Not another incremental technology upgrade. Not another short-lived hype cycle. But a genuine shift in productivity and capability that changes how organisations operate and how careers are built. 

As a CEO of a talent business, I sit in the flow of real hiring decisions, real budgets, and real commercial trade-offs. What I’m seeing doesn’t feel theoretical. It feels early. It feels uneven. But it also feels meaningful. 

Recently, I had the pleasure of listening to Jeremy Khan, AI specialist and keynote speaker from Fortune, who described this phase of the AI revolution as the “Jagged Teeth” stage. Progress is sharp and irregular. Breakthroughs are followed by setbacks. Confidence surges, then stalls. Capabilities leap forward in one domain while lagging in another. 

That framing resonated with me. 

Because what we are experiencing right now does not feel smooth or linear. It feels disruptive at the edges, experimental in the middle, and quietly transformational underneath. 

History tells us this pattern is not new. 

Major productivity shifts rarely show up immediately in the data. Technology appears first. Excitement builds. Investment flows. But measurable gains only emerge when organisations change how work is structured, how capital is deployed, and how people are trained. 

Which brings us to the lesson we keep forgetting. 

AI, productivity, and the lesson we keep forgetting

There is a chart I keep coming back to. 

It shows US nonfarm business productivity surging from the mid-1960s to the mid-1970s, then stalling for almost two decades before accelerating again in the late 1990s as PCs and the internet became mainstream. 

That chart matters because it tells us something uncomfortable: technology does not automatically translate into productivity. 

The first productivity boom was not about new invention alone. It was about integration. Electricity, logistics, manufacturing scale, telecommunications. These capabilities were embedded deeply into operating models. Firms invested heavily in physical capital, redesigned workflows, professionalised management, and aligned education and skills accordingly. 

Technology, capital, organisation and people moved together. That is when productivity grows. 

The slowdown that followed was not a lack of innovation. The PC had arrived. Software was emerging. Data was being captured. But early productivity gains were muted because organisations layered new tools on top of old structures. Work was digitised, but not redesigned. Processes were enhanced, not reimagined. 

We may be at a similar inflection point now. 

AI capability is advancing at extraordinary speed. But capability alone will not deliver productivity. The gains will only show up at scale when leaders rethink how work is structured, how teams are composed, how decisions are made, and how entry pathways into careers evolve. 

In other words, this moment is not just about technology. It is about organisational courage. 

The long slowdown was not a lack of innovation

From the mid-1970s to the early 1990s, technology continued to advance. Computers were already in offices. Software existed. Data was being captured. But productivity stalled. 

Why? 

Because technology sat alongside work rather than reshaping it. Early IT lived in silos. Organisations digitised processes but rarely redesigned them. Roles remained structured around old assumptions. Capital investment slowed. Measurement lagged reality. 

You could see computers everywhere, but not in the productivity statistics. 

There is an important lesson here, and it is one that deserves empathy rather than criticism. 

Boards and investors quite reasonably expect returns on technology investment. When significant capital is deployed into new systems or AI capability, the expectation is that productivity gains will follow quickly. That pressure is understandable. 

The reality, however, is that structural change does not happen in parallel with tool adoption. 

Workforce planning takes time. Business process redesign takes time. Strategy reshaping takes time. Organisations often pause, elongate hiring cycles, or delay senior appointments while they work out what their future operating model should look like. 

We see this directly in our own permanent recruitment market. 

Processes are extended. Roles are put on hold. Executive searches are delayed while leadership teams reassess structure, automation potential, and long-term headcount design. It is not a lack of ambition. It is a period of recalibration. 

This is what transition looks like. 

The risk is not that AI fails to deliver. The risk is assuming the returns should appear before the redesign has happened. 

PCs ultimately delivered productivity gains not because they existed, but because organisations eventually rebuilt around them. Computing moved from back offices onto desks. Processes were re-engineered. Data flowed across functions. Decision-making sped up. Networks mattered more than hardware. 

The lesson is simple: productivity gains arrive after redesign, not after adoption. 

PCs worked because organisations changed

The productivity pickup of the late 1990s did not happen because PCs were invented. It happened because firms had the discipline and courage to reorganise around them. 

Computing power moved from back offices onto desks. Processes were rebuilt. Data flowed across the organisation. Coordination costs collapsed. Decision-making sped up. Networks mattered more than hardware. 

The lesson is simple. Productivity gains arrive after redesign, not after adoption. 

This is the uncomfortable truth about AI

AI feels different because adoption is faster and the tools are more powerful. But the structural risk is exactly the same. 

