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.

Read more

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.

In today's rapidly evolving marketing landscape, CMOs face increasing complexity, expanding responsibilities, and higher expectations—all while facing pressure to drive growth and demonstrate ROI. The key to unlocking marketing's full potential requires fundamentally reimagining your marketing operating model, not through incremental adjustments.

The Expanding Marketing Mandate

Marketing leaders now wear multiple hats, with responsibilities extending far beyond traditional marketing functions. Marketing departments increasingly own or influence product innovation, sales/e-commerce, AI strategy, pricing decisions, and more. This expanded scope creates both opportunities and challenges.

Successful CMOs recognize this evolution requires more than adding new technologies or hiring specialized talent. It demands a holistic transformation of how marketing operates—connecting teams, ways of working, and expertise to drive growth.

Three Pillars of a Future-Fit Marketing Operating Model

Organizations with robust marketing operating models outperform peers by creating clear links between marketing activities and business outcomes. To achieve this, focus on three interconnected pillars:

1. Connecting Teams Through Organizational Structure

The most common challenge facing marketing organizations is siloed structures with insufficient cross-functional collaboration.

Effective organizations address this by:

• Mobilizing beyond reporting lines: Creating cross-functional teams with shared goals and end-to-end accountability

• Strategic centralization: Establishing centers of excellence for key capabilities like AI/generative AI, while maintaining local flexibility for market execution

• Breaking down barriers: Dismantling silos between brand teams, creative, performance marketing, analytics, and technology functions

Companies that have brought multiple functions under a unified marketing umbrella have seen improved collaboration and more coherent growth strategies.

2. Connecting Ways of Working Through Governance and Culture

Clear governance and an agile culture are essential for pivoting quickly and adapting to changing consumer landscapes.

Leading organizations focus on:

• Establishing clear decision rights: Defining precise roles, responsibilities, and process flows to enable faster decision-making

• Fostering agility: Creating a test-and-scale culture where teams can experiment without fear of failure

• Building ownership mentality: Assigning clear growth objectives rather than predefined activities

Organizations that dedicate time to proactive planning, spend analysis, backlog management, and ideation create more efficient marketing operations.

3. Connecting Expertise to Growth Drivers Through Capabilities

Marketing leaders must invest in growth capabilities, balancing new technologies with human expertise.

Key focus areas include:

• Embedding analytics and customer data: Building integrated data ecosystems for comprehensive customer views

• Embracing AI and generative AI: Developing capabilities that enhance creative output, personalization, and efficiency

• Developing "T-shaped" talent: Building multidisciplinary competencies across customer centricity, full-funnel marketing, measurement, and marketing technology

• Creating strategic partnerships: Cultivating an ecosystem of external partners aligned with business outcomes

Building Your Connected Operating Model

To transform your marketing operating model successfully:

1. Define a clear North Star: Establish your marketing vision and tie it to concrete business objectives before restructuring teams

2. Focus on new ways of working: Prioritize cross-functional processes and capability building over rearranging org charts

3. Take a holistic approach: Address the entire marketing ecosystem rather than solving isolated issues

4. Celebrate early wins: Demonstrate value to build organizational momentum and acceptance

The Path Forward

The most successful marketing transformations combine hiring, acquisition, and upskilling—with the heaviest emphasis on upskilling existing talent. Make capability building a strategic priority, not an afterthought.

Companies leading in this space make learning journeys mandatory for all employees—including C-suite executives—signaling that digital transformation is a critical strategic initiative rather than a peripheral concern.

Conclusion

A connected marketing operating model isn't just nice to have—it's essential for competing in today's complex landscape. Organizations that successfully connect teams, ways of working, and capabilities can realize significant improvements in customer satisfaction, sales conversion, cost efficiency, and employee engagement.

The time to transform is now. As customer expectations continue to rise and the competitive landscape evolves, the organizations that reimagine their marketing operating models will be positioned to drive sustainable growth and deliver superior customer experiences.

In today's rapidly evolving business landscape, generative artificial intelligence (GenAI) fundamentally reshapes how companies connect with customers. This revolutionary technology is not just another digital tool but a catalyst for transformation across the entire marketing ecosystem.

