Author:

Phil Cornier

Summary

Marketing has been a leading adopter of generative AI, yet most organizations have little to show for it on the bottom line. McKinsey calls this the “gen AI paradox”: nearly 90% of CMOs are experimenting with AI, but fewer than 10% have captured value across end-to-end workflows. The constraint is not the technology. It’s that pilots have been bolted onto legacy processes rather than embedded in redesigned ones.

Agentic AI changes the equation. Unlike generative AI, which assists with isolated tasks, agentic systems plan, decide, act, and coordinate across multistep processes. McKinsey estimates agentic AI could power as much as two-thirds of current marketing activities, drive 10–30% revenue growth through hyperpersonalization, and accelerate campaign creation and execution by 10 to 15 times.

Capturing this value requires a deliberate five-step playbook: build a granular taxonomy of marketing activities, define reusable agent archetypes, identify the full set of agents and ensure they can integrate with surrounding systems, redesign workflows with clear human roles, and roll out in waves starting with high-value workflows. Across each step, interoperability, not model capability, is usually the binding constraint, which is why a modernized foundation of unified data, identity, and APIs is a precondition rather than an afterthought.

The transformation reshapes marketing in three structural ways: topline growth from always-on personalization, productivity gains of an order of magnitude, and a reallocation of budget from operational machinery to working spend that reaches actual customers. It also redefines human roles. Roughly three-quarters of marketing roles will need fundamental reshaping, with marketers shifting toward strategy, audience understanding, stakeholder relationships, and oversight of agent fleets and the infrastructure behind them.

The takeaway is straightforward but hard to execute: agentic AI is not a feature to switch on, it is a way of working to build. The organizations that will win are the ones doing the unglamorous, granular work of mapping workflows, modernizing foundations, and redesigning marketing as a discipline where small human teams direct large agent fleets toward outcomes that were previously out of reach.

The Introduction: The Gen AI Paradox in Marketing

Marketers were among the first to embrace generative AI. From copywriting to image generation, from briefing assistants to campaign ideation, marketing teams have piloted more gen AI use cases than almost any other corporate function. And yet, despite all the activity, very few marketing organizations can point to meaningful enterprise-wide impact on the bottom line.

This is what McKinsey calls the “gen AI paradox”: the technology is everywhere, except on the P&L. Activity has surged (more concept images, more draft headlines, more variant copy), but business outcomes haven’t followed. McKinsey’s research finds that nearly 90% of CMOs are experimenting with AI use cases across some part of marketing, yet fewer than 10% have captured value across end-to-end workflows.

The reason is structural. Most early gen AI deployments solved isolated tasks inside legacy processes. They lived as bolt-ons, not redesigns. The underlying martech stack, with its multiple CMS instances, separate digital asset management systems, and fragmented CRM and analytics layers, was never built for real-time, agentic workflows operating against a shared data model. The result is a patchwork of disconnected pilots that increase output volume without improving the work itself.

This pattern is not unique to marketing, but marketing reveals it more starkly than most functions. Marketing is highly cross-functional, dependent on dozens of integrated systems, and judged on outcomes (brand strength, customer acquisition cost, lifetime value) that no single AI tool can move on its own. A faster headline generator does not, by itself, change CAC. A better image tool does not, by itself, raise conversion. The value lives in the workflow, and the workflow is exactly what most pilots leave untouched.

Agentic AI is the technology shift that promises to break this pattern, but only for organizations willing to reinvent how marketing actually gets done. The evidence so far is clear: companies that simply add agents to existing processes get marginally faster execution of the same work. Companies that redesign the work itself get something categorically different.

What Makes Agentic AI Different

Generative AI tools largely assist human work. You ask them to do something; they respond. Agentic AI is built on the same foundation models, but it can plan, decide, act, and execute multistep processes on its own, and coordinate with other agents to do so.

The shift is from passive tool to active collaborator. An AI agent doesn’t just draft an email. It can analyze audience segments, decide which segments to target, draft variants, run them through compliance checks, push them into the activation platform, monitor performance, and reallocate spend in real time. One marketing professional can supervise a team of agents handling work that previously required a much larger team and many more handoffs.

This unlocks an operating model McKinsey describes as the “agentic team”: a small group of two to five humans supervising what amounts to an agent factory of 50 to 100 specialized agents running an end-to-end process. In marketing, that process might be launching a campaign, localizing a brand asset across 30 markets, or running always-on personalization across millions of customer touchpoints.

The economic prize is significant. McKinsey estimates that:

  • Agentic AI could power as much as two-thirds of current marketing activities.
  • Organizations implementing agentic marketing workflows could see 10–30% revenue growth from hyperpersonalization.
  • Campaign creation and execution could accelerate by 10 to 15 times.

Some early content pilots have already cut end-to-end production cycles by 4x.

But none of this is automatic. The gains come from reimagining workflows around agents, not from layering agents onto legacy ones.

The Five-Step Process for Building an Agentic Marketing Workflow

McKinsey’s research identifies a five-step playbook that leading organizations are using to redesign marketing for an agentic operating model. Each step is deceptively simple to describe and genuinely hard to execute.