Most organisations today are experimenting at the edges. Drafting support. Search. Summaries. Copilots embedded into existing workflows. Productivity pilots in pockets of the business. 

That is progress. But it is not transformation. 

From what I see across our clients, the real hesitation is not about the technology. It is about what follows. Redefining roles. Rethinking workforce plans. Reworking incentives. Deciding which layers of decision-making still make sense in an AI-assisted environment. 

Just as in the 1970s and 1980s, the real gains will only appear when businesses redesign how work is done. How decisions are made. How data flows. How roles are defined. How performance is measured. How incentives are aligned. 

AI will not deliver productivity by sitting next to broken processes. 

Live coding, Codex, and the ERP question

Where this becomes particularly interesting is in software development itself. 

The emergence of live coding environments and increasingly capable systems such as Codex raises a more structural question. If AI can meaningfully accelerate development cycles, reduce dependency on large outsourced teams, and enable rapid iteration, what does that mean for traditional ERP-heavy operating models? 

We may see organisations gradually unshackling from highly customised, multi-year transformation programmes and instead bringing more development capability back on-site. Smaller, more agile internal teams augmented by AI. Faster experimentation. Reduced reliance on long change cycles. 

We are also seeing the early signs of something even more disruptive: the rise of the single-person software company. Individuals now have access to tools that allow them to design, build, test and distribute full software products with minimal overhead. What once required a team of ten may soon be achievable by one. 

We do not yet know how far or how fast this will go. But it challenges long-standing assumptions about scale, team size, and the economics of software creation. 

What this means for service businesses

For professional and service-based organisations, the parallel is stark. 

Buying AI tools is relatively easy. Embedding them into core workflows is where the real work begins. 

The opportunity is not marginal efficiency. It is structural change. Reimagining how demand is forecast. How pricing decisions are made. How talent is matched. How delivery is optimised. How time is genuinely freed up for higher-value work rather than absorbed by new layers of coordination. 

The constraint is rarely the technology. It is data discipline, process clarity, and organisational will. 

The leadership moment

History is clear. Productivity follows redesign, not invention. 

If this really is the start of another structural shift, three things matter more than anything else. 

1. Embed AI at the core.

 It must sit end-to-end in the operating model, not bolted onto the edges. 

2. Redesign work around what is now possible. 

Roles, workflows and incentives should reflect AI capability, not legacy assumptions. 

3. Treat data as infrastructure. 

Clean, shared, trusted data is not optional. It is the capital base of the AI era. 

If we get this right, AI may well power the next productivity expansion. 

If we do not, we will repeat a familiar story. Powerful tools. Ambitious expectations. Modest aggregate results. 

The difference will not be the technology. 

It will be the decisions leaders make now. 

The post What history teaches us about the AI productivity boom  appeared first on La Fosse.

US hyperscalers alone are expected to spend roughly $646 billion in capex this year. That is around 2% of US GDP and broadly equivalent to the entire GDP of countries such as Singapore, Sweden or Argentina. That level of capital deployment signals that this transition is structural, not cyclical. Governments can shape the edges, but they cannot stop the direction of travel. 

That does not make me pessimistic. Quite the opposite. 

History suggests we have been here before

Major technological shifts, from the democratisation of the desktop computer to the rise of the web, initially created friction and short-term displacement before unlocking significant productivity and GDP growth. There was often a lag of close to a decade before the full economic benefit materialised. 

We are likely in the early stages of a similar productivity cycle now. 

The key risk is not AI itself. The risk is how we respond. If we concentrate AI power and economic gains in too few hands, the outcome could be destabilising. If we combine innovation with broad-based access, skills development and responsible governance, the upside for productivity and living standards could be significant. 

The UK economic picture is more nuanced than headlines suggest

A tight fiscal stance is unlikely to boost hiring confidence in the short term. But the underlying picture is more complex. 

January saw the UK record a £30.4 billion budget surplus, the largest monthly surplus since records began in 1993, supported by stronger than expected tax receipts. Self-assessed income tax and capital gains tax receipts reached £46.4 billion, materially higher than the same month last year. Retail sales and private sector activity have also shown signs of improvement. 

That tells us there is resilience in the system. The question is how that fiscal headroom is used. 

With GDP growth of just 0.1% in the final quarter of 2025, annual growth of 1.3%, inflation still at 3% and unemployment edging up to 5.2%, the UK is operating in a cautious economic environment. Add to that higher employer National Insurance costs, and it is difficult to see the conditions for broad-based hiring acceleration. 

We do not expect to see widespread hiring growth. 

From headcount growth to capability growth

What we are seeing is a fundamental shift in how businesses think about workforce planning. 

