Stop Asking If You’re AI-Ready: Ask This Instead

The Wrong Starting Point

I am sure you have seen this sentiment over the last few years, and I see this mistake all the time. Companies pouring millions into AI initiatives, hiring data teams, upgrading infrastructure, and running endless strategy workshops all in pursuit of “AI readiness.” They get consultants in, build maturity models, and benchmark themselves against competitors. Yet, months, nay, sometimes years later, they have little to show for it.

Sound familiar?

No real impact. No measurable results. Just a growing sense of frustration.

Here’s the hard truth and what I’ve observed and learned over the last 25 years of my career. Two-word terms such as “Business Readiness” (yeah, remember those business readiness surveys and statements), naval gazing about how mature a company is, and endless questionnaires popping up! Here’s the hard truth:

“AI readiness is a myth.”

It’s not something you achieve before starting, it’s something you build as you go, driven by real business problems. Oh, I like that! But here’s the thing. Countless organizations are stuck in this “readiness” loop, convinced that if they just tick the right boxes, AI will somehow transform their business, and they will all be ready!

It won’t, and it never will, unless they shift their approach.

The Symptoms That You Will Recognize

Perhaps you are already feeling the pain of this approach, even if you haven’t named it yet.

• You’ve had “AI” on the agenda for years, but nothing meaningful has been implemented.
• Your AI projects never make it past the proof-of-concept stage, and they sit in limbo, waiting for a “readiness” that never comes.
• Your AI team is frustrated because they have ideas, but they can’t get business buy-in.
• Your leadership team is losing patience, and the board wants to know where the ROI is, but all they see are PowerPoint decks. (Sorry if that one hurt!)
• You’ve invested heavily in data infrastructure but without a clear business case, it’s just an expensive foundation with no house on top.

If these sound familiar, believe me you are not alone. This is a widespread problem, and it has clear underlying causes.

But Why Does This Keep Happening

You can blame anyone you want. But the real reason it fails isn’t anyone else but you and your team. I know that’s not what you want to hear because you want to put the blame at someone else’s door, maybe the consultant or the contractor or the article you read last week or a motivational speaker or a book that you read.

No, don’t go there as these are just excuses that are spectacularly flawed! We have seen this cycle repeat across industries because and through new trends, etc., because of a few common but flawed assumptions.

1. Tech-first thinking: this isn’t just for AI, it’s been the same way for ERP, CRM, Big Data and now AI is being treated as an IT project rather than a business driver. The focus is on data lakes, platforms, and governance before even identifying the problem AI is meant to solve.
2. Chasing AI for the sake of it: oh yes and don’t tell me you aren’t! Most companies out there feel the pressure to “do AI” because competitors are, rather than focusing on where it actually makes a difference.
3. No clear ownership: you know I’m working with a client at the moment and just ownership of programs is tough for them to address can you imagine when it’s AI! AI itself can be spread across data teams, IT, and business units, with no one fully accountable for turning it into business value. Siloed approaches and no joined up collaboration!
4. Lack of integration: most AI solutions exist in silos, disconnected from the workflows and decisions they’re meant to improve. AI is meant to enhance the decision making process and be integrated into your workflows, but most don’t get this because they are playing with a new sandbox and frothing at the mouth!

It’s like trying to build a Formula 1 car without knowing what race you’re entering. You can have all the components, but if you don’t know the track, the weather conditions, or even the driver, the car is completely useless.

The Shift From Readiness to Impact

I’m all for asking the right questions, in fact in my early career in strategy it was all about asking the right questions. Maybe, we have lost that art, and I think the companies that succeed with AI don’t ask, “Are we AI-ready?” Instead, they ask:

• What specific business challenge can AI solve today?
• What decisions will change because of it?
• How will AI fit into our existing workflows?
• How will we measure success in terms that matter i.e. revenue, cost savings, and efficiency?

This shouldn’t be a massive shift and one that is deeply profound as if you have just worked out the theory of black holes. It just means that AI stops being a theoretical exercise and becomes a focus and practical tool for value creation. It forces companies to move beyond endless discussions about maturity models and instead focus on the real outcomes and results they need.

