Stat of the Week. In BCG’s 2026 survey of 300 chief marketing officers (CMOs), 91% of those at business-to-consumer companies, and 76% at business-to-business companies, said AI-moderated no-click discovery is currently reshaping funnels. (BCG, June 2026.)

Usually, when so many people agree, it means the debate is over. That’s not the case here. Marketing leaders agree that discovery now happens inside systems they don’t control, but they’re still guessing about what that really means. Many believe the sales funnel is shrinking, and they see fewer clicks as a sign of less demand. But if you look at how shoppers actually behave, there’s a different story. Since AI became part of the buying process, the journey has gotten longer, with new steps where customers check if recommendations are any good. Brands have lost the chance to be part of the initial impression, but they still own—and now have more influence over—what happens when shoppers come to verify. This means marketing budgets should shift: instead of spending to make introductions you can’t control, invest in the moments you still own.

AI adds, not reduces, steps in the journey

The best proof comes from observing real shopping behavior. The Interactive Advertising Bureau (IAB) and Talk Shoppe watched over 450 AI-assisted shopping sessions and surveyed 600 US consumers aged 18 to 64. Their main finding was about the number of steps shoppers take. Before using AI, the average shopper took 1.6 online steps before buying. After interacting with AI, that number jumped to 3.8 (IAB and Talk Shoppe, October 2025).

Nearly everyone in the study added at least one more step after using AI before making a purchase. In fact, 95% took extra actions, and 89% said they double-check what AI tells them. These extra checks drive more traffic to retailer and marketplace sites. After an AI interaction, visits from serious shoppers almost tripled. Seventy-eight percent visited a retailer’s site, and one in three clicked directly from the AI platform.

So, while 'no-click' might describe the search result, it doesn’t describe the purchase process. Buying now involves more steps than before. Shoppers still arrive and compare options, but they do so at a different stage in their decision-making. This shifts the challenge to positioning your brand effectively.

Understanding how AI views your brand is the first change to make

In an AI-driven discovery world, brands no longer control how visible they are. More and more, consumers make decisions with the help of assistants and recommendation engines that interpret, summarize, and rank brands for them. If these engines don’t show your product, it can become invisible, no matter how strong your brand or how much you spend on media (BCG, June 2026).

Introductions to new products are still happening. In the IAB study, almost 90% of shoppers said AI helped them discover products they wouldn’t have found otherwise, and 64% had a new product suggested during their session. For people who use AI to shop, it’s now the second most influential source, just after search engines and ahead of retailer sites and recommendations from friends and family. So, introductions are still happening at scale, but they’re controlled by something else and in ways that are hard to see from the outside.

An appliance manufacturer spent nearly a year debating the wrong metric. Over nine months, organic visits to its product pages dropped by about 20%. The team assumed this meant demand was falling, so they shifted budget from upper-funnel media to promotions. But nothing changed—sessions kept dropping at the same rate, while conversion rates for the remaining sessions went up. The visitors who did arrive were further along in their decision process. When the team finally asked buyers in a post-purchase survey where they first heard about the product, over a third said it was through an AI assistant. The session count had actually been tracking a filtering step that now happened off the company’s site, so the team spent a year arguing over a number that no longer reflected reality.

Lack of trust is the step you can still win

Each of these extra steps is a chance for brands to make an impact. Only 46% of consumers in the IAB study said they fully trust AI shopping recommendations. When asked what would help build trust, 88% wanted to know where the information came from, and 87% wanted verified customer reviews. Another 75% wanted an explanation of how the answer was created (IAB and Talk Shoppe, October 2025). In other words, your page should give visitors clear sources and reasons to trust what they see.

Most brand websites aren’t set up for this, since they were designed to make introductions. They usually start with a positioning statement and put reviews at the bottom of the page as social proof, far from where someone looking to verify will see them. Shoppers who have already been introduced by AI and are now checking need to see sources and comparisons right at the top.

BCG says the key decision is whether to meet customers inside AI-driven experiences or focus on building direct relationships. Starting with direct relationships is usually cheaper, since you don’t need permission from a platform or have to guess which assistant will win. Some companies are already seeing results from this approach. BCG reports that 31% of consumer CMOs and 20% of business CMOs say their agentic marketing transformation is already delivering significant, measurable revenue impact, though this covers the whole transformation, not just this choice or discovery alone (BCG, June 2026). Getting your brand cited by these systems is a separate challenge, with its own tools and team, and deserves more attention than can fit here.

Rebuild the consumer experience before the next campaign

All of this points to one clear action, and there’s a simple question that shows how urgent it is.

Update your most-visited landing page for visitors who already know about you. Use the trust list as your guide: put the sources for your claims and real customer reviews near the top. Measure success by what these visitors do, since session volume no longer reflects your market. Before debating this, add a question to your post-purchase survey asking where customers first heard about you, listing assistants as options instead of just 'online.' Most teams rely on guesses, like the appliance manufacturer did, but a month of real answers will give you clarity faster than any attribution model.

The teams that succeed won’t be the ones who guess which assistant will win. Instead, they’ll be the ones who realize early that their customers are still coming—just later in the process and with better questions. If most visitors to your homepage already know about you from somewhere else, what purpose does your homepage serve now?

Sources

• BCG, “Moving the Agentic Marketing Transformation from Illusion to Reality (BCG CMO Survey 2026),” June 15, 2026 (n=300 CMOs globally across business-to-consumer and business-to-business sectors, supplemented by structured interviews with 50 of them. The report states that “statistics cited in this report come from the survey unless explicitly noted as engagement-based observations,” and every BCG figure used here is survey data; its engagement-based observations are deliberately not cited. Verbatim: “91% of B2C CMOs (and 76% of B2B CMOs) say that AI-moderated, no-click discovery is currently reshaping their already nonlinear funnels.” This page is titled two different ways: the browser title reads “Making the Agentic Marketing Transformation a Reality” while the on-page heading reads “Moving the Agentic Marketing Transformation from Illusion to Reality.” The heading is the one given here.)

• IAB and Talk Shoppe, “When AI Guides the Shopping Journey: Opportunities for Marketers in the Age of AI-Driven Commerce,” October 28, 2025 (US; 450+ screen-recorded AI shopping sessions plus a survey of 600 consumers aged 18–64. The step counts describe shoppers in the study who used AI, measured before and after the AI interaction, and are observed rather than recalled. Verbatim: “Prior to AI engagement, the average shopper completed 1.6 steps online before making a purchase. However following AI interaction, that number jumped to 3.8.” The trust and traffic figures are quoted from IAB’s own announcement of the study at https://www.iab.com/news/ai-ranks-among-consumers-most-influential-shopping-sources-according-to-new-iab-study/.)

Stat of the Week. US commerce media advertising revenue reached $63.4 billion in 2025, up 18.0% year over year, though the growth rate is down from 23.0% the year before. (IAB and PwC, April 2026.)

Many marketing budgets still treat commerce media as just a retailer negotiation, debated with a few major chains and placed somewhere between trade spend and paid media. This view has been outdated for two years, and by 2025, sticking with it started to have real costs. Now, the category includes more sellers outside of the retail sector, shifting the focus for marketing leaders from simply sizing up the opportunity to choosing the right suppliers. The key question is which transaction-data partners deserve a real commitment and on what terms, especially as many of your current partners are quietly becoming media sellers. The best approach is to work with fewer partners, invest more deeply in them, and agree on reporting terms before making any commitments.

Commerce media has outgrown the retail sector

It's important to look at the scale before discussing strategy. The Interactive Advertising Bureau (IAB) and PwC report that US commerce media will reach $63.4 billion in 2025, up $9.7 billion from the previous year. The entire US internet advertising market hit $294.6 billion, growing by 13.9% (IAB and PwC, April 2026). When a channel grows faster than the overall internet advertising market for two years in a row, it is no longer just an experiment.

The same report notes that growth is slowing. Commerce media is expected to grow by 18.0%, down from 23.0% the previous year. IAB says this is partly because it's harder to maintain high growth on a larger base, and partly because brands are asking retailers to prove that the spending is truly incremental. Another IAB forecast, based on a January survey of 205 US ad decision-makers, predicts commerce media will grow 12.1% in 2026, compared to 9.5% for all US advertising, which includes TV, radio, print, and direct mail as well as digital (IAB, January 2026). Growing about a quarter faster than such a broad market, on a $63 billion base, shows this is now a solid part of the plan.

The real change is in how the category is defined. IAB now says commerce media is advertising powered by commerce data across on-site, off-site, and in-store environments, plus new and growing players beyond retail (IAB and PwC, April 2026). The key point is that the definition now focuses on who holds the data, not just who owns a store.

Two assets make a business a media seller

If you look at what the new entrants have in common, the list is short. To qualify, a business needs just two things.

First, they need a record of what people actually bought. Purchase history is a clear fact. A card issuer, airline, or delivery marketplace can target buyers more confidently than a publisher who guesses based on what someone reads. This also helps settle any disputes after a campaign, since the seller can see if a purchase happened.

Second, there must be a moment when the customer is making a decision. This could be at checkout, on a booking screen, in a statement, or during a service call—times when the customer is focused and can see their options. This makes the advertising space more valuable than the same impression elsewhere.

Almost any business that requires a login and a payment step meets these criteria, and the market is responding. In June, AdExchanger reported that The Trade Desk created a travel and hospitality media network with Uber Advertising, Booking.com, United’s Kinective Media, and Marriott Media (AdExchanger, June 2026). One goal is to give advertisers access to travel data they might not have considered before, such as marketing inside hotels. For example, supermarkets can't offer insights about when someone is planning a trip.

A few years ago, buying this kind of media would have been much harder. In 2025, programmatic advertising revenue reached $162.4 billion, up 20.5% (IAB and PwC, April 2026). This automation allows media teams to add a mid-size network without needing more staff.

Breadth is the expensive part

The main challenge in this channel is no longer supply. IAB points out that what slows brands down is the work involved in running campaigns across different retailer systems, separate buying platforms, and fragmented workflows, as well as inconsistent measurement (IAB and PwC, April 2026). There are also questions about how well incrementality is tested. Each of these costs is paid for every partner, but media plans often overlook this.

A packaged-food company spent a year testing nine networks: three grocery chains, a delivery marketplace, a convenience retailer, a card-linked offer platform, and three regional accounts. This made sense, since a learning budget should be spread out while there is still something to learn. Each network reported a positive return by its own standards, so the year looked successful. The problem appeared in the next planning cycle, when the team had to decide which network to scale up. Looking back, the four smallest networks took up about half of the analysts' time but only accounted for 6% of the spend, since each required its own creative specs, portal, and manual product mapping. These four networks didn't underperform—they just never received enough funding to justify the effort. The next year, the same budget went to two networks, each getting four times the spend, with reporting terms agreed in advance. This finally gave the team a clear answer they could use.

The buyer on the other side may be AI-agents

There is an important question in this category: will the customer still be the one making the choices? Accenture’s 2026 survey of 25,590 people in 16 countries found that 74% would let an AI agent handle routine tasks, like negotiating a deal or resolving a complaint, as long as the agent follows their instructions (Accenture, 2026). This shows people are willing to delegate errands, but fewer are ready to give up decision-making. Thirty-two percent would let an agent choose what to buy if they still make the payment, and only 9% are open to fully autonomous purchases. Willingness drops sharply when it comes to spending money.

There are two possible paths, but both lead to the same approach. If agent-mediated buying stays close to the current 9%, commerce networks will keep selling directly to people, and nothing changes. If more people let agents make buying decisions, then businesses with purchase records and payment steps will become the main channels for agents, turning today’s media partners into tomorrow’s customer routes. In either case, spreading your budget thinly across many sellers does not pay off. It is better to know two partners well enough to negotiate effectively.

Fund fewer partners, and set the terms first

You can address most of these issues before the next planning cycle starts, and you do not need a new budget line to do it.

Begin by listing which of your current partners have already become media sellers, even if you have not officially changed the relationship. The bank behind your co-brand card, the marketplace delivering your product, and the platform your customers use to book travel are all now part of this category. Each of these agreements was originally made for a different purpose. This list is often longer than your media plan shows, and it is where your negotiating power already exists.

Next, decide what a network needs to report for its results to be compared with everything else you buy. Agreeing on these definitions before signing is very different from asking for them later, and it is one of the few points of leverage a mid-size advertiser has with a large seller.

This category is still new enough that its terms are being shaped, and those terms will be set by advertisers who commit deeply to a few partners. Which non-retail network has surprised you this year? Reply in the comments with your answer.

Sources

• IAB and PwC, “Internet Advertising Revenue Report, Full-Year 2025,” April 2026 (US market; all figures realized. Commerce media $63.4B, +18.0%, an increase of $9.7B, with the report stating the rate is “down from 23.0% in the prior year”; total internet advertising $294.6B, +13.9%; programmatic $162.4B, +20.5%. The definition quoted, “on-site, off-site, and in-store environments, as well as new, growing entrants beyond retail,” and the operational-constraint passage are the report’s own words).