Boards are no longer asking how many people they should add. They are asking what capability they need to compete in an AI-enabled economy. 

Any hiring expansion will be targeted. The main areas of growth will be technology and AI-led businesses racing to build capability and secure competitive advantage. Across the wider economy, hiring will be concentrated in areas that directly support productivity improvement: AI, data, cyber security and automation. 

We are not seeing a dramatic overnight shift in roles, but there is a gradual move towards greater specialisation. Organisations are looking for individuals who can apply AI and automation in specific commercial contexts rather than broad, generalist digital profiles. 

There is also rising demand for transformation leadership, both interim and permanent, to embed these capabilities into operating models at scale. 

Within engineering teams, AI-assisted development tools are beginning to influence workforce planning. While demand for some traditional UX and UI roles has softened, there is growing interest in strengthening core engineering teams with developers leveraging AI tools to increase velocity and output. 

In short, growth in hiring will be strategic and capability-led, not volume driven. 

The real challenge is skills, not wages

I do not think the central challenge is wage inflation. It is skills inflation. 

We are already seeing demand for AI-literate talent outpace supply. Employers are not simply bidding up salaries across the board. They are competing hard for a relatively small pool of individuals who can design, deploy or commercially exploit AI-enabled systems. That creates capability gaps rather than broad-based wage pressure. 

Youth unemployment remains materially higher than the national average, with 16 to 24-year-old unemployment sitting in the mid-teens as a percentage. This is a major systemic issue. At the same time, businesses are struggling to find AI, data and automation capability. 

Bridging that gap through modern, AI-focused apprenticeship and training routes is a clear economic opportunity. 

The direction of travel on apprenticeship flexibility is encouraging. Giving businesses greater freedom to use their apprenticeship levy to upskill existing employees, as well as create alternative pathways for early career talent, is exactly the right lever to pull. 

Where I would like to see more urgency is in scale and speed. AI adoption is not a five-year transition. It is happening now. The policy framework needs to move at the same pace as the technology. 

What is missing from the policy agenda

If anything is missing, it is a more ambitious productivity agenda linked to capital investment. 

Businesses have already shifted from headcount growth to output growth. Boards are asking how to deliver more value with the same or fewer people through automation, AI deployment and process redesign. That structural shift is happening irrespective of policy. 

What would materially accelerate UK productivity is stronger and more targeted incentives for capital expenditure, particularly in digital infrastructure, AI systems and automation. 

One of the very few clear economic advantages of the UK operating outside the European Union is greater flexibility over fiscal and regulatory decision making. That autonomy gives us the ability to move faster and more decisively in support of productivity and investment than many of our peers. From a business perspective, it can be frustrating if that flexibility is not fully utilised. 

If we want to unlock sustained GDP expansion, we need to make investing in productivity-enhancing technology as attractive as possible. Without that, we risk talking about growth without fully enabling it. 

The public sector cannot afford to wait

The public sector employs around 6.18 million people, roughly one in six UK workers. Public sector pay alone represents close to 10% of GDP, and broader government, education and health output accounts for somewhere between 12% and 19% of GDP. That scale means even marginal productivity gains have significant economic impact. 

In that context, automation is not optional. It is necessary. 

The real challenge is not pure AI talent volume. It is leadership, delivery capability and operating model redesign. The public sector needs the right senior sponsorship and execution expertise to modernise effectively. In many ways, this is about catching up with transformation that has already been underway in the private sector for several years. 

A note on CEO responsibility

My broader reflection would be this: in a fast-moving and AI-accelerated environment, it is every CEO’s responsibility to learn, adapt and lead thoughtfully. 

There is a real risk of knee-jerk decision making driven by fear of missing out on the latest AI tools that promise transformational results. In reality, meaningful productivity gains rarely come from technology alone. They require business process re-engineering, clear workforce planning and disciplined execution. That is one reason why, despite substantial investment, aggregate productivity gains from AI have so far been limited. 

Careful planning matters. A clear data strategy matters. Understanding what genuinely moves the needle for your specific business matters far more than adopting tools because competitors are doing so. 

This is easier said than done. The pace of innovation is extraordinary, and leaders must immerse themselves in the learning curve. But the goal should not be to chase every wave. It should be to apply AI responsibly, deliberately and in a way that enhances human capability rather than replaces it indiscriminately. 

The balance we need to strike

It is not the government’s primary responsibility to protect specific roles or slow technological progress. Its responsibility is to create the right conditions for sustainable growth. 

