The gap between confidence and trust

When we asked C-suite executives how confident they felt in their own AI expertise, 70% described themselves as “very confident”. 

When we asked the rest of the organisation how much they trusted C-suite AI expertise, the picture looked very different. 

Confidence in C-suite AI capability, by seniority:

• C-suite self-assessment: 70% very confident 
• Directors: 48% very confident in C-suite 
• Senior management: 50% 
• Middle management: 36% 
• Entry-level staff: 33% 
• Intermediate staff: 27% 

The further you get from the boardroom, the less trust there is in leadership’s ability to make informed AI decisions. 

Why this gap matters

A confidence gap might seem like a perception problem. It is not. It is a business risk. 

When employees do not believe leadership understands AI, three things happen: 

1. People stop flagging problems. If staff assume leadership will not understand the issue or will dismiss their concerns, they stay quiet. Small problems become big ones. 
2. AI initiatives lose momentum. Adoption stalls when the workforce does not trust the strategy behind it. People comply rather than commit. 
3. Trust erodes beyond AI. Confidence gaps are rarely contained. If staff question leadership judgement in one area, it spreads to others. 

The behaviour behind the numbers

The trust gap is not irrational. It reflects what employees are seeing. 

Our research found that C-suite executives are the most likely to engage in high-risk AI behaviours: 

• 93% of C-suite have made AI-informed decisions based on inaccurate data 
• 73% have uploaded confidential company data into AI tools 
• 78% have used AI for work they are not trained to do 
• 40% report serious business impact from AI-related errors 

These are not junior mistakes. They are leadership behaviours. And the rest of the organisation is watching. 

What needs to change

Closing the confidence gap requires more than communication. It requires visible action in four areas: 

Board-level expertise 80% of C-suite executives themselves say their company needs a dedicated AI specialist at board level. The demand is there. The appointments are not. 

Strategy that reaches everyone 56% of C-suite say their AI strategy matches reality “very well”. Only 16% of entry-level staff agree. If the strategy is not visible and understood at every level, it is not working. 

Governance with accountability Clear rules mean nothing if they do not apply to everyone. When senior leaders bypass safeguards, it signals that governance is optional. 

Honest self-assessment The leaders who will succeed are those willing to scrutinise their own confidence, competence, and decision-making. Seniority does not equal capability. 

Take the next step

If you’re concerned about AI readiness in your organisation, our Inovus team offers a free 30-minute consultation to discuss your AI strategy and data foundations. 

Book your free consultation

Read the full research

This article draws on findings from AI in the Workforce: The Hidden Risk for UK Businesses, our independent research with over 2,000 UK tech workers. 

The full report includes a practical framework for what to fix first. 

The post The AI confidence gap your board isn’t talking about appeared first on La Fosse.

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.

The room at Hiscox was buzzing. Seven speakers, each bringing their unique perspective on building high-performing teams and navigating the AI revolution. From CIOs to agile coaches, delivery leads to transformation specialists, the wisdom shared was both practical and profound. 

Giles Lindsay – CIO: Beyond Framework Thinking 

“We talk about high performing teams like they’re a formula, but performance isn’t built by frameworks alone. It’s built by trust, feedback and focus, and AI is now changing the very fabric of how we define performance.” 

Giles opened with a challenge that resonated throughout the evening. Rather than forcing one-size-fits-all frameworks, the smartest teams contextualise their approach based on what they’re actually building. 

His insight? Support teams handling reactive work often benefit more from flow-based methods like Kanban than from time-boxed sprints. Sometimes Kanban’s pull-based approach makes more sense. Sometimes even ITIL works better because it measures responsiveness to need. 

The breakthrough came when teams started having “non-threatening conversations” with stakeholders about measuring outcomes, not just outputs. Instead of celebrating delivery dates, they began asking: “How do we measure when this creates real value?” The result: better focus, improved predictability, and higher quality metrics. 

Sophie Johnson – Enterprise Delivery Coach: The Blended Scorecard Approach

“When I think about high performing teams, the first things that come to mind are they’re efficient and they’re effective.” 

Sophie brought a holistic view from the enterprise transformation trenches. Her secret weapon? A blended metric scorecard that breaks down performance into four key areas: value, process and flow efficiency, quality, and people metrics. 

“If you optimise in any one of those spaces at the detriment of the other, it means you don’t have a high performing team or organisation.” 

Her approach tackles the common divide between business and technology by ensuring teams remain both efficient and effective across all dimensions. It’s not about picking one metric to rule them all – it’s about balance. 

Patricia Manley – Head of Project Delivery: Embracing the Chaos

“I really, really thrive in a mess and I love working in teams that are in this forming/storming phase.” 

Patricia brought refreshing honesty about the reality of delivery leadership. Working with project managers and delivery teams across everything from data centres to digital products, she’s learned that high performance isn’t about perfect processes. 

Her approach focuses on helping teams define who they want to be, then walking with them every step of the way until they believe in themselves. When teams have vision, framework, and understand what success looks like, they create “that sense of ownership, purpose and empowerment” that drives real performance. 

Her Net Promoter Score of 77 speaks volumes – teams actively request to work with her delivery unit because of this people-first approach. 

Nathan Davies – Agile Transformation Lead: The BlackRock Scale

“When we agree on a common idea and we work towards it, we create something very special.” 

Leading agile transformation for a 3,400-person team at BlackRock, Nathan knows what good looks like at scale. With 18 Scrum Masters and coaches managing approximately 70 squads, he’s learned that simplicity beats complexity every time. 

His philosophy draws inspiration from Jürgen Klopp (whose autographed picture features in every video call): success comes from shared understanding and common purpose, not from JIRA boards or Azure DevOps configurations. 

Nathan’s experience spans from the early days at Egg, where they were described as “the world’s best online development team,” to current enterprise transformation. His key insight: establish discipline, governance, and nurture positive team culture while maintaining that safe space for trust. 