• IAB, “2026 Outlook Study: A Snapshot of U.S. Ad Spend, Opportunities, and Strategies for Growth,” January 2026 (Projection, not a realized result: forecast US ad-spend growth of 9.5% for 2026 — a total-advertising basis that includes linear TV, radio, print, OOH, and direct mail — with commerce media at 12.1%. Email survey of US buy-side ad investment decision-makers, n=205 (44% brands, 53% agencies, 3% other), fielded 11/24/25–1/16/26; the by-channel commerce media estimate is answered by n=115 of them. IAB states the 9.5% is lifted by cyclical events and that removing them lowers growth to a 7.1%–7.8% range, so the article’s “roughly a quarter faster” is the conservative reading. Cited to the study PDF rather than the January 28 press release, per the content plan’s follow-links-to-the-primary rule; the two agree on every figure. This is also the study the revenue report’s separate “30% faster than the overall market” line is footnoted to, so the forecast is cited here rather than to the revenue report).

• AdExchanger, “The Trade Desk Forms A Travel And Hospitality Media Network,” June 17, 2026 (trade reporting by James Hercher; names Uber Advertising, Booking.com, United Airline’s Kinective Media and MARRIOTT MEDIA as partners. No figures are given in the article, and none are cited from it).

• Accenture, “Talk to my AI agent: The new rules of brand value,” Consumer Pulse 2026 (n=25,590 consumers across 16 countries). Stated openness, not observed behavior: 74% would delegate routine tasks “provided the agent acts strictly on instruction,” 32% “would let an agent decide what to buy, provided that they make the payment themselves,” and 9% “are open to an AI agent making fully autonomous purchases.” The 74% appears on the same page in a second, different framing (trusting an agent more than a best friend) — the delegation framing is the one used here, and the body says in-line what this 74% measures.

Stat of the Week. Only 16% of business-to-business companies use marketing data for real-time decisions, the evidence BCG gives for one of three obstacles it attributes to running sales, marketing, and customer success as separate functions. (BCG, September 2024.)

The last few articles have focused on the key number: what marketing tracks and misses, whether media measurement can be trusted during budget shifts, and where growth funding sits in the budget. Each issue is important to solve, but even fixing all three might not create real change because the main problem is structural. The number that would show improvement usually spans three different teams, so no one owns it. The next section explains the simplest way to fix this, and it does not require reorganizing.

Everyone is accountable for a proxy

Think about how accountability is divided. Marketing is responsible for pipeline created, sales for closed deals, and customer success for retention or renewal rates. Each metric has a clear owner and reflects what matters to the business, but none of them fully capture it.

BCG points out that marketing, sales, and customer success teams often work in separate silos, each with their own processes and metrics, and few of these have a real impact on the bottom line (BCG, September 2024). The 16% figure is a sign of this problem, since data stuck within one team cannot inform decisions fast enough. While BCG does not disclose the sample size for this number, it references nearby surveys and its own benchmarks. Anyone who has tried to align three dashboards will recognize this challenge.

This leads to a situation where everything appears successful. Each team reports strong results, every metric moves in the right direction, but the overall outcome those metrics are supposed to reflect does not change. No one is at fault, and no one is truly accountable.

BCG suggests a very simple solution. Assign a small cross-functional team to focus on one main metric that is tied to a real business outcome, like revenue growth or return on marketing spend, and make that team responsible for moving only that number (BCG, September 2024). The rule of focusing on 'nothing else' is important, since cross-functional projects often fail when they try to track too many metrics at once.

The unit is a team, not a program

According to BCG, a demand squad is a small, agile team made up of people from sales, marketing, and customer success. They work in two- to three-week sprints. Squads are organized based on what matters most to the business, such as region, customer segment, or another key area (BCG, September 2024).

What makes demand squads easy to adopt is that, according to BCG, they do not need extra budget, resources, or major changes to the organization. This is rare for an operating model change, since most fixes require a reorganization, a new department, or a new platform, which often makes them hard to implement. A squad simply shifts focus and can be started by a director without needing to hire anyone new.

This approach is also easy to reverse. If a squad does not achieve results in two quarters, it can be dissolved without disrupting anything else. This makes it easier to get approval to try.

Evidence, and what it is worth

It is important to be honest about the evidence, since this idea has been around long enough to pick up the usual consulting hype.

At one medical-device company, the problem was clear. The marketing team met its lead goals, sales hit its targets, and clinical support met every service-level agreement. Yet, over four quarters, the time from first purchase to first reorder increased by about seven weeks. No team was responsible for this gap, since marketing stopped at the handoff, sales moved on after closing, and clinical support only tracked its own response times. The first squad focused on this interval as its main metric, met weekly with members from all three teams, and closed most of the gap in two quarters by changing what happened in the first 10 days after delivery. They did not need extra budget, just someone to take ownership.

Another example comes from a large tech company that created two demand squads, one for each main region, and these squads identified, tested, and scaled 16 growth ideas. This led to one-year revenue gains of 15% to 30% in the company’s main business areas (BCG, September 2024).

When a company that advises others on operating models reorganizes itself this way, it shows how thinking has evolved. In 2025, Accenture combined five separate service lines—strategy, consulting, Song, technology, and operations—into one integrated unit under a single leader, while keeping its geographic and industry structures the same (Accenture, June 2025).

Where squads fail

There are four main ways squads can fail, and each one shows warning signs early if you know what to look for.

• Sometimes the main metric is just another proxy. For example, if a squad is responsible for 'marketing qualified leads accepted by sales,' it has simply recreated the handoff it was supposed to eliminate. You can spot this when the metric improves but the actual business outcome does not change.

• Another failure is when the squad lacks authority. If a cross-functional team has to get approval for every change, it becomes just another meeting. This is clear when the first two sprints only produce recommendations that need someone else’s sign-off.

• A squad can also turn into a standing committee. This happens when they stop working in sprints and start adding more metrics to their agenda. Adding a second or third metric is an early warning sign.

• Another problem is measuring activity too soon. Real results take a quarter or two to show up, but impatience often leads to requests for activity reports by the third week, such as asking how many use cases have been tested.

The main issue in all these failures is dilution. Each one adds more tasks to the squad’s responsibilities, which is why BCG’s rule of focusing on 'nothing else' is so important.

Your first squad

There are three steps you can take this quarter, and none of them require extra budget.

First, pick the metric. Choose one that no single team can influence alone and that your finance partner would see as a true business outcome. Examples include time to reorder, first-year revenue per account, or yield on a specific spend line. Pipeline volume does not meet these criteria.

Next, choose the two teams most involved with that metric. While three teams is ideal, starting with two is fine. Make sure the people who actually do the work are included.

Then, set the sprint schedule and stick to it. Plan for two to three weeks per sprint, hold regular meetings, and enforce one key rule: do not add anything else to the agenda. Protecting this focus is the main point, and it is often the first thing to slip during a busy quarter.

Over the past decade, marketing has improved its measurement, which was needed and, as discussed here, is still not complete. The next improvement is structural and is available to anyone ready to give one number to one team and let them focus on it.

What is the one metric in your business that spans three teams but is not owned by any of them? Reply and share your answer.

Sources

• BCG, “An Overdue Upgrade for B2B Go-to-Market Functions,” September 2024 (the 16% figure carries NO disclosed sample base or methodology in the published article, though adjacent figures are sourced to a BCG survey and to BCG’s own benchmarking analysis, so the precise claim is an undisclosed base; the 15–30% incremental-revenue result is ONE unnamed large technology company, self-reported to BCG, stated as gains “in the company’s main business segments” with no statement of what the range spans).

• Accenture, “Accenture Changes Growth Model to Reinvent Itself for the Age of AI,” June 2025 (corporate announcement, changes effective September 1, 2025; self-reported with no outcome data and a forward-looking-statements disclaimer, cited only as evidence of adoption).

Stat of the Week. Nonworking spend, meaning agency fees, content production, marketing technology, data and analytics, campaign strategy, measurement, and overhead, runs at 20% to 45% of the total marketing budget across industries. It’s also the part of the budget that rarely gets scrutinized with the same rigor as media. (BCG, June 2025.)

The request to cut costs usually comes before there’s a real plan. When revenue drops and budgets tighten, marketing is told to do more with less. This often leads to debates about how much to cut, but rarely does anyone ask what the budget actually includes.

Asking what makes up the budget is more helpful and leads to better answers. In a tough year, arguing to keep the same budget size rarely works. Instead, focus on changing how the budget is used. The funds you need are already there.

The default approach is measurably expensive

Let’s look at the usual reaction, since it’s worth considering. A 2023 BCG brand analysis found that cutting marketing budgets during uncertain times causes long-term harm to sales growth, conversion, market share, and shareholder return (BCG, June 2025). The harm doesn’t show up right away. Savings appear in the current quarter, but the real costs show up in later years.

This doesn’t mean marketing budgets can’t be reduced. The key point is that cutting everything by the same percentage removes both valuable demand generation and waste, often before anyone decides what’s what. In a tough year, you still need to find savings, but simply cutting by a flat percentage is the least thoughtful way to do it.

A fifth to nearly half of potential savings sits outside media

There’s a clear answer to what makes up the budget, and it’s often bigger than teams expect. BCG says nonworking spend makes up 20% to 45% of the total budget, including agency fees, production, marketing tech, data, analytics, campaign strategy, measurement, and overhead (BCG, June 2025). Working spend, which is the media itself, usually makes up 55% to 80%. These ranges overlap, so they don’t add up to 100%. BCG also points out that returns are usually measured in the working spend.

McKinsey makes a similar distinction within media spending. Working media spending depends on how many people see your ads, how often, and the quality of those ads. Nonworking media spending covers the creative, production, and research that go into making the ads, and isn’t tied to how many people see them (McKinsey, 2018). BCG’s definition is broader, including marketing tech, data, measurement, and overhead, but McKinsey’s version is a more focused look at media spending.

These two definitions help explain a common habit in budget planning. The part of the budget that’s measured gets reviewed every quarter, while the unmeasured part is often left alone, year after year. This can lead to a fifth to nearly half of spending being based on old habits. BCG gives both ranges but doesn’t share its sample or survey details, so you’ll need to check your own numbers. Most teams haven’t done this analysis.

Where the nonworking money actually goes

The ways to save money aren’t flashy. BCG points out that many costs come from old processes that haven’t been reviewed in years. Their list starts with combining vendors, especially where advertising and marketing agencies overlap (BCG, June 2025). It also includes reviewing internal processes like how brand briefs are developed, setting rules for production versus media spending, and changing the operating model, such as bringing some work in-house or offshoring simple tasks.

McKinsey’s zero-based approach shows what these savings can add up to. One EU consumer goods company reviewed over 50 spending categories and improved spending efficiency by 15%, with more than 70% of the savings coming from nonworking media levers (McKinsey, 2018). This is just one example, reported by McKinsey, but it shows that these results are possible. The key takeaway is that most of the savings came from outside the media spend.

Content production is another area BCG highlights, where some brands spend too much on work that doesn’t reach many people. Accenture’s work with Telefónica shows how automation can help: they produced over 300 pieces from a simple template in two days, compared to about 40 assets in several weeks before (Accenture). Accenture says this saved 83% in costs. While asset count and marketing value aren’t the same, this example shows how much production costs can drop with better design.

When one hotel group had a slow first half, they cut 12% from media spending in every market, thinking it was fair to share the pain. But they left agency fees, retainers, and production untouched, since those didn’t have clear performance numbers. The cut only affected the measured part of the budget. A year later, nonworking spend made up a bigger share of a smaller budget, making things worse. The savings just went into the general budget and didn’t fund anything specific. No one was careless; they just cut the easiest line.

Reallocate, then lock it in

Most programs miss the final step. If savings aren’t quickly reassigned, they quietly disappear into the general budget, turning the whole process into just another budget cut with extra paperwork.

BCG gives an example from a global apparel and footwear company. Over a year, the company tracked its spending, found savings in both working and nonworking areas, and redirected those savings to social content and brand-building (BCG, June 2025). The key was making sure the new allocation was built into their operating model, so it would last through future planning cycles.

Reinvesting savings also helps make your case internally. You can show where a fifth of the budget was going and suggest a better use for it. This is a stronger argument than just asking to keep your current budget.

Two things to accomplish before the planning cycle starts

• Check your budget split. Ask finance for your working and nonworking totals from the last two years, as a percentage of the budget. Most marketing teams haven’t seen this breakdown. The trend is more important than the exact numbers, since a rising nonworking share means old habits are shaping your budget.

• Decide what you’ll do with the savings before you propose a cut. Clearly state what the freed-up money will fund, with a specific number, in the same meeting where you suggest the reduction. Savings with a clear purpose are more likely to last through the planning cycle.

Being told to do more with less is common in tough years, but it’s not all bad. This challenge forces marketing teams to ask important questions they might otherwise avoid. Teams that tackle it often end up with a budget that’s only a little smaller but much better organized.

If you had to cut one budget line, which would it be? And what would you use those savings for? Share your answers.

Sources

• BCG, “For CMOs, the Future Starts with Smarter Spending,” June 2025 (BCG states the 20–45% nonworking range, the 55–80% working range, and the nonworking lever list bare, with no disclosed sample or survey base; the only survey disclosed in the article is a separate GenAI C-suite poll, and BCG’s one methodological tag, “in our experience,” is attached to a different claim, the 10–30% total-spend unlock, which this article does not use. The 2023 brand-analysis finding on cutting during uncertainty is BCG’s own prior research, cited within this article. The four-part program is one client’s case, not a prescribed BCG method).

• McKinsey, “Zero-based productivity—Marketing: Measure, allocate, and invest marketing dollars more effectively,” August 2018 (the working/nonworking definition, which McKinsey scopes explicitly to media spending; the 15% efficiency gain with more than 70% of opportunities from nonworking media levers is a single unnamed EU consumer-packaged-goods client, realized and self-reported by McKinsey, labeled as such in-article).