Step 1: Build a Detailed Taxonomy of Marketing Activities

You cannot redesign work you don’t understand. The first step is to break down priority workflows into the full chain of activities they actually contain, from initial brief to final measurement, and map every supporting system: CRM, CMS, digital asset management, analytics platforms, data pipelines, activation environments.

This is granular work. It surfaces the dependencies, handoffs, and friction points that current tooling has obscured for years. It also reveals the real reason most pilots stall: not that the model can’t do the task, but that the task is embedded in a chain of upstream and downstream work the model has no way to touch.

Step 2: Define Agent Archetypes

Rather than designing agents one task at a time, organizations group them into reusable archetypes that can be applied across multiple workflows. McKinsey cites a consumer brand that classified its agentic needs into six archetypes:

  • Content generator: produces text, images, video assets
  • Knowledge: extracts and synthesizes information from internal and external sources
  • Localization: adapts content for markets, languages, and cultural contexts
  • Analyzer: evaluates performance, audience response, and signals
  • Planner: sequences activities, allocates resources, designs campaigns
  • Operator: executes actions in activation platforms, manages campaigns live

Designing for archetypes, rather than for one-off agents, is what allows the system to scale. The same content-generator archetype can be specialized for email, paid social, programmatic display, and product copy without rebuilding from scratch each time.

Step 3: Determine the Full Set of Agents Needed

Within each archetype, organizations identify the specific agents required across all priority workflows, and (critically) make sure each one can integrate with the data platforms, content repositories, and activation systems it needs to act on.

This is where most agentic projects hit their hardest wall. McKinsey’s research is emphatic on this point: interoperability, not model design, is usually the limiting factor. The model is rarely the problem. The problem is that the customer data lives in one system, the brand assets in another, the compliance rules in a third, and the activation platform exposes a brittle, partial API. An agent can only act as well as the systems around it allow it to act.

This is why a modernized technology foundation, including unified identity, unified data layers, flexible model-serving infrastructure, and reliable APIs across content and activation systems, is a precondition for scaled agentic marketing, not an optional accessory.

Step 4: Define Future-State Workflows with Clear Human Roles

Once agents are mapped, the workflow itself can be redesigned. This step is often misunderstood: it is not about deciding which tasks humans “still” do. It is about deciding what humans should now be doing, given that an agent fleet handles the operational layer.

McKinsey’s view is that human marketers should shift toward:

  • Strategy: what the brand stands for, where it competes, what bets to place
  • Audience understanding: what genuinely resonates and why
  • Stakeholder relationships: internal partners, agencies, retailers, talent
  • In-person activations: experiences, events, anything irreducibly human
  • Infrastructure oversight: data quality, content metadata, orchestration rules, API governance, agent quality monitoring

The skills that go with these roles are also new. McKinsey lists prompt engineering, agent collaboration, output refinement with human expertise, applied ML literacy, and quality monitoring as the emerging core capabilities of an agentic marketing team.

Step 5: Prioritize Implementation in Waves

Trying to agentify everything at once is the surest way to deliver nothing. Leading organizations roll out in waves, starting with high-value workflows that build adoption and confidence. They also make a deliberate choice on each component about whether to build custom or buy off-the-shelf, factoring in technical readiness and reuse potential.

McKinsey describes a consumer-brand rollout that happened in three waves:

  • Wave 1: built an ideation engine to accelerate concept generation
  • Wave 2: added pretesting and compliance checks into the same flow
  • Wave 3: extended the system globally for localization and scalable testing

This sequencing is deliberate. Each wave delivered standalone value while building the foundation (data, governance, integration patterns) that the next wave depended on. By the third wave, the team had an end-to-end pipeline running at four times the speed of the pre-AI process.

Three Ways an Agentic Workforce Transforms Marketing Operations

When the five steps come together, the impact extends well beyond efficiency. McKinsey’s research points to three structural shifts in how marketing creates value.

1. Topline Growth Through Hyperpersonalization

The 10–30% revenue uplift cited by McKinsey comes from the ability to run always-on, AI-enabled campaigns that personalize at a level no human-driven workflow could sustain. Crucially, much of this activity becomes self-serve: rather than every campaign requiring a brief, an agency, and a six-week production cycle, internal stakeholders can spin up compliant, on-brand campaigns themselves, with the agent fleet handling production and activation.

The cross-functional dimension matters too. Because agents can move data and decisions across teams and channels in real time, the historical silos between brand, performance, CRM, and trade marketing start to dissolve.

2. Productivity at a Different Order of Magnitude

The 10–15x acceleration in campaign creation and execution McKinsey reports isn’t a marginal improvement. It changes what’s possible. Concepts that were previously infeasible because they couldn’t be produced, tested, and localized in time now become routine. Synthetic audience testing, which uses AI-generated audience simulations to evaluate campaigns before deployment, collapses what used to be weeks of pretesting into hours.

Continuous optimization in flight is another shift. Agentic systems can adjust budgets, creative variations, and audience targeting incrementally and constantly, reducing the need for the manual checkpoint reviews that have governed media execution for decades.

