At Hudson & Hayes, we spend a lot of time helping organisations move beyond AI experimentation and into meaningful transformation. While the technology continues to evolve at pace, many leaders are still grappling with the same fundamental questions: Where do we start? How do we avoid costly mistakes? And how do we ensure AI delivers measurable business outcomes?
To explore these questions, we sat down with Helena Clennell, one of our Consulting Partners and Advisors. Helena brings decades of experience leading large-scale business transformation programmes across shared services, finance, HR, operations, and technology. Having worked with organisations across both the public and private sectors, she has a unique perspective on what separates successful AI initiatives from those that struggle to gain traction.
In this conversation, Helena shares her observations on the current state of AI adoption, the importance of business-led transformation, and why combining deep industry expertise with technical delivery capabilities is critical for success.
One of the biggest trends I'm seeing is organisations successfully implementing AI within specific parts of a process.
That could be conversational AI supporting customer queries, AI-enhanced automation within finance processes, or intelligent workflows that help manage administrative tasks. There are plenty of examples where businesses are applying AI effectively to solve targeted challenges.
What I'm seeing less often, however, is truly end-to-end transformation through agentic AI. Many organisations are talking about autonomous agents working together across entire business processes, but relatively few have reached that level of maturity.
Another challenge is that some organisations are still trying to apply AI to processes that were inefficient to begin with. If you automate a poor process, you simply accelerate the inefficiencies. The organisations achieving the best outcomes are taking a step back and rethinking the process itself before introducing AI.
I also think it's important that AI transformation is led by the business, not purely by IT.
The business understands the workflows, the pain points, and the opportunities. At the same time, IT needs to be involved early enough to address governance, security, architecture, and data considerations. The most successful programmes bring both perspectives together from the outset.
Finally, employee engagement and upskilling remain hugely important.
There's still a lot of fear around AI. People read headlines about jobs disappearing and naturally become concerned. In reality, many industry forecasts suggest AI will create new opportunities and reshape roles rather than simply eliminate them.
The organisations seeing the strongest adoption are investing time in helping people understand what AI is, how it works, and how it can augment their role. When people feel informed rather than threatened, they often become the source of the best ideas.
Ideally, very early.
Not necessarily because IT should be driving the initiative, but because successful transformation depends on stakeholder engagement and alignment.
Early involvement allows organisations to identify constraints, discuss governance requirements, understand security considerations, and start shaping the operating model they'll need as AI adoption grows.
Longer term, organisations will need to answer important questions around ownership and accountability. How much should be centralised? How much should sit within business functions? What role should IT play versus operational teams?
Bringing those conversations forward helps avoid friction later.
That said, every organisation is different. In some cases, it can work well to approach IT once a clear set of high-value opportunities has already been identified.
Rather than saying, "We're thinking about doing something with AI," it's often more productive to say, "We've identified several opportunities that could reduce costs, increase capacity, improve customer experience, or drive revenue. Let's discuss how we make them happen."
Understanding your organisation's culture and decision-making structure is key.
The pace was one of the first things that stood out.
Very quickly after the initial discussions, we were able to articulate both what we believed could be achieved and how we proposed delivering it. It wasn't about producing extensive reports or lengthy proposals. It was concise, practical, and focused on action.
That pace continued throughout the engagement.
The work was highly collaborative, with client teams and Hudson & Hayes working together rather than operating as separate groups. There was a real emphasis on solving problems rather than producing deliverables for the sake of it.
What also worked particularly well was the team composition.
The project brought together business and functional expertise, transformation specialists, process improvement capability, technical delivery expertise, and architectural thinking. Each perspective added value in different ways.
My role was to bring deep subject matter expertise in shared services and business operations, while others contributed technical, architectural, and industry-specific perspectives.
That combination allows conversations to happen at every level of the organisation. Whether engaging operational leaders, functional stakeholders, or technology teams, there's someone who understands their challenges and can speak their language.
Partly, it's the structure of the organisation.
Decision-making is close to delivery. Leadership remains actively involved, which means decisions can be made quickly and obstacles can be removed without unnecessary layers of governance.