• Accenture, “Telefónica paints the town with “Kisses”,” undated (vendor-authored case study, n=1, published by the firm that delivered the work; no methodology, baseline, or measurement window disclosed. Only the 300-pieces-in-two-days versus 40-in-several-weeks comparison and the campaign-scoped 83% cost saving are used).

Stat of the Week. 15% to 20% of a marketing budget can be reinvested elsewhere or returned to the bottom line without losing marketing return on investment (ROI). That range comes from a sample of the 400 marketing ROI engagements McKinsey had run with clients published in November 2013. (McKinsey.)

Some numbers stick around long after their original proof fades, and this one has lasted longer than most. The 15% to 20% figure appears in board meetings, agency presentations, and budget talks when someone wants to cut the marketing spend. People quote it confidently, often without knowing its source, and it has been used this way for more than ten years.

The number itself holds up. What hasn’t lasted is the idea that you could actually identify which 15% to 20% to cut. That made sense in 2013, but it’s much harder in 2026 because the tools for finding waste have changed a lot since then. It’s still worth reviewing your budget, but you can’t start the same way as before.

The number is real, but it’s now 12 years old.

Credit where it’s due, since the estimate was never a marketing slogan. McKinsey drew it from a sample of the 400 times its teams had worked on marketing ROI with clients. The finding was a typical range of 15% to 20% of budget that could be reinvested or returned without losing return (McKinsey, November 2013).

McKinsey explained the idea more clearly than the number itself. People often repeat the old Ogilvy line about not knowing which half of the budget is wasted. McKinsey argued it’s more like a fifth, and if you understand your data, you should be able to identify which fifth.

That last point is key. The range is so broad that it’s hard to prove wrong, so the real value was in saying you could find the wasted spend. Seeing similar numbers across different clients suggests you probably have savings in your budget too, but actually finding them is a measurement challenge.

The instruments changed underneath it

Since then, the industry has changed how it measures results. New privacy rules, changes to platforms, and the gradual loss of tracking tools that used to connect ads to outcomes have made measurement more fragmented than it was when the original estimate was made.

In a February 2026 report, IAB surveyed 430 US planning and analytics leaders at brands and agencies (IAB, February 2026). Of those who use advanced measurement tools, 60% to 75% said these tools fall short in key areas like rigor, coverage, timeliness, trust, or efficiency. The report goes further, describing marketing and media measurement systems as fundamentally broken, with data spread across disconnected systems that make consistent cross-channel measurement impossible.

Keep in mind, this is what practitioners say about their own tools, not an independent audit. The AIB report is also sponsored by vendors and introduces an IAB initiative, so it’s not completely neutral. Still, it matters because the sources used are people in the roles who would handle your budget reallocation.

The IAB points out exactly where the problems are. Marketing mix models tend to underrepresent connected TV, retail media, creator-led formats, gaming, and commerce media, which makes the returns from these channels look different than they really are. Buyers see gaps in how every channel is measured, even traditional media.

Reallocating from a broken model shifts where the error occurs

That level of detail is more important than general complaints. When you reallocate budget based on a model’s output, any channel the model doesn’t measure well is at risk of being judged unfairly. If measurement isn’t even across channels, the one that looks cheapest might just be the one that’s measured best.

One insurance company learned this lesson over nine months. They did a thorough review, put every budget line into one model, and made the biggest cuts where the measured return seemed weakest—mainly in retail media and connected TV. These areas only had about a year of good data, compared to several years for search and direct mail, so the model’s estimates were less reliable than they appeared. The costly mistake was that the problem didn’t show up right away. The reallocation looked like a win at first, but the shortfall appeared in the second half of the next year, after the review had already been approved and filed.

The order of operations is what has to change

The solution is to change the order of steps, which costs less than the big measurement programs most teams try first. The old way was to measure everything, rank it, and then reallocate based on those rankings. But if the rankings aren’t equally reliable, this approach can lead to confident mistakes.

To flip the process, start by identifying the two or three key allocation decisions that really matter for the year, since most annual plans have fewer important choices than budget lines. These decisions are usually specific, like whether to keep or cut the brand budget, whether retail media deserves more funding, or if the always-on search budget is just capturing demand you already had. Then, focus your measurement efforts only on what’s needed to make those decisions. Running a geo holdout or an incrementality test on one debated line can answer a key question in a few months, and costs much less than rebuilding your whole measurement system. The evidence you get is the kind finance teams trust, because it’s built to answer their specific questions.

This new order also makes it easier to get internal funding, since you’re linking a test to a decision that already involves spending money.

Start smaller than the problem

• What are the two or three decisions that really matter in this plan? First, write them out as questions with numbers, then remove any measurement requests that don’t help answer them.

• Which channels in your model have less than two years of reliable data? First, label those lines as low-confidence before the reallocation meeting, so no one confuses a rough estimate with a poor result.

• What can you test on one debated budget line this quarter? Start by picking the line with the most internal disagreement and set up a holdout test for it. Resolving a big argument is more valuable than just improving the model a little.

That 15% to 20% is likely still in most budgets, and the reason to pursue it is even stronger as budgets get tighter. What’s different now is that you have to earn the right to act on it one decision at a time, and each resolved debate makes the next budget change easier to justify.

Which decision would you test first? Let us know which budget line you’d choose.

Sources

• McKinsey, “Smart analytics” can tap up to 20% of lost ROI,” November 2013 (the 15–20% is a typical range observed across a sample of roughly 400 McKinsey client marketing-ROI engagements, not a survey finding; vintage stated in-article. Quotes from Jonathan Gordon paraphrased, not quoted verbatim.)

• IAB, “State of Data 2026: The AI-Powered Measurement Transformation,” February 2026 (n=430 US buy-side brand and agency planning and analytics decision-makers, fielded by BWG Strategy; the 60–75% is a range across several separate questions and sub-bases, and is self-reported perception of measurement performance, not a measured audit. Report sponsored by Dstillery and OptiMine; IAB states findings were not influenced by sponsors.)

Stat of the Week. Measurement came last. In BCG’s 2025 global survey of 200 chief marketing officers (CMOs), measurement ranked at the bottom of the funding priorities for improving the digital customer experience and the marketing technology stack that enables it. (BCG, June 2025.)

Measurement is unique in marketing because it can feel finished even as it becomes outdated. When this happens, everything seems fine. Reports keep coming in on schedule, the numbers look right, but each year, what gets measured quietly narrows and few people notice.

A BCG survey of 200 CMOs found that measurement was the lowest priority for investment to improve digital customer experience and technology (BCG, June 2025). It’s easy to see why. Measurement often looks complete, while other areas seem more urgent or unfinished. This thinking makes sense until you realize what happens when your measurement system slowly stops working as well as it should.

It’s easy to put measurement at the bottom of the list, and there’s a reason for that.

BCG points this out too. They say measurement is a lower priority partly because many companies have already invested heavily in first-party data and measurement, giving them a solid base (BCG, June 2025). After such big upgrades, it’s natural to shift new spending elsewhere.

But BCG also warns about the risk. They say putting measurement last 'may also indicate a blind spot, because considerable untapped potential remains,' and that the details are often dull and expensive (BCG, June 2025). For real marketing ROI, BCG says many companies still don’t invest enough in three things: connecting marketing to extra sales, agreeing on how to measure upper-funnel and nontraditional spending, and using automatic measurement to improve personalized content during campaigns.

None of these three problems are about accuracy, so better models or cleaner data won’t fix them. They’re really about what your measurement system covers, which is harder to notice. A system can be very precise about what it tracks, but if it’s tracking the wrong things, it still seems complete.

An average customer isn’t the same as a real customer.

A good example of this issue is how averages are used. Most marketing measurement depends on blended numbers, like cost per acquisition for the whole program or lifetime value for all customers, because these are the metrics leaders usually accept.

Bain’s research with B2B companies found that promoter customers have an average lifetime value three to twelve times higher than detractors, depending on the segment and industry (Bain, 2014). The main takeaway is the big gap between promoters and detractors, not their average. The difference between your best and worst customers is a multiple, not just a small percentage, and it changes so much by segment that using a single average hides important insights.

When you compare that big range in customer value to a blended cost per acquisition, the numbers stop being reassuring. If new customers can be worth so much more or less, it’s easy for a campaign to look good on paper but bring in the wrong people. This mistake happens often. For example, one industrial distributor ran a campaign that hit all its targets: cost per new account fell by 20% and volume went up. But no one checked who these new customers were. They bought once, cared only about price, and rarely came back. The same team had to serve both these new and long-term accounts. The older accounts brought in most referrals, but referrals dropped the next year and didn’t show up in any report, since there was no metric for them. By the measures used, the campaign still looked like a win.

Programs can look successful even as relationships with customers get weaker.

This problem isn’t just found in industrial distribution. It also shows up in consumer loyalty, where metrics focus on the program and not the real relationship. EY’s 2026 loyalty study makes this clear. Loyalty programs look good by standard numbers, but it’s not clear customers feel the same value (EY, 2026). Enrollment and ROI seem strong, but people join fewer programs, check rewards less, and feel less positive, even when the program works as planned. At the same time, churn is going up.

A program can look like it’s doing well and still be declining, because measurement only tracks the program itself. Enrollment counts sign-ups and ROI counts redemptions, but neither tells you if the real relationship is getting weaker. This is the same issue the distributor had, just in a different industry.

The good news is you don’t need a new platform to fix this. The three areas BCG talks about are about what you analyze, and the loyalty gap EY describes can be found in data most companies already have. What’s needed is someone willing to admit that measurement isn’t finished.

Here are four questions you should try to answer, even if you can’t right now.

You don’t need a big project to tackle these. The real benefit is finding out which ones you can’t answer yet.

• Can you tell what your last campaign really added, apart from what would have happened anyway? If you can’t separate extra sales from normal sales, your ROI number is questionable at best.

• Do you know how much your best and worst customers are worth, for each segment? A single average can’t show if you’re gaining or losing with new customers.

• Which of your upper-funnel or nontraditional spending doesn’t have an agreed way to measure it? Not everything that’s unmeasured is unmeasurable, and the list is usually shorter than you think.

• What could you change during a campaign, using measurement that updates automatically? This is where you’ll see the fastest results, and BCG’s approach assumes you have measurement that updates by itself.

Measurement often gets less funding because it never looks broken. If you treat it as something you need to maintain, your next budget talk will be based on real evidence, not just gut feeling.

Sources

• BCG, “How CMOs Are Scaling GenAI in Turbulent Times,” June 2025 (published June 2, 2025; annual global survey, n=200 CMOs, fielded April and May 2025; “measurement ranked last” is a ranking of stated funding priorities, and the “blind spot” reading is BCG’s own).

• Bain & Company, “Do Your B2B Customers Promote Your Business?,” February 2014 (the 3–12x promoter-to-detractor lifetime-value range is Bain’s observed range across client work, not a survey; varies by segment and industry; vintage dated in-article).

• EY, “2026 EY Loyalty Study: the gap between performance and experience,” 2026 (third edition; cited directionally — the study’s published summary carries no isolated headline percentage, so no figure is quoted).

Stat of the Week. Customer-experience leaders grew revenue more than 2x faster in the United States than the companies falling behind over 2016–21, and growth winners deliver roughly 30% higher total returns to shareholders (McKinsey, Experience-led growth, March 2023.)

Most large companies go through a similar customer-experience review each year, and it usually ends the same way. The journey maps look great, the satisfaction dashboard shows good results, and the roadmap is set. Then someone from finance asks what the program actually delivered, and the mood shifts. The journey maps can’t answer that question, because the program focused on mapping everything, fixing what seemed broken, and reporting scores. None of these steps show the real impact, so when budgets get tight, the program that can’t prove its value is often the first to be cut.

It’s ironic that the same evidence supporting CX investment is what these reviews fail to show. McKinsey found that U.S. companies leading in customer experience grew revenue more than twice as fast as those falling behind from 2016 to 2021, and these leaders delivered about 30% higher total returns to shareholders (McKinsey, March 2023). Customer experience does drive growth, but only when programs are designed for growth, not just for reporting. To do this, marketers need to work backward: start by defining the impact they want, identify the key customer experiences that influence it, redesign those from start to finish, and report on the results from the initial goal to the post-launch review.

The forward-designed program can’t defend itself

Forward design fails in a clear and predictable way because it focuses on journey maps instead of outcomes. Journey-mapping programs create more documentation and score more touchpoints, but this coverage doesn’t have a clear end point or a financial owner. As a result, the backlog keeps growing and it’s hard to show what’s working. McKinsey suggests a different approach with three main steps: set a clear goal, redesign specific journeys to meet that goal, and build the skills, technology, and measurement needed to keep the changes going (McKinsey, March 2023). The whole model depends on the first step, because stating the goal as an impact outcome turns the other steps from just activities into real investments.

Work backward from impact

Designing backward begins by identifying the financial impact. For example, instead of saying “improve retention,” focus on the renewal-churn cost for this year’s plan. Instead of “reduce effort,” look at the cost to serve your two most contact-heavy segments. Defining the impact does three things: it gives the program a financial owner who wants results, it sets a clear stopping point, and it helps choose the right work, since most of the journey-map backlog won’t affect the defined impact.

