IPG’s AI Strategy: What It Means for Media and Creative

IPG’s AI strategy is one of the more coherent plays to emerge from the holding company world. Rather than announcing a single AI product and calling it a transformation, IPG has been building AI into its media planning infrastructure, creative production workflows, and data capabilities in a way that reflects how agencies actually operate at scale. The question worth asking is not whether the strategy sounds good on a slide deck, but whether it changes the commercial reality for clients.

The short answer is: in parts, yes. In other parts, it is too early to tell, and the holding company incentive structure creates friction that no AI announcement fully resolves.

What IPG Has Actually Built

IPG’s AI positioning centres on a few specific capabilities rather than a single unified platform. Mediabrands, its media investment arm, has been developing AI-driven planning and buying tools under its Kinesso data and technology unit. The pitch is that Kinesso aggregates audience data, applies machine learning to media allocation decisions, and connects planning to outcomes more tightly than a human-led process can at speed.

Acxiom, which IPG acquired in 2018, is the data infrastructure underneath much of this. It holds one of the largest consumer data assets in the United States, and IPG has been positioning it as the engine for first-party data strategies as third-party cookies have declined. The combination of Acxiom’s data depth and Kinesso’s activation layer is the most commercially serious part of the AI story IPG is telling.

On the creative side, IPG has invested in AI-assisted production through agencies like MullenLowe and McCann, and has been integrating generative AI tools into content workflows. The creative AI story is less differentiated than the media and data story, partly because the tools available to IPG agencies are largely the same tools available to anyone, and partly because creative quality is harder to systematise than media efficiency.

If you want a broader view of how AI is reshaping marketing practice beyond the holding company world, the AI Marketing hub at The Marketing Juice covers the practical and strategic dimensions without the vendor framing.

Where the Media AI Argument Is Strongest

I spent a significant part of my career running performance marketing operations across multiple markets, managing budgets that ran into nine figures annually across more than thirty industries. The honest truth about media planning at scale is that the cognitive load is enormous and the margin for error is real. You are making allocation decisions across dozens of channels, hundreds of audience segments, and thousands of creative variants, often with data that arrives with a lag and signals that contradict each other.

This is where AI genuinely earns its place. Not because it makes better creative judgements, but because it processes signal volume that humans cannot process at speed. The Kinesso proposition, at its core, is that machine learning can identify allocation inefficiencies faster than a human planning team and adjust in closer to real time. That is a defensible claim if the data inputs are clean and the model is properly validated.

The risk is that AI media tools can optimise confidently toward the wrong objective. I have seen this happen with algorithmic bidding systems that drove down cost-per-click while quietly destroying conversion quality. The machine was doing exactly what it was told. The problem was the brief, not the algorithm. IPG’s AI media tools face the same constraint: the quality of the output depends entirely on the quality of the objective that goes in.

For marketers evaluating AI-driven media planning, the Ahrefs AI and SEO webinar offers a useful parallel in how AI tools interact with search intent signals, which is increasingly relevant as paid and organic media converge.

The Creative AI Question Is More Complicated

IPG’s creative AI story is where I would apply more scrutiny. The efficiency gains from AI-assisted production are real: faster asset generation, lower cost of variation, quicker iteration cycles. If you are producing localised content at volume across multiple markets, AI tools reduce the production burden materially. That is a genuine commercial benefit.

But production efficiency and creative effectiveness are not the same thing. When I judged the Effie Awards, the work that won was almost never the work that was cheapest or fastest to produce. It was the work that understood something true about the audience and expressed it in a way that created a response. That understanding comes from human insight, not from a model trained on historical creative outputs.

The risk with holding company creative AI is that it optimises for production speed and cost reduction in ways that are visible on a procurement scorecard, while the harder-to-measure quality of strategic thinking quietly declines. Clients who evaluate their agency relationship primarily on production efficiency will get exactly what they measure for.

For teams thinking through how AI fits into content and creative workflows without losing strategic quality, HubSpot’s breakdown of AI copywriting tools is a grounded starting point, and Moz’s analysis of AI content and E-E-A-T is worth reading for anyone concerned about how AI-generated creative performs in search.

The Structural Tension No One Talks About

There is a tension at the heart of every holding company AI announcement that rarely gets named directly. Holding companies make money from complexity. The more channels, the more markets, the more specialisms involved in a client’s business, the more revenue flows through the network. AI, at its most commercially potent, reduces complexity. It automates tasks that previously required headcount, consolidates decisions that previously required multiple specialists, and compresses timelines that previously justified large retainers.

This creates a genuine strategic dilemma. IPG, like every major holding company, is simultaneously trying to sell AI as a value-add that justifies its fees and managing the internal reality that AI adoption at scale will put pressure on the headcount model that underlies its cost structure. These two things are in tension, and no press release resolves that tension.

I ran an agency for a number of years. I know what the P&L looks like and where the margin lives. The honest version of an AI strategy for an agency involves some uncomfortable conversations about what the business model looks like when AI handles a meaningful proportion of the work that currently justifies billable hours. IPG has not had that conversation publicly, and I would not expect them to. But clients should be having it privately.