The other factor is the blend of expertise.
Many traditional consulting models separate senior advisors from delivery teams. The most experienced people may only become involved at key checkpoints or during quality assurance reviews.
The risk is that valuable experience isn't embedded throughout the engagement.
What I've seen at Hudson & Hayes is a much more hands-on approach. Senior advisors and industry specialists work alongside delivery teams throughout the process. That means lessons learned, practical experience, and real-world insight are incorporated from the beginning rather than reviewed at the end.
When you combine experienced transformation leaders, industry specialists, technical builders, and process experts, you create a team that can move quickly without sacrificing quality.
The first step is leadership alignment.
Not necessarily the entire executive team, but the leaders responsible for the functions involved need a shared understanding of what AI can do, what it can't do, and what success looks like.
It's also important to invest in education early. Most people have some awareness of AI, but often with very different levels of understanding, expectations, and concerns.
Once you've established that foundation, avoid trying to transform everything at once.
One of the biggest mistakes organisations make is attempting to boil the ocean.
Find one, two, or three high-value opportunities and move quickly. Demonstrate value. Learn from the implementation. Identify governance, data, and operational challenges early.
Those initial wins create momentum and provide practical insights that can inform broader transformation efforts.
Eventually, organisations need to take a more holistic view of their operating model. But timing matters.
If you start with large-scale theoretical redesign without delivering tangible outcomes, people quickly lose confidence.
The most successful organisations balance quick wins with a longer-term vision.
What stood out most was that the solutions were focused on real problems.
They're not examples of organisations implementing AI simply because it's the latest trend.
The strongest use cases address meaningful operational challenges that impact both organisations and the people they serve.
Take healthcare as an example. Administrative inefficiencies, missed appointments, long waiting lists, and resource constraints are well-documented challenges. Solutions that help reduce waste, improve communication, and support better outcomes have the potential to create genuine value.
The same principle applies across education, customer service, operations, and workforce management.
What impressed me was the focus on solving tangible business and societal problems rather than building technology for technology's sake.
That's ultimately where AI creates the greatest impact.
Technology should help people work more effectively, make better decisions, and focus their time where it matters most.
When AI is applied with that mindset, the opportunities are enormous.
As organisations continue to navigate the rapidly evolving AI landscape, the winners are unlikely to be those chasing every new technology trend.
They will be the organisations that remain focused on business outcomes, invest in their people, rethink outdated processes, and move quickly from ideas to execution.
Helena's experience reinforces a principle we see repeatedly across our own work at Hudson & Hayes: successful AI transformation is not just about technology. It's about combining strategy, operational expertise, technical capability, and practical delivery into a single, focused approach.
When those elements come together, AI becomes more than an innovation initiative. It becomes a catalyst for meaningful business change.
Whether you're exploring your first AI use case or scaling transformation across the enterprise, Hudson & Hayes helps organisations identify high-value opportunities, design practical solutions, and deliver measurable outcomes at pace.
Get in touch with our team to discuss how AI can create real impact across your organisation.
Over the last two years, most enterprise AI activity has existed in a phase of experimentation.
Organisations launched pilots, trialled copilots, explored proofs of concept, and ran workshops to better understand what generative AI might mean for their business. In many ways, that period was both necessary and inevitable. The pace of technological advancement was extraordinary, and for many senior leaders the initial challenge was simply understanding what was genuinely possible beyond the noise, hype, and vendor positioning.
What is becoming increasingly clear, however, is that the market is now beginning to move into a different phase entirely.
The conversation is no longer centred on whether AI can create value. Most organisations have already seen enough to conclude that it can. The more pressing question is whether they can operationalise it effectively within the realities of enterprise environments.
That distinction matters.
Demonstrating AI capability in a controlled environment is relatively straightforward. Embedding it safely into operational processes, governance structures, frontline workflows, and existing technology estates is considerably more difficult. In many organisations, the limiting factor is no longer the underlying technology itself, but the organisation’s ability to deploy it coherently, responsibly, and at scale.
As a result, the conversation around enterprise AI is becoming less about models and more about operational integration.