A subscription business is a clear example of this shift. The CX team managed over 300 journey maps, updated by a dedicated staff, but couldn’t say which ones actually made a difference. When a new leader asked them to work backward, they focused on churn cost. The analysis was surprising: most churn wasn’t due to dissatisfaction, but to things like failed card payments at renewal, failed retries, and no way to recover. The team stopped updating the map library and redesigned just one episode—the failed payment at renewal. In six months, they recovered enough subscribers to pay for every CX project that followed. Three hundred maps described the customer’s world, but one episode made a real impact.

The episode is the unit that ships

The subscription example highlights a key idea from Bain’s Customer Episode Design: redesign should focus on the customer episode—the specific task the customer wants to complete, like “resolve the failed payment” or “return the product”—instead of the broad journey map. Bain says clients have seen operating expenses drop by 20% or more with episode-level redesign (Bain). An episode is small enough to redesign and launch in a quarter, big enough to have a real impact on cost and loyalty, and clearly defined so you can measure the true cost. A large map library can’t do that.

Choose moments the customer would name

The total impact comes from several episodes, so you need to prioritize them. EY’s “moments that matter” approach is helpful here. It’s a method, not a statistic: find the few moments that matter most to customers, like the claim, the outage, the renewal, or the first bill, and use these to guide which episodes you focus on first (EY). The prioritized roadmap then becomes your playbook.

How to get started

You can complete the backward design in a quarter, and the first steps only take a week.

• What impact can your CX program prove? Start by choosing one financial impact—like churn cost, cost to serve, or repeat rate—that you would bet the program on. Bring the person responsible for that impact into the conversation before you begin any journey work.

• Which episode has the biggest effect? Start by listing the five episodes that influence your chosen impact. Rank them by the moments your customers would recognize, and focus on redesigning the top one from start to finish, aiming for the same impact in your post-launch review.

CX budgets aren’t cut because experience isn’t important—they’re cut because the program couldn’t show its results. If you design your program backward from a clear impact, you can prove its value. Companies that do this are already growing twice as fast as those still focused on mapping journeys.

What financial impact would you connect to your next CX project? Share your answer.

Sources

• McKinsey, “Experience-Led Growth: A New Way to Create Value,” March 2023 (2016–21 US comparison window dated in-article; >2x revenue growth, ~30% higher TRS; three-pillar model).

• Bain, “Customer Episode Design,” Client-results claim (20%+ OPEX reduction; no sample/year published — attributed as a client claim in-article).

• EY (EY Studio+), “Customer Experience — moments that matter,” Method only — cited as a selection lens, no figure attached (dossier flag).

Stat of the Week. Personalized offers return as much as three times what mass promotions return, by BCG’s read of retail results, while retailers on average still put less than 5% of promotional spending into personalized offers. (BCG, Personalization in Action, 2024.)

Most marketing teams no longer wonder if they should use personalized offers—they’re focused on how to do it. The first mistake often happens here: teams usually pick the biggest, boldest project for their first pilot, like a full journey overhaul or a platform migration, just because it promises the largest opportunity and gets the funding.

A smarter way is to keep the first pilot small and focused. Start by optimizing offers, use a five-step process with a control group, and treat the main goal as earning support for the next pilot.

Offers deliver measurable results quickly

Starting with offers provides a highly measurable pilot. An offer either outperforms a control group or it doesn’t. No attribution model is necessary. And the cycles are short: offers run weekly, so a test-and-learn loop completes in weeks where a journey redesign completes in quarters.

A quick-service restaurant group learned this lesson the hard and expensive way. Their first personalization budget went to a year-long overhaul of their app’s ordering journey, picked because it was the boldest idea. The new app launched and improved in many ways, but the program almost failed in review because there was no control group. No one could prove if the pilot met its goals. What saved the project was the smallest idea on the list: a six-week personalized-offer pilot for one loyalty segment, with a proper control group. This showed a clear increase in margin that finance accepted right away. The next year’s budget was approved based on those six weeks, not the twelve-month overhaul.

The five-step pilot

Design a pilot where each step delivers a defensible result.

1. Segment. Choose one group where you have strong behavior data and a real way to influence them, like lapsing loyalty members or frequent buyers with narrow interests. Don’t start with everyone.

2. Design the offer. Change only the factor you think matters most for this group—like depth, timing, category, or channel—and keep everything else the same. That way, if you see a win, you know what caused it.

3. Build your test-and-learn loop. Use weekly cycles, set clear rules for what moves forward, and agree on how you’ll measure results before you start. This process helps you turn small wins into bigger ones.

4. Measure incremental margin. Use a randomized control group and report the difference in sales volume and margin—not just clicks, redemptions, or engagement. If you don’t see real incremental gains, you’re just giving discounts.

5. Pre-agree on the scale path. Write down, before launch, what result unlocks which next step. A pilot that clears its bar and still waits a quarter for a decision has lost momentum.

A result you can defend

If you don’t have a control group, every result can be debated, and planning turns into a battle of opinions. The QSR group’s six-week results passed finance review because the comparison was built in from the start. Share what met the goal, drop what didn’t, and use your agreed plan to move quickly to the next, bigger test while the results are still top of mind.

How to get started

Two decisions put the pilot in motion.

• Which segment should you test first? Start by picking the one group where you can launch a unique offer within 30 days using data you already trust. Then, write a one-page pilot plan using the five steps.

• What result would your CFO accept? Agree with finance before you start on the sales and margin targets, and on the control group setup. That way, your results are already approved when they arrive.

Offers aren’t the end goal of a personalization program, but they’re the quickest and easiest way to prove it works. Run the six-week pilot, secure support, and use that momentum to tackle bigger challenges with credibility you can’t get from a slide deck.

Reply with the first segment you’d test, and the offer dimension you’d vary.

Sources

• BCG, “Personalization in Action,” 2024 (retail results; “as much as three times” qualifier carried in-article; on average <5% of promotional spending personalized; $100M+ topline stated as an opportunity for large retailers).

Stat of the Week. 20%–30% lower cost to serve through AI-powered next-best-experience, 15%–20% higher customer satisfaction, and a 5%–8% revenue lift. (McKinsey, October 2025.)

In the last ten years, top marketing engines have focused on questions customers never actually asked. Next-best-action (NBA) decisioning looks at every possible move a brand could make, such as offers, upsells, or win-back emails, and sends the one most likely to convert, always with the brand’s goals in mind. But what customers really want to know is, what do I need right now? McKinsey’s answer to this is next-best-experience (NBX), which shifts decision-making to focus on the customer’s current needs. Their client work shows this change can lead to a 15%–20% increase in satisfaction, a 5%–8% boost in revenue, and a 20%–30% drop in cost to serve (McKinsey, October 2025).

There’s another reason to make this change now. Accenture’s research shows that AI agents are starting to interact with brands on behalf of customers, acting as go-betweens that don’t care about your brand. When software is involved, flashy campaigns matter less than the actual experience, which is what NBX aims to improve. The best approach is to shift away from next-best-action as your main method and rebuild around the customer’s key moments, starting with the one you currently handle the least well.

The engine answers the wrong question

NBA became popular because it measures the chance of conversion across thousands of possible interactions, and it beats batch-and-blast methods by every marketing metric. But it misses what happens outside those numbers. NBA engines focus on actions that help the brand, so moments where no offer converts, like a third support call about the same billing error, a delivery that never arrived, or a renewal that quietly failed, are ignored. Yet, these are often the moments that matter most to customers.

A telecom company ran a top NBA program. Its outbound next-best-offer engine reached millions of subscribers each month with personalized upgrade offers, and its conversion rates led the industry. But during that time, customer exit interviews kept highlighting one problem no one was fixing: long hold times after repeat billing complaints. The engine didn’t respond to this because there was no offer to make, so valuable customers waited on hold while the system calculated discounts for people who hadn’t even asked for help. When the team finally set up a trigger for this customer moment—a proactive callback for anyone who contacted support twice in a week about the same charge—it outperformed every other offer in keeping customers. The best result came from an action that wasn’t an offer at all.

Decisioning around the customer’s moment

NBX keeps the same decisioning tools: the data, the models, and the orchestration, but changes the main question from “what do we want this customer to do?” to “what does this customer need most right now?” McKinsey gives the example of a global payments processor whose model predicts which merchants might reduce business in the next week, so the right help, like a technical fix, fee forgiveness, or a better product, arrives while the merchant can still be saved (McKinsey, October 2025). The way success is measured also changes. An NBX program tracks satisfaction and cost to serve, along with revenue, because the value of meeting a need often shows up as the call that never happened.

Deloitte’s research on service organizations finds the same thing: AI-first service companies raise satisfaction while lowering costs to serve (Deloitte Digital, 2026). Both firms’ work shows a clear pattern: the best results come where the stakes are highest for the customer, not just where the conversion model predicts the biggest return.

The agent in between

Accenture’s Consumer Pulse 2026, a survey of 25,590 consumers in 16 countries, shows there’s little room left for experiences that only look good in a campaign report. 74% of consumers say they would trust a personal AI agent more than their best friend to make purchases for them, and 37% of loyal shoppers would let an agent switch brands if it finds a better fit (Accenture, 2026). An agent doesn’t read your campaign; it looks at your prices, your availability, your service record, and whether the last three deliveries arrived on time. As AI-assisted shopping grows, the focus shifts from winning the customer’s attention to earning the agent’s recommendation. The agent’s scorecard is the real experience, moment by moment, which NBX manages and NBA does not.

How to get started

The good news is you can start implementing an NBX strategy on a small scale. The first pilot only needs to focus on one moment, not a whole new system.

• What is one moment your customers have to handle alone that they shouldn’t? Start by looking at 90 days of repeat contacts and complaint themes, and identify the single most important moment your current decisioning ignores. That moment should be your first NBX.

• Can you pilot this in 30 days? Start by setting up just that moment: create a trigger, take one proactive action, and have a holdout group. Measure satisfaction and cost to serve, not clicks. If the telecom’s callback story sounds familiar, the results from this first step can help fund the next one.

The brands that succeed will be those whose experience stands up to AI agents. Rebuilding decision-making around the customer’s moment is how you build that experience, one step at a time.

What is one use case you would pilot in 30 days? Reply and share the moment.

Sources

• McKinsey, “Next Best Experience: How AI Can Power Every Customer Interaction,” October 2025 (ranges are McKinsey estimates from client work, labeled in-article; payments-processor example from the same article).

• Accenture, “Consumer Pulse 2026 (Talk to My AI Agent),” 2026 (n=25,590 across 16 countries; realized survey figures).

• Deloitte Digital, “The Future of Service 2026,” 2026 (n=600 organizations + 3,000 consumers; cited directionally in-article — no specific percentages).

Stat of the Week. $2 trillion: the revenue BCG projects will shift to the companies that are best at personalized marketing over the next five years. The companies leading BCG’s Personalization Index already grow revenue 10 percentage points faster each year than everyone else. (BCG, Personalized, October 2024.)

If you ask a marketing team if they do personalization well, they’ll almost always say yes and show you their tools. They have a customer data platform, a segmentation strategy, and automated journeys. But customers don’t see these tools—they only notice whether the offer feels right for them or not. BCG has measured the cost of this disconnect. Their Personalization Index shows that companies excelling at personalized marketing grow revenue 10 percentage points faster each year than others, and they estimate $2 trillion in revenue will shift to brands that get personalization right over five years (BCG, October 2024). Many teams who think they’re already personalizing well will lose out on this revenue.

What sets the top companies apart isn’t usually effort or budget—it’s how they measure success. Most teams judge personalization by what they’ve built, but the leaders focus on how the customer feels. Two frameworks help turn this customer perspective into action: BCG’s five promises of personalization, which explain how good personalization should feel, and Bain’s Elements of Value, which show what personalization should deliver.

The prize is other companies’ revenue

It’s important to understand that the $2 trillion opportunity is about shifting existing revenue, not creating new demand. BCG expects money to move from companies whose customers don’t feel personalized to those whose customers do. This changes the game, because what works today may not work tomorrow. If customers feel understood by another brand, they’ll take their business there, and the 10-point growth gap will show up in your sales numbers.

You can see the difference in what leaders measure. Top companies track results customers notice, like repeat purchases, share of wallet, and whether people feel known by the brand. Teams that fall behind focus on activity, such as counting campaigns sent or segments created. Activity is easy to report, but customers don’t notice your activity—they notice if your brand’s actions feel personal to them.

Five promises, graded by the customer

BCG’s five promises—Empower Me, Know Me, Reach Me, Show Me, and Delight Me—work because they use the customer’s voice. Your customers are already judging you by these standards, whether you use them or not.

• Three of the promises focus on fit. Know Me is about earning my trust and permission to use my data to improve my experience. Reach Me is about showing up where I am, at a time that works for me. Show Me is about making sure what you offer is relevant to me, not just to a group I might fit into.

• The other two promises are about payoff. Empower Me is about putting my needs first and helping me reach my goals. Delight Me is about constantly improving until the experience sometimes feels magical. These two promises build trust—fit gets attention, but payoff earns loyalty.

A grocery chain shows how tools can look successful even when the customer promise is missed. They invested in channel intelligence, so every weekly promotion reached shoppers through their preferred channel—app notification, email, or printed coupon. But the offer was the same for everyone. For example, a shopper who bought gluten-free products for two years still got the same barbecue bundle as everyone else, sent to her favorite app. The dashboards called it a win because channel engagement went up, but the customer saw it as proof the brand never paid attention to her needs. The chain personalized the delivery, but not the offer. They met the Reach Me promise but failed at Know Me, which is the promise customers expect from brands they trust.

