Tim Richardson – Building a future-ready organisation

FSP are a specialist in advisory-led enterprise transformation delivered through integrated capabilities – Data & AI, Cyber Security, Enterprise Cloud and Change Delivery. Over the next couple of months, we’re running a series interviewing leaders from each area, to explore the trends, challenges and opportunities shaping enterprise transformation today from their perspective.

In this article, we interview our Principal Consultant, Tim Richardson. From creating the right foundations for enterprise transformation, to embedding AI into business processes and delivering change through iterative, value-driven approaches, Tim shares his perspective on what organisations need to do to build adaptable, future-ready businesses.

Read the article below to discover Tim’s key takeaways and expert perspectives.

Having worked on transformation initiatives for many years, how have you seen enterprise transformation evolve?

Tim Richardson:

“When I started working in transformation, the focus was largely on implementing technology to improve specific areas of a business. Success was measured by whether the technology worked.

That evolved into platform modernisation, where organisations built shared foundations rather than solving the same problems repeatedly. Cloud became central to this approach, making it easier to scale and innovate.

Today, transformation is much broader. Organisations are no longer asking, “How do we implement this technology?” but “How does our business need to change, in order to get the most value from it?” Cloud, Data, Cyber Security and AI are all important, but they’re only enablers. Real transformation depends just as much on operating models, governance and an organisation’s ability to adapt.

The biggest shift I’ve seen is from technology-led projects to business capability redesign. Businesses are focusing less on adopting new technology and more on rethinking how they operate, serve customers and create value.

Another major change is pace. Technology is evolving so quickly that fixed three-year transformation plans are becoming less effective. The organisations seeing the most success, are setting a clear long-term ambition but delivering it through smaller, iterative steps, continually reviewing and adapting their approach as technology and business needs evolve.” 

What are the biggest challenges organisations face when trying to modernise and transform?

Tim Richardson:

“One of the biggest challenges is that organisations often focus too heavily on technology and not enough on the people and processes needed to make transformation successful.

A framework I often refer to is BCG’s 70-20-10 model, which suggests that around 70% of the effort in an AI transformation should be spent on people and processes, 20% on technology and data, and only 10% on the AI models themselves. In reality, many organisations reverse this, investing most of their time and resources in the technology, while overlooking the organisational change required to make it work.

Another common challenge is a lack of strategic alignment. Different teams often modernise their own areas in isolation, without a clear business vision connecting those initiatives. This can lead to fragmented ownership, disconnected systems and poor-quality or inaccessible data.

We’re also seeing many organisations struggle to move AI projects beyond the pilot stage. A pilot may prove a concept, but scaling it successfully requires trusted data, clear ownership, governance and, most importantly, people who are ready to adopt new ways of working.

Ultimately, transformation rarely fails because of the technology. It fails when organisations underestimate the effort needed to change how the business operates and bring people along on the journey.”

Why do some organisations successfully realise transformation value, while others struggle?

Tim Richardson:

“The organisations that succeed and those that struggle often start in a similar place, with comparable technology, budgets and ambition. The difference is, that successful organisations keep their transformation focused on the value they create for customers.

Rather than pursuing technology for its own sake, they can clearly explain how every initiative contributes to a meaningful business outcome. Those that struggle often become distracted by technology-led projects or AI proof of concepts without establishing how they support the organisation’s wider goals.

Another key differentiator is mindset. Instead of asking, “Where can we apply AI?” successful organisations ask, “How should our business change because of AI?” That shift in thinking helps them redesign processes and business models, rather than simply automating existing ones.

Finally, the most successful transformation programmes bring business and technology together from the outset. When business leaders, technology teams and delivery partners work as one, decisions are made with a shared understanding of the outcomes they’re trying to achieve, making it far more likely that transformation delivers lasting value.”

What role do modern technology platforms play in enabling enterprise transformation?

Tim Richardson:

“Modern technology platforms are transformational when they improve the speed, safety or cost of delivering business change. If they don’t achieve at least one of those outcomes, they’re simply infrastructure rather than true transformation.

A strong platform provides reusable capabilities, such as identity, data, integration and security, allowing organisations to build new solutions without solving the same challenges repeatedly. By putting these foundations and governance in place early, businesses can deliver future initiatives faster, more consistently and with less risk.