Matthew Carr – CTO Consultant: The Agility Detective 

“So as it says on there, I’m an agility consultant. And I’m also known as the Gordon Ramsay of the technology world.” 

Matthew’s approach is refreshingly direct. Called in when something feels “a little bit off” despite good metrics, he digs beneath the surface to find what’s really happening. 

His recent case study involved a team with perfect JIRA boards, beautiful burn-down charts, and hitting velocity targets – but something wasn’t right. His detective work revealed the human element that metrics can’t capture. 

His philosophy: “Deliver the most valuable thing to obtain feedback fast while keeping waste to a minimum.” It’s not about the delivery method – whether Scrum, XP, or Kanban – it’s about getting that critical customer feedback that tells you whether you’re building the right thing. 

Pardeep Dhanda – Agile Practice Director: The Community Builder

“Harvard have run a study which has been going on for over 100 years. They wanted to answer this question of what does make us more successful and live longer… What they found was the people that have the most and the deepest human connections between the ages of 45 to 50 typically go on to live beyond 80 and be happier and be more successful.” 

Pardeep’s insights went beyond work teams to explore the power of community building. His Visual Jam community, started during lockdown, grew from a few local people to over 4,000 global members. 

The impact was profound: a furloughed community member learned visual skills through the sessions, hand-drew her presentation visuals for a job interview, and got hired. For Pardeep, it was “a real tearful moment” that demonstrated community’s transformative power. 

His research-backed insight: face-to-face interaction generates 20% more ideas and greater creativity than virtual collaboration. Human connection isn’t just nice to have – it’s a competitive advantage. 

Nisha Joshi – Agile Delivery Consultant: The Psychological Safety Champion

“If psychological safety is a predictor for high team performance, if it’s a predictor of togetherness in a community, if it’s a predictor of harmony in a marriage. Then I would ask you to consider are you a safe space?” 

Nisha brought the evening together with her interactive exploration of psychological safety. Starting with a £20 note experiment that demonstrated how we hesitate when we feel exposed, she revealed the neurological truth: social rejection activates the same brain regions as physical pain. 

Her personal story of her parents’ marriage illustrated psychological safety in action. When shift work threatened their decision-making time, her father recognised their “safe space was disappearing” and created Saturday as their dedicated day for walking, talking, and connecting. 

“You need to design for psychological safety. You need to design for it at leadership level and also at team level.” 

Her framework, based on Timothy R Clark’s four stages of psychological safety, shows how teams progress from inclusion safety through learner safety, contributor safety, and finally challenger safety. 

The AI Conversation: Fishbowl Insights

The evening’s AI discussion sparked passionate debate. The consensus? We’re not doomed, but we need to adapt quickly. 

“We’re not going to fear AI. We have to fully embrace it.” 

One speaker compared our moment to 19th-century industrial transformation – disruptive but ultimately creating new opportunities. AI is becoming the ultimate agility enabler, shrinking the cycle from idea to market feedback from months to minutes. 

Practical applications are already emerging: tools like Lovable are revolutionising product discovery, allowing teams to create clickable prototypes from simple descriptions within minutes. But the human element remains crucial – “always keep a human in the chain” for validation, safety, and creative problem-solving. 

The Bottom Line 

Seven speakers, one powerful message: high-performing teams aren’t built by frameworks, tools, or metrics alone. They’re built by trust, psychological safety, shared purpose, and genuine human connection. 

Whether you’re contextualising agile approaches, building community, or embracing AI transformation, the fundamentals remain constant: create safe spaces for people to do their best work, focus relentlessly on value creation, and never forget that behind every metric is a human being trying to make something better. 

As we plan our next Agile Assembly, one thing is certain: the conversation is just getting started. 

Want to join the next Agile Assembly? Keep an eye on our events calendar. Because the future of agile leadership isn’t just about what we build – it’s about how we build it together. 

The post Seven Voices on High-Performing Teams: Insights from Agile Assembly appeared first on La Fosse.

The Chief Technology Officer role remains one of the most sought-after positions in technology leadership, yet also one of the most misunderstood. La Fosse recently brought together seasoned CTOs and CPTOs to cut through the romanticised perceptions and share the unvarnished truth about what it really means to step into these senior technology roles. 

The reality check: it’s not what you expect

The conversation opened with a sobering reality that few CTO roles match initial expectations. The technical playground that many engineers envision simply doesn’t exist at the CTO level. The role demands far less hands-on technical work than anticipated, with significantly more organisational responsibilities that many don’t expect. 

The common thread across all CTO experiences is that there’s no one above you who can answer technical questions. Yet this technical authority comes with responsibilities that extend far beyond technology itself. 

The great title confusion: no two CTO roles are the same

Perhaps the most crucial insight shared was the massive variation in what CTO roles actually entail across different organisations. The title itself can be almost meaningless without understanding the specific organisational context and structure. 

This variation creates a critical challenge for anyone considering the transition. Due diligence becomes essential when exploring CTO opportunities. Understanding the full technology leadership structure, including whether there are CIOs, CPTOs, and other C-level technology roles, can make the difference between success and stepping on colleagues’ toes. 

Different organisational structures dramatically affect the role’s scope and effectiveness, with some companies having multiple C-level technology roles that can create confusion and conflict. 

The courage to seize the moment

Despite the challenges, timing and courage are crucial when CTO opportunities arise. Many successful transitions happen when individuals find themselves already performing CTO-like responsibilities and have the courage to formalise the role. 

The advice from experienced leaders is clear: when the opportunity presents itself, you must be prepared to act decisively. These positions don’t follow linear career paths, and opportunities may not present themselves again if missed. 

The difficult conversations nobody talks about

The discussion revealed the darker aspects of CTO responsibilities that many don’t anticipate. People management challenges, particularly around restructuring and redundancies, often fall squarely on the CTO’s shoulders. 

This responsibility creates a fundamental conflict for those transitioning from engineering management roles. As a VP of Engineering, your primary concern is the engineering team. As a CTO, your allegiance shifts to the executive team, creating difficult decisions that can feel conflicting. 