What to personalize toward

Realizing that customer experiences are broken doesn’t show you what to fix first. That’s where Bain’s Elements of Value helps. Their 2016 research, based on over 10,000 US consumers, lists 30 things customers care about, from saving time and cutting costs to reducing anxiety, self-actualization, and social impact (Bain, September 2016). Even after a decade, it’s still the best guide to what customers really value.

When you combine the two frameworks, each promise gets a clear goal. For example, Reach Me, which is about saving time, could be a reorder shortcut that pops up when the pantry is low. Delight Me, focused on reducing anxiety, could mean sending updates before the customer has to ask. Know Me, aimed at quality, would make sure the gluten-free shopper never gets the barbecue bundle again. The promises show how personalization should feel, the elements show what value to deliver, and together they help teams focus on what customers actually want—not just what’s easy to automate.

How to get started

Changing your scorecard is the real work, and it begins with a strategy, not just a new platform.

• Which promise do your customers think you break most often? Start by showing the five promises to real customers this month and ask them to grade you. The difference between their grades and your dashboard is your real backlog.

• Which element of value does your best customer segment care about most? Start by looking at your highest-retention customers and, based on their behavior, identify the two or three elements they clearly value. Then see if your personalization delivers those elements, or if it just sends messages.

The $2 trillion in shifting revenue will go to companies that match their customer promises with what customers value most.

Which of the five promises is your weakest link? Reply and name it: Empower, Know, Reach, Show, or Delight.

Sources

• BCG, “Capturing the $2 Trillion Personalization Opportunity with AI (*Personalized*, Abraham & Edelman),” October 2024 (book + Personalization Index; $2T labeled projection in-article; growth gap stated in percentage points).

• Bain / Harvard Business Review, “The Elements of Value,” September 2016 (n=10,000+ US consumers; vintage flagged in-article).

Stat of the Week. 21%: the share of organizations with a mature governance model for agentic AI, against the 74% who expect at least moderate agent use across the business by 2027. (Deloitte, AI agents are scaling faster than their guardrails, April 2026, n=3,235 leaders / 24 countries.)

When an agent fails in production, there are no obvious signs. The campaign still launches on time, the dashboard shows everything is fine, and mistakes like an unauthorized discount or an unapproved claim can reach hundreds of thousands of people without any error message. Agents fail in ways that are hard to spot, so problems leak out quietly and go unnoticed until someone investigates.

We can now see the gap between how quickly companies are using AI agents and how well they control them. Only 21% of organizations say they have a mature governance model for agentic AI, but 74% expect to use agents widely by 2027 (Deloitte, April 2026, n=3,235 leaders across 24 countries). As Deloitte puts it, AI agents are growing faster than the controls meant to keep them in check.

Most leadership teams react by treating governance as an all-or-nothing choice: either pause the agents until controls are ready, or accept the risks to move faster. But this approach misses the point, since agents are valuable because they are fast, and slowing them down takes away that benefit. Instead, governance designed for speed does three things: it matches each task’s freedom to how easily mistakes can be fixed, it sets clear criteria every output must meet before release, and it assigns an owner to any step that can’t be reversed. All three fit on a single-page governance plan.

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Stat of the Week. 15,505: the number of products on the 2026 marketing-technology landscape, up just 0.79% in a year, growth so flat that Scott Brinker, who has mapped the category for 15 years, asks whether we have finally hit “peak martech.” (chiefmartec, 2026 Marketing Technology Landscape, May 2026.)

For 15 years, martech kept expanding. Each spring brought more logos, every budget cycle added a new tool, and everyone expected the stack to keep growing. That has changed. Brinker’s 2026 landscape lists 15,505 products, just 0.79% more than last year, which he says could be the peak for martech (chiefmartec, May 2026). It looks like buying more tools is no longer the way forward.

This shift matters because the stack is about to face a new kind of user. Agentic systems can plan, decide, and act much faster than people, but the tools they use were built for humans with logins, dashboards, and manual steps. McKinsey calls this the generative-AI (gen-AI) paradox: there are more pilots, but real value stalls because the old systems marketing relies on “were never designed for real-time agentic workflows or shared data models” (McKinsey, April 2026).

In 2026, the main challenge for agentic marketing is the system’s architecture, not a lack of ambition. Agents pick up every delay, silo, and manual step your stack already has, and they do so at scale because they act much more often than people. The marketing teams that will benefit first are focusing on core technology, checking if their stack can support machines, and fixing the basics before adding more agents.

The stack was designed for people, and it’s obvious.

AdExchanger reported this spring that many brands still wait weeks just to move one audience from a CRM system into paid media (AdExchanger, March 2026). Human teams work around these delays, planning campaigns around exports like they plan around holidays. But an agent, built to adjust targeting in real time, would just be stuck waiting for a person to step in.

Last year, a retail bank tested a new agentic campaign system that could put together a promotion in one afternoon, including audience logic, budget, and creative options. But the audience data itself took almost two weeks to move from the CRM, through a warehouse export, and into the ad platforms, passing through three teams. The agent did its job, then had to wait, because everything else moved as slowly as a service ticket.

Four questions your stack has to answer

To see if your architecture is ready for agents, ask these four questions in order of importance. Most stacks fail at least two.

• Can software access the data without a person getting involved? If audiences are moved by export and upload, every automation you add will still depend on that manual step, limiting the system to the speed of the person running it. You need connected systems before you can have real automation.

• Do your systems agree on who the customer is? Agents need to see the whole journey, which only works if CRM, website, service, and media all use the same customer data, not four different versions. It’s no surprise that Accenture’s research found 47% of C-suite executives say getting the data strategy right is a top challenge for gen AI (Accenture, January 2024).

• Can software use your tools, or are they only for people? If a platform can only be used through its screens, agents can’t see it. No matter how good the tool is, an agent can only work with what it can access programmatically.

• Does anything happen in real time? Nightly batch jobs worked when campaigns launched once a week. But if an agent makes decisions in seconds and then waits a day to act, it’s only autonomous in theory, not in practice.

Be honest when you score yourself. Any question you can’t answer yes to shows a layer you need to rebuild next.

Rebuild the core before the agents arrive

The good news is that this work pays off even before you launch any agents. Accenture’s research from January 2024 found that companies called Reinventors were 1.8 times more likely to rate their digital core as strong and 9 times more likely to be fixing technical debt than others (Accenture). Connected data and clean interfaces make everyday work faster and smoother, and the benefits grow even more when agents start using them.

Brinker’s data shows the same trend. While the overall market has stopped growing, the segments that are still expanding are the ones that connect things: content management is up 21.4%, integration platforms (iPaaS) are up 8.0%, and governance is up 7.1%. These layers help systems work together and let software act safely across them (chiefmartec, May 2026). Buyers are now investing in better connections between their existing tools instead of buying more tools.

How to get started

Start with something small and specific.

• Where does your stack fall short on the four questions? First, score your stack this week for data access, a single customer graph, callable tools, and real-time capability. Then, make a plan to fix the biggest gap.

• How long does it really take to activate one audience? First, measure the full process from the CRM to going live in an ad platform, and count every time a person has to get involved. That number shows your stack’s true speed limit.

• Which connection would an agent need first? Start by picking the workflow you want to automate, fix the key integration it relies on, and give one agent live access to it before buying anything new.

The map isn’t expanding anymore, and now the advantage goes to teams whose systems machines can actually use.

Check your stack against the four questions this week, and let us know which one it fails first.

Sources

• chiefmartec, “2026 Marketing Technology Landscape Supergraphic: Peak Martech Achieved (Maybe),” May 2026 (annual census, n=15,505 products). Category growth within the flat total: CMS +21.4%, iPaaS +8.0%, governance +7.1%.

• McKinsey, “Reinventing Marketing Workflows with Agentic AI,” April 2026. Architecture diagnosis quoted verbatim (”never designed for real-time agentic workflows or shared data models”).

• AdExchanger, “The Boring Infrastructure That Could Make Agentic AI Happen For Ad Tech,” March 2026 (reported).

• Accenture, “Reinvention, by the numbers,” January 2024 (vintage flagged in-article). Reinventors 1.8x “strong” digital core, 9x tech-debt remediation; 47% name data strategy a top gen-AI challenge.

Stat of the Week — About 80% of CMOs now report significant investment in AI-specific upskilling, up roughly 10 points from 2025, per BCG. The next evolution will be to shift the budget to fund a role most org charts haven’t written yet.

For years, marketing teams focused on hiring specialists. Each channel had its own expert, like a paid-search lead, email marketer, or e-commerce strategist. Now, agentic AI is changing that approach as channels begin to operate as stages in a consumer journey rather than independently from each other.

Agents can’t supervise themselves. As more agents work together and hand off tasks, companies need a new way to manage them. This requires someone to design the system and decide when a machine’s suggestion should be reviewed by a person. This person is the orchestrator—a role that most organizations haven’t defined yet. Without it, there’s a gap between what AI promises and what it delivers. McKinsey reports that almost 90% of CMOs are trying out AI, but less than 10% see value across their full workflows. This is the gen AI paradox: the technology is everywhere, but it rarely impacts the bottom line.

This year, the most valuable skill isn’t using agents—it’s managing them. The focus is shifting from doing the work to designing and supervising the systems that do it. McKinsey describes this as the CMO’s role growing from managing brand and demand to orchestrating data, technology, and AI-driven execution. Next, we’ll look at what this new job involves, why it matters, and how to build the right team.

From channel specialist to orchestrator-in-chief

Marketing teams now need people who can design and oversee the agents running the channels, not just those who manage a single channel themselves.

Tasks that involve a lot of execution are the first to be handled by agents. These include things like creating content variations, building audiences, managing bids and budgets, and drafting initial plans. McKinsey estimates that agentic AI could eventually handle up to two-thirds of today’s marketing activities.

Some work still needs a human touch, like strategy, taste, brand judgment, and designing and supervising the agent network. This is what McKinsey calls a “hybrid human–agentic workforce.” People set up and oversee networks of agents that do most of the execution. As McKinsey explains, one marketer can now supervise a team of agents. The marketer’s job shifts from doing the work to defining quality and deciding when a person should step in.

The job has changed faster than most organizational charts. Many teams are in transition, with agents in place but no clear supervising role. To close this gap, companies need to redesign roles instead of just adding more agents.

For example, a consumer brand tried to improve its creative process by giving each specialist an AI assistant. While some output improved, overall productivity stayed the same because the workflow didn’t change. Things improved when the team redesigned the process. One orchestrator managed a group of agents from start to finish, with people stepping in at key points for judgment and brand decisions. The tools stayed the same, but the way they were used changed.

Reinvention pays; optimization plateaus

The top performers are building their operating models around the orchestrator role instead of just improving the old system. As a result, the gap between these leaders and others is growing.

Accenture’s research found that about 9% of companies have learned to keep reinventing how they work. These companies saw a 15-point revenue growth advantage from 2019 to 2022. Accenture expects this gap to grow 2.4 times by 2026.

What sets these companies apart? They don’t just buy new tools—they change how work is done and improve their technical foundations. Accenture found they are 1.8 times more likely to rate their digital core as “strong” and 9 times more likely to invest in fixing technical debt. This is the difference between improving what you have and building what agents need.

Early leaders are already changing how they work. They aren’t waiting for the orchestrator role to become standard. BCG says that AI-focused marketing teams are more flexible, with fewer silos, new roles, and much more cross-channel coordination than traditional teams. The main change is building teams around objectives, not channels, with an orchestrator leading each one.

Staffing the orchestrator: the upskilling roadmap

It’s hard to hire enough orchestrators, so the goal is to develop these skills within your current team. Top marketers are already moving in this direction.

Budgets are shifting. Around 80% of CMOs say they are investing heavily in AI training at all levels, up about 10 points from 2025. A similar number are adding training in responsible AI and ethics. The money for this role change is already there, even if the skills are still being built.

The orchestrator needs a different set of skills than a specialist. Their job is to design agent workflows, set up handoffs between agents and people, check agent output for quality and compliance, and understand data well enough to know when to trust results. These are supervisory skills, more like managing a team than running a single channel.

A new role needs the right organization. This means smaller teams with fewer silos, organized by objectives, and a governance layer that, as BCG notes, didn’t exist three years ago. The orchestrator works within this structure, not outside it.

How to get started

Ask yourself these questions as you create your plan.

• Which team role should you reinvent first? Start by choosing one workflow that involves a lot of execution, like creative variation or budget pacing, and assign someone to manage its agents as the orchestrator.

• Are you redesigning your operating model or just improving the old one? Before buying new tools, plan out how one workflow will look with agents doing the execution and people stepping in at key checkpoints.

• Can your team manage the systems it’s putting in place? Start by creating a training program in workflow design, handoffs, and quality monitoring for those who will run the agents, and set a timeline for when it will begin.

You don’t need to hire someone new for this. You can create the orchestrator role within your current team and get started right away.

Which marketing role did you choose to get reinvented first?

Sources

• McKinsey, “Reinventing Marketing Workflows with Agentic AI,” April 2026 (survey of 35 Fortune 250 consumer and technology CMOs).

• Accenture, “Reinvention, by the Numbers,” January 2024.

• BCG, “Moving the Agentic Marketing Transformation from Illusion to Reality,” June 2026 (2026 CMO report, n=300 CMOs + 50 interviews).