However, technology is only part of the equation. Successful platforms also require the right operating model, enabling teams across the organisation to access and use these capabilities without unnecessary barriers.

Cloud, data and AI platforms have each played an important role in accelerating transformation, but their real value comes when they work together to support business processes and outcomes. Ultimately, a platform should make every future change easier, faster and more cost-effective than the last.”

How has cloud changed the way organisations approach transformation?

Tim Richardson:

“Cloud did its most visible transformational work some years ago, but it laid the foundations for most of what’s happening now. It made technology modular, scalable and consumption-based, and that pushed organisations towards thinking in products and platforms: product operating models, automation, DevOps, and much faster experimentation.

The experimentation point is the one I’d draw out. Because cloud collapsed the lead time for standing up infrastructure and environments, organisations can try five or ten smaller ideas and find out what actually works, rather than locking into one big multi-year decision and hoping. The whole “fail fast” idea only became practical once cloud made experiments cheap and reversible. Before that, every experiment carried the cost of procuring and standing up its own kit, so experiments were rarer and each one felt like a commitment.

Cloud also created a much stronger link between architecture, security and operational cost. Decisions that used to sit in separate teams started showing up on the same monthly bill.

Also, it forced a different conversation about money. Cloud shifted investment from CapEx to OpEx, from a big capital outlay written down over three to five years, to paying monthly for what you consume. Because the bill moves with consumption, it introduced FinOps and the discipline of governing variable cost. That shift is paying off again now, because AI runs on much the same economics: inference is a consumption cost, charged per transaction, for as long as you run the service. The organisations that built real FinOps capability in the cloud era are finding AI costs a lot easier to govern than the ones that didn’t. So cloud’s real legacy isn’t the infrastructure itself. It’s the adaptable, iterative, consumption-aware way of working that almost everything since has depended on.” 

What are the risks of treating cloud, cyber, data & AI and change as separate initiatives?

Tim Richardson:

“Each initiative optimises locally and the whole business loses. A team over here does its part well, a team over there does its part well, they each hit their own objectives, and none of it joins up to see the business value. You also end up paying twice, because separate initiatives tend to build duplicated platforms, duplicated tooling and duplicated governance, all solving the same problems in slightly different ways.

The specific failure modes are predictable. Cloud without cyber builds fast and leaves the doors open. AI without good data produces unreliable outputs. Data without process ownership creates assets nobody uses. And technology without change delivers something nobody adopts.

Cyber deserves particular mention, because it needs to run through every technology change a business makes and it’s still so often treated as an afterthought. It has to be a design discipline built in at the start, not a gate at the end. When it’s at the end, one of two things happens. Either it stops the project dead and months of work gets reworked, or, worse, it gets pressured into waving things through. Both are common, and both are avoidable.

The same late-involvement pattern shows up everywhere. Data teams disconnected from the business build assets that delivery teams never touch. AI teams build pilots that don’t pass governance or never scale into production, because they were designed without it in mind. Change teams get brought in after the solution is designed and released, by which point every decision that determines whether people adopt it, has already been made.

However good the technology, if nobody uses it, it was for nothing. That’s the whole argument for running these as one joined-up change agenda rather than four separate ones. The value only exists where they meet.”

What characteristics do successful transformation programmes have in common?

Tim Richardson:

“A few things show up again and again. They’re outcome-led and tied to business value, with genuine executive sponsorship behind them rather than just a signature on a business case. And they’re capability-based, looking not only at the technology being implemented, but at the business’s ability to keep using it and to keep making good technology decisions in the future: knowledge management, change and adoption, governance.

Ownership is a big one, at every level. Most technology change exists to affect a business process, so you need buy-in at the process, product and service level, not only at the top. The programmes that work tend to have clear strategic intent; what are we changing and why, and a named chain of ownership behind it: an executive owner, a business owner, a technical owner, a risk owner. A missing owner is one of the more reliable places a programme starts to unravel. Governance is built into the delivery rather than added at the end.

The two I’d single out, because they’re the ones most often missing, are benefits realisation and repeatability.