The question to ask your holding company partner is not “what AI tools are you using?” It is “how does your commercial model change as AI takes on more of the work, and how does that benefit us?”

What the Omnicom Merger Changes

The announced merger between IPG and Omnicom, which emerged in late 2024, adds a significant variable to any assessment of IPG’s AI strategy. Mergers of this scale involve years of integration work, and technology infrastructure is typically one of the most contested areas. Kinesso, Acxiom, and IPG’s other AI-adjacent capabilities will need to be evaluated against Omnicom’s equivalent assets, and decisions will be made about which platforms survive and which get rationalised.

For clients currently invested in IPG’s AI ecosystem, this creates real uncertainty. The data partnerships, the audience infrastructure, the AI-assisted planning tools: all of these sit on top of systems and commercial relationships that may look different in two or three years. That is not a reason to avoid IPG, but it is a reason to understand what is contractually portable and what is not.

From a market structure perspective, the merger would create the largest advertising holding company in the world by revenue. Whether that scale creates genuine AI advantage, through greater data access, more investment capacity, more client diversity to train models against, or whether it creates bureaucratic drag that slows AI adoption, is genuinely unknown at this point. Scale in AI is not automatically an advantage if the organisational structure cannot move quickly enough to use it.

How to Evaluate IPG’s AI Offer as a Client

If you are a senior marketer currently working with IPG or considering it, the AI conversation deserves more rigour than most procurement processes apply. Here is how I would approach it.

First, separate the data infrastructure from the AI tooling. Acxiom’s data assets are real and have genuine commercial value, particularly for brands with complex audience segmentation needs or limited first-party data of their own. That is a different conversation from whether the AI planning tools on top of that data are materially better than alternatives.

Second, ask for specific outcome evidence rather than capability demonstrations. Any holding company can show you a dashboard or a planning interface. What you want to see is documented cases where the AI-assisted approach produced measurably better business outcomes than the prior approach, with a clear methodology for how that was measured. If the evidence is thin, that tells you something.

Third, understand the human layer. AI tools in agency environments are only as good as the people configuring them, interpreting their outputs, and translating them into decisions. The quality of the strategists and planners working on your business matters more than the sophistication of the tools they use. I have seen mediocre thinking dressed up in impressive technology, and I have seen sharp thinking extract value from relatively simple tools. Do not let the AI conversation distract you from the talent conversation.

For teams building their own AI evaluation frameworks, Semrush’s guide to AI content strategy and their analysis of AI optimisation tools offer useful benchmarks for what mature AI-assisted workflows look like in practice.

The Broader Holding Company AI Race

IPG is not operating in isolation. WPP has made significant AI investments through its WPP Open platform. Publicis has built its AI layer around the Publicis Spine data infrastructure and has been arguably the most aggressive of the major holding companies in positioning AI as a core commercial differentiator. Dentsu has its own AI practice. Every major network is making the same argument in slightly different language.

This matters because it means the AI capabilities IPG is describing are increasingly table stakes rather than genuine differentiation. If every holding company has AI-assisted media planning, AI-assisted creative production, and first-party data infrastructure, then AI stops being a reason to choose one over another and becomes a baseline expectation. The differentiation will come from execution quality, talent, and the specific verticals or use cases where each network has built genuine depth.

Early in my career, I watched the shift from traditional media planning to digital happen in a way that initially felt significant and then quickly became normalised. Every agency acquired digital capabilities, every pitch deck featured digital expertise, and within a few years the question was not “do you do digital?” but “how well do you do it?” AI is on the same trajectory. The holding company that builds the most durable advantage will be the one that embeds AI into genuine strategic capability rather than using it as a pitch narrative.

There is a lot more on how AI is reshaping agency and brand-side marketing practice in the AI Marketing section of The Marketing Juice, covering everything from workflow integration to the strategic questions that tools alone cannot answer.

What IPG Gets Right

It is worth being fair about what IPG has done well. The Acxiom acquisition, which was controversial at the time and expensive, looks strategically coherent in a post-cookie world. Owning a first-party data asset at scale, rather than renting access to third-party data, gives IPG a structural position that is genuinely hard to replicate quickly. That was a long-term bet that is now paying off in a way that pure-play AI tooling would not.

The Kinesso build, rather than acquiring a finished product, means IPG has more control over its AI infrastructure than holding companies that have bolted external tools onto existing workflows. That control matters when clients have specific data governance requirements or when the market shifts in ways that require rapid reconfiguration.

And IPG has been relatively disciplined about not overclaiming. Compared to some of the more theatrical AI announcements from competitors, IPG’s positioning has been grounded in specific capability areas rather than sweeping claims about AI reinventing the agency model. That restraint is commercially sensible and, frankly, more credible.

For those interested in how AI imagery and visual production is evolving alongside these broader agency AI plays, Moz’s analysis of generative AI imagery is a useful reference point, and Ahrefs’ AI tools webinar series covers the practical tooling landscape in depth.

About the Author

Keith Lacy is a marketing strategist and former agency CEO with 20+ years of experience across agency leadership, performance marketing, and commercial strategy. He writes The Marketing Juice to cut through the noise and share what works.

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