The guidance for managing this tension focuses on authentic decision-making. Following your heart and doing what you genuinely believe is right for the business becomes essential, even when those decisions are difficult for teams you care about. 

The enterprise architecture blind spot

An unexpected theme emerged around the critical importance of enterprise architecture in certain organisations for  CTO success. Many of these organisations lack proper enterprise architecture functions, which can create a significant blind spot for CTOs.

In these companies, this gap can impact strategic technology decisions and organisational alignment, highlighting how
enterprise architecture skills become increasingly important as AI and data initiatives require
strong foundational structures.

In smaller, or less regulated companies, this is less of a concern and often is picked up by senior developers in conjunction with the CTO.

What makes the role worthwhile

Despite the challenges, the unique opportunities that CTO roles provide consistently emerge as the primary draw. The breadth of impact extends well beyond technology, offering input into commercial vision and organisational strategy. 

Technology leaders are no longer confined to back-room roles. There’s a growing recognition that tech should have a voice at the top table, though this requires fighting for that position in many organisations. 

The networking and learning opportunities, combined with involvement across multiple business areas, create a stimulating environment for those ready to embrace the broader responsibilities. 

The not-so-one-way door

One encouraging insight is that CTO roles aren’t necessarily permanent commitments. Internal promotions in particular can offer flexibility, allowing individuals to step back if they discover the role isn’t suitable. 

This flexibility provides reassurance for those hesitant about making the transition, though it’s more feasible with internal moves than external CTO appointments. 

The challenges: time, politics, and technical distance

When examining what leaders dislike most about CTO roles, several consistent themes emerge. Limited personal time, including challenges taking adequate holiday, represents a practical concern that impacts work-life balance. 

More significantly, the political aspects of senior leadership prove challenging for many technically-minded individuals. The energy required to navigate organisational politics can be draining for those who prefer direct, solution-focused approaches. 

The distance from hands-on technical work represents perhaps the biggest adjustment. The excitement of exploring new technologies and solving technical problems becomes limited, replaced by higher-level strategic considerations. 

The workforce evolution: AI’s impact on roles

Looking forward, the traditional boundaries between CTO and CPTO roles are blurring, particularly with AI enabling product managers to create prototypes and demos independently. This technological shift is driving role evolution and potentially consolidating responsibilities. 

Economic pressures are also forcing organisations to reconsider multiple C-level technology roles, with many companies finding such structures duplicative and expensive. 

Making the decision: honest self-assessment

The final consideration centres on honest self-assessment. Before pursuing CTO roles, individuals must genuinely examine whether they’re prepared to leave behind hands-on technical work. 

Many talented engineers discover they prefer remaining close to technology rather than moving into the broader organisational responsibilities that CTO roles demand. This preference is perfectly valid and important to recognise before making the transition. 

Managing change and communication

Successful CTOs must excel at change management, requiring clear communication about end states and extensive effort in explaining changes to diverse stakeholders. The ability to modify messages for different audiences becomes crucial, whether speaking to technical teams or broader business stakeholders. 

Starting with why becomes essential. People need to understand the reasoning behind changes, even if the specific destination isn’t completely defined. 

Remote leadership considerations

The modern CTO role increasingly involves remote team management across multiple locations and time zones. Success requires dedicated effort in communication, including regular town halls and structured updates that maintain connection across distributed teams. 

While fully remote CTO roles are possible, they demand additional investment in communication structures and relationship-building to maintain effectiveness. 

The path forward

The conversation revealed that successful CTO transitions require more than technical expertise. They demand political acumen, people management skills, strategic thinking, and perhaps most importantly, a genuine desire to operate at the intersection of technology and business. 

For those considering the step up, the guidance is clear: understand exactly what the role entails in your specific organisation, be honest about what you’re willing to give up, and when the opportunity arises, have the courage to seize it. 

The technology landscape continues evolving, and with it, the nature of senior technology leadership roles. Understanding these realities helps ensure that career transitions align with personal goals and organisational needs. 

La Fosse continues to facilitate these crucial conversations, providing technology leaders with the insights needed to make informed career decisions and navigate the complexities of senior technology roles. 

The post To CTO or Not to CTO: Navigating the Leadership Decision appeared first on La Fosse.

The mobile development landscape has matured dramatically, yet mobile engineers still face unique challenges when stepping into broader technology leadership roles. La Fosse recently brought together experienced mobile engineering leaders to discuss how they’ve navigated this transition and what advice they’d give to others looking to expand their influence beyond the mobile domain. 

Escaping the “mobile person” label

The conversation opened with a challenge familiar to many mobile engineers: being pigeonholed as “the mobile person” when aspiring to broader leadership roles. 

Neil Sheppard, Head of Mobile Engineering at Zopa, shared his approach: “One of the things that happened at Moonpig was I’d been a mobile guy for a few years, and my boss said, ‘you’re head of mobile, you’ve got two direct reports. It’s quite a small scope.’ So I said, ‘Okay, well, I need to increase my scope.'” 

His solution was proactive: volunteering to lead the company’s migration from data centre to AWS, despite having limited cloud experience. “It was never really about doing anything technical. When you get to that level of role, it’s more about orchestrating and making sure that people are unblocked,” Neil explained. 

This approach worked, leading to additional platform migration projects and eventually breaking free from the mobile-only perception. 

The mindset shift: from end of chain to orchestrator

Mobile engineers typically work at the end of the development chain, consuming APIs and rendering interfaces for end users. The transition to leadership requires a fundamental mindset shift. 

“With mobile, you tend to be at the end of the chain,” Neil explained. “But when you start to branch out from being mobile, you find that you’re no longer end of the chain.” 

The key insight emerged around expanding impact beyond the immediate mobile domain: “It might be a technical problem, might be a marketing problem, or it might be a process problem, whatever it might be, but really try to branch out to much wider impacts. The wider the impact, the more visible the work, the more valuable it is,” added Vesselin Iliev, Head of Engineering at N Brown Group. 