Most businesses are good at writing a business case. Give me a million pounds and within two years it returns two million in savings or value. The case gets signed off. Far fewer track whether it actually delivered what it promised. That tracking isn’t bureaucracy. It’s what tells you whether to course correct, stop, or keep investing. Without it, you don’t really know whether the programme is working. It matters even more with AI, where some organisations are incentivising usage as if that were the point, as though a bigger AI bill meant better results. It doesn’t. Usage is a cost, not a benefit. The question is what it changed in real business outcomes. Count the outcomes, not the licences.

And the best programmes build repeatable patterns rather than one-off wins. The aim isn’t a single transformation that finishes and gets signed off, it’s a capability that keeps going, because the next wave of change is already on its way.”

What do leadership teams often underestimate when embarking on transformation?

Tim Richardson:

“The honest answer is that AI-era transformation is genuinely new territory, and it’s easy not to know what you don’t know. The best leadership teams are open about that. The harder situations are where a team assumes their experience of previous transformations will carry across. Some of it does, but not all, and it’s often the parts that don’t, that catch people out.

The most common pattern is the one I mentioned earlier, reversing the 70-20-10. A leadership team can treat the technology as a silver bullet and then be caught out by the friction that follows, both in adoption and in the operating model itself. The technology was never going to be the hard part.

Data and knowledge quality is a big one at the moment. Leaders know they need data, but it tends to get treated as technical hygiene when it’s actually a strategic constraint. Knowledge in a business is inherently human-readable. It’s designed for a person to read a document, navigate a wiki, or ask a colleague. Making that same knowledge machine-readable, governed and reliable enough for AI to act on is a serious piece of work, and it’s routinely underestimated.

So is the judgement that sits inside people’s heads. You’ll have a written policy you want an agent to follow, but the person who applies that policy today knows when not to follow it to the letter. They flex it sensibly, based on context and experience. An agent won’t do that on its own. That tacit judgement has to be surfaced and made explicit before an agent can be trusted with the work, and leadership teams rarely budget for it.

Middle management is another blind spot. Adoption tends to stall not at the frontline and not in the boardroom, but in the layer in between, with the managers who have to redesign how their teams actually work. They need to be brought in early, not handed the change once it’s built.

Then onto culture and literacy. Does the organisation have a culture where people know they’re allowed to use data, or AI, to make decisions? And if it does, do they have the literacy to actually do it? Both get skipped, and both decide whether anything you build ends up being used.”

How can organisations balance ambition with practical delivery?

Tim Richardson:

“Set a bold direction, but deliver it through iterative waves that create evidence as you go.

A real example. A business wants to speed up its change process by 300%. That’s a bold ambition, and it’s the right kind, because almost everything involved in transforming the organisation flows through the change process, so speeding that up compounds across the whole business. But you don’t attempt it in one move, you break it into iterations. Take a step towards the ambition, measure it, do the benefits realisation. Going in the right direction? Take another step. Not having the effect you expected? Adjust, and take the next step along a slightly different line.

The route from A to B is rarely a straight line. It’s more of a wiggly arrow. If you fix the whole route on day one and then just execute for three years, you tend to arrive at B and find you actually needed to be somewhere closer to C. Iterating is what lets you steer along the way.

Both halves of this matter, and organisations tend to drop one or the other. Without the bold ambition, a business just modernises what it already does, applying new technology to yesterday’s business model, which is comfortable and rarely worth the investment. Without the practical delivery, big abstract programmes lose credibility, because people have generally been through the multi-year programme that promised a lot and delivered late. Small, evidenced steps build credibility with each wave, and that credibility is what funds the next one.

One more thing. What counts as “practical” keeps shifting underneath you. The technology decisions underpinning delivery need revisiting regularly, every quarter or so, to check they still hold, because the model landscape, the platform capabilities and the costs will have moved. This is the same iterative approach applied to the plan itself, rather than only to the delivery.”

What advice would you give organisations looking to build stronger foundations for transformation?

Tim Richardson:

“Start by being clear about what “foundations” actually means, because most people hear that question as “How do I get the technology foundations in place?” The foundation that matters is the organisation’s ability to change in a safe, repeatable and sustainable way. The technology and the platforms sit underneath that, not the other way round.

So build around business value rather than a technology trend. What value are we trying to affect? What outcomes? Who owns the processes, the outcomes, the data, the governance, the platforms and the change itself? The ownership questions are the ones to answer before any technology conversation, because a lot of later pain traces straight back to nobody being clear on who decides.

