The question isn’t whether you are AI ready, it’s whether you are value ready.

There’s been a growing chorus over the last 12 months and every other post on LinkedIn, every conference panel, and every glossy consultancy report seems to be asking, “Are you AI ready?” As if it’s some mythical threshold, like being admitted into an exclusive club for the enlightened.

If you are reading this and you know me then yes you know that I’m sometimes blunt! “AI readiness” is a red herring. It’s an empty term that gets thrown around to scare businesses into thinking they’re behind the curve, just like all the terms before this one. It’s the classic FOMO effect, so that companies will buy more shiny new toys and sink more money into the ever growing data and tech marketing machine!

It’s a distraction that leads organisations down a rabbit hole of endless preparation, rather than focusing on outcomes. Be value ready, not tech ready!

For most businesses, being AI ready has become synonymous with having the perfect data and technology foundations. Do you have a clean, centralised data lake? Are your integration layers seamless? Is your tech stack up to scratch?

Sure, these things are important. But the fixation on data and technology often creates a mindset where companies think they can’t act until everything is “just right.”

What’s the result of this fixation? Months, actually often years of expensive groundwork without a single result to show for it. Yes, you know who you are, spending the company’s money as it’s not your own, and layering the cuteness into your profile.

The truth is perfection is a myth.

Let’s look at it since many companies started this data journey more than 30 or even 40 years ago! Data hasn’t changed, it will always be messy, because every year we layer on more tech, new clunky systems that are the panacea for business transformation and don’t live up to their game. Data architecture being snubbed because who needs data modelling! Data governance gets another boost and is then confined in the basement without light and energy! If you are waiting for an ideal state before you implement AI, you’ll never get started.

You may or may not agree with my sentiment so far, but I don’t say these things just to shock. I’ve been working in data for almost three decades now, yes, I might be a spring chicken compared to some of my colleagues who have been in it for ovee 40 years. But I’ve been in enough data leadership positions since the early 2000s, to know that this never ending cycle of shiny new things is just that. I’m sure, nay extremely confident, that this year there will be an abundance of distractions that will lead companies down the garden path and the gap between the haves and have nots will get even wider! I spoke to Malcolm Hawker about this on his podcast mid-week, he contested that we won’t have much hype this year, and I asserted that we will. It’s not a game, but, let’s just ride it out.

So, instead of obsessing over AI readiness, ask yourself this simple question:

Are we ready to create value?

Here’s what I mean:

1. Focus on Use Cases, Not Perfection: start with clear, tangible problems that AI can help solve. It could be predicting customer churn, optimising supply chains, or improving fraud detection. The key is to start small and be very specific. Again don’t do it for the sake of doing it.
2. Embrace Imperfect Data: newsflash no company has perfect data. Yikes! What separates value-driven organisations is their ability to work with what they have. They test hypotheses, run pilots, and refine their models over time. Waiting for “perfect data” is just another excuse to delay action.
3. Build Decision-Making Muscle: being value-ready means knowing how to act on insights. This requires a culture that’s comfortable making decisions quickly, experimenting, and learning from failure. A tech stack won’t help you here, but strong leadership and clear decision making processes will. As I always say, start with the decision.

One of the biggest mistakes leaders make is thinking that AI is a technology problem. It’s not. AI is a business tool, a means to an end, not the end itself. Also, remember that AI has been around for a very long time, in fact since the 50s. Most of those who are looking at it now, are revelling in the GenAI fever.

Take the example of a retailer that spent years building a state of the art data platform. They poured millions into the tech but had no clear strategy for how to use it. Meanwhile, their competitor focused on a single use case improving inventory forecasting with AI. The result? Faster stock replenishment, happier customers, and increased revenue, delivered in months, not years. Don’t you wish that was you?

Yes, yes, the lesson here is that technology isn’t the answer, it’s just an enabler. What matters is how you use it.

Now, like me, if you have read Randy Bean and Tom Davenport‘s yearly Data & AI Executive Leadership survey, you will see one statistic that stands out from the crowd. Well, actually it doesn’t just stand out, it’s a bloody alien staring back at you speaking a different language and you just go about your daily travail without even blinking! Here it is:

“Exhibit I shows that cultural challenges continue to represent the greatest impediment to organizational transformation. These impediments are largely due to human factors relating to people – business process change, organizational alignment, talent and skills, change management, resistance to change.”

I rest my case me Lord and the uncomfortable truth is that most organisations don’t have a data problem; they have a culture problem. Since I started seeing this early on in my corporate roles, I decided to have deep conversation with those of my friends whom were in the business of psychology. I even decided to enrol into a Masters in Organisational Psychology! Culture is something that baffles me and I’m not the only one, all sense of self and common sense is instantly knocked out of many that I’ve worked with. Somehow, people in the corporate machine, suffer from amnesia or similar. The endless transforming year in year out, with some new concept or other, then, falling on the mediocrity sword, by repeating the same behaviours while expecting a different outcome.

It will never ever work, no way Jose, and you are delusional if you think it will.

You can invest in the best AI tools on the market, but if your teams don’t trust data, resist change, can’t modify behaviours, change mindsets, or lack the skills to interpret insights, it’s all for nothing. Being value ready means having a culture where people are empowered, to make bold decisions and to get out of their own ways.

At a webinar this week with Katalyze Data (formerly Amadeus Software) and the 848 Group I presented this on a slide. The reason why organisations need a unified data and AI strategy. Being value ready requires:

• Leadership buy in: AI initiatives (in fact any initiatives) fail without visible support from the top.
• Collaboration across functions: AI isn’t an IT project, it’s a business wide effort.
• Training and education: your people need to understand how AI works and how to apply it.
• Operating model: how will we make the strategy real and embed AI into the cultural fabric of the culture. Often the missing piece of the puzzle which so many ignore.

The irony is that the organisations delivering real value from AI aren’t the ones shouting about readiness. They’re the ones taking action, changing mindsets and behaviours of their employees, working with what they have, experimenting quickly, and tipping their hats to value.

When I did woodwork at school, I wasn’t the best carpenter, but I loved making cupboards or shelves or just tinkering and coming up with new ideas and designs. In fact a set of shelves I did make stood the test of time for over 20 years! So, I liken AI to a tool in a carpenter’s workshop. You don’t need a perfectly organised toolbox to get started. You just need the right tool for the job at hand and the skill to use it. I think there may well be a little joke there about tools! 😊

Gosh I loved those shelves!

The only questions that matter in your assessment and due diligence are these:

• What value can we deliver today?
• What small wins will build momentum?
• What problems are worth solving with AI?

Because when you focus on value, readiness becomes irrelevant. You will figure it out as you go. That’s what my trumpet teacher Mr Cobb always used to tell me “if you don’t know the sheet music, go figure it out!” Having lessons was fine, and yes, he was a tough taskmaster, but it make me dig out the records (showing my age) for the pieces I needed to learn and duly figure it out!

Drop the Checklist Mentality

All leaders out there whether you are in the C-Suite or the various business lines (yes, I’m not creating another set of departments just to fit in data and tech), it’s time to ditch the obsession with AI readiness. Stop chasing perfection. Stop waiting for flawless data or the ideal tech stack. Because it ain’t coming anywhere to a town near you, any time soon!

Instead, and as another reminder focus on outcomes.

Pick one problem, deliver one result, and build from there. Success with AI doesn’t come from being ready, it comes from being bold enough to act.

Thanks for reading.