Building trust across teams

A crucial skill for mobile engineers transitioning to leadership is learning to trust and build relationships with other engineering teams. As Vesselin noted: “You don’t have to learn everything. What you have to learn as you grow is how you can trust and build economy, build ownership with other teams and other people.” 

This represents a shift from the mobile engineer’s tendency to want to understand every technical detail to focusing on enabling others and removing blockers across the entire technology stack. 

Advocating for mobile’s unique value

The discussion also addressed how to champion mobile development in organisations that might undervalue it. The advice was pragmatic and data-driven. 

“For me, it goes back to understanding what the business wants,” shared Greg Pugh, Head of Engineering at Bourne Leisure. “We can say we’re engineering-led teams, but we are business-led. Part of our roles as leaders is to understand what the business wants, what they drive towards, and then build the right products to support that.” 

The importance of data emerged as a key theme from Vesselin: “Show me the data. Is that an assumption that someone has, or is that real data?” He discovered that despite assumptions about their customer base being older and less mobile-oriented, “55% of traffic actually came through mobile devices,” with better return on investment than web. 

The mobile advantage: demonstrating what web can’t do

Mobile offers unique capabilities that web simply cannot match. As Greg put it: “Web development is so homogenised, so basic. It’s not woven into people’s lives in the way that mobile is woven into people’s lives.” 

The end-to-end experience mobile enables creates opportunities to demonstrate value: “That journey that was an email that dropped into their inbox, or was a push notification that took them to just the right place to do the thing and then triggered another journey, that is our unique mobile superpower,” he explained. 

Even simple features like haptics can make a powerful impression on leadership teams unfamiliar with mobile’s capabilities. 

Leadership lessons beyond mobile

The conversation revealed that many challenges faced by mobile engineers transitioning to leadership are universal leadership challenges, not mobile-specific ones. 

Key insights included: 

• Intent matters: Understanding whether you actually want to move into leadership, or whether it’s happening by default, is crucial for success. 

• Leverage existing experience: Most mobile engineers have broader technical backgrounds they can draw upon when demonstrating capability beyond mobile development. 

• Look for growth opportunities: Actively seeking projects outside your immediate domain demonstrates ambition and capability to stakeholders. 

• Focus on business impact: Understanding and aligning with business objectives is essential for any technical leadership role. 

The multiplier effect of good leadership

One of the most compelling points made was about the often invisible value that good technical leadership provides: “Somebody who’s caring about those individuals is multiplying the effectiveness of those individuals. Decisions are being made before they get to engineers, and some of those decisions are really, really stupid. If you can stop those happening, that’s the value you’re providing,” observed one of the panellists. 

This insight resonates beyond mobile engineering, highlighting how effective technical leaders shield their teams from poor decisions and enable them to focus on meaningful work. 

Moving forward

The mobile engineering community continues to mature, and with it, the career paths available to mobile engineers are expanding. The key is recognising that the skills developed in mobile engineering, combined with intentional efforts to broaden scope and demonstrate business impact, create strong foundations for technology leadership. 

As these leaders demonstrated, success comes not from abandoning mobile expertise, but from leveraging it as a springboard to broader influence and impact. 

La Fosse continues to bring together technology leaders across specialisms to share insights and support career progression. These conversations provide valuable peer learning opportunities for engineers at all stages of their leadership journey. 

The post Breaking into Leadership: The Mobile Journey appeared first on La Fosse.

Turning AI potential into business reality

Our exclusive roundtable, facilitated by Mal Minhas (CPTO, Gumtree), Simon Farnsworth (CTO, ITV) and Lucie Cassius (Head of Client Growth, La Fosse), brought together technology executives from leading organisations to share candid insights about their AI journeys.

The consensus was clear: successful AI isn’t about chasing the latest model or algorithm, it’s about strategic implementation that drives measurable outcomes in three key areas:

• Revenue growth through enhanced products and services
• Cost reduction across operations
• Increased velocity in business processes and decision making

“Most organisations are prioritising revenue growth rather than cost reduction alone,” noted one participant, highlighting a shift towards seeing AI as a growth enabler rather than simply an efficiency tool.

From theory to practice: AI use cases that deliver

The most compelling discussions centred around practical applications already delivering results:

• Content creation: Companies leveraging generative AI to transform marketing and creative outputs
• Development acceleration: Engineering teams using AI-powered coding assistance to dramatically increase velocity
• Product enhancement: Businesses implementing AI-supported listings to drive revenue, with examples ranging from advanced SEO tagging at News UK to product enhancements at Checkatrade

Investment priorities: people first, technology second

A recurring theme throughout the discussion was the critical importance of balanced investment across:

• People: Training, hiring, and upskilling existing staff
• Technology: Tools, infrastructure, and AI models
• Processes: Governance, workflows, and best practices

Interestingly, attendees emphasised that successful AI transformation often hinges more on people than technology. “Finding cultural champions who already understand your business and upskilling them in AI can be more effective than hiring external AI specialists,” shared one technology leader.

Several participants cautioned against premature optimisation or deep dependency on single AI models, advocating instead for starting with smaller experiments that can scale as confidence grows.

Data: the foundation that makes or breaks AI success

The roundtable highlighted data readiness as perhaps the most critical, yet often overlooked, factor in AI success.

Leaders emphasised that without robust data governance, observability, and infrastructure, even the most sophisticated AI initiatives will falter. Specific focus areas included:

• Ensuring data quality through comprehensive monitoring and alerting mechanisms
• Building scalable data infrastructure that integrates with existing platforms like Salesforce
• Establishing clear ethics policies and principles around AI usage and data privacy

Organisational structure: no one-size-fits-all approach

One fascinating insight was the variety of approaches to AI governance and leadership. While some organisations have created dedicated AI roles, others have distributed responsibility across existing technology functions.

“There’s no standardised approach yet for AI governance,” one attendee observed. “Leadership varies between CTO, CPTO, CDO, COO, or hybrid models, depending on organisational context.”