While traditional AI has enhanced business operations for years, GenAI represents a significant leap forward. Unlike conventional AI systems trained for specific tasks, GenAI can generate original content, analyze complex data patterns, and create personalized customer experiences at unprecedented scale and speed.

The Business Case for Generative AI

The most forward-thinking organizations are already deploying GenAI across three key areas:

Customer Experience Enhancement
GenAI enables hyper-personalization at scale, allowing businesses to craft individualized interactions that resonate with customers on a deeper level. Companies can now analyze customer behavior in real-time and deliver tailored recommendations, communications, and solutions that address specific needs. This level of personalization was previously impossible without significant manual effort.

Growth Acceleration
By leveraging GenAI for targeted promotions and dynamic audience segmentation, companies can effectively identify and activate high-value customer segments. Marketing campaigns that once required months of content design and customer targeting can now be executed in days or weeks, often with personalization that drives higher engagement and conversion rates.

Productivity Optimization
GenAI streamlines workflows by automating routine tasks, allowing marketing professionals to focus on strategic, high-value activities. GenAI tools can handle time-consuming processes with remarkable efficiency, from content creation to data analysis, freeing human talent for more creative and complex challenges.

Strategic Implementation Approaches

Organizations exploring GenAI typically follow three implementation paths:

1. Quick-Win Pilots: Many companies start with off-the-shelf GenAI tools integrated into existing workflows. These early efforts deliver immediate value by generating copy, personalizing campaigns, and analyzing customer feedback more efficiently. They also help organizations build crucial capabilities for more advanced applications.

2. Customized Solutions: Leading companies adapt generic GenAI models with their proprietary data and specific business needs to differentiate themselves. Training these models on brand guidelines or historical marketing campaigns creates bespoke solutions that continuously improve and enhance competitive advantage.

3. End-to-End Transformation: The most ambitious organizations are reimagining their entire marketing and sales functions with GenAI at the core. Almost all marketing tasks are AI-assisted in this transformed future, from content creation to customer research, enabling genuinely unique customer experiences that drive growth.

Building a Technology Foundation

Successful GenAI implementation requires a robust technology foundation built on five pillars:

• Data: Enhanced data collection and analysis capabilities incorporating comprehensive customer information, content engagement metrics, and robust analytics infrastructure.

• Decisioning: Advanced AI models that predict customer behavior and determine the most effective actions, offers, and content for each interaction.

• Distribution: Sophisticated architecture that delivers seamless messaging to the right audiences at the right time across all channels.

• Content Supply Chain: Integrated systems for managing content production, ensuring consistency and relevance across all touchpoints.

• Analytics: Comprehensive frameworks that track performance, validate ROI, and facilitate continuous optimization.

Getting Started with GenAI

For organizations looking to harness the power of GenAI in marketing, here are practical next steps:

1. Create a vision and roadmap: Develop a clear view of how GenAI can address your business challenges and prioritize use cases based on potential impact and feasibility.

2. Build the team: Establish a cross-functional team that combines technical expertise, domain knowledge, and strategic thinking to drive implementation.

3. Launch pilot projects: Start with low-complexity use cases where off-the-shelf tools can deliver quick wins, learn from these experiences, and scale successful approaches.

4. Implement governance: Establish guidelines, oversight mechanisms, and risk management processes to ensure responsible use of GenAI technology.

Looking Ahead

The rise of GenAI in marketing is not just a technological shift but a fundamental reimagining of how businesses connect with customers. Organizations harnessing this technology effectively will create more meaningful customer relationships, drive sustainable growth, and gain significant competitive advantage.

Those who wait on the sidelines risk being left behind as competitors transform their marketing capabilities. The future of marketing is being shaped by generative AI today, and the companies that embrace this transformation will be the ones that thrive tomorrow.

In today's digital-first marketplace, delivering personalized experiences has evolved from a competitive advantage to table stakes. Organizations across industries recognize that personalization drives measurable business outcomes — improved customer acquisition, increased retention, and enhanced lifetime value. Yet despite widespread recognition of its importance, many organizations struggle to implement personalization effectively at enterprise scale.

