Think IT Isn’t Responsible for Revenue? Think Again.

Love it or hate it, AI has an impact on revenue.

PwC predicts that AI technology could generate $15.7 trillion in revenue by 2030, boosting the GDP of local economies by an additional 26%. And a study we conducted here at Akeneo found that two-fifths (40%) of consumers were willing to pay an average of 25% more for a more personalized, tailored experience powered by AI.

At peak moments, AI-assisted experiences are now moving the market at scale: during the 2024 holiday period, AI-influenced online sales reached $229B globally as shoppers interacted far more with chatbots and AI assistants. Amazon recently announced that it expects its AI shopping assistant, Rufus, to generate an extra $10 billion in sales this upcoming holiday season.

And of course, LLM-powered search and shopping is only continuing to grow in popularity as it slowly poaches searchers from more traditional methods; AI overviews can reduce click-throughs from traditional search results by anywhere from 15% to 64%, depending on the industry and search type, and 60% of searches now terminate without users clicking through to another site. 

 Now, it’s pretty obvious how this transition from traditional search and shopping experiences to AI-powered ones can affect customer-facing teams like marketing, eCommerce, and product teams: 56% of marketers say their company is taking an active role in implementing and using AI, and 73% say AI plays a role in creating personalized customer experiences.

But there’s a quieter, equally critical shift happening behind the scenes: IT departments are quickly becoming the gatekeepers of AI readiness.

The Impact of AI Commerce on IT Teams

In a commerce environment where speed and relevance drive conversions, poor data quality is a direct threat to revenue. Gartner estimates poor data quality costs organizations an average of $12.9 million per year, while Forrester finds that fragmented data directly contributes to lost revenue, slower time to market, and higher return rates.

As AI-powered search becomes standard, IT leaders are under mounting pressure to adapt their tech stacks to meet an entirely new set of requirements. 

Traditional automation is built on preprogrammed workflows, but in the age of intent-driven search and AI search agents, IT teams must design systems that allow AI agents to sense, decide, and act dynamically based on changing conditions, often without waiting for human approval.

This requires an architecture capable of supporting real-time data integration across core systems like ERP, PIM, OMS, DAM, and CDP, ensuring that AI tools have immediate access to accurate information. It also demands seamless interoperability, so AI can call APIs for pricing, content, or fulfillment data whenever needed, without bottlenecks. 

Just as importantly, these systems need feedback loops that enable AI agents to learn from results, refine their decision-making, and improve over time. Without these elements in place, AI simply can’t deliver on its promise of more intelligent, adaptive commerce experiences.

But meeting these requirements comes with challenges. IT leaders must avoid the costly trap of duplicate tech investments as AI capabilities spread across multiple platforms, and they must keep system sprawl and operating costs under control in order to maintain performance and manageability as stacks grow more complex. 

With MIT recently announcing that 95% of GenAI pilot programs fail and Gartner reporting that over 40% of current agentic projects will stall out before 2027, it’s become more important than ever before to ensure that these AI investments are successful, but the question still remains; how?

How IT Can Ensure AI Success

Here’s the harsh reality: while ERPs and MDMs play essential roles in managing operational and generalized data, they weren’t designed to handle the kind of rich, contextual product information that powers revenue and fuels AI.

This is where Product Information Management (PIM) comes in.

Think of PIM as the end-to-end supply chain for product data. Just as supply chains ensure raw materials get transformed into finished goods and delivered to customers, PIM ensures product information is enriched, organized, and distributed wherever it’s needed.

This matters because AI can only work with the information it’s given. A large language model or generative search tool isn’t magic; it can’t invent accurate specs or infer missing details. To interpret buyer intent and deliver relevant recommendations, these tools need structured, complete, and contextualized product data. 

For IT leaders, PIM directly addresses the core priorities of integration, governance, and scalability. 

Integration

On the integration front, modern PIM systems are built API-first, making it easy to connect with ERPs, MDMs, DAMs, eCommerce platforms, and increasingly AI engines. Instead of custom workarounds or brittle connectors, IT departments gain a flexible, future-proof hub that allows product data to flow where it’s needed with a PIM.

Governance

Governance is another critical advantage with PIM as organizations can establish clear workflows and validation rules to ensure that data is complete, accurate, and compliant with internal standards and external regulations. This reduces the risk of bad data reaching buyers (or worse, regulators) and builds the trust that’s essential when AI is making recommendations on a company’s behalf; as Gartner predicts, enterprises using AI governance platforms will enjoy significantly higher customer trust ratings in the years ahead.

Scalability

Finally, there’s scalability. AI-powered commerce is evolving quickly, and businesses need tech stacks that can adapt just as fast. A PIM system offers the agility to expand into new channels, markets, or business models without rebuilding the entire data infrastructure each time. Whether it’s supporting a new conversational AI interface, integrating with a marketplace, or enabling real-time product updates, PIM provides the flexible foundation IT leaders need to scale with confidence.

Ultimately, investing in a PIM gives IT leaders and their organizations the agility to adapt quickly, the governance to ensure accuracy and compliance, and the integration layer needed to connect every tool in the stack. Most importantly, it positions businesses to fully leverage AI commerce tools and deliver the kind of seamless, trustworthy product experiences that today’s buyers expect.

IT As the Gatekeepers for AI-Driven Revenue

The interface of commerce is shifting from search bars and storefronts to intelligent, adaptive systems that work on behalf of the customer.

At the heart of this transformation are IT teams. Marketing, product, and commerce leaders may shape the customer-facing experiences, but those experiences are only as strong as the infrastructure behind them. 

IT holds the keys to making AI commerce possible: ensuring real-time data flows, connecting systems through APIs, governing information for trust and compliance, and maintaining the agility to adapt as technologies evolve.

The path forward for IT leaders is clear: embrace openness, centralization, governance, and continuous evolution. By doing so, they will not only keep their organizations competitive but also help define what AI-powered commerce looks like in the years ahead.