TL;DR

After 30 years in technology, I kept seeing the same pattern: excitement about the latest tech drowning out focus on the actual problems worth solving. Projects losing sight of what matters - the business, its people, and whether the solution actually makes things better. I’ve made these mistakes myself, which means I can recognise the warning signs. I built Justified AI to help businesses use AI for justifiable reasons - technically, financially, strategically, practically, and with their people in mind. We stay involved from strategy through to production, and sometimes the honest answer is “not yet” or “not AI”. I’d rather tell you that upfront.

Problems First, Technology Second

I’ve worked in technology for about 30 years, and I’ve watched a lot of hype cycles come and go. The dot com boom. Web 2.0. Social media. And now AI.

Each time, there’s genuine excitement about what the technology makes possible - and quite rightly so. But each time, I’ve also watched that excitement overshadow something more fundamental: focusing on the actual problems worth solving.

With every new wave, there’s a rush to apply the shiny new thing. Less often do I see people stop and ask: what problem are we actually trying to fix here? And is this the right way to fix it?

That question - or rather, the lack of it - is at the heart of why I started Justified AI.

The Journey Back to What Matters

I’ve always loved technology. It started when my dad brought home our first PC - an Amstrad PC1512 in the late ’80s - and I taught myself to program. My physics degree fed the same fascination: computational modelling, using data to understand how things work.

But somewhere along the way, I realised what I really loved wasn’t the technology itself. It was using technology to solve real problems and making a difference to people’s lives through it. Technology that actually matters, not technology for its own sake.

I spent 16 years in education technology, where I first got fascinated by machine learning - using student data to predict which learners might need extra support. From there I worked in email security, building systems to detect phishing attacks. I’ve led engineering teams, worked in product management, and consulted with clients across a range of industries.

Throughout all of it, what I kept coming back to was helping people make better use of the data and technology they already had. And because I’ve actually worked across all of these areas - not just advised on them - I can speak multiple languages: technical, strategic, and operational.

When Projects Lose Sight of What Matters

Over those years, I watched the same problems repeat themselves.

The beautifully crafted AI strategy that wasn’t grounded in reality. Grand visions for how AI would transform the business, but without really understanding the beating heart of how that business works and its people.

The technically impressive solution that missed the point. Too much time refining the impressive technical approach, not enough attention to the simplest thing that would actually solve the problem the business really had.

And the project that solved the wrong problem entirely. The fundamentals - what problem are we solving, what does success look like - got lost in the complexity of the process and the excitement of the solution. Nobody asked the right questions early enough, and nobody was measuring the right things to know whether they were on track.

I often think of it as putting the cart before the horse. If you start with the technology and then look for problems it can solve, you’ve got it backwards. The horse should be the problem. The cart should be the AI.

The fundamentals haven’t changed since I started out: understand the problem, measure what works, make sure what you’re doing actually improves things from a business perspective.

I’ve Learned This the Hard Way

I’ve made these mistakes myself - getting excited about a technology and then looking for ways to apply it, rather than starting with the problem. That excitement about technology is real, and it’s part of why I do this work. But I’ve learned over the years that I need to temper it, because what I really love is getting my head around the problem and figuring out the right way to make things better.

Because I’ve been down these wrong paths, I can recognise the warning signs. I built Justified AI so that my clients can benefit from what I’ve learned rather than making the same mistakes themselves.

Why We’re Called Justified

I called us Justified because I believe we should be able to justify why we’re using AI, how we’re using it, and what difference it will make. That means five things:

Technically justified. We use the right approach for your specific problem - not a particular technology because it’s fashionable or exciting. If a simpler solution works better, that’s what we’ll recommend.

Financially justified. Any AI investment needs a clear business case. You should understand the expected return before you invest, and have a way to measure whether you’re getting it.

Strategically justified. Any AI solution should align with where your business actually wants to go. If it doesn’t fit your strategy, it doesn’t matter how clever the technology is.

Practically justified. Can your team actually build and maintain this? Does it work with your existing systems? Is it sized to your real constraints - your budget, your skills, your timescales? We design solutions that fit your reality, not an ideal world.

People justified. Your team is your greatest asset. Any AI solution inevitably changes how people work - their processes, their roles, sometimes their sense of security. We consider those impacts from the start and address concerns honestly, in language that makes sense to the people affected.

And sometimes, being honest means saying “not yet” or “not AI”. I’d rather give you that advice than sell you something you don’t need.

How This Works in Practice

In practice, this means starting with your problems, not our toolkit. That’s why our discovery workshops begin by understanding your business, your challenges, and your constraints - before we talk about technology.

A recent example: a retail technology company engaged us to explore how they could use AI across two potential use cases. When we dug into the detail during discovery, one turned out to be technically complex with significant risk around whether the return on investment would justify that complexity. The other was more practical and aligned with what their customers had been asking for. We agreed together to focus there.

What we delivered wasn’t a generative AI system or an LLM - it was a machine learning and optimisation solution that identifies which product categories boost each other’s sales and recommends profitable layout changes across store estates. We chose the appropriate technology to solve the problem in the simplest way, with clear success criteria aligned to their business goals. We identified other opportunities to use generative AI along the way, but stayed focused on what mattered to them now. Because we proposed the right solution for their particular problem, they were able to move forward with confidence.

It means staying involved from strategy through to production, so nothing gets lost in translation between what was planned and what gets built.

And it means speaking the right language to the right people: ROI projections and risk assessment for your board, architecture decisions for your technical team, practical reassurance for the people whose work will change.

Is This For You?

Justified AI is for businesses who’ve been burned before by AI projects that didn’t deliver, because technology was put ahead of the problem.

It’s for leaders who are wary of being sold to, who want someone to be straight with them rather than bamboozle them with jargon.

And it’s for organisations who need someone who can translate between the technical world and the business world, because that gap is where most AI projects fall down.

If any of that sounds familiar, I’d love to have a conversation. And if AI isn’t right for you yet, I’d rather tell you that upfront than ask you to invest in something that won’t make a difference for you.