Questions around ownership, assurance, governance, workflow redesign, architecture, adoption, and change management are increasingly becoming the defining challenges. How are outputs validated? How does AI integrate into existing operational processes? Who governs it? How does it align with enterprise risk frameworks? How do organisations move from isolated experimentation to repeatable delivery capability?
These are transformation questions far more than technology questions.
One of the more interesting developments over the last year has been the role highly regulated and governance-heavy organisations are beginning to play within this transition. There has long been an assumption that sectors with complex governance requirements would struggle to adopt AI at pace. In practice, many are now proving surprisingly well positioned for operational deployment.
The reason is relatively simple. Once AI moves beyond experimentation, many of the capabilities required for successful adoption already resemble the disciplines mature organisations have spent years developing: operational controls, structured delivery governance, risk management, assurance processes, accountability models, and formal approval pathways.
What initially appeared to be barriers increasingly look like enablers.
This is also why the next phase of enterprise AI is unlikely to be defined by standalone chatbot experiences alone. The more significant shift is happening within operational workflows themselves.
AI is increasingly being embedded into procurement processes, engineering assurance activities, compliance workflows, operational reporting, drafting support, service operations, knowledge retrieval, and decision support environments. In many cases, the most meaningful deployments are not highly visible from the outside. They quietly reduce friction, accelerate workflows, improve consistency, and support operational teams handling large volumes of repetitive or information-heavy activity.
The organisations creating meaningful value are often not the ones producing the most impressive demonstrations. They are the organisations investing in the operational foundations required to deploy AI responsibly and repeatedly across the enterprise.
This is also why “AI delivery” is beginning to emerge as a discipline in its own right. Successful enterprise AI deployment increasingly requires a combination of operational understanding, transformation leadership, governance, architecture, workflow design, change management, and technical capability.
Most organisations no longer struggle to imagine AI use cases.
The real challenge now is operationalising them.
And that is where the next phase of enterprise AI has already begun.
Across the organisations we work with, one pattern keeps coming up: teams are pushing to build AI quickly, but they don't have the right delivery structure in place to support it.
This isn't a criticism. The pressure to move fast on AI is real. But without the right operating model, you end up with one of two problems.
The first is under-governance. AI tools get built inconsistently, security and compliance gaps appear, and there's no shared standard for how decisions get made. The second is over-governance. Existing IT processes, designed for large, complex, long-running projects, get applied to every AI initiative regardless of size or risk. Everything slows down, teams get frustrated, and innovation stalls.
Neither works. What organisations actually need is a governance model that fits the work.
We recently worked with a UK transport organisation that was facing exactly this. There was strong demand across the business to start building AI solutions. But there was no defined delivery model, no agreed governance process, and the existing IT framework was too heavyweight for many of the use cases teams wanted to pursue.
The goal was straightforward: enable AI experimentation at pace while keeping the right controls in place.
We started by working with IT, security, and business teams together. Rather than imposing a framework from the top, we mapped the full journey from initial idea through to deployment, then looked at what actually varied across different types of work.
The key insight was that AI initiatives aren't all the same. Some are small, self-contained tools with no external connections. Others involve shared infrastructure and enterprise data. Others sit somewhere in the middle.
Treating all of them the same way, either with no governance or with the same process as a major system integration, creates unnecessary risk or unnecessary friction depending on which direction you default to.
So we categorised initiatives by complexity and risk, then designed delivery pathways to match.
Low complexity: fast track
For contained, low-risk use cases, localised tools, citizen development projects, solutions with no external connectors, the framework allows lightweight governance and rapid approvals. These projects don't need a six-week sign-off process. They need clear guardrails and a quick path to deployment.
Medium complexity: structured review
For solutions with shared ownership across IT and business teams, or where clear environment and security standards need to be met, additional review points are built in. These projects move faster than a traditional enterprise delivery process, but with more rigour than the fast track.
High complexity: enterprise alignment
For initiatives involving integrated systems, enterprise data, or higher security and compliance requirements, the framework aligns to existing enterprise delivery processes. Formal architecture and infosec governance applies here. These projects carry real risk and deserve full scrutiny.
