If there was one message that emerged from this year’s Cannes Lions, it was that agentic AI has moved from future concept to present-day priority. Major platforms rolled out tools that let AI agents plan, create, and even complete purchases with little to no human hand on the wheel — including new agentic ad experiences designed to take consumers from “saw the ad” to “bought the thing” in a single conversation.

The takeaway wasn’t subtle. Agentic AI is moving from experimentation into everyday marketing operations. As marketers explore how AI can streamline workflows and improve campaign performance, success won’t be determined by the sophistication of algorithms alone. It will depend on the quality of the data powering them.

As a data partner, we’re less focused on what AI looks like on a keynote stage and more focused on what fuels it behind the scenes. So once the Cannes headlines settle, here’s the question that actually matters: what does it take to make agentic AI work well, not just work?

Behind Every AI Agent Is a Data Decision

Every agent demoed at Cannes was making decisions based on data it was given. That’s easy to overlook when the demo is polished, but it’s the whole ballgame. An agent can only plan, bid, or personalize as well as the consumer data feeding it. Bad inputs don’t just produce mediocre outputs; in an agentic system, they get compounded and acted on at machine speed with far less human intervention along the way.

That’s the practical shift agentic AI introduces. It’s not just automating tasks, it’s automating decisions. And decisions are only as good as what they’re based on.

Three Questions to Ask Before You Hand Agents More Control

Every AI recommendation is only as good as the data behind it. As agentic AI begins making more decisions across planning, activation, and optimization, a trusted consumer data foundation becomes critical.

Before agentic AI starts making more calls, run a quick gut check on what’s actually feeding those systems:

• Where’s your data coming from, and how was it built? Agents perform only as well as their inputs. Aggregated or context-free data is risky, especially right as you’re handing over more control.
• How fresh is it? Consumer behavior moves fast. An agent making real-time bidding or audience calls on stale data will make confidently wrong decisions.
• Does it move? With agents transacting across DSPs, retail media, and walled gardens, data stuck in one platform limits what any connected agent can do.

Building the Foundation Before You Need It

Fortunately, preparing for an agentic future doesn’t require overhauling your marketing organization overnight. It starts with strengthening the data foundation AI depends on. A few places to start:

• Inventory your current data sources. Flag anything aggregated, stale, or lacking clear provenance.
• Pressure-test your data partners. Ask specifically how they handle identity resolution, refresh cadence, and privacy compliance.
• Build in traceability. When an agent makes a decision, you should be able to trace the data behind it.
• Start small. Pilot agentic workflows in reporting or audience discovery before extending them into budget allocation or bidding.

Data Will Be the Competitive Advantage

Over the next several years, AI capabilities will become broadly accessible across the industry. As that happens, the gap between organizations won’t come down to who has access to the most capable AI. It will come down to who’s feeding their AI data they can actually trust.

That’s the work we do at Alliant. We help marketers build the trusted data foundation agentic AI depends on through deterministic purchase data, identity resolution, predictive consumer insights, and audience solutions designed for transparency and interoperability—so that when an AI agent makes a decision, it’s working with data that holds up.

Ready to audit your data foundation before handing more decisions to AI agents? Contact Alliant to talk through what that could look like for your team.