Stat of the week — 8%: the share of CMOs who run campaigns where multiple AI agents operate autonomously, against the 96% who say AI is already transforming marketing end-to-end.

Most marketing leaders have watched similar AI demos. In these, an agent reads a brief, creates a campaign, sets the budget, chooses channels, and reports back while the marketer observes. The idea is that marketing can mostly run on its own, with people overseeing instead of doing the work themselves.

Survey results show a different reality. This year, 96% of CMOs told BCG that AI is changing their work from start to finish. But only 8% actually run campaigns where several agents work on their own. Another 42% use generative AI mainly to help with specific tasks. In short, expectations are about a year ahead of what’s really happening.

The gap between what people believe and what they actually do is the main story this year. The teams that succeed won’t be the ones who talk the most about AI or spend the most money. Instead, they’ll honestly assess where they are and improve step by step. We’ll look at how you can figure out your group and what to work on next.

The data agrees: belief is ahead of operation

Deloitte’s research shows that about 38% of organizations are testing agentic AI, but only 11% have it in full use. McKinsey found that almost 90% of Fortune 250 CMOs are experimenting, but less than 10% are seeing value across all their workflows. Nearly everyone is trying AI, but true autonomy is still uncommon. AdExchanger summed up this issue at recent industry events, asking if the agentic 'oasis' is real or just an illusion.

This doesn’t mean the technology is overhyped. It just shows that adoption is still in the early stages.

Why the gap exists: data, orchestration, and talent

The gap between 96% and 8% isn’t because of a lack of ambition. Instead, it’s due to a few key structural challenges that need attention.

Three forces are affecting full adoption:

Agents are limited by the data they can access. Many organizations still need weeks to move a single audience from their marketing database to a digital channel. This delay makes real-time agentic work impossible. Demos use clean, connected data, but most real systems still need to fix their basic data issues.

Orchestration needs clear ownership. One agent writing copy is just a tool, but when agents work together, it becomes a new way of operating. This setup needs a supervisor, clear rules, and a way to handle problems. McKinsey calls the goal a hybrid human–agentic workforce, where a marketer oversees agents instead of doing all the work. Many teams have added agents but haven’t assigned a human supervisor.

Teams are still learning new skills. About 80% of CMOs say they are investing in AI training, which is up 10 points from 2025. The investment is real, but teams are still building their expertise. You can’t supervise what you don’t fully understand yet.

Taken together, these are actually good signs. A structural gap can be closed. Data connections can be made, orchestration can be assigned, and teams can build the needed skills over time. This is more about careful planning than anything magical.

From 42% to 8%

Knowing how ready you are for AI is the first step to moving forward. There are four stages between belief and full autonomy:

• Stage 1: Assisted (about 42%). Generative AI helps people with specific tasks. This is helpful, but not yet a big change.

• Stage 2: Piloting (about 38%). Agentic workflows are tested in a small part of marketing, with close supervision.

• Stage 3: Agent-led. Agents handle multi-step workflows from start to finish, with people checking in at certain points.

• Stage 4: Autonomous (the 8%). Multiple agents work together on a campaign with very little human involvement.

One global retailer called its campaign engine 'autonomous.' But on closer look, a person was approving almost every step, from budget to creative to audience. This isn’t a failure—it’s a good example of stage two being mistaken for stage four. The real risk was believing they were further along than they actually were.

Early leaders take a different approach. Instead of saying they’re at the top, they choose one workflow, move it up one stage, and show the results before expanding. This steady progress adds up as AI spreads through the organization.

How to get started

Ask yourself these questions to help shape your plan.

• Which stage are you really at? Share the four stages with your leadership team and agree on where you stand. Usually, the honest answer is one stage lower than most people think. First step: Agree on your starting point and stick with it.

• Which single workflow will you improve? Pick one that is high-volume and low-risk, like audience building, creative variation, or budget pacing. First step: Choose the workflow and decide which stage you’ll move it to by next quarter.

• What needs to happen for an agent to work in your system? Usually, it’s about making sure the agent can access the right data and knowing when a person should step in. First step: Connect the data for that workflow and set a clear point where a decision goes back to a person.

• Who is responsible for managing the workflow on your team? First step: Choose that person before you add more agents.

Tech Watch. The “oasis or mirage” question ran through this spring’s industry panels, including POSSIBLE 2026, with practitioners divided on whether agentic media buying is shipping or still on slides. The clear bottleneck most often named isn’t the models. It’s the data and identity plumbing the agents depend on.

This year, the marketing teams that do best will be the ones who can clearly say where they are now and take the next real step forward.

Where are you on the 8-to-42 spectrum? Let us know your stage.

Sources

• BCG, How CMOs Are Moving Agentic Marketing from Illusion to Reality (June 2026, n=300 CMOs + 50 interviews) — bcg.com

• Deloitte, AI agents are scaling faster than their guardrails / State of AI in the Enterprise (2026, n=3,235 leaders) — deloitte.com

• McKinsey, Reinventing Marketing Workflows with Agentic AI (Apr 2026, n=35 Fortune 250 CMOs) — mckinsey.com

• AdExchanger, Is Agentic Commerce An Oasis Or Mirage? (2026) — adexchanger.com

The idea is appealing: AI agents that can manage budgets, personalize customer experiences, and turn engagement into sales without people involved. Many marketing leaders are moving quickly toward this goal. In fact, BCG research shows that companies using AI in marketing see 60% higher revenue growth than others. However, as automation increases, there is a risk that is not often discussed: what if AI agents make confident but wrong decisions?

The Hallucination Problem Meets High-Stakes Decisions

AI hallucinations happen when models give answers that sound confident but are not based on real facts. In automated marketing, this can be especially dangerous. While a chatbot might just give a strange reply to a customer, an AI agent in charge of budgets could move millions of dollars to the wrong place before anyone realizes.

Modern marketing automation systems are all connected. For example, if an AI agent looks at campaign data and mistakes a random spike for a real trend, it might move budget to a channel that is not actually performing well. This mistake can then affect other systems, like those that create new ads for the wrong audience. At the same time, the personalization system, using the same bad data, could send irrelevant messages to important customers.

This chain reaction is risky because each automated system assumes earlier AI decisions are correct.

Where Hallucinations Hit Hardest

Budget optimization is where the risks are greatest. When AI agents quickly move money between channels, as many top companies do to catch new opportunities, one wrong idea about channel performance can pull money away from campaigns that are actually working. The same speed that makes AI useful also means mistakes can spread before anyone can step in.

Personalizing customer experiences also comes with risks that are less obvious but just as serious. Generative AI tools can create messages that seem believable but give wrong information about products, make up features, or promise things the company cannot deliver. For example, a European telecom found during testing that its AI ordering system once offered to deliver a truck of soup when a customer tried to trick it. This was funny in a test, but it could be a big problem if it happened with real customers.

Conversion optimization can be hit hardest by AI hallucinations. AI models that predict what customers will do next and suggest actions can become too confident in patterns that are not real, especially if they are trained on small or biased data sets. This means automated systems might focus on signals that do not matter and miss real chances to convert customers.

Building Guardrails Without Losing Speed

The answer is not to stop using automation, but to build systems that keep people involved at important moments. Top organizations are using a tiered approach, where AI agents work on their own within set limits but send bigger decisions to humans for review.

It is also important to add checks that compare AI results with different data sources before taking action. For example, if an AI agent suggests a big budget change, other models should double-check the analysis before any money is moved.

Finally, companies should avoid judging AI marketing success only by how efficient it is. The real goal is not to take people out of the process; it is to free them up to focus on strategic oversight, which helps prevent automated systems from confidently heading in the wrong direction. The future of marketing will favor companies that use AI’s speed and scale but also know when to step in if the technology makes a mistake.

Marketing has always been at the intersection of creativity and analytics, but we're now at a pivotal moment where agentic AI, artificial intelligence that can autonomously execute complex tasks with minimal human supervision, is fundamentally reshaping how marketing operates. This shift transcends traditional automation, with AI systems now capable of independently planning, creating, optimizing, and executing marketing initiatives.

As AI technology advances at an unprecedented pace, marketing leaders face tremendous opportunities and strategic imperatives. Forward-thinking companies are already moving beyond experimentation, integrating agentic AI deeply into their marketing workflows to drive personalization, efficiency, and growth. The question is no longer whether to adopt agentic AI, but how to deploy it strategically to create a sustainable competitive advantage.

From AI tools to an AI partner

The marketing landscape has evolved significantly over the past decade. What began with basic analytics tools has progressed to AI systems that can autonomously generate creative content, optimize campaigns in real-time, and even make strategic recommendations based on complex market data.

This evolution represents a shift from seeing AI as simply a set of tools to viewing it as a strategic partner in marketing. Where once marketers had creative constraints, they're now experiencing a world where AI can help make the right offer at the right time for the right person, in communications that feel cohesive rather than disjointed.

The most sophisticated organizations are now developing AI marketing flywheels where human marketers focus on strategy and oversight while AI autonomously handles execution and optimization. This approach generates measurable results across customer experience, productivity, and revenue growth.

Building the AI marketing flywheel

In this model, human marketers focus on strategic priorities, defining audience strategy, campaign objectives, and creative direction, while AI autonomously handles execution, optimization, and analysis. The AI system can predict trends, gather insights in real-time, auto-populate campaign plans, identify high-value audiences, generate personalized content, and suggest real-time adjustments.

This approach creates a virtuous cycle where AI execution produces data that informs human strategy, guiding the next iteration of AI execution. The result is marketing that continuously improves through human creativity and machine efficiency.

Agentic AI opportunities in marketing

Personalization at scale

Personalization has been a marketing goal for years, but traditional methods have fallen short due to cost constraints and technical limitations. Agentic AI is changing this equation by enabling truly individualized marketing at scale. Leading companies redirect spending from mass promotions to highly personalized offers, creating tailored experiences that resonate with specific customer segments.

AI can now analyze customer behavior at a granular level, developing personalized content that feels unique to each recipient. This level of personalization extends beyond simple name insertion to include tailored product recommendations, individualized pricing strategies, and content that resonates with specific audience segments.

Content creation and optimization

Marketing leaders are using agentic AI to transform content creation processes. Instead of spending weeks developing campaign materials, marketers can generate multiple variations in hours or minutes. AI can analyze performance data across hundreds of creative assets to identify what elements (colors, calls to action, etc.) drive the best results, then incorporate these insights into new campaigns.

This capability allows marketers to test far more creative variations than was previously possible, rapidly identifying and scaling what works.

Autonomous campaign management

Agentic AI is revolutionizing campaign management through real-time optimization and budget allocation. AI can now autonomously shift budgets across platforms and channels to maximize outcomes, enabling marketers to capitalize on emerging opportunities without manual intervention.

These systems can analyze large datasets to forecast outcomes based on different allocation scenarios, then continuously adjust spend to optimize for specific business objectives. This capability has helped organizations reduce media budget planning time while improving key performance indicators like ROAS (“return on advertising spend”).

Audience discovery and targeting

Traditional audience targeting relies on predefined segments that often fail to capture the complexity of consumer behavior. Agentic AI can identify and reach highly specific audience segments based on real-time signals and behaviors.

This approach enables marketers to develop more precise targeting strategies that adapt to changing consumer preferences. Some B2B companies have used these capabilities to expand their reach while simultaneously reducing lead acquisition costs.

The organizational shifts driven by agentic AI

Successfully implementing agentic AI in marketing requires more than just technology investment. It demands fundamental changes to organizational structure, talent, and processes.

Cross-functional integration

Leading companies understand that scaling AI successfully requires building strong partnerships across functional areas. These partnerships are essential for promoting new workflows, securing funding, and implementing talent strategies.

IT teams need to overhaul legacy systems and redesign infrastructure. Finance must validate AI business cases and ensure sustained resource allocation. HR has to secure talent with specialized AI marketing skills. Legal teams must assess data privacy, intellectual property, and compliance risks.

Talent and skills development

As AI transforms marketing roles, organizations must invest in developing new capabilities. This involves both hiring specialized talent and upskilling existing teams.

Marketing leaders are creating AI literacy programs that show how the technology can complement and enhance specific roles. Some companies have made AI tools a standing agenda item in weekly team meetings, with marketers experimenting with the technology and regrouping to discuss successes and refine techniques.

From experimentation to transformation

Companies typically progress through several stages as they mature in their use of marketing AI, from building essential foundations to achieving transformative integration:

Stage 1: Building the foundation

At this initial stage, organizations address gaps in foundational capabilities. They implement AI-ready data management tools, test AI-powered campaigns with first-party data, and redesign creative processes to make assets modular and channel-agnostic.

The focus here is on using built-in AI tools within existing platforms to make rapid progress while establishing a clear vision for where AI can add the most value.

Stage 2: Scaling priority use cases

Companies expand AI-powered use cases across media and creative functions in the scaling stage. They enhance the scope and granularity of data fed to AI systems, enabling more precise insights and decision-making.

This phase involves introducing balanced human-AI workflows backed by responsible governance frameworks. Organizations begin to implement more sophisticated analytical models to predict consumer behavior and optimize marketing investments.

Stage 3: Developing advanced capabilities

As organizations progress, they develop the ability to build real-time audience targeting and fluidly shift resources across channels based on AI insights. They increase the volume and relevance of creative content by integrating AI throughout the creative lifecycle.