The Value Proposition of Personalization

Successful personalization transforms how customers engage with brands by delivering relevant content, offers, and experiences precisely when they matter most. When implemented effectively, personalization creates mutual value: customers receive tailored interactions that better meet their needs, while organizations benefit from improved marketing efficiency, increased conversion rates, and stronger customer relationships.

The business case is compelling. Organizations that master personalization can reduce customer acquisition costs, increase revenue, and improve marketing efficiency. However, few companies successfully execute personalization at scale despite these potential unlocks.

The Framework: A Blueprint for Personalization Success

Implementing personalization at scale requires a comprehensive approach addressing technology architecture and organizational capabilities. Drawing on implementation experience across diverse industries, the most successful organizations build their personalization capabilities around five interconnected dimensions:

1. Data Foundation

Personalization begins with data accessibility and integration. Most organizations already possess valuable customer data, but it typically resides in disconnected systems managed by different stakeholders. Creating a centralized customer data platform (CDP) that connects disparate data sources enables a unified customer view that can be consistently leveraged across channels.

Leading organizations implement three critical data management components:

• A customer data platform that centralizes first-party data

• Identity-resolution capabilities that match known customers with otherwise anonymous digital IDs

• A data management platform that activates first-party data and enriches it with third-party insights

2. Decision Engine

Effective personalization requires sophisticated decision-making capabilities that determine the next best action for each customer. Advanced organizations build centralized analytic models that create propensity scores at the individual level, enabling precise predictions about customer behavior and preferences.

Machine learning and AI now play increasingly critical roles in identifying patterns that static models might miss, continually optimizing decisions based on real-time customer signals and business rules. While no single platform today fully serves as a comprehensive decision engine, organizations should maintain active experimentation with emerging tools that deliver incremental capabilities.

3. Distribution Capabilities

The distribution dimension connects data, decisions, and design to the marketing technology systems that deliver personalized experiences guided by analytics. Integration between channels enables coordinated communications that react to customer actions in real-time rather than relying solely on predetermined campaigns.

4. Content Supply Chain

As personalization scales, content needs grow exponentially. Traditional content approaches that rely on bespoke assets cannot support the volume required for personalization at scale. Instead, organizations must adopt a composable content strategy where creative assets are broken into content fragments that can be dynamically assembled based on customer context.

This modular approach requires:

• A content factory operating model that produces flexible, reusable assets

• A well-structured taxonomy and tagging system

• A digital asset management system for storage and retrieval

• Dynamic creative optimization capabilities for real-time assembly

5. Analytics

Leveraging emerging capabilities in artificial intelligence, analytics can unlock the potential of consumer data to drive personalization at scale. Combining real-time behavioral data with historical buying signals, a durable identity graph, and enriched demographics can effectively predict what products and pricing will resonate with consumers. Activating these models through a decision engine can deliver the most compelling content in the right channel and the exact right moment to influence a consumer to take the action of your choice.

Implementation: Starting Small, Scaling Fast

While the technology blueprint may appear daunting, successful organizations typically start with targeted initiatives that demonstrate value quickly. The key is progressing iteratively:

1. Begin with the data you have today instead of waiting for perfect information architecture

2. Identify high-value customer signals and develop relevant trigger-based responses

3. Establish cross-functional teams empowered to test and scale rapidly

4. Use small wins to build momentum and self-fund future transformation efforts

Organizations that outperform in personalization share a common approach: they address technology and organizational challenges in tandem. Cross-functional teams with clear executive sponsorship create the operating model needed to capitalize on technological capabilities. These teams typically include marketing, analytics, creative, operations, and technology experts working together to identify signals, develop responsive triggers, and continuously refine the personalization approach.

Looking Forward: The Evolution of Personalization

As customer expectations evolve, organizations must view personalization as an ongoing capability rather than a finite project. The technology landscape supporting personalization continues to mature rapidly, requiring a flexible architecture incorporating emerging capabilities.

The organizations leading in customer experience will systematically build their personalization capabilities across all five framework dimensions, creating a cycle of customer insight, engagement, and value creation. In an era where customer expectations continue to rise, personalization at scale represents not just a marketing strategy but a fundamental business capability that drives sustainable competitive advantage.