One additional piece of the work was helping teams understand when to use off-the-shelf tools versus when to build something bespoke.
Platforms like Copilot Studio are well-suited to a range of use cases. But they're not the right answer for everything. Part of good AI governance is giving teams a clear, shared understanding of where different tools are appropriate, so technology decisions are made deliberately, not by default.
At the end of the engagement, the organisation had a framework that gave them:
The framework is also repeatable. It works for both early-stage experimentation and enterprise-grade delivery, because it scales to the work rather than forcing everything through the same gate.
The organisations making the most progress with AI aren't just moving quickly. They're building on foundations that will hold as they scale.
Getting governance right early isn't about slowing things down. It's what makes sustainable delivery possible.
Why scaling agents requires a new kind of delivery ownership
As organisations move from individual copilots to portfolios of agents, a new role is beginning to emerge inside enterprise delivery.
The agent product owner.
This role exists because agents behave differently from traditional automation. They are not static deployments. Their value depends on continuous iteration as policies evolve, workflows change and expectations increase.
Organisations using generative AI now typically deploy it across an average of two business functions, with leading adopters reaching three or more. Forecasts suggest that around 25% of enterprises using generative AI will deploy AI agents in 2025, rising to approximately 50% by 2027. As agent footprints expand, ownership must become more deliberate.
Agent product owners shape scope with subject matter experts, define acceptable outputs, prioritise enhancements and monitor adoption patterns after launch. They also act as a bridge between operational users, governance teams and delivery environments.
This matters because adoption rarely fails at launch. It fails afterwards.
Where ownership is unclear, prompts drift from policy, logic becomes outdated and trust weakens. Where ownership is defined, agents improve steadily and expand into adjacent use cases.
Only 14% of organisations have deployed AI agents at scale, while 71% report they do not yet fully trust autonomous agents in enterprise environments. Establishing clear ownership is one of the mechanisms that can help close this gap.
Most organisations already have elements of this capability across service owners, analysts and automation leads. Formalising agent product ownership brings those responsibilities together around decision-support delivery.
Agent delivery is becoming a product discipline.
Operating models now need to reflect that shift.
Copilot licences sitting idle. Data platforms that could run agents today, sitting untouched because nobody was empowered to start. A survey published this week found that 59% of companies are spending over $1 million a year on AI, but only 29% are seeing real returns. That gap has a name, and the name isn't technology.
The organisations we work with that are genuinely ahead have one thing in common. They started before they were ready. Imperfectly, with limited resources, learning as they went. The ones still waiting for the right moment, the right framework, the right sign-off are falling behind people who were never more qualified than them- they just started.
The dust is not settling
There is a version of this story that ends questionably for procurement. Leaders see the pace of AI development and decide to wait. Wait for the technology to mature. Wait for best practice to emerge. Wait until things stabilise.
The problem is that the pace is not slowing. Agentic AI- systems that can reason, plan, and execute tasks autonomously- has moved from experiment to production deployment across industries faster than most enterprise technology roadmaps anticipated. What was a niche concept eighteen months ago is now running in financial services, logistics, and supply chain functions at serious scale.
Waiting for the landscape to settle is not a neutral position. It is a decision to let the gap widen.
The adoption gap is real, and it is widening
The organisations struggling are not short of investment. They have the licences, the platforms, the vendor relationships. What they are short of is momentum.
We see this consistently. Teams using Copilot as a slightly better search engine. Procurement functions sitting on data infrastructure that agents could be working on today, where nothing has been built because no-one has been given the authority to start. The investment is there. The capability is there. The activation is missing.
The new Writer enterprise AI survey captures this precisely: 75% of executives admit their AI strategy is more for appearance than actual guidance. That is not a technology problem. That is a leadership one.
The barrier to entry has never been lower
What has changed fundamentally over the past two years is access. A procurement team with the right skills and curiosity can build agents, automate workflows, and redesign processes without waiting for IT sign-off, without a seven-figure implementation, without a three-year roadmap. That was not true in 2021. It is emphatically true now.