At this stage, marketing teams collaborate closely with stakeholders to implement next-generation talent strategies that support the new AI-driven processes. Predictive AI models forecast outcomes and directly inform media activation efforts.

Stage 4: Creating the transformative flywheel

The ultimate goal is to create a transformative AI marketing flywheel where human strategists and AI systems work harmoniously. While few organizations have fully reached this stage due to technical limitations, the vision is clear: AI will automatically predict trends, generate insights, populate campaign plans, identify high-value audiences, and create personalized content at scale.

In this future state, marketers will be freed from tactical execution to focus on strategic direction, creative oversight, and business innovation. The most advanced companies believe they are twice as close to achieving this vision compared to their peers.

Taking action

Marketing executives looking to harness the power of agentic AI should focus on five strategic priorities:

1. Assess current capabilities

Conduct a thorough assessment of your organization's AI marketing maturity. Identify key steps in your end-to-end workflow where AI co

uld play a role, particularly in measurement, insights, media optimization, and creative development.

2. Develop an AI vision and roadmap

Set clear, measurable AI objectives aligned with broader business goals. Start with two to three priority use cases where AI can deliver immediate value, while developing a longer-term vision for how AI will transform your marketing operations.

3. Build cross-functional alignment

Launch a cross-functional AI task force that includes marketing, engineering, IT, finance, HR, legal, and agency partners. Ensure key stakeholders understand the value proposition and their role in enabling AI transformation.

4. Invest in data and technology foundations

Evaluate your current marketing technology stack and identify gaps that must be addressed. Focus on creating integrated data assets that can power AI-driven decision-making, with particular attention to customer data, content management, and measurement capabilities.

5. Develop talent and culture

Invest in upskilling existing marketing teams while recruiting specialized AI talent. Foster a culture of experimentation and continuous learning, with clear processes for testing, measuring, and scaling successful AI applications.

Change is unavoidable and also an opportunity

The marketing function stands at the threshold of its most significant transformation in decades. Agentic AI is not simply another technology to evaluate; it represents a fundamental shift in how marketing will operate in the coming years.

Organizations that take bold steps today to integrate AI into their marketing operations will shape the future of their industries. As AI becomes increasingly sophisticated, the gap between leaders and laggards will only widen, potentially creating insurmountable competitive advantages.

Marketing leaders must now commit to comprehensive AI transformation strategies that reimagine how marketing creates value. By combining human creativity with the power of autonomous AI systems, organizations can unlock unprecedented levels of personalization, efficiency, and growth.

The question is not whether AI will transform marketing, but which organizations will lead this transformation and which will be left behind.

Marketing leaders are facing an unprecedented convergence of challenges as we navigate 2025. With shrinking budgets, intensifying market competition, and the disruptive power of AI transforming the industry, marketing organizations need more than traditional approaches to survive and thrive. The solution lies in embracing agile marketing methodologies - a proven approach that's becoming less of a nice-to-have and more of a strategic imperative.

The Current Marketing Reality: More Than Just Budget Constraints

Modern marketers are operating in a pressure cooker environment where multiple forces are converging simultaneously. Budget constraints have forced organizations to demand more output with fewer resources, while market volatility requires unprecedented adaptability. The emergence of AI as a transformative technology adds another layer of complexity, forcing teams to rethink their entire operational framework.

What makes this moment unique isn't just the presence of these challenges individually, but their intersection. Marketers aren't just dealing with tight budgets - they're managing them while trying to remain competitive, implement new technologies, and deliver increasingly personalized customer experiences. This convergence creates a scenario where traditional, waterfall approaches to marketing simply can't keep pace.

The pressure isn't just coming from external market forces. Internal stakeholders are demanding greater accountability, faster results, and clearer ROI metrics. Marketing teams find themselves caught between the need for strategic long-term planning and the urgency of responding to rapid market changes. This tension often leads to reactive decision-making and fragmented execution, ultimately compromising the effectiveness of marketing initiatives.

Why Agile Principles Are Transforming Marketing Organizations

Agile marketing represents a fundamental shift in how marketing teams organize, plan, and execute their work. At its core, agile methodology borrows proven principles from software development, adapting them to the unique challenges of marketing. This transformation goes beyond implementing practices like sprints or daily standups - it's about adopting a mindset that embraces change, values customer feedback, and prioritizes iterative improvement.

The agile approach breaks down traditional silos by creating cross-functional teams that work collaboratively toward shared goals. These teams operate in short, focused cycles, allowing for rapid adjustment based on real-time feedback and changing market conditions. This flexibility is crucial in today's environment, where customer preferences can shift overnight and new competitors can emerge seemingly from nowhere.

What sets agile marketing apart is its emphasis on data-driven decision making and continuous learning. Rather than committing to lengthy campaigns based on assumptions, agile teams test, measure, and adjust their approaches frequently. This experimental mindset not only improves outcomes but also accelerates the organization's ability to adapt to market changes.

The transformation also involves restructuring how teams prioritize work. Instead of trying to do everything at once, agile teams focus on a smaller number of high-value activities, ensuring that resources are allocated efficiently and impacts are measurable. This focused approach leads to better quality deliverables and more meaningful business outcomes.

The Competitive Advantage of Full Agility

Organizations that fully embrace agile methodologies consistently outperform their partially agile or non-agile counterparts across multiple dimensions. The benefits extend beyond operational efficiency to include improved team morale, better alignment with business objectives, and enhanced adaptability to market changes.

Fully agile marketing teams demonstrate remarkable productivity gains. By implementing practices like sprint planning, daily standups, and retrospectives, these teams can execute campaigns significantly faster while maintaining or improving quality. The structured approach to work management, including the use of kanban boards and work-in-progress limits, ensures that teams aren't overwhelmed and can focus on delivering value systematically.

Perhaps more surprisingly, agile approaches lead to reduced stress levels among team members. The transparency and predictability that come with agile practices help create a more manageable work environment. Team members report greater satisfaction knowing exactly what's expected of them and having clear visibility into how their work contributes to broader organizational goals.

The collaboration improvements are equally noteworthy. Agile teams consistently demonstrate better communication across functions, clearer prioritization of work, and stronger alignment between marketing and other departments. This enhanced collaboration translates into campaigns that are better integrated across channels and more effectively support overall business objectives.

AI Integration: Where Agile Makes the Difference

The relationship between agile marketing and AI adoption reveals a powerful synergy. Organizations that have successfully implemented agile methodologies are dramatically better positioned to leverage AI technologies effectively. This connection isn't coincidental - the core principles that make teams agile also facilitate successful AI integration.

Agile teams approach AI implementation with the same experimental mindset they apply to marketing campaigns. They're comfortable with testing new tools, iterating based on results, and scaling what works while quickly abandoning what doesn't. This willingness to experiment removes one of the biggest barriers to AI adoption: the fear of failure.

The organizational structure of agile teams also supports AI integration. Cross-functional agile pods naturally include the diverse skill sets needed for AI implementation - from data scientists to content strategists to technical specialists. This integration ensures that AI tools are adopted in ways that align with marketing objectives and enhance rather than replace human creativity.

Moreover, agile practices like frequent retrospectives and continuous improvement cycles accelerate the learning curve associated with new AI technologies. Teams that regularly reflect on their processes and outcomes can quickly identify opportunities to leverage AI for greater efficiency and effectiveness.

The feedback loops inherent in agile methodologies also support AI optimization. As teams implement AI tools, they can rapidly gather data on performance, adjust approaches based on results, and refine their use of technology to achieve better outcomes. This iterative approach to AI adoption leads to more sophisticated and effective implementations over time.

Building the Agile Marketing Transformation

Successfully transitioning to agile marketing requires more than copying practices from software development teams. It demands a thoughtful approach to transformation that considers the unique challenges and opportunities within marketing organizations.

The journey typically begins with leadership alignment and education. Marketing leaders need to understand not just the mechanics of agile but also the cultural shifts required. This includes embracing concepts like failing fast, prioritizing based on value rather than politics, and accepting that perfection is less important than progress.

Next comes the careful formation of agile teams or "pods." These cross-functional units should include representatives from various marketing disciplines - from strategy and creative to analytics and operations. The key is ensuring that each pod has the skills and authority needed to execute campaigns from ideation through delivery without excessive external dependencies.

Training and skill development play a crucial role in the transformation. Team members need to develop new capabilities, from facilitation skills for daily standups to estimation techniques for sprint planning. The most successful transformations invest heavily in building these core competencies rather than assuming teams can figure things out on their own.

The implementation of agile practices should be progressive and adaptive. Starting with pilot teams allows organizations to test and refine their approach before scaling across the entire marketing function. This measured approach helps identify potential challenges early and allows for adjustments based on what works within the specific organizational context.

Overcoming Common Implementation Challenges

The path to agile marketing isn't without obstacles. Many organizations struggle with resistance to change, particularly from stakeholders who are accustomed to traditional marketing approaches. Overcoming this resistance requires demonstrating quick wins and maintaining open communication about the benefits of agile methodologies.

Integration with non-agile functions can pose significant challenges. When marketing operates in an agile manner but legal, finance, or IT continue with traditional waterfall approaches, friction often emerges. Successful organizations address this by creating clear interfaces between agile and non-agile teams and by gradually extending agile principles to other functions where appropriate.

Agency relationships often require rethinking in an agile context. Traditional agency models, built around annual retainers and quarterly campaign planning, don't align well with agile's iterative approach. Forward-thinking organizations are restructuring these relationships to support more flexible, outcome-based partnerships that can adapt to agile ways of working.

Measurement and metrics systems typically need updating to support agile marketing. Traditional metrics focused on campaign completion and budget adherence give way to more dynamic indicators that track value delivery, team velocity, and iterative improvement. This shift requires not just new dashboards but also new ways of thinking about marketing success.

The Skills and Capabilities for Agile Success

Building an agile marketing organization requires developing specific skills and capabilities that may not exist in traditional marketing teams. Product ownership skills, typically found in software teams, become essential as marketers need to ruthlessly prioritize features and initiatives based on customer value and business impact.

Facilitation capabilities are crucial for running effective agile ceremonies. Team members need to learn how to conduct efficient standups, productive sprint planning sessions, and insightful retrospectives. These skills ensure that agile practices add value rather than becoming administrative overhead.

Data literacy takes on new importance in agile marketing. Teams need to be comfortable with continuous testing, A/B experimentation, and interpreting results quickly to inform next steps. This goes beyond traditional marketing analytics to include real-time performance monitoring and rapid decision-making based on emerging data.

Collaborative problem-solving becomes a core competency. Agile teams regularly encounter challenges that require cross-functional input and creative solutions. The ability to work effectively in diverse teams, communicate clearly across disciplines, and contribute to collective problem-solving determines the success of agile initiatives.

Leading the Agile Marketing Organization

Leadership in agile marketing organizations looks fundamentally different from traditional marketing management. Leaders shift from command-and-control approaches to servant leadership, focusing on removing obstacles and empowering teams to make decisions close to the work.

This leadership transformation requires developing new capabilities. Agile leaders need to be comfortable with uncertainty, able to provide direction without prescribing solutions, and skilled at creating environments where teams can experiment and learn. They must also be adept at managing stakeholder expectations in an environment where plans frequently evolve.

Communication strategies must evolve to support agile ways of working. Instead of occasional formal status reports, agile leaders maintain ongoing dialogue with teams and stakeholders. They create mechanisms for transparent sharing of progress, challenges, and learning, ensuring alignment without stifling autonomy.

Performance management systems need reimagining to reflect agile values. Rather than evaluating individuals in isolation, agile leaders assess team performance, collaboration quality, and the speed of learning and adaptation. They celebrate experiments that fail but generate valuable insights as much as they reward successful outcomes.

Measuring Impact and Demonstrating Value

Agile marketing requires new approaches to measuring and communicating value. Traditional metrics like campaign reach or budget adherence give way to more sophisticated indicators that capture the dynamic nature of agile work.

Value-based metrics become central to agile marketing measurement. Teams track customer outcomes, business impact, and the velocity of value delivery rather than just activities completed. This shift helps justify the investment in agile transformation by connecting marketing efforts directly to business results.

Agile metrics also include team health indicators. Measuring things like sprint velocity, cycle time, and team satisfaction helps organizations understand whether their agile practices are truly improving their capability to deliver. These metrics provide early warning signals when agile implementations need adjustment.

The most sophisticated agile marketing organizations develop balanced scorecards that combine traditional marketing KPIs with agile-specific metrics. This approach ensures that the benefits of agility are captured while maintaining connection to overall marketing objectives.

The Future of Marketing is Agile

As we look ahead, agile marketing is transitioning from a competitive advantage to a baseline requirement for success. The convergence of AI, evolving customer expectations, and market volatility will only intensify, making the flexibility and responsiveness of agile approaches even more critical.

Organizations that resist this transformation risk falling behind competitors who can adapt more quickly to changing conditions. The gap between agile and non-agile marketing organizations will likely widen as agile teams become more adept at leveraging new technologies and responding to market dynamics.

For marketing leaders, the message is clear: agile transformation isn't just about improving current operations - it's about building the capabilities needed for future success. The investment in developing agile capabilities today will determine whether marketing organizations can thrive in an increasingly complex and fast-paced business environment.

The path forward requires commitment, patience, and a willingness to learn and adapt. But for organizations that successfully embrace agile marketing principles, the rewards include not just improved performance and efficiency, but also more engaged teams, better customer outcomes, and stronger business results. In 2025 and beyond, agile marketing isn't just a methodology - it's the foundation for sustainable marketing success in an ever-changing world.