How This Can Be Fixed

When we work with clients, we don’t start by asking how mature their data is or whether they have the perfect AI governance framework in place. We start by identifying where AI can deliver measurable impact. Or is that just too simplistic for you? It’s not technical enough, or doesn’t sound like you have to go out and buy a new platform! Nope don’t do it, you don’t need that extra tool. This is what you need to do:

1. Define the Business Problem: any new fad including AI must have a purpose. We use a great vision canvas and process that starts by identifying pain points and where AI can make a material difference, whether it’s reducing churn, cutting costs, or improving efficiency. Producing a vision that is robust and connected to these challenges and opportunities.
2. Align AI with Existing Workflows: AI isn’t useful if no one acts on its insights. Have you ever wondered why journey mapping is done to understand how a customer might track through your website or use your product? Yeah, I bet you don’t know. It’s to ensure we understand the flow, touch points, triggers, value exchanges, decisions! Just the same for AI, it’s about making sure it fits into the decision-making processes, so it actually gets used.
3. Start with Targeted Business Use Cases: Oh boy oh boy oh boy! The number of times I’ve seen companies go straight to the tech use case is horrendous! Instead of a grand tech motivated AI strategy, we focus on one or two high-impact business areas, proving value before scaling further. It ain’t difficult and again we use a framework that has been finessed over the last 10 or so years.
4. Measure Success in Business Terms: AI teams love talking about accuracy rates and model performance. I don’t think it’s just these teams that do that even data governance teams do the same and focus on data metrics, rather than business ones. So, in our approach we focus on metrics that matter to the executives and operational teams, you know revenue growth, customer satisfaction, demand forecasting, conversion rates etc.
5. Scale What Works: I have seen people invest loads of money and time on something that will actually never prove it’s worth! Then they ask why it didn’t work! We approach this through the lens of financial metrics that make sense and making sure that we work with the CFO and their team to product the right projections and estimates. But here’s the secret, even when we have selected those two or three business use cases, we still have to prove whether it will work or not. So, if an AI initiative proves its worth, we expand it. If it doesn’t, we cut it quickly.

This approach isn’t theoretical; it’s practical, and it works.

This Works in Practice

Take the case of a financial services firm we worked with. They had been stuck in “AI readiness” mode for over two years, investing in data platforms and hiring AI experts but failing to move beyond strategy discussions.

We flipped their approach. Instead of debating their AI maturity, we focused on a specific business problem: customer churn. It’s not sexy, but, it’s an area where they were having big problems.

We built a churn prediction model that integrated directly into their customer service workflows. It didn’t just spit out insights, it gave clear, actionable recommendations to retention teams, telling them which customers to focus on and what intervention would work best.

The result?

• Churn rates dropped by 18 percent within six months.
• Customer lifetime value increased by 12 percent.
• People understood what they were supposed to do, and therefore, adoption accelerated because it was finally delivering clear value.

No abstract AI strategy. No endless discussions about readiness. Just a direct impact on the business.

The Results of Shifting the Mindset

Why is this important? It helps behaviour change and people shift away from the “AI readiness” mindset. Readiness in my vocabulary is an ugly term that has been used for centuries to sell people nothing but rubbish that is typically consigned to the financial bin! If you do it right and get out of the readiness mindset, you can have these results that we have seen time and time again:

• Faster AI adoption: in fact faster anything not just AI as you can apply this to any form o business problem. The reason why? Because it’s tied to clear, measurable business outcomes.
• Higher ROI: this one is always the clincher as most people say it’s difficult to prove. Hogwash! If you use our approach, you will actually invest in AI where it actually makes a difference.
• Less internal friction: do you want to solve problems or just create new ones. Because if you go down the route of “readiness” believe me there will be much naval gazing and not action!

AI is not something you “get ready” for. It’s something you deploy where it matters most in your business.

So, stop asking if you’re AI-ready. Start asking how AI can drive real business impact. That’s the only question that counts!