This is one of the most significant shifts in enterprise technology in a generation. The limiting factor used to be budget or vendor capability. Neither is the constraint anymore.
The constraint is mindset. Specifically, whether leaders are willing to give their teams permission to build, and whether those teams believe it is their job to try.
What the leaders who are ahead are doing differently
They are not doing AI perfectly. Nobody is. What they are doing is treating AI as something their function owns, not something that happens to them.
They are identifying one workflow- supplier onboarding, spend analysis, contract review, market intelligence- and asking what it looks like if AI handles the parts AI is good at. Then they build it, learn from it, and move to the next one. They are not waiting for a centre of excellence to hand them a blueprint. They are becoming the blueprint.
The organisations achieving real returns share a pattern: they connect AI directly to outcomes, they assign ownership, and they give the people closest to the work the authority to act. That last part is the one most procurement functions have not figured out yet.
The question that matters
The leaders who will define what procurement looks like in five years are asking a simple question right now: if AI handles everything it is capable of, what do my people actually do?
That is not a technology question. It is a question about what a procurement function is for, what value it genuinely creates, and what human judgment is actually worth in that context. The functions that answer it well- and act on the answer- will look completely different from the ones that did not. The functions that wait for someone else to answer it first will spend years catching up.
The gap between those two groups is opening now. The time to close it is also now.
What we learned from a room full of procurement leaders talking about AI
We recently brought together procurement leaders from across sectors to share what's actually working and what isn't when it comes to AI transformation in procurement.
Here are the five things that stood out.
Most business cases for AI in procurement default to headcount savings. That's both limiting and unconvincing. Procurement's real value is increasing commercial outcomes and reducing risk- and in a world of ongoing supply chain disruption, the ability to rapidly assess exposure is arguably more valuable than any efficiency gain. Make sure your leadership team is hearing that story.
A common theme from every organisation on the call: licences are not the same as adoption. The shift from "we have Copilot" to "we use Copilot well" requires structured, role-specific learning, peer accountability, and a shared resource like a prompt library. Treat it like any other behaviour change programme.
There's no universal AI strategy for procurement. A mature enterprise with existing platforms needs a different approach to a lean team building from scratch. Before you develop a roadmap, be honest about where you actually are, and design accordingly.
The organisations seeing the best outcomes are building AI capability alongside external partners, not handing the work over. Delivery in waves, shared ownership, knowledge transfer built in from day one, that's the model that creates durable internal capability.
The most useful reframe of the entire session: stop asking "where are our AI use cases?" and start asking "what does a future procurement function look like, largely automated, with humans doing what only humans can do?" That question forces a genuine rethink- and it's the one that leads to the biggest value.
Check out the full webinar recording below, and if you would like to talk through where your organisation sits on this journey, get in touch.
The hype around AI in procurement is real. But so is the gap between ambition and delivery.
Having worked with organisations across private equity, transport, healthcare, and the public sector on AI transformation, we've gathered lessons that don't tend to appear in vendor brochures. Here are seven of the most important.
Unlike ERP implementations, AI doesn't arrive ready to operate. It requires training, iteration, and continuous feedback. The organisations that succeed are the ones that understand this upfront and build their delivery models accordingly. Think of it less like installing software and more like developing talent.
Generic AI training rarely sticks. The most effective education we've delivered connects AI directly to people's day-to-day roles. A procurement leader needs to see what their morning looks like differently, their emails, their briefings, their supplier reviews, not a theoretical overview of large language models.
If your organisation is at ground zero with AI adoption, the single highest-leverage activity is creating a shared prompt library. Map your team's recurring tasks to specific prompts. Share them. Standardise them. This alone can save individuals an hour or more per day and begins to normalise AI as part of how work gets done.
There is no one-size-fits-all AI strategy for procurement. A large, mature organisation with Ariba or Coupa already deployed needs a different approach to a lean team building a procurement function from scratch. Before you develop your roadmap, be honest about where you are and design accordingly.