A maturity assessment is a sensible place to start, and not only of the technology. How mature is the strategy itself? Does the organisation actually understand how value flows through it? Are there value stream maps, and are they backed by strong, current processes, or by ones nobody has revisited in years? Are the right platforms, governance, workforce, adoption capability and benefits processes in place? An honest look across all of that tends to show where the real investment is needed, and it’s often not where the organisation assumed.

Then sequence the work sensibly. People and process first, because you can’t redesign processes you don’t understand, and you can’t understand them without knowing who owns them. Data readiness next. Technology after that. It can feel slow, but in my experience, it’s the quickest route that actually holds.

Two final things. Don’t underestimate what the people side takes. Change and adoption is so often treated as an afterthought when it’s central to whether any of this works. Secondly, be explicit about what you’re choosing not to do. A strategy that spreads its funding thinly across everything tends to lead on nothing. Some of the best foundation work I’ve seen came from an executive team writing down what they were deliberately leaving alone, and then holding the line when the pressure came to drift back to it.”

Looking ahead, what trends do you think will have the biggest impact on enterprise transformation over the next few years?

Tim Richardson:

“That’s an easy one, because it’s already happening. The biggest shift is AI moving out of personal productivity and into the workflow itself, embedded in business processes and decision-making rather than sitting alongside them as a tool individuals reach for. We’re already working with organisations thinking hard about how the business fundamentally operates with AI in it, and I think those are the ones that will pull ahead of the organisations still applying AI to the business model they already have.

That shift needs everything I’ve been talking about: stronger governance, real-time data, platform engineering, and adaptive strategies and operating models. Agentic AI is very much part of the picture, but it’s a destination rather than a starting point. Agents belong in bounded, well-understood, well-governed processes, and only once the data, platform and governance foundations are actually in place. Organisations that prioritise agents before establishing these foundations often encounter higher costs, increased risk and disappointing outcomes. As a result, they may conclude that the technology doesn’t work, when in reality the issue is a lack of organisational readiness.

I also think knowledge architecture becomes a serious differentiator. AI is lowering the barrier to building applications, so the technology itself stops being much of an advantage, because everyone can buy the same models. What stays defensible is what’s genuinely yours: client relationships, proprietary data, and the knowledge and expertise the organisation holds. The domain knowledge sitting in your best people’s heads, codified so the organisation can reuse it, becomes company IP rather than personal know-how. Getting that knowledge into a governed, machine-readable state will be some of the most valuable work businesses do over the next few years, and very few have started.

There’s a customer-expectation angle too. Things that differentiate today – instant response, personalisation, always-on service, become table stakes within a year or two, because AI keeps moving the baseline. Differentiation has a shorter shelf life than it used to, which only strengthens the case for adaptability over any fixed multi-year plan.

And structurally, transformation stops being a programme with an end date. The real divide over the next few years won’t be between organisations that use AI and those that don’t, because nearly all of them will. It’ll be between those that treat it as another technology deployment and those that build a standing capability to keep redesigning work, governing risk and adapting as the technology moves. That second group is the one that will still be getting value from this in five years.”

What is the single most important lesson you’ve learned from supporting enterprise transformation programmes throughout your career?

Tim Richardson:

“If I’m honest, anyone in this industry who claims to know exactly what the next five years look like will almost certainly be proven wrong, and probably faster than they’d like! So I’ll say upfront that this is a best guess!

What I am confident about is the direction: data is only going to become more central to how organisations actually run. Not a supporting function, but the thing everything else depends on.

As AI keeps maturing, businesses will lean on trusted, well-managed data more and more, to automate the everyday stuff, to give customers a genuinely better experience, and to make decisions at a speed that just wasn’t possible before. But here’s the part I don’t think changes: none of that removes the need for strong governance or clear strategic thinking. If anything, the opposite. The faster and more autonomous things get, the more it matters that the foundations underneath are solid. You can’t have systems making decisions at machine speed on data you don’t fully trust.

So, my view is pretty simple. The organisations quietly doing the hard work now and getting their data foundations right, are the ones who will be best placed to take advantage of whatever comes next, whatever form it actually takes. The rest will spend those years playing catch-up. And that gap is going to get very visible, very quickly – if not already!”