What matters most is clarity around who owns AI strategy and implementation, regardless of where it sits in the org chart.

Breaking down silos: AI as a business decision

Perhaps the most definitive conclusion was that AI strategy must be business-driven rather than technology-led.

“AI should ultimately be viewed as a service to business functions rather than a standalone tech initiative,” summarised one CTO. This requires education and digital literacy across all levels of the organisation to ensure alignment between technical capabilities and business objectives.

The experience paradox: junior staff leading the way

In a notable reversal of traditional experience dynamics, many attendees observed that junior team members often show higher enthusiasm and adaptability towards AI compared to more senior staff who may be sceptical or resistant.

This presents both challenges and opportunities: leveraging the passion and aptitude of younger employees while ensuring their efforts remain aligned with broader strategic objectives.

Actionable recommendations

Based on the collective wisdom shared during our roundtable, several clear recommendations emerged:

• Identify cultural champions: Find enthusiastic internal staff who understand company culture and operations, then upskill them in AI instead of hiring externally.
• Prioritise data governance: Conduct thorough reviews of data readiness and implement strong governance and monitoring tools to ensure high-quality inputs for AI models.
• Start small and iterate: Begin with manageable experiments before committing deeply to specific AI models or technologies.
• Establish clear ownership: Define who owns AI strategy within your organisation, understanding this may vary between technical and business functions.
• Focus on business outcomes: Ensure AI projects are driven by clear business objectives rather than purely technical ambitions.
• Broaden AI literacy: Provide training and resources across the company to increase AI understanding and drive adoption.
• Leverage junior talent: Create opportunities for younger, tech-savvy employees to contribute meaningfully to AI initiatives.
• Develop clear AI policies: Formulate and communicate AI ethics, policies, and principles company-wide.

Continuing the conversation

This roundtable is part of La Fosse’s ongoing commitment to bringing together technology leaders to share insights and solve common challenges.

The insights shared during this session underscore the importance of practical, business-focused approaches to AI implementation. As organisations continue to navigate the rapidly evolving AI landscape, the value of peer learning and shared experiences becomes increasingly apparent.

La Fosse regularly hosts exclusive events for senior technology leaders across various specialisms. These sessions provide a forum for open discussion and knowledge exchange in a trusted environment.

The post Beyond the hype: AI Leaders share real-world strategies for business impact  appeared first on La Fosse.

At La Fosse, we’ve spent more than seven years helping organisations avoid expensive hiring pitfalls. We know what works, and what doesn’t. Here are some of the most common recruitment mistakes we see, and how to avoid them…

1. Writing vague job descriptions

A job advert is your shop window. If it’s unclear, full of buzzwords, or lacking in detail, you’ll either attract the wrong candidates, or none at all.

What to do instead: Be honest and be specific. Focus on the skills and experience the role really needs, and explain how it fits into the wider team. A strong job description helps candidates picture themselves in the role, or more importantly, know if it’s for them.

2. Hiring in a hurry

We understand the urgency when roles need to be filled, projects need to move, and your team is under pressure. But rushing the hiring process can lead to poor decisions, and unnecessarily high turnover.

What to do instead: Speed doesn’t have to mean shortcuts. With a clear plan and the right support, you can move quickly and make smart choices. That’s where a trusted partner like La Fosse can step in – keeping momentum high while making sure the candidates are a great fit.

3. Relying too heavily on ‘culture fit’

Hiring someone who ‘fits in’ sounds great on paper. But if it becomes a tick-box for shared hobbies or similar backgrounds, you risk building a team that all thinks the same – and that can hold your business back.

What to do instead: Look for a culture add. Focus on people who share your values but bring new ideas and different perspectives. That’s how you build stronger, more diverse, and more creative teams.

4. Overlooking internal talent

Sometimes the best person for the job is already in the business – but gets passed over because they’re not on the hiring radar.

What to do instead: Create pathways for progression and give people the tools to step up. Not only is this good for retention, it also saves you time and money compared to bringing in someone new.

5. Treating recruitment as a one-off task

Too often, businesses see hiring as a single action – post a job, review CVs, interview, hire. But great recruitment is part of a bigger picture. It’s about building teams that grow with you.

What to do instead: Think long-term. Partnering with an experienced recruitment team – like us here at La Fosse – means you’re not starting from scratch every time. We help clients build an all-encompassing approach to hiring, so they can scale faster, smarter, and with less friction.

Why La Fosse?

We’ve been helping businesses make smarter tech hires for over seven years. From entry-level placements to senior leadership superstars, our four specialist divisions cover every corner of the tech space – making sure you get the right people, right when you need them.

If you’re ready to stop making the same hiring mistakes and start building a better team, we’re ready to help.

Ready for success? Submit a vacancy, apply for a job, or get in touch with the La Fosse Team.

The post The biggest hiring mistakes companies make – and how to fix them appeared first on La Fosse.

According to McKinsey, data-driven organisations are not only 23 times more likely to acquire customers, but they’re also six times as likely to retain customers and 19 times more likely to be profitable. So, is data in the driving seat for enterprise valuations? And if so, how can that value be quantified and optimised?

At the acquisition stage, what should you be looking for in terms of data and its value?

Go back to basics – look at the amount of data available, the architecture and infrastructure of where the data is collated and how it’s stored, how the tech stack is built, the structural approach and data strategy, and the period it’s been captured for.

When you have billions of data points, it’s easy to get lost. Consider the overarching operational decisions that will optimise value and how the data aligns with them. For example, if a key focus is marketing, does the data give you a clear customer picture?  Think about the insights you can monetise to help drive growth and, therefore, value. In a study by Bain & Company, only 4% of companies said they have the right resources to draw meaningful insights from their data.

At the acquisition stage, a lot of the exploration and due diligence process is about identifying opportunities. Look at the data itself and understand what’s actually visible – the quality, the way it’s collated, the way it’s stored – but also look at the talent behind it – the people who run and govern the data, how it’s reported, how the value is extracted from it.