Not every AI use case requires a bespoke build. A useful framework puts opportunities into four categories: those you can solve with a standard prompt today; those you can configure using tools like Copilot Studio; those that require a custom build; and those where a vendor solution is the right answer. Knowing which category each use case falls into dramatically improves prioritisation and speed to value.
One of the clearest patterns from real-world delivery: organisations that build AI capability alongside an external partner, rather than handing it over entirely, retain more knowledge, drive faster adoption, and are far better placed to scale. Delivery in waves, with shared ownership and regular knowledge transfer, is the model that works.
If your business case is built entirely around headcount reduction, it will underperform in delivery and in credibility. Procurement's value proposition is increased value and reduced risk. AI contributes to both. In a world where supply chain shocks arrive without warning, the ability to rapidly assess exposure is arguably more valuable than any efficiency saving. Make sure your CFO is hearing that story.
Underneath all of these lessons is a more fundamental change in how procurement leaders need to think. The question to ask isn't "how can we use AI to do what we already do, faster?" It's "what does a future procurement function look like- where AI handles the automatable, and our people focus on judgement, relationships, and strategic value?"
That question leads somewhere far more interesting.
Many large enterprises have a clear-eyed view of what AI could do for them. The strategy decks are full of transformation narratives. And yet, when the rubber meets the road, the overwhelming majority end up investing almost exclusively in making their existing operations incrementally cheaper.
The ambition is there. The execution tells a different story.
The distinction between Efficiency AI and Opportunity AI is one I first encountered through Nathaniel Whittemore on the AI Daily Brief podcast, and it has stuck with me ever since.
Efficiency is about doing the exact same things with fewer resources. Opportunity is about doing things that were previously impossible.
It is a clean and useful framing; but the more important question, and the one I keep running into in practice, is not whether leaders understand the difference. It’s why, despite understanding it perfectly well, they almost always end up defaulting to Efficiency.
I recently ran an opportunity workshop for the business services function of a large organisation. The room was full of sharp, forward-looking people. And yet, within twenty minutes, the conversation had migrated entirely to using AI to squeeze margins and cut processing hours. The underlying assumption was tacit but unmistakable: we have to earn the right to pursue Opportunity AI by mastering Efficiency AI first.
It makes sense on paper. But in practice, treating efficiency as a stepping stone leaves legacy businesses dangerously exposed. If you spend your strategic energy shaving 10% off operational costs, you leave the door wide open for a competitor with zero technical debt to render your entire operating model obsolete. This has always been true. In the age of AI, it is existential.
It’s easy to point the finger at a lack of vision, but in my experience, that’s rarely the culprit. The leaders in these rooms are not blind to the future. The real problem is usually far more mundane: technical friction.
Efficiency AI is attractive to legacy businesses precisely because it is low-friction. You don’t need a pristine, unified data lake to deploy an AI co-pilot to your team, or to buy an enterprise licence for a tool that summarises your meetings. It sits neatly on top of existing systems. It gives the board a measurable, immediate win without disturbing twenty years’ worth of accumulated technical debt.
Opportunity AI is the exact opposite. When a business unit tries to build a fundamentally new, AI-driven operating model, they immediately hit a wall. The business stakeholders are thinking in 2026, but their infrastructure is stuck in 2012. They crash into siloed databases, rigid compliance structures, nine-month enterprise architecture reviews, and entirely legitimate CISO apprehension. The hard reality is that the truly transformative capabilities of Opportunity AI require clean, real-time data and modern architecture; most large enterprises have neither.
This is precisely the wedge that AI-native newcomers are exploiting. Start-ups are not inherently more innovative. They simply do not have legacy systems dragging them down. They build on modern stacks from day one, and the compound advantage of that clean foundation grows with every passing quarter.
If an enterprise tries to force an Opportunity AI initiative through standard IT governance and legacy infrastructure, the project will die before the first prototype is ever built. You cannot build tomorrow’s operating model on yesterday’s plumbing.
The answer is not to tell a CEO to ignore immediate, tangible cost savings. Nor is it to allow the pursuit of those savings to slowly cannibalise your long-term bets. The answer is structural separation: parallel tracks, protected by a deliberate firewall between them.