3. Reallocation of Spend from Operations to Working Spend

A significant portion of every marketing budget goes not to reaching consumers but to the operational machinery of producing, reviewing, and activating campaigns. Agentic workflows compress that operational layer, which means more of the budget can flow to working spend (actual customer reach). For large enterprises, even a single-digit shift in this ratio can mean tens or hundreds of millions in newly available reach.

The shift also changes the economics of long-tail marketing. Many activities that were previously uneconomic, including micro-segmented campaigns for smaller audiences, hyperlocal creative variations, and always-on lifecycle communications for low-value customer cohorts, become viable when agents can produce, test, and activate them at near-zero marginal cost. Markets, segments, and moments that used to fall below the threshold of human attention now get served, often with measurable incremental return.The combined message from Ottawa is that the slow phase is over. The frameworks exist, the case studies exist, and now the financing exists. What the government will be watching for next is uptake: how many businesses actually take advantage of the program, and whether the productivity bump shows up in the data a year or two from now. Program success metrics will likely include the number of loans deployed, the mix of software-focused versus physical AI projects, the share of Canadian-developed solutions adopted, and longer-term productivity outcomes among participating firms.

The international context also matters. Several peer economies have launched comparable programs, and Canada has been criticized in past cycles for slower diffusion of digital technology among SMEs. LIFT reflects an awareness that AI adoption among small businesses is not just a productivity question but a competitiveness question. Canadian SMEs that delay adoption do not simply miss out on internal efficiencies; they risk losing market share to international competitors that have already integrated AI into pricing, fulfillment, customer service, and operations

Workflow automation tools enable teams to connect applications, trigger processes, and move data across systems without heavy engineering effort. These tools focus on execution at the workflow level, allowing organizations to automate repetitive tasks, integrate AI outputs into business processes, and streamline operations across platforms.

Why Most Companies Will Struggle, and What Separates the Leaders

The gap between 90% of CMOs experimenting and fewer than 10% capturing value isn’t going to close on its own. The execution challenges are real and worth naming clearly.

Workflow redesign is hard, granular work. Most organizations underestimate how much process archaeology is required to map current-state activities at the level of detail an agentic redesign demands.

Legacy martech is the binding constraint. Years of accreted CMS, DAM, CRM, and analytics tooling, often integrated only at the surface, were never designed to expose the kind of clean, real-time APIs that agents need. Modernizing this foundation is multi-quarter work that doesn’t show up in marketing’s quarterly results.

Governance is non-negotiable. Marketing is consumer-facing by definition, which means errors are visible, brand-damaging, and sometimes legally consequential. The McKinsey research highlights brand and legal governance, capability gaps, technology under-investment, and data bottlenecks as the top concerns marketing executives flag. Agentic governance, which defines what agents can decide on their own, what requires human approval, how outputs are monitored, and how risks are escalated, has to be designed in from the start, not bolted on after launch.

Capability and culture lag the technology. Roughly three-quarters of marketing roles will need fundamental reshaping. That’s not a training problem; it’s a job-design problem. Organizations that try to manage this as a series of upskilling courses rather than a redesign of how marketing operates will see uneven adoption at best. The harder cultural work is teaching teams to operate “above the loop,” supervising fleets of agents rather than executing tasks themselves, which inverts decades of how marketing performance has been measured and rewarded.

Trust is the gatekeeper. As agentic systems take on more autonomous decision-making in consumer-facing contexts, customer trust, regulatory scrutiny, and brand exposure all rise. Organizations need transparent reasoning, auditable decisions, and clear escalation paths long before agents are operating at scale.

What separates the leaders is straightforward to state and difficult to do:

  • They redesign workflows, not tools. Agents are embedded in end-to-end processes, not bolted onto legacy steps.
  • They invest in the foundation (unified data, identity, model serving, API governance) in parallel with use cases, not after.
  • They treat agents like managed talent. Governance, quality monitoring, and feedback loops are first-class concerns, not afterthoughts.
  • They scale with a new operating model. Cross-functional human and AI teams, shared data products, and flat decision structures with high context-sharing across teams.
  • They start with the outcome. Define the business result the workflow exists to deliver, then design backward to the agents and humans needed to deliver it.


Conclusion: Augmentation, Not Replacement

It is tempting to read research like McKinsey’s and conclude that the future of marketing is one where humans are progressively pushed out of the workflow. That’s not what the evidence suggests.

The future is one where humans are pushed up the workflow, out of the operational tasks that agents handle better and faster, and into the strategic, relational, creative, and judgment-intensive work that defines a brand’s actual position in the world. Human judgment, cultural understanding, qualitative sense-making, and strategic thinking remain essential. What agents bring is the speed, precision, and scale of execution that finally allows the strategic layer to operate without being dragged down by the operational one.

The organizations that will win the next phase aren’t the ones with the most pilots or the flashiest demos. They are the ones doing the unglamorous, granular work of mapping their workflows, modernizing their foundations, redefining their roles, and rebuilding marketing as a discipline where small teams of humans direct large teams of agents toward business outcomes that were previously out of reach.

Agentic AI isn’t a feature you turn on. It is a way of working you have to build. The companies that understand the difference are the ones that will move from the gen AI paradox to genuine, durable, bottom-line impact.