Protecting employment in the long term is a shared responsibility between the public and private sectors. Businesses must invest in their people, reskill their workforce and design operating models that generate opportunity. The public sector must modernise responsibly. The government’s role is to provide clear governance, sensible guardrails and competitive tax and policy incentives that enable both sectors to grow. 

If we focus too heavily on protecting existing roles, we risk constraining productivity. If we focus solely on efficiency without supporting transition, we risk social friction. 

The right balance is about enabling growth while accelerating skills development, not attempting to freeze the labour market in its current form. 

If we can combine ambition with discipline, and maintain a human-first mindset rather than concentrating power and wealth in too few hands, the long-term opportunity is significant. 

The post AI, Productivity and the UK Labour Market: A CEO Perspective appeared first on La Fosse.

Interim leadership at C-suite level has moved from exception to established practice. And the reasons are not hard to understand. 

Why boards are thinking differently

When outlook is uncertain, budgets are tighter, or change needs to happen at pace, boards often choose interim leaders to bring in expertise without locking themselves into a permanent appointment. 

From personal experience, I hired almost 30% of my own C-suite initially on an interim basis before converting some roles to permanent. We needed very specific expertise at a pivotal moment in our growth, and interim gave us flexibility while still raising the bar. 

That flexibility is not a weakness. It is a strategic choice. 

The structural drivers are not going away

In the short term, the interim market is cyclical. In the medium to long term, it is structurally growing. 

The wider UK recruitment industry generates more than £40 billion in annual turnover (REC Recruitment Industry Status Report 2024/25). Interim executive search represents a smaller proportion of that total, but it is a high-value segment. Day rates are higher, mandates are strategically critical, and assignments are often linked to moments of material change: restructuring, digital transformation, private equity-backed growth. 

The structural drivers remain strong: 

• Private equity ownership 
• AI adoption 
• Cyber risk 
• Digital transformation 

All of these require experienced operators who can step in and execute. Over time, that supports continued expansion and professionalisation of the sector. 

Where demand is strongest

Demand is clearest for roles that help businesses adapt structurally to technological and capital shifts. 

That includes: 

• Interim transformation directors 
• Chief Technology Officers and Chief Data Officers 
• AI programme leads 
• Cyber specialists 
• Chief Financial Officers with strong change and capital discipline experience 

These are complex, time-bound mandates that require objectivity, pace, and deep subject-matter expertise, which naturally lends itself to interim capability. 

That said, none of us has perfect visibility on the horizon. Artificial superintelligence, quantum computing, automation: these could reshape large parts of the workforce. My own view is that we will see a short-term growth slowdown before things pick up quickly, likely faster than in past cycles. Productivity might drop at first, but then it should rise sharply. 

Compressed, non-linear change creates volatility. And volatility tends to increase demand for leaders who can step in quickly and guide organisations through it. 

Why interim is becoming a deliberate career choice

There is now a well-trodden route into becoming a professional interim. It can be a deliberate choice, with clear expectations around what you are brought in to fix, stabilise, or build. 

For experienced leaders, interim leadership offers the opportunity to have high impact over relatively short periods of time, and to repeat that impact across different companies, ownership models, and industries. That breadth of exposure is hard to replicate in a single long-term role. 

However, there are trade-offs. You sacrifice some longevity, deeper cultural imprint, and the upside of long-term incentive plans. But those can often be balanced by higher day rates and the professional autonomy that comes from being hired purely for delivery. 

For the right personality, it is a conscious, performance-led career model. 

The pitfalls worth knowing

It is important to be clear and honest about the risks. 

Interim work is a little like boxing. You are only as good as your last fight. Your last mandate largely determines your next one. Reputation compounds quickly, both positively and negatively, and the market has a long memory. 

Income is not always linear, and you are responsible for managing your own pipeline between assignments. There is no corporate safety net. 

In interim leadership roles, you are often stepping into organisations at moments of stress or uncertainty, so you need to build trust quickly, operate with humility, and make decisive calls. It suits people who are commercially disciplined, emotionally robust, and comfortable being judged purely on results. 

What makes the difference

If you are looking to move into interim leadership, a few things matter more than others. 

Be clear on your niche. Interim leaders are hired to solve a specific type of problem repeatedly. You need to evidence outcomes, not just experience. 

Be proactive with references. Offering credible board-level referees early in a process signals seriousness and builds trust quickly. 

Build depth, not breadth. Work with a small number of credible interim search firms, rather than trying to blanket the market. Most first mandates come through reputation and relationships, not volume applications. 

Keep an open mind. Some of the strongest interims I know have stepped into permanent roles because they wanted to leave a deeper cultural imprint. Going into an assignment fully committed, rather than keeping one eye on the exit, is the best approach. 