Think of this as a three-part lifecycle.
You do not evaluate Efficiency and Opportunity initiatives in isolation. You assess the entire value chain at once, identifying immediate friction points alongside open white space. The logic is simple: let the quick, low-friction efficiency wins relieve operational pressure and generate the funding runway for your bolder, longer-horizon bets. One track finances the other.
This is where most enterprises trip up. Once the ideas are on the whiteboard, they cannot be built under the same roof.
Efficiency AI stays embedded in the core business. Operations leaders own it, it runs on existing IT infrastructure, and it is held accountable to traditional metrics: margins improved, process times reduced, immediate ROI.
Opportunity AI must be physically and financially spun out. Give the team ring-fenced funding that will not get raided when earnings look soft, and a clean, isolated cloud environment detached from the legacy stack. The firewall is not bureaucratic caution; it is the only thing that keeps the Opportunity initiative alive long enough to prove itself.
Crucially, you must change the scorecard. If you measure an Opportunity AI project using traditional corporate ROI, you will kill it before it takes its first breath. Standard metrics; hours saved, margin improvement; are lagging indicators. They measure the optimisation of a process that already exists. With Opportunity AI, the process has not been invented yet. You are not optimising; you are searching for a fundamentally different business model. That requires different instruments:
Time-to-First-Prototype: How fast can the team get a functioning, unpolished, unscalable version of the model in front of a real user? This metric forces the team to strip away corporate perfectionism, identify their riskiest assumption, and test it immediately.
Iteration Velocity: Opportunity AI is inherently experimental. The team will get it wrong on the first try. You are not measuring their initial accuracy; you are measuring the speed of their learning loop. How quickly can they deploy, gather data on why it failed, adjust the model, and push the next version?
New Unit Economics: If the legacy business spends £50 and three hours to manually process a complex request, the isolated team must prove they can fundamentally break that equation; not improve it by 15%, but shatter it entirely.
Proving those new unit economics is the bridge back to the core business. But here is the catch: if the Opportunity initiative stays in isolation too long, it becomes an organ transplant that the host body eventually rejects. We have all seen isolated innovation labs build something brilliant that dies the moment they try to hand it back. Usually, the legacy systems cannot support it. The firewall that protected the initiative must eventually come down.
But you cannot simply throw the prototype over the fence to IT and hope for the best. You need hard, unambiguous triggers to force integration at the right moment; not too early (when the initiative is too fragile) and not too late (when it has become a separate business that no longer fits):
The Unit Economic Crossover: The project must prove in its isolated state that its fundamental unit economics are vastly superior to the legacy process. The financial justification should be undeniable before IT is asked to do the heavy lifting of integration.
The Data Ceiling: When the only thing preventing further growth is access to live, core operational data; and the team has exhausted what they can do with sandboxed or historical data; it is time to force the integration conversation.
Risk Parity: Before convergence, the incubation team must demonstrate that they have built sufficient security, data privacy, and hallucination guardrails that the risk profile of the new system matches or beats the legacy one.
When those conditions are met, the narrative shifts entirely. You are no longer asking the core business to absorb a risky, theoretical experiment. You are asking them to scale a proven, superior operating model with a documented financial case. That is a very different conversation.
Mastering Efficiency AI does not earn you the right to pursue Opportunity AI. It just buys you a little time; and perhaps not as much as you think.
The organisations that are going to lose the next decade are not just the ones that failed to invest in AI. They are also the ones that invested heavily; and spent all of it making their existing operating model marginally cheaper to run. They will have dashboards full of efficiency metrics, impressive productivity reports, and a business model that an AI-native competitor has made irrelevant before those reports reach the board.
The organisations that will win are not necessarily the boldest or the best-resourced. They are the ones that build the structural discipline to pursue both tracks at the same time; the firewalls, the ring-fenced environments, the divergent scorecards; and resist the constant organisational gravity that pulls every Opportunity initiative back toward the comfort of incremental optimisation.
The stepping stone logic feels rational. It is, in fact, the path of least resistance dressed up as strategy. And in the age of AI, the path of least resistance leads somewhere very specific: to a business that is exceptionally well-optimised for a world that no longer exists.