As you move to the development stage, how do you start to unlock your data to help drive strategy?

As you begin to develop a strategy for a new business acquisition, the reality of making big changes and switching direction can be daunting, so utilising data to make small adjustments to begin with may make more sense. Looking at developing a data lake? Start with a data puddle that solves some small problems first. As you develop and understand the data better, you can connect the puddles to become ponds and then the ponds will connect to eventually become a lake.

If you acquire a company that doesn’t have a data culture, people change is the hardest element. Pick specific examples of where you can use data, show your people how to use it, and embed utilising data throughout your strategy and the day-to-day of your employees. Once you have people who know how to use the data, you’re most of the way there.

If a company is built from day one as data-centric, the CEO will likely have enough knowledge to drive the data strategy. Frequently in legacy companies, a CDO hire is made to facilitate data transformation, and there’s less buy-in from the board and cultural momentum because it’s not an embedded business element. The share of leading global firms with a CDO rose to 27% in 2022, up from 21% the previous year. The role is particularly prevalent in Europe, with over 40% of top European firms looking to a CDO for data leadership. Boards need to be aligned with data transformation; they don’t all need to be experts, but there should be a decent level of understanding. If not, bring in advisors and consultants to support the message. Data requires a high level of governance – it’s a huge business benefit/risk area and needs to have that oversight and insight from the board.

Establish some overall principles for your data. Whilst it’s important to get input from across the business, the data strategy should be created by the leadership team and communicated down. Having a holistic vision and standardising it across your systems will help to align your workforce, but don’t be too rigid on this; mapping out how to achieve your vision from start to finish leaves little room for flexibility and it’s unlikely that your plan will follow an exact path.

Data plays a vital part in optimisation – what do you need to consider at this stage?

Organisational design is at the heart of optimisation, and data is a core element of this. Consider where data sits within the business – is it within IT, with the CDO, or with the strategy team? Find correlations between different areas of the business and establish how data links them to identify the best team structure. There’s a common debate around whether a centralised data team is more or less effective than a dispersed team, but effectiveness is dependant on how the business uses its data. A central team gives more focus; a dispersed team embeds data (and buy-in) throughout the organisation.

Compliance is one of the board’s main responsibilities, so regulation and risk aversion can become the focus rather than how to harness the data for growth. Have an advisory team or committee formulate the commercial thinking to shine a light on profitability or value; when the board has compliance at the forefront, working out how to monetise data doesn’t work without that advisory help. Corporate boards may tend to over-index on risk, but this is where challenge and flexibility come into play. In a Private Equity environment, value must be created quickly. Risk is therefore reduced because the focus is on speed and there is less buy-in required from the board.

As generative AI becomes a more popular and familiar tool, the potential to use it to enable business growth is a huge draw. Fundamental process and infrastructure elements need to be stable before bringing in the next shiny thing – and data plays an important role. Generative AI utilises data points to develop and become potentially autonomous, a conceptually exciting possibility. However, there are considerations in terms of ethics, data security, and governance that must be addressed first. The best way to avoid risk is to not do anything, but there needs to be a balance or there’s no growth.

When it comes to liquidity, how do you communicate the value of your data?

The ability to demonstrate the security of your data is an important factor. Trust and brand reliability are key elements at the liquidity stage, and managing customer data plays a role in that. Security must be an embedded behaviour that every person in the company embraces. It becomes an essential and non-negotiable building block of modern corporate culture. The value of a company’s data is directly linked to its quality and integrity, therefore its architecture and security become fundamental value drivers.

Buyers and investors will look at how your company data can be considered as a stand-alone asset – yes it’s intrinsically linked to the business, but does it have a singular value? Identifying how other businesses can use your data is the key. Without understanding what can be done with the information, you can’t attach a value to it. For example, if you’re able to demonstrate impressive ROI on a commercial project, using data insights alongside the narrative to illustrate how it was done, that data is clearly much more valuable than a basic dashboard. Data on its own is not where the value lies; the critical judgement and application of the data is.

Insights were provided during a La Fosse Private Equity panel event by:

• Richard Wazacz – CEO of Travelex, former CEO and Co-founder of Octopus Choice and Octopus Cash
• Carla Stent – Board Chair and iNED including Telecom Plus plc, Evelyn Partners, Marex plc, Post Office, and former COO and CFO of Barclays GRCB, and Virgin Group
• Lorenzo Bianchi – CDTO of Sector Alarm Group, former Digital Transformation Operating Partner at KKR, and former Google senior leader
• Eser Tireli – VP of AI and Data Solutions at Bain & Company
• Elizabetta Camilleri – Chair of PE-backed Access Partnership and Togetherall, NED at BOV and Boring Money, Board Advisor to multinationals and startups
• Facilitated by Jonathan McKay – Chairman at La Fosse, Forward Partners UK, Move.ai, and Driftrock
• Hosted by Jack Denison – Global Head of Executive Search and Interim Management at La Fosse

To discuss our data recruitment solutions, please contact our team.

The post Is data in the driving seat for enterprise valuations? appeared first on La Fosse.

So, as such a vital component in organisational success, how do you ensure that architecture has a real presence in the boardroom and is ultimately seen as a major contributor, rather than as a cost centre whose primary role is to keep the lights on?

Long-term strategy vs short-term execution

When we hear about enterprise architecture failing, it’s usually down to little buy-in from the C-suite or lack of sponsorship from those with a seat at the top table. There’s either miscommunication or a lack of understanding of what architecture really is and how it can help the business. We’ve all heard examples of CIOs and CTOs bringing in enterprise architecture thinking it will solve all problems.

Before even considering a Chief Architect hire, the leadership team must be clear on what can be achieved and how the role will benefit the organisation, with a realistic timeframe for delivery. The situation can be likened to the pattern of football club managers being hired, only to lose their jobs after a short spell of losses. They may have inherited a poor squad, or the owner has unrealistic expectations of what’s possible to achieve. In both cases, it’s the manager (or Chief Architect) who suffers, the organisation loses confidence and trust, and successful implementation falls out of reach – a vicious cycle.

