There is a moment in the history of every major technology shift when access stops being the advantage.
It happened with the internet. In the early years, having a website was enough. Then everyone had one. The advantage moved — to the people who understood how to use it. How to design experiences, build communities, generate demand.
The technology became table stakes. The thinking became the differentiator.
We are at that moment with Agentic AI.
According to McKinsey’s State of AI 2025 report — 88% of McKinsey’s State of AI 2025 report found that 88% of enterprises now regularly use AI in at least one business function. The tools are everywhere. The budgets are being spent. The mandates are coming down from the top.
And yet only 6% of those organisations are achieving meaningful, measurable bottom-line impact from their AI investments.
Read that again. 88% have access. 6% are getting results.
That gap is not a technology problem. It is not a training problem. And it is not an adoption problem — though that is what most organisations have been treating it as for the past three years.
The problem is the strategy.
And here is the part that should give marketing leaders pause: the people best positioned to fix it are already in the room. They just haven’t been given the right question yet.
Why the diagnosis that keeps failing
The most common response to AI underperformance in marketing functions has been more training. More courses, more workshops, more internal communications about the importance of AI.
It is a rational response. It is just the wrong one.
The 2024 State of Marketing AI Report found that 67% of respondents identified lack of education and training as the top barrier to AI adoption. That finding has been remarkably consistent across years of similar research.
So has the result: marginal improvement, at best.
Here is what the persistence of that data actually tells us. When the same diagnosis produces the same response and the same result, year after year, the problem is not execution. It is the strategy.
Training teaches people how to use a tool. But AI is unlike any tool we have had before.
Every tool that came before it was passive. A spreadsheet does what you tell it. A CRM stores what you give it. A campaign platform executes what you configure. None of them think. None of them react. None of them make decisions.
You can teach AI Agents to reason, interpret context, weigh inputs, and produce judgments. You can give it a goal and let it find the path.
Which means deploying AI without deciding what it should think about, react to, and decide on is not just inefficient. It is like hiring a highly capable person and never telling them what their job is.
Training does not answer that question.
It does not decide which tasks in the marketing function should be handled by an agent and which require human judgment. It does not design the handoffs, define the outputs, or build the infrastructure for autonomous execution.
Training only fills a knowledge gap.
Most organisations have made a category error with AI.
They treat AI Agents as another software.
Software is a tool. You buy it, licence it, train people to use it, measure utilisation. The value is in the features. The ROI is in the efficiency gains. It has an implementation timeline and a renewal date.
Agentic AI is not that.
Agentic AI — the capability now scaling rapidly across enterprise organisations — does not sit in a stack. It changes what the stack is for.
Agents can own a process, not just assist with a task.
They can perceive their environment, set goals, make decisions, take actions, and learn from the results. Continuously. Without human intervention at every step.
Gartner projects that by 2028, 33% of all enterprise software applications will include agentic AI — up from less than 1% in 2024. That is a 33-fold increase in four years.
The organisations treating AI as a subscription cost are not just missing the opportunity. They are accumulating a design debt that will compound against them as this capability scales.
What actually separates the winners
Let’s go back to the 88% versus 6% gap, because it is worth sitting with.
McKinsey’s research does not just show the gap. It identifies the single factor that most distinguishes organisations achieving meaningful impact from those that are not.
Out of 25 attributes tested across organisations of all sizes, one factor has the strongest contribution to EBIT impact from AI.
The redesign of workflows.
AI high performers are 2.8 times more likely than their peers to have fundamentally redesigned their workflows around AI.
That is a design advantage, not a technology advantage.
Meanwhile, 42% of companies abandoned most of their AI initiatives in 2025. The average organisation scrapped 46% of its AI proof-of-concepts before reaching production. Only 26% have demonstrated the capability to move from pilot to production at all.
The pilot worked. The design was not there to scale it. The tool performed. The workflow was not built to use it. The training happened. The task ownership was never defined.
The silo is not a culture problem
One of the most common patterns in marketing functions that have been actively investing in AI is fragmentation. Individual teams build their own approaches. Content develops one workflow. Demand generation develops another. Brand does something else entirely.
A 2025 survey found that 71% of executives report AI applications being created in silos — and 68% report that fragmentation is creating active tension between teams.
The temptation is to diagnose this as a collaboration problem. It is not.
When there is no central decision about what AI should own across a function, every team makes its own decision. The silo is not the failure. The silo is the symptom.
Fix the design, and the silo resolves itself. Not because people suddenly start collaborating better — but because the structure of the work makes alignment natural.
Adobe’s 2026 Digital Trends Report adds a further dimension. The top challenge causing AI misalignment is not resistance to change, insufficient tools, or lack of budget. It is executive misunderstanding of AI — cited by 61% of respondents.
In that vacuum, teams improvise. They experiment. They build workflows that do not connect, do not scale, and do not produce the compound value that systematic design would create.
Why marketing leaders are the right people to lead this
This is not a technology problem. It is an organisational design problem.
And marketing leaders — not IT departments, not AI specialists, not consultants — are the right people to solve it.
Here is why.
Marketing sits at the intersection of data, customer behaviour, creativity, and commercial outcomes. It is the function that touches the customer most directly, generates the most varied and continuous data, and produces work that spans the full range — from highly repetitive to deeply creative.
That complexity is what makes marketing hard to design. It is also what makes it the most important function to design well.
Marketing leaders also bring something that technology teams do not. A deep, intuitive understanding of what human judgment in marketing actually looks like. They know which decisions require a strategist and which do not. They know the difference between a brief that needs a creative director and one that needs a system. They know what good looks like at every stage of the process.
That knowledge is exactly what you need to design the division of labour correctly.
Until now, marketing leaders have never had to make that knowledge explicit in this way. Job descriptions, briefs, OKRs — these are all forms of design. But none of them required leaders to specify what a thinking system should own, where its authority ends, and what it should hand back to a human.
That is a new kind of decision. And it is one that marketing leaders are better equipped to make than anyone else in the organisation.
The gap is compounding
The Salesforce State of Marketing 2026 report found that 75% of marketers have adopted AI, yet most are still using it to deliver generic, one-way campaigns. Every marketer has access to the same AI models. What separates the winners is not the tools. It is the design decisions behind how those tools are used.
The gap between organisations that are designing deliberately and organisations that are not is not a fixed distance. It is a growing one.
That is the marketing renaissance.
Not a new tool. Not a new platform. Not a new training programme. A fundamentally different way of thinking about how a marketing function is built — one that starts with the question of what agents should own and what humans should own, and designs everything else from there.
When agents take over the work that is repetitive, processable, and execution-driven, human capacity does not disappear. It becomes available for the work that only humans can do. Strategic judgment. Creative direction. Stakeholder relationships. Decisions that require context, experience, and accountability.
The organisations that get this right will not just outperform their competitors. They will build marketing functions that are more resilient, more adaptive, and more satisfying to work in.
Where to begin
The scale of this challenge can feel paralysing. Fifty tasks to redesign. Multiple teams to align. A function to rebuild while still running.
Pick one team. List every recurring task they perform. Apply three questions to each one:
- Is it repetitive and rule-based?
- Does it require human judgment that cannot be specified in advance?
- What is the cost of getting it wrong?
You will find tasks that shouldn’t exist. You will find tasks that agents could own today. You will find decisions that have been made by habit for years that nobody has ever examined. And you will begin to see the shape of a marketing function that doesn’t just use AI — but is designed around it.
That is where Renaissance begins. Not with the tools. With the thinking.