AI Is Still Waiting for the Clean Data That BI Never Got

The views and opinions expressed are those of Olga Sakka and do not represent the official policy or position of any organization.

For a decade, enterprise data was allowed to stay messy because a person always sat between the data and the decision, catching what didn't add up before it went anywhere. Agents remove that person. They read whatever's in the warehouse, take it at face value, and act on it, with no instinct for when a number is wrong. The promise driving this year's AI budgets is that software can finally run that stretch on its own, and it's a promise the enterprise data world has heard, and broken, once before.

That warning comes from someone who has spent 25 years inside the same company reading its numbers. Olga Sakka is Head of Applied AI and Business Value at Avon, the 140-year-old beauty business now operating globally under private-equity ownership, where she leads AI adoption across a deliberately lean organization with every project tied to a business outcome. Before the AI remit she spent more than two decades as a data scientist and strategic analytics leader in Avon's commercial function, sitting, as she puts it, between decisions and numbers. She still builds, turning ideas into working internal tools with platforms like Lovable and Claude Code.

She keeps steering every AI conversation back to the one thing the tools can't fix for anyone: whether the data beneath them is clean enough to trust. After 25 years of asking, she's stopped expecting a yes. "Tell me one company that has clean data," she says. "Tell me one." But it doesn't leave her stuck. It sets the order of operations.

A mess that predates the agents

Olga keeps her team disciplined by sorting every AI project into three business values. "The first is cost, retiring something we used to pay for externally and building it in-house," she says. "The second is time and productivity, letting someone do more with less. The third is the biggest unlock, and it needs real agentic AI: revenue growth. Cost, time, and revenue, in three words." It's a framework built to keep spending honest against outcomes rather than novelty. None of it clears the bar, though, without the layer underneath, and that layer is where a quarter-century of enterprise habit catches up with the ambition.

Enterprise data was rarely in good shape, Olga says, not even for the reporting it was first built to serve. For years the fix was a person. Experienced analysts carry the definitions that make a number mean something, which sales count and which don't, and most of that context has never been written down anywhere a system could find it. "The good AI today is still in one person's head," she says.

The challenge lands because she isn't only describing Avon. It's a condition she sees across the enterprises she talks with, most of them further along in ambition than in readiness. "This isn't new with AI. It was already the problem in the BI era ten years ago, and it still is." When agents act without human review, the contextual checks people have traditionally supplied need to be built into the workflow.

Governance as onboarding

The most common request Olga fields is a swap. "'Can we replace Tableau with Claude?' That's the question I get, and it's the wrong one," she says. "Tableau is traditional software, a reporting machine. A language model isn't another reporting machine you swap in for the old one." Her reframe is to stop installing AI and start onboarding it like a hire. "I like people to view AI as an extra colleague. You don't just say, 'take that data and do the reporting.' You explain what the data means, where the semantic layers are, where it's hosted. Our whole data warehouse is a house, and you decide whether you're opening the door to the whole house or to one room."

"I've seen this story before," she says. "Ten years ago, the CTOs said, 'we don't want ten analysts doing Excel reports, we'll bring in Tableau.' And the analysts said, 'fine, but once you connect Tableau to Snowflake, the dashboard shows the wrong information if the data isn't correct. So let's fix the data first.' This is the same. This is exactly the same." A wrong dashboard misinforms a person who can still catch the error. When an agent works from that same data without human review, the business knowledge that would have caught the error has to be documented in advance.

That onboarding is where governance does its real work. Before an agent gets loose in the warehouse, Olga wants it sandboxed to a single room of well-understood data, with per-agent rules for what it can and can't touch, because a model optimizes for finishing the task and will fill an ambiguous gap with a confident guess rather than stop. "Now we need our agents to actually do good work, and nobody really knows what an agent is yet." Newer and more capable models keep shipping regardless, and the people signing off on them are often the furthest behind on what the systems can actually do.

Building readiness, use case by use case

Avon works through use cases one at a time. Each one has to show clear business value and carry the right safeguards before it goes anywhere, and that assessment sets the pace. Olga's method for keeping the pace honest is documentation. "I want everyone to document what actually needs doing, like writing a recipe. You list the ingredients, but sometimes the ingredients aren't good enough, so there's an extra step, and someone has to go shopping first." It resets what executives expect the technology to do for them. "There is no AI to save you," she says. "There is AI to serve you. You use it to help construct your thinking about what you should do to create the data."

What makes that pace possible is a leadership team fluent enough to accept it, and Olga is clear that the fluency is still rare across the industry. She keeps raising it, tier by tier, because the ambition keeps outrunning what even a C-suite understands about the tools. Her own C-suite is further along, which is what makes the approach workable. The people signing off on her projects understand what the technology can do and what has to be built before it can do it. "I have a CTO, a CEO, and all of my C-level people who are AI native," Olga concludes. "It helps a lot to have leadership teams in this way."