Getting buy-in at board level

When it comes to interaction with the board, communicating value is key to getting buy-in. Especially with technical or lesser-understood business functions, the ability to demonstrate the positive impact of your team is an important skill. Think about:

• Shifting mindsets – there’s always been an air of intimidation from the technology team, that they are the all-knowing experts of everything IT-related. This is obviously not the case, and the majority of Chief Architects we’ve spoken to have admitted to panicking anytime a shiny new technology is launched and the board demands to know how it can fix their problems (Chat GPT/AI anyone?). If you make yourself vulnerable and admit that you’re not the expert, but can find someone who is or develop your own knowledge, you’ll gain trust and reduce the ‘us and them’ divide.
• Knowing your audience – numbers are the language of the C-suite. When engaging them, if you can evidence what you’re saying with specific, compelling metrics, they’re more likely to take note. Bear in mind that success is dependant on the stakeholders you’re engaging. For example, a CFO will be more interested in cost-saving and revenue increase numbers, but a CPO will have different drivers. Get to know your stakeholders, their motivations, and ultimately what helps them to succeed in their role, and then tailor your approach to it.
• Alleviating pain points – it may be a business process that could be easily streamlined or upgrading legacy tech, but by uncovering your stakeholder pain points early on, you can find some immediate successes and easy wins. If you can help someone, you’ll get them on-side. Even if it’s not necessarily your area of expertise, gain trust by making things easier; it’ll quickly open doors and help develop stronger relationships. Consider the bigger picture – could mulitple people or branches of the business benefit from something you’re working on? Find and improve connecting functions, not only making efficiencies but bringing different elements of the business toether.
• Being properly prepared – We hear so often that the challenge is getting into the boardroom in the first place, but once you do, do you have a clear plan? Being prepared is about having a business case fully worked out, knowing the answers to follow-up questions, and mapping out next steps. Be ready to make decisions and take accountability, but also think about suitable options for projects that require more stakeholders involvement. Be clear that architects aren’t just problem-solvers; they’re also idea-generators. Considering architecture at the ideation stage is vital with any new projects or initiatives. Ask some of your less-technical peers for feedback on how you communicate your technical points; peer review gives a different perspective to ensure your message hits the mark.

Balancing advancement with BAU

Whilst it’s true that digital transformation and technical innovation are key success drivers, there’s a need to balance new projects with the day-to-day. Without a solid architectural foundation, those development areas won’t have a stable footing to start out on. It’s easy to get caught up in the shiny and exciting stuff, but the engine needs to keep running.

Data can be helpful here – what’s working well? What efficiencies could be made? What small changes can help to support those efficiencies? Identify where minimal changes can have a big impact and showcase your team’s value in these areas whilst leaving space to carry out BAU tasks.

Sometimes, tech ideas come from less technically minded business functions. For example, the Product Team may ask for what they believe is a simple and straightforward update but is actually a complex undertaking. Manage expectations by collaborating with them and plotting the realistic scope of the project. If the alignment with overall business strategy isn’t apparent, or the effort outweighs the end result, where does the value lie

Building a tech culture

Culture may be the word of the moment, but it’s a key business focus for a reason. Building a tech culture is about embedding technology throughout your organisation, enabling your workforce to utilise tech tools, and using them to support growth and success.

Collaboration is a huge part of successful tech culture – there needs to be a mutual respect and understanding between different branches of your business to facilitate growth. For example, the Product Team can lead the creation of a business value case, but they must be aligned with the Architecture Team to understand the full scope and plan implementation.

Building trust is also important here. It takes time to establish your credibility and value, but each team needs to be able to rely on the other. There’s a common opinion that architects offer options, but rarely take a stance; as with the board, build trust by showing that you can make decisions and guide the narrative.

In any workplace, understanding people is at the heart of good culture. Take time to talk to people, find out what drives them, how they make decisions, what’s important in their day-to-day role. If you’re able to understand the vision of the people you working with, it’s easier to feed into and facilitate that together.

Reading list:

• Patterns of Enterprise Application Architecture, by Martin Fowler
• Empowered, by Marty Cagan and Chris Jones
• Influence, by Robert Cialdini
• Surrounded by Idiots, by Thomas Ericson

The post Elevating Architecture to the Boardroom appeared first on La Fosse.

Start with culture

It’s no secret that a successful business has a successful culture at the centre, so embedding an expectation of excellence within that culture is a great place to start. Company culture is made up of three things: the things that are written down, the things that are said, and the things that are demonstrated and believed. As a leader, it’s your job to set the tone and really show up; you must live and breathe your company’s culture and elevate the environment.

The next stage is driving those messages and actions throughout your teams and the wider organisation. Visit employees in each location, encourage input from everyone on what’s going well and what could be improved, and welcome suggestions on different ways of working. It’s only by collating different opinions that you’ll understand how embedded those cultural elements are. Remember, culture needs to be championed across the company from top-to-bottom, so buy-in at board level is equally as important.

Hire for personality

Tech comes and goes (after all, it’s just a tool), but the person using the tool and using it well is where success lies. The most important thing you can do is hire good people, and ‘good’ doesn’t necessarily mean technically proficient. Skills can be taught; personality, problem-solving ability, and work ethic cannot.

Of course, there’s a basic skill-level requirement, but it’s not the leading factor. Having a detailed understanding of the soft skills and ways of working already present in your teams can help you to identify what you’re missing, and therefore recognise the value-add candidates. Bring on people who are smarter than you; if you’re wise enough to realise your gaps and shortfalls and then hire people to fill those gaps, you’re on to a winning formula.

Optimise collaboration

Online technical collaboration tools make working over the web pretty simple, but emotional contagion is a huge factor of working as a team, and being physically together at regular times is vital to understand the more subtle nuances of interaction. Combining the two can work well; the time spent together in person can focus on communication, the time spent online can focus on technical work. Ensure you’re enabling both environments as much as possible.