In today's rapidly evolving B2B landscape, a quiet revolution is reshaping how companies sell to and engage with their customers. The integration of artificial intelligence, particularly generative AI, into B2B sales and marketing processes represents the most significant transformation in commercial operations in decades.

The New B2B Sales Landscape

The traditional B2B sales model has fundamentally changed. Where organizations once relied heavily on in-person sales teams, today's successful companies embrace an omnichannel approach following what industry experts call the "rule of thirds" - customers evenly split their preferences between in-person interactions, remote engagement (phone/video), and digital self-service across all stages of the buying journey.

This shift reflects evolving buyer behaviors. Today's B2B customers use an average of ten different channels throughout their decision-making process - double the number from just a few years ago. They expect seamless experiences across all touchpoints and will readily switch suppliers if these expectations aren't met.

E-commerce has emerged as an indispensable channel, even for high-value transactions. Customers now demonstrate increased comfort making substantial purchases online, with many willing to spend six figures or more through remote or self-service channels.

The AI Revolution in B2B Sales

Within this transformed landscape, AI and generative AI are becoming essential tools for competitive advantage. Leading companies are implementing several key use cases:

AI-Powered Opportunity Identification: AI helps identify high-potential opportunities, segment customers, and target prospects more effectively by analyzing vast amounts of data to uncover patterns that would be impossible for humans to detect.

Personalization at Scale: Gen AI enables tailored communications, product recommendations, and sales approaches based on individual customer needs and behaviors, moving beyond generic messaging to true one-to-one engagement.

Value-Based, AI-Enabled Pricing: Organizations can optimize pricing strategies through AI that determines price points based on customer willingness to pay, competitive positioning, and market conditions, driving significant margin improvements.

Digitally Enabled Seller Automation: AI automates repetitive sales tasks, freeing representatives to focus on relationship building and complex problem-solving. This includes meeting preparation, proposal generation, and simplified administrative processes.

Talent Enhancement and Coaching: AI provides insights into sales performance, identifying coaching opportunities and offering personalized guidance to improve effectiveness across the entire team.

Hybrid Sales Teams for the Future

The workforce model is evolving alongside these technological advancements. Successful organizations are adopting hybrid sales approaches where representatives blend in-person engagement with digital tools and remote interaction. Companies with flexible, multi-location work environments consistently outperform those adhering strictly to traditional models.

This shift requires new capabilities. Top performers are investing in upskilling their teams, particularly focusing on digital fluency, data interpretation, and relationship management in virtual environments. The ideal B2B seller today combines technological savvy with strong emotional intelligence – able to leverage AI-powered insights while maintaining the human touch that remains essential in complex sales.

Building an Integrated Technology Stack

For many organizations, the challenge isn't merely adopting individual AI applications but creating an integrated technology stack that enhances the entire customer journey. Winners in this space build comprehensive architectures that connect marketing, sales, and service functions through unified data, intelligence, and activation layers.

This integration allows companies to track the complete customer decision journey, understand buying signals across channels, and deliver consistent experiences regardless of how customers choose to engage. Commercial technology stacks must move beyond siloed implementations to provide a single source of truth about customer relationships.

The Path Forward

For B2B leaders looking to capitalize on these trends, several actions are critical:

1. Embrace the omnichannel reality: Invest in providing seamless experiences across in-person, remote, and self-service channels, recognizing that customers expect all three options.

2. Make e-commerce central: E-commerce is no longer optional or secondary - it's becoming the primary revenue driver for many organizations.

3. Adopt a data-first approach: Unify data across marketing, sales, and service to create comprehensive customer views and enable AI applications.

4. Prioritize impactful AI use cases: Rather than chasing every AI possibility, focus on applications that directly address key business challenges and opportunities.

5. Build for the long-term: While quick wins are valuable, sustainable advantage comes from foundational investments in data, technology, and talent.

The companies that will thrive in this new environment aren't merely those with the most advanced technology. Rather, success will come to organizations that effectively combine technological innovation with human expertise, creating sales experiences that are more responsive, personalized, and valuable than ever before.

As artificial intelligence continues to evolve, the boundaries of what's possible in B2B sales will expand further. The organizations that experiment, learn, and adapt today will be best positioned to lead in tomorrow's market.

In today's rapidly evolving consumer landscape, the omnichannel concept has transformed from a competitive advantage to a fundamental requirement for survival. As consumers seamlessly traverse physical and digital spaces, organizations must develop coherent strategies that create value across multiple touchpoints while maintaining brand consistency and operational efficiency.

The Omnichannel Necessity

The digital revolution has fundamentally altered consumer behavior patterns. Modern shoppers routinely engage with brands across numerous channels—researching products online, comparing options on mobile devices, seeking validation through social platforms, and making purchases through various combinations of digital and physical interactions. This behavioral shift places unprecedented demands on supply chain capabilities and organizational structures.

However, many organizations approach omnichannel haphazardly, implementing disconnected initiatives that consume resources without delivering proportional value. This fragmented approach erodes profitability and creates disjointed customer experiences that fail to build meaningful relationships.

Strategic Omnichannel Models

Successful omnichannel implementation requires deliberate strategic choices aligned with organizational capabilities and customer expectations. Three distinct strategic models have emerged, each representing different levels of integration and organizational commitment:

Commerce Model: This approach prioritizes seamless transaction experiences across channels. Organizations focus on operational integration that enables conveniences like online ordering with in-store pickup, simplified returns across channels, and inventory visibility. While this represents the entry point for omnichannel capabilities, leading practitioners elevate the approach through initiatives like curated in-store experiences complemented by advisory services.

Personalization Model: Organizations adopting this strategy move beyond transactional efficiency to create tailored experiences across touchpoints. This requires sophisticated data management, cross-channel integration, and robust technology infrastructure. Successful practitioners leverage unified customer data to personalize interactions across digital platforms and physical stores, creating persistent relationships that transcend individual transactions.

Ecosystem Model: The most advanced approach extends beyond core offerings to integrate brands into customers' lifestyles. Organizations have created immersive platforms combining products, experiences, and communities. This model enables expansion into adjacent business opportunities while developing customer relationships beyond traditional engagement boundaries.

Implementation Fundamentals

Regardless of strategic direction, five critical elements underpin successful omnichannel transformations:

1. Cross-Functional Integration: Omnichannel excellence requires breaking organizational silos. Successful initiatives establish cross-functional teams with clear authority and resources to implement solutions that optimize enterprise-wide value rather than departmental metrics.

2. Customer-Centric Strategy: Effective omnichannel strategies begin with deeply understanding customer preferences and behaviors. This includes recognizing that "customers" often encompass multiple stakeholders—particularly for B2B organizations where end consumers and channel partners have distinct needs requiring specialized approaches.

3. Optimized Network Design: Supply chain networks must evolve beyond traditional efficiency models to support omnichannel requirements. This includes rethinking inventory positioning, fulfillment approaches, and potential collaboration with external partners to enhance capabilities without unsustainable infrastructure investments.

4. Specialized Capabilities: Organizations must develop or acquire advanced capabilities like real-time inventory visibility, distributed order management, cross-channel analytics, and flexible fulfillment. These specialized functions require both technological investments and talent development.

5. Agile Implementation: The dynamic nature of consumer expectations requires iterative approaches that enable rapid testing, learning, and adjustment. Organizations should implement capabilities in manageable segments rather than attempting comprehensive transformations, enabling continuous refinement based on performance metrics.

Avoiding Common Pitfalls

Many organizations struggle with omnichannel implementation due to predictable challenges. Leaders should vigilantly avoid:

• Technology-driven initiatives disconnected from strategic objectives or customer needs

• Fragmented investments that create partially implemented capabilities across multiple areas

• Resistance to decommissioning legacy approaches when they conflict with omnichannel objectives

• Inadequate attention to organizational alignment and incentive structures

• Attempting to replicate competitor approaches without understanding their unique organizational context

The Path Forward

Organizations pursuing omnichannel excellence should begin by establishing clarity around strategic objectives and honestly assessing current capabilities. This foundation enables the development of sequenced implementation roadmaps that prioritize initiatives based on value-creation potential.

Success requires focusing resources on the capabilities that enable your chosen strategic model rather than attempting to excel across all potential dimensions. The most effective organizations rigorously evaluate which investments unlock the most significant value for their specific customer segments and business models.

As consumer expectations evolve, organizations that establish coherent omnichannel strategies aligned with core business objectives will capture disproportionate value while building resilient market positions. Those that approach omnichannel as a series of tactical responses will face increasing competitive disadvantages as consumer expectations and technological capabilities continue advancing.

The omnichannel journey represents a fundamental transformation in how organizations engage with consumers. Those who approach this shift strategically, with a clear vision and disciplined implementation, will establish sustainable advantages in increasingly complex and competitive markets.

Introduction

The advertising landscape is undergoing a profound transformation with the emergence of commerce media. This innovative approach to advertising is revolutionizing how brands connect with consumers, creating unprecedented opportunities for commerce brands, advertisers, and publishers alike. At its core, commerce media directly connects audience impressions with actual transactions and business results, enabling more targeted, relevant, and effective advertising strategies.

The Vanguard: Retail Media Networks

Retail media networks (RMNs) represent the first significant manifestation of the commerce media revolution. Retailers, from major e-commerce platforms to grocery chains, are capitalizing on their first-party customer data to build high-margin businesses that offer advertisers unique access to valuable audiences and insights.

Several key factors have catalyzed the exponential growth of RMNs:

• The accelerated shift to e-commerce

• Increasing importance of first-party data

• Growing recognition of the value of shopper insights

• Demonstrable high returns on advertising spend

This evolution creates a paradigm shift extending beyond simple ad placements, touching every aspect of the advertising ecosystem.

Beyond an Amazon-Only Story

While Amazon pioneered retail media, most advertisers now use multiple RMNs. The market has expanded dramatically, with numerous retailers launching their own networks in recent years, including Walmart, Target, Kroger, CVS, Walgreens, and Home Depot, among others.

This diversification benefits advertisers by providing access to unique, highly targeted audiences across various shopping contexts and customer journeys.

A Cross-Category Phenomenon

The impact of commerce media extends beyond consumer packaged goods (CPG). While CPG companies are enthusiastic adopters, brands across automotive, financial services, jewelry, luxury goods, consumer electronics, beauty products, and other categories are equally committed to increasing their RMN investments.

This broad-based adoption signals that commerce media's value proposition resonates across diverse industries, addressing fundamental marketing challenges that transcend specific product categories.

Incremental Growth, Not Budget Reallocation

A common misconception is that commerce media spending cannibalizes marketing budgets that already benefit brands, such as shopper and co-op marketing. The vast majority of spend flowing into commerce media represents incremental growth from new budgetary sources.

This creates a win-win scenario: commerce brands gain additional high-margin revenue streams, while product brands invest in channels that deliver measurable results justifying the expenditure.

Full-Funnel Marketing Approach

Contrary to another misconception, commerce media serves multiple objectives beyond lower-funnel or shopper marketing. Advertisers place equal emphasis on performance marketing and brand building when deploying commerce media strategies.

This balanced approach means commerce brands must develop capabilities to offer comprehensive, end-to-end campaigns that address the end-to-end customer journey rather than focusing solely on conversion-oriented tactics.

The Path Forward for Commerce Brands

For commerce brands looking to capitalize on this opportunity, several strategic imperatives emerge:

1. Protect the Customer Experience: Ensure that advertising enhances rather than detracts from the shopping experience.

2. Demonstrate Performance: Meet advertisers' high expectations by showing precise, measurable results.

3. Deliver Unique Insights: Help brands discover audience insights that inform product innovation and marketing strategies.

4. Segment Strategically: Provide elevated service to high-value advertisers while building self-service capabilities for the broader market.

5. Build Partner Ecosystems: Develop relationships with technology, content, and service partners to create a seamless experience.

Advertiser Strategies for Success

For brands and advertisers, maximizing the value of commerce media requires:

1. Internal Alignment: Create partnerships between marketing and sales teams to ensure joint visibility and accountability.

2. Holistic Evaluation: Assess marketing investments across all channels, including brand, trade, shopper marketing, and traditional media.

3. Clear Expectations: Hold commerce media partners to high-performance standards based on demonstrable results.

4. Insight Extraction: Use commerce media data to uncover new customer segments and inform product development.

The Publisher Response

Traditional publishers face dual disruption from the decline of cookies and the rise of commerce media. To remain competitive, they can:

1. Develop E-commerce Capabilities: Move toward commerce by building marketplace functionalities.

2. Engage with Lower-Funnel Audiences: Incorporate live-commerce elements to create innovative shopping experiences.

3. Partner with commerce brands: Collaborate on data sharing and content creation rather than competing directly.

Conclusion: The Future of Commerce Media

We are only at the beginning of the commerce media revolution. As the landscape evolves, we anticipate further innovation in how advertising connects to commerce across various contexts, from connected TV to virtual reality.

Companies that strategically position themselves in this ecosystem—keeping customer experience as their North Star while forging the right partnerships—stand to benefit substantially. The ultimate winners will be those who shift advertising's focus from mere audience delivery to accelerating business growth through more relevant, valuable customer experiences.

This transformation represents one of the most significant opportunities in marketing today, with implications that will reshape how brands connect with consumers for years to come.