A final thought for CEOs hiring interim leaders

Clarity is everything. If you are hiring an interim, define the problem precisely. Be explicit about outcomes and success measures. Treat the interview process with the same rigour as a permanent executive hire. Precision attracts the right talent. 

Businesses are operating in shorter cycles, with faster technological change and tighter capital discipline. They need access to specialist capability at moments that matter. Interim is part of that structural shift. 

A healthy interim market reflects a mature and flexible labour economy. AI embedded across business processes could unlock significant productivity gains, whether that plays out over a decade or accelerates over a few years. Either way, the need for leaders who can navigate change at pace is not going away. 

The post The rise of interim leadership: why more boards are choosing flexibility at the top appeared first on La Fosse.

But that model is losing momentum. 

AI is changing how work gets done. Not by removing people, but by shifting where value sits. The arbitrage is no longer geographic. It is cognitive. The real advantage now comes from organisations that combine human judgment with intelligent systems that learn and adapt. 

We no longer sell capacity. We build capability. 

The companies who win next are not the ones with the lowest-cost delivery centres. They are the ones who can connect talent, insight, and automation into systems that improve every week. 

Execution is getting cheaper. Context, problem framing, trust, and adaptability are getting more valuable. 

The staffing industry has a decision to make. Keep selling human capacity as a commodity. Or step up into enabling capability. 

At La Fosse, we are choosing capability.

This means: 

• Recruiting talent who work fluently with AI in their workflow 
• Training people to think, adapt, and lead alongside automation 
• Evolving delivery from headcount supply to human-plus-system teams 
• Positioning the Academy as a capability accelerator, not just an entry pipeline 
• Using market insights and proprietary knowledge as the differentiator 

This is how we create value in an AI-shaped economy. 

AI will not eliminate the need for people.
It will eliminate the value of uncontextualised labour. 

Our edge is not the hands we place.
It is the capability we enable between people and systems. 

This is the future we are building. Starting now. 

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I remember it like it was yesterday, the long hours, face to face relationships, listening in on calls, and coaching in real time were the environment that really kick started my career.

The trade-offs of the past

It wasn’t perfect, and not enjoyable at times, but overall it was rewarding and set me up for success. That’s not to say it needs to be that way today. Back then, the hours often reflected when our customers and candidates were operating. With the advancement of technology, greater focus on wellbeing, and clearer links between flexibility and productivity, this is now a multifaceted debate, and not one I think even global juggernauts or leading economies have the answer to (yet).

Flexibility vs presence

At La Fosse, we run a 3-4 day in-office rhythm (depending on the division). I know it’s not for everyone, but when the environment is fun and vibrant, it creates better outcomes for our colleagues, customers, and candidates alike. And whilst I recognise that flexibility needs to be offered to secure and nurture the best talent, there’s no denying that at the entry level of a high-intensity sales role like recruitment, you need to be visibly present and learning from experienced people around the office. Humans aren’t designed to be locked away solo; we are a species that relies on interaction, influencing, engaging in person, and building within communities. This is where learning accelerates, confidence builds, and careers really take shape.

The balancing act

We don’t claim to always have this environment, but we’re doing what we can to listen and adapt, whilst also making decisions to create a healthy and happy workplace that benefits our communities of candidates and customers.

What the CEOs are saying – The boss is back

The FT recently ran a piece on the return of stricter office cultures. Amazon, JPMorgan, and even Sergey Brin are pushing for 5 days in the office and an “always-on” mindset. Brin even remarked that “60 hours a week is the sweet spot of productivity,” and urged employees to be in the office every weekday, calling under-performers who work less than that “highly demoralizing” to everyone else. What’s driven the shift in narrative – Trump, sluggish economies, trust, results?

Learning from the Dutch

On the other hand, the Netherlands is quietly moving toward a four day workweek. It already has the shortest average working hours in the EU (around 32 hours per week), the highest part time rate in the OECD, strong employment (~82%), and high per hour productivity, reportedly without economic downside, plus happier children.

The future office landscape

Looking ahead, I personally think tech disintermediation will change the landscape. Central hubs where our colleagues, customers, and candidates come together to knowledge share, connect, and get real value from those connections will be the future. Tech will strip away the mundane work, and it’s the higher order skills of influencing, negotiating, and networking that will reap the rewards. This may feel far off, but they’re ultimately the fundamentals of a great recruiter (and if we’re honest, always have been the greatest skillsets).

That’s the future I’m betting on: embracing technology to elevate the human skills that matter most in the office landscape.

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