Why Avoiding AI Failures Starts with Data Governance

Harnessing AI’s true power goes beyond plugging in algorithms and hoping for magic. It demands a rock-solid strategic foundation that aligns your data, eliminates bias, and transforms your workforce operations from good to efficient.

Recently, I sat down with Kay Smart, Head of Global Talent Acquisition at Reckitt on an episode of Skills Connect. Kay has been driving a jaw-dropping transformation that is as technically fascinating as it is effective. You can watch the recording here, but this part of the conversation I’ll write about in this week’s newsletter was particularly interesting to me.

Bridging the AI Divide: Why Governance is Non-Negotiable

When I asked Kay about how Reckitt approached AI for talent acquisition, something she said really fascinated me: “We realized that training an AI for recruitment couldn’t be separated from our internal mobility efforts. Without a unified data foundation, our AI would end up perpetuating inefficiencies and biases.”

That insight got me thinking—how many organizations are still approaching AI and skills transformation in silos?

The answer: far too many.

At Reckitt, Kay and her team discovered that if their AI was going to deliver meaningful, unbiased results, it needed consistency and rigorous governance. Misaligned job requirements across their 68-country operation were skewing their AI algorithms, causing what we call “model drift,” where AI's predictions lose accuracy over time.

And here’s where it gets even more interesting: Reckitt didn’t just build a tool. They constructed a global governance framework that ensures AI makes consistent, high-quality recommendations. It’s a beautiful marriage of technology and human oversight, and it’s one of the reasons Reckitt is leading the way in responsible AI adoption.

Strategic Insight: AI is only as powerful as the data it’s trained on. If your organization isn’t prioritizing data governance and uniformity, your AI isn’t just underperforming—it’s potentially causing harm.

Meet Your People Where They Work

Another thing we discussed was the importance of viewing AI as a strategic partner, not a magic fix. As Kay pointed out, Reckitt's team initially faced pushback from recruiters who felt AI was just adding extra work. We know recruiters move fast—they’re practically Olympic sprinters when it comes to sourcing talent!

So, how did Reckitt solve this? By integrating AI right where their people already worked, like Successfactors.

Let me tell you, this idea blew me away: it wasn’t about building a shiny new UI for the sake of it. It was about embedding AI in a way that streamlined existing workflows, not complicated them. And that’s a huge lesson for any company looking to implement AI: meet your people where they are, and respect how they already get things done.

Strategic Insight: We’ve learned that the UI is secondary to data quality. Your AI needs good, structured work and skills data to make accurate, valuable recommendations. Focus on building an unbreakable data foundation first and foremost.

Savage Take: Managing Model Drift

Model drift can derail your AI’s performance faster than you can say “bad hire.” At Reejig, we regularly do independent audits, ensuring our algorithms stay aligned with our evolving skills data. This keeps them from causing harm and focuses on skills and potential rather than personal characteristics.

Wrapping Up

So, what’s the big takeaway here? AI is a supercharger, but only if you’ve laid the groundwork for it to succeed. It won’t fix bad data, misaligned job expectations, or disconnected talent strategies. But if you do it right—like Reckitt has—you’ll unlock a level of workforce agility and efficiency you never thought possible.