Artificial Intelligence and Marketing: How to Make Your Customer Data Actionable with AI?

Forrester’s recent report, The Customer Data Platforms Landscape, Q1 2026, shows that AI is disrupting the CDP market by redefining the way brands interact with their audiences. Forrester identifies AI as the main disruptor in this market, marking a major shift from simple data management to an era of orchestration and augmented decision-making. This change is all the more radical as 72% of marketing decision-makers consider the implementation of a CDP to be expensive to maintain, making immediate value creation essential. However, while the promises of AI (content generation, prediction, hyper-personalisation) are immense, many companies are coming up against a technological wall – their data is not ready. Because for AI to truly work its magic, it needs clean raw material – “AI-ready” data.

What data does AI need? How is it become possible to switch from simple manual execution to true intelligent orchestration? 

To understand these issues, Arthur Lacroix, Head of Product Marketing at imagino, explains how to modernise your approach to data to feed the engagement loop of tomorrow.  

The strategic shift of AI agents and conversational interfaces 

The massive arrival of AI agents and conversational interfaces in 2026 is completely redefining our relationship with marketing tools. Until now, marketing teams had to navigate between several tools to segment, analyse or launch campaigns. Now, they can simply express a business intent. But for this promise to become a reality, AI agents must be able to access reliable, governed and usable data in real time. 

It is precisely to meet this challenge that our Customer Engagement Platform welcomes Imogen, our native artificial intelligence. Able to understand your intent in natural language, it helps you to analyse your data, generate audiences and journeys, make better decisions, and continuously optimise customer engagement. Leveraging our foundation of unified, live data, our AI agent accompanies marketers in a conversational way at every stage of the engagement loop. 

What is the difference between “classic” customer data and truly “AI-ready” data? 

For years, companies have built platforms that can collect and centralise information. It was the era of the Customer Data Platform. But for Artificial Intelligence, this approach is no longer enough. “Classic” data is often stored in silos, duplicated between several tools or updated in batches. It is impossible  to exploit by modern algorithmic models.  

“AI-ready” data is based on three fundamental pillars: 

• Unified: AI must have access to a single, perfectly reconciled customer view, bringing together web history, e-commerce, CRM, loyalty and customer service. If a customer changes their last name and the system creates a new entry, the AI will send redundant communications.  
• Fresh: AI must be able to read and act on “living” data, updated in near real-time. Using copied or obsolete data significantly limits performance compared to a streamed approach. 
• Governed: Data must be structured and regulated (consents, GDPR, access rights) to allow legal and secure access and use by AI models. 

“The future of AI in marketing lies in the ability to operate directly on living data, where it resides, and not on yet another copy. This is what will make the difference between those who achieve average results and those who continuously optimise their direction.” 

How does AI-usable data enable intelligent orchestration?  

Faced with AI’s ability to generate thousands of campaigns and content in a few clicks, the major risk is marketing fatigue. Inboxes are saturated and customer attention is more difficult to hold than ever. Intelligent orchestration, fed by AI-ready data, makes it possible to change the paradigm: it is a question of doing less, but doing it better.  

AI decision intelligence is not just about determining the best channel (email, SMS, push) or the best message. It excels in its ability to analyse the context in real time to know what’s the best way to trigger an action, including when it is better not to send anything. 

 
“One of the biggest misconceptions about AI is that it’s only for generating more interactions. In reality, a truly successful system knows when nothing should be done. Doing nothing is a choice. It means preserving the customer relationship and avoiding over-solicitation.” 

With AI, how does each past interaction make the next one more effective? 

Customer engagement is no longer a linear process, but a continuous AI-driven loop: Understand > Imagine > Activate > Optimise, all around a central foundation: Govern. 

In this model, every interaction generated (a click, a purchase, a cart abandonment, or even a lack of reaction) is instantly fed back into the unified customer profile. AI (like Imogen, the AI developed by imagino) analyses these new signals on the fly. For example, it detects a drop in engagement and predicts a risk of churn. Armed with this insight, the AI will recommend the most relevant “Next Best Action” or “Next Best Offer”, automatically activate it on the appropriate channel, and analyse the result to refine the next interaction. 

But this virtuous loop has an Achilles heel – the quality of the incoming data. If you leave an autonomous system running on erroneous, outdated, or siloed data, AI will make bad decisions at high speed. This is why AI-ready data (fresh and unified) is the essential fuel for this continuous improvement. 

How is AI transforming the marketer’s job? 

For years, marketing teams have spent much of their time setting up campaigns and analysing reports. AI does not replace this, but it profoundly changes how it’s done. 

With the current AI Assistance model, the marketer asks the machine to perform specific actions: “Write an email subject“, “Create a landing page“, or “Translate this message“. Tomorrow, with AI Decisioning, the marketer will express a strategic intention: “How is it possible to reconnect with loyalty program members who have not interacted in the last 90 days?“. AI will then take over to analyse the data, recommend and launch a multichannel journey tailored to each user’s profile. 

Rather than doing everything themselves, the AI-Powered Marketer approves the recommendations suggested by the tool, oversees the action of the intelligent agents, and focuses on continuous optimisation of the strategy. This new model allows the marketer to orchestrate millions of personalised interactions while maintaining control of the brand, customer experience, and business objectives. 

 
“The future will not see AI replace the marketer, but a marketer augmented by AI. The most successful teams will be those that combine human creativity, governance and artificial intelligence to drive ever more relevant customer experiences.” 
 

For a marketing team, what is the very first step to take to make their data actionable by AI? 

The answer may come as a surprise, but the very first step is not to frantically attack the quality of the data. The absolute priority is architecture. This is the “Architecture First” principle. 

Before a company can fully exploit AI, it must choose and deploy a modern data lake (such as Snowflake, who is an imagino partner, Databricks, or OVH Cloud). This infrastructure must become the single source of truth validated by the entire company, not just the IT or Marketing teams. 

All interactions (inbound, e-commerce, stores, CRM) as well as the insights generated and data from third-parties must then converge on this centralised architecture. Secondly, it is necessary to rely on open platforms, capable of communicating via modern protocols (such as the MCP – Model Context Protocol), allowing external AIs (Claude, Gemini) to come and query, read and orchestrate this living data without friction, in complete security. 

 
“You can have the best possible data quality. But if your company is not aligned with a single centralised architecture, it will not work. To successfully integrate AI, you have to think Architecture First.”