A UK transport organisation set out to accelerate its AI journey, building on early experimentation with Microsoft 365 Copilot and initial agent development in Copilot Studio. There was already strong internal momentum, with multiple teams exploring how AI could improve productivity, streamline operations, and enhance decision-making.
However, this momentum brought a critical inflection point. Rather than rushing into rapid deployment, the organisation recognised the need to establish the right foundations—ensuring that any AI capability developed could scale securely, consistently, and under clear governance.
The engagement focused on moving from isolated experimentation to a structured, repeatable, and IT-led AI delivery model.
The organisation faced a familiar but complex challenge: balancing speed of innovation with the discipline required for enterprise-scale delivery.
There was strong ambition to build AI agents quickly, but without the right guardrails, this risked fragmented solutions, inconsistent standards, and potential governance issues. At the same time, growing interest across teams created pressure to define ownership, responsibilities, and a clear path forward.
Key challenges included:
Without addressing these challenges, the organisation risked losing control of its AI estate—leading to inefficiencies, duplication of effort, and increased security or compliance exposure.
The approach centred on a “done with” model—working side-by-side with internal teams to build capability while simultaneously delivering tangible outputs. This ensured that knowledge was embedded, not outsourced.
Hands-on education sessions were delivered to upskill the IT team across key areas, including:
These sessions were practical and applied, enabling teams to immediately translate learning into action.
A secure and scalable technical foundation was established to support ongoing AI development.
Microsoft Foundry played a key role as a unified platform for managing AI models, agents, and data integration—enabling a consistent and scalable development approach.
A core focus of the engagement was designing governance that could scale with demand.
This provided the structure needed to maintain control without slowing down innovation.
Rather than delivering a one-off solution, the engagement focused on creating a repeatable blueprint for AI delivery.
This ensured the organisation could scale AI initiatives independently, without ongoing reliance on external support.
The work was delivered in close partnership with the IT team:
This embedded both confidence and ownership within the internal team.
The engagement delivered both immediate value and long-term capability, positioning the organisation for scalable AI adoption.
Quantitative & Tangible Results
Qualitative Impact
Before vs After
By focusing on foundations rather than speed alone, the organisation has positioned itself to scale AI agents in a way that is both controlled and sustainable.
The combination of capability building, governance design, and technical enablement has created a platform for long-term success—where AI can be developed confidently, securely, and at pace.
With a clear delivery model, established guardrails, and an empowered internal team, the organisation is now equipped to move beyond experimentation and into enterprise-scale AI adoption—turning early momentum into lasting transformation.
The traditional approach to operating model design was often static. In a "technology-enabled" world, we designed a process and then looked for a tool to support it. Today, we live in a "technology-led" world where AI, RPA, and digital tools should be core design principles from the outset.
The Principles of Modern Design When redesigning your operating model today, you must consider three critical shifts:
Designing these models requires a specific set of skills. It’s about understanding the "murky world" of complex processes and applying pragmatic, expert-led guidance to simplify them.
Pragmatic Guidance Over Pure Theory Theory is easy; implementation is hard. Successful transformation requires a toolkit that works in the real world. One of our recent partners noted:
"Hudson & Hayes provided valuable assistance as we designed a new Operating Model in support of a large corporate merger. Their transformation expertise and pragmatic guidance helped us successfully navigate the complexity of the program." – Product Strategy Director
The Value of Practical Learning Our training isn't just a lecture; it's a workshop where you apply TOM principles to your own business challenges. Participants walk away not just with knowledge, but with a transformation plan ready for execution.
As Damian Jarocki, Contract Lead at E.ON, shared: "Genuinely one of the most valuable training sessions I have participated in... definitely the most practical and packed with the best tools!"
Ready to future-proof your organisation? Learn how to design an operating model that thrives in a digital world. Join our next Operating Model Design Training cohort and gain the confidence to lead your organisation’s transformation.
Get in touch to discuss in-house or virtual training options
© Hudson & Hayes | Privacy policy
Website by Polar
