Climbing the data and AI literacy ladder

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The dashboards, the tools, the shiny platforms that promise to transform your business, they’re not what’s holding you back. People are.

That was the core message from a recent LinkedIn Live hosted by Orbition Group, featuring Greg Freeman (CEO of Data Literacy Academy) and Pete Williams (Director of Data, Penguin Random House UK). And if you’re serious about data, AI, or digital transformation, it’s one worth sitting with.

Here’s the real talk they shared, stripped down, practical, and grounded in the messy reality of enterprise change.

Where we are now: Everyone’s talking AI. Few are ready for it.

According to Greg, data and AI literacy isn’t new, but people doing it is. For years, the industry’s been shouting about democratising data. But outside the data bubble? Most of the business is still stuck.

There’s a silent majority of employees, frontline managers, sales leads, ops teams, who don’t just lack confidence with data. They’re actively avoiding it. Not because they’re lazy or resistant. Because they’ve never been given a reason to care, or the tools to engage.

And then along comes AI.

Now the expectations are sky-high, the budgets are moving, and execs want results. But if the only people engaging with data and AI are the same 10–15% who always raise their hands, what happens to the rest?

Let’s stop assuming people “just get it”

Pete told a brilliant story from early in his career. He’d rolled out a new MI system to a big UK supermarket chain. After a year, he noticed usage was low. When he asked why, the answer was blunt:

“I look at this, but I don’t know what to do with it.”

It’s a perfect metaphor for how many data programmes still operate today. We build better charts. We train people how to use filters. But we don’t help them think with data. We don’t connect dashboards to decisions.

So what happens? People revert to gut feel. Or worse, they disengage entirely.

Misconception #1: “This isn’t my job”

When non-technical staff say “I don’t do data,” it’s easy to roll your eyes. But here’s the thing: they’re not wrong. Most haven’t been trained, supported, or incentivised to think differently.

That doesn’t mean they’re off the hook. As Pete put it, “There are no decisions in business that are truly free of data.” Even the gut feel merchants are using inputs, they just happen to live in their heads.

The opportunity? Help them connect the dots. What are the five questions they should ask on a Monday morning? What signals should they look for before taking action?

It’s not about turning everyone into analysts. The goal is always to make decisions more informed, more consistent, and more aligned to what the business actually values.

Misconception #2: “Tech will fix it”

One of Greg’s sharper points: “Silicon Valley has spent 30 years making you believe software will solve everything.”

The reality? You can pour millions into data platforms, AI tools, and cloud migrations, but if your people don’t know how to interpret the outputs, challenge assumptions, or spot the opportunities… it’s money down the drain.

Buying the Ferrari is easy. Teaching people to drive it confidently, in your business context, that’s the work that needs to be done to get those pie in the sky outcomes tech companies promise.

So what does good look like?

A few things stood out from both Greg and Pete’s stories:

  • Confidence > capability. Skills matter, but confidence is the unlock. If someone’s scared to ask a “stupid” question, they’ll never use the tools they’ve been given.
  • Context is king. Teach people to connect data to their roles, not some abstract use case. What does ‘good’ look like in this team, on this system, in this market?
  • ROI isn’t just commercial. It’s human. Pete shared that 36% of people who went through their data literacy programme were promoted. 44% took on more responsibility. That’s not just training, that’s real business transformation.
  • Executive engagement matters. Not just in sign-off, but in behaviour. If your C-suite says data matters but still makes decisions on gut feel and vanity metrics, your culture won’t shift. And people will notice. That's why role-modelling starts at the top.

Measuring success? Start where the business already cares

Greg laid out a simple way to show impact:

  1. Find the value hotspot. Where is the business already spending money? Where does data literacy support a bigger transformation?
  2. Quantify it (as best you can). Even if it’s directional, attach a number to the upside of changing behaviour.
  3. Tell the story. Don’t just collect feedback. Share it. Make the invisible visible. Success stories are contagious.

Final thought: This is a climb, not a leap

If you’re wondering why your data strategy isn’t landing, or why AI adoption feels like pushing water uphill, it’s not because the tools are wrong.

It’s because the people haven’t come with you.

Changing that takes time. It takes trust. It takes a proper, human-centred approach to learning, one that starts with curiosity and ends with impact.

But if you get it right, you won’t just build skills. You’ll build belief. And that’s when transformation starts to stick.

Unlock the power of your data

Speak with us to learn how you can embed org-wide data literacy today.