Testing collaboration at the interview stage can also be a great indication of individual work practices and approaches. Try giving your candidates a problem and then work with them to find out how they solve it – you’ll see practical examples of problem-solving, critical thinking, resilience, curiosity, and reaction to failure, all of which will help you to understand how each person will work within your team.

Understand drivers

In a competitive marketplace, understanding what attracts and motivates candidates isn’t where the conversation ends; to retain your staff, you must ensure they feel fulfilled in their roles. Find out what drives them – recognition, working with others, remuneration, trust in managers, challenge, career progression – and keep talking about this on an individual level as their roles develop. Remember, the things that make one person happy are not necessarily what make another person happy.

Commonly in tech, innovative and dynamic ways of working are an attractive prospect for candidates and employees, and being open to suggested new ways of working, alternative tech stacks, or unfamiliar software can not only motivate but also show a willingness to be flexible.

Focus on happiness

Defining success within teams can be difficult, but ‘The Happiness Factor’ is the leading indicator of whether people will deliver, bring people along with them, and help build a positive culture.

Autonomy is a huge element of happiness; people don’t want to be order-takers, and so allowing for self-management (to an extent) can help to create a more positive environment. If you’ve hired the right people, trusting them to do their job should come next.

As with understanding drivers, talk to your teams about what makes them happy. Send out regular surveys and ask for feedback, welcome suggestions, and check in on your employees with regard to their mental health and well-being.

Manage your vendors

If you’re using external agencies and vendors to hire into your teams, treat them as closely as you would an employee or internal hiring manager, and hold them to the same standard. Communicate the cultural elements that are vital to your business’ success and ensure they are hiring against them. Align your interview questions to uncover ‘personality’ factors, collaboration styles, and soft skill sets. Your vendors are a representation of your business; excellence should be front of mind for them, too.

Reading list:

• Riding the Waves of Culture by Charles Hampden-Turner and Fons Trompenaars
• Communication for Engineers by Chris Laffra
• Legacy by James Kerr
• Team Topologies by Matthew Skelton and Manuel Pais

The post CTOs Anonymous: In search of excellence in a global workforce appeared first on La Fosse.

From maintaining a robust IT framework to keeping an eye on emerging trends, ITSM leaders are a key driver of innovation and business growth. 

But how can ITSM leaders bring value and navigate their many challenges? 

During our recent ITSM Summit event, we brought together ITSM leaders to discuss how to get the most from your teams and what the future holds for the industry.  

Here’s a round-up of the key themes from the night. 

Set up for success

ITSM, like any other service, needs clearly defined goals to measure success.  

It may sound obvious, but it’s vital to be clear on the role of ITSM in your business. What does the user want? What do they need? If your service meets or exceeds expectations, you’ll have a seat at the table and get the visibility and influence you need to drive value. 

When you’re working on large matrix projects, make people accountable and get cross functional teams to work together. This will ensure projects don’t take too long and cost too much. Fast feedback loops are a useful way to keep teams engaged. Collate feedback, produce improvement plans and showcase delivery. 

The tools you use are also a key factor in user perception. ServiceNow is used by nearly 85% of Fortune 500 companies and 70% of our ITSM Summit attendees, but it may not be the best fit for you. Gartner’s magic quadrant can help you understand where you stand. 

Move from traditional to agile

There’s a growing expectation for ITSM to be flexible and adaptable to business needs. Essentially, you shouldn’t work for the processes, the processes should work for you. 

Cloud transformation has made it easier for ITSM teams to work to within an agile framework, and make the most of the flexible roadmaps, ongoing adjustments and constant collaboration that comes with it.  

It’s also allowed for a closer relationship between ITSM and DevOps. While the teams may have a different focus, they can come together to deliver shared objectives. Integrating tools and systems between the teams is a useful way to share knowledge and align on strategic projects. 

While agile brings many benefits it’s important to not lose sight of traditional ITSM principles. Strong processes and detailed documentation complement agile ideals and make for a strong structure that delivers a better user experience, reduced risk, improved culture and better adherence to regulations. 

Optimise with automation

Automation and AI will undoubtedly have a part to play in the future of ITSM. However, its implementation should be linked to business objectives. If automation doesn’t help you achieve your goals, it’s not much more than a vanity project. 

Depending on your use case, automation can bring about significant benefits. For example, it can allow you to simplify processes to allow service desk agents to focus on continual service improvement (CSI). Virtual assistants, chatbots and machine learning can all help optimise your process, cut costs and improved user experience. 

In a poll of ITSM leaders after our event, over 50% said that optimising processes and productivity were their focus points for the next 12 months, with integrating AI the next most popular goal. 

This highlights that process is still a key focus for ITSM leaders and AI and automation is likely to be part of that journey. But it’s important to stress that a human element and a personal touch will always be needed. 

Build great teams

Alongside having a clear strategy and access to relevant tooling, building a strong team culture team is key. The book Radical Candor by Kim Scott outlines how leaders can be more effective by combining sincerity with care. 

Modern ITSM should move away from a command-and-control culture to one that promotes collaboration and problem solving. An example being the simple switch from ‘change manager’ to ‘change enabler’ mindset. This will help ITSM SMEs to think like a developer, becoming more agile and able to adapt to challenges, and foster an environment for continuous improvements. 

The change in team culture can also be seen at leadership level. A recent trend has showed a shift from a traditional CIO approach to the people- and product-led CDO vision.

But team culture is only as effective as talent you have available. Hiring staff that can successfully manage change and are comfortable working with a range of stakeholders will remain a core part of delivering value.

Get in touch with ITSM recruitment experts

At La Fosse, we help leaders build great teams. We have built strong relationships with a large network of ITSM professionals over 10 years, making us well placed to find the match for any of your future hiring.

If you have an immediate hiring need, or would like to discuss the ITSM market, please submit a brief.

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