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.

At our AI Reality Check roundtable, we gathered senior leaders from financial services, media, pharmaceuticals, law and consulting to tackle the hard questions about what’s working, what’s failing and why. 

The 70% problem 

Let’s start with the uncomfortable truth: 70% of AI projects fail in their first year. That’s not a technology problem. It’s an expectation problem. 

Ollie Whiting, CEO of La Fosse, put it in historical context. The desktop PC took over a decade to achieve meaningful productivity gains. The web followed a similar pattern. We’re four years into the AI revolution and somehow expecting instant transformation. 

“The impatience of boards, investors and shareholders to get ROI over the line in such a short space of time is one of the key reasons for failure. We’re letting history repeat itself and wandering a bit blindly into this.” 

Who actually owns AI governance? 

Ask ten organisations who’s responsible for AI governance and you’ll get ten different answers. Legal thinks they own it. Security thinks they own it. The CTO wants centralised control. Individual business units are just getting on with it. 

The roundtable revealed a common pattern: ambitious governance forums that aim to track every AI initiative, but reality falling short. As one participant from Pacific Life Re put it, the intention is good but the execution is fragmented. Different territories have different regulatory understandings, and when something goes through legal and compliance first, the immediate answer is often no. 

The Guardian’s Chief AI Officer, shared their approach: principles first, product second, monitoring third. They’ve published AI principles, built governance into their product development and continuously take the temperature of both staff and readers. Media organisations face particular scrutiny, with readers anxious to know whether AI is involved in journalism. 

The AI veneer is cracking 

Remember when every company rushed to build mobile apps in the early 2010s? Those apps were essentially mobile websites, and they quickly revealed all the cracks in back-end infrastructure. Five years of data infrastructure spending followed. 

We’re about to see the same pattern with AI. Organisations are throwing agents onto badly designed processes and wondering why they don’t deliver value. The shiny AI tool you bought last quarter? It’s probably falling over because the whole end-to-end process hasn’t been designed. 

Anu Doll, Founder of Synexra, provided the strategic anchor for the session, arguing that AI’s true value lies in weaponising a firm’s competitive moats through an Agentic Operating Model. Her framework identifies the high-leverage capabilities where intelligence creates genuine market distinction rather than mere efficiency. By bridging the “Autonomy Gap”; the distance between strategic ambition and foundational readiness, Synexra ensures that infrastructure, data and governance are hardened to support “Autonomous Flow,” transitioning teams from task executors to Intelligence Orchestrators of unique, high-growth value chains.

The democratisation imperative 

Here’s a stark reality from La Fosse: 40-50% of a recruiter’s working week is spent on tasks that could be automated. They could be driving double the productivity doing work they actually enjoy. But they can’t, because AI hasn’t been democratised. 

Too much energy is being spent on centralised governance and not enough on getting AI into end users’ hands with the right guardrails. The desktop PC only delivered productivity gains when everyone had one on their desk. The web only transformed business when it was democratised. AI will follow the same pattern. 

One participant, working with Soho House, described the advantage of smaller organisations: no labyrinthine governance structures, no siloed AI officers blocking everything. Instead, they’re showing business people how tools like Claude work, planting seeds and watching ideas develop. That’s where the real ROI comes from. 

The leadership learning gap 

Research shared at the roundtable revealed a troubling lack of trust in board-level AI decision-making among tech professionals. Part of this is communication. Does your front-line team know about the AI training the exec team did over Christmas? Probably not. 

But it’s also about humility. As one CEO put it, leaders need to admit they might not have the answers they had for the last decade. The CTO who doesn’t understand business processes is destined to fail. The Chief AI Officer who only knows AI and not the heritage of technology is equally doomed. 

The consensus: AI literacy must be mandatory from top to bottom. Cross-functional leadership isn’t optional. Gone are the days of siloed executives who only understand their own domain. 

Don’t forget the humans 

When ROI is measured in headcount saved and roles reduced, employees get scared. Redundancy announcements and layoffs erode psychological safety, regardless of the productivity gains promised. 

But reframe the conversation around personal productivity, around how many hours a week can you save, and something shifts. People feel empowered. They want to perform better. They engage with the tools rather than fearing them. 

This isn’t soft thinking. It’s fundamental to successful AI adoption. The organisations that crack this balance between transformation and cultural safety will be the ones that succeed. 

What’s next? 

This roundtable was the start of an ongoing conversation. We’re committed to bringing together leaders who are navigating AI implementation in the real world. 

If you want to be part of the next discussion, or if you’re wrestling with AI challenges in your organisation, get in touch. Sometimes the best insights come from people facing the same problems. 

Download our AI in the Workforce whitepaper

Join our next panel event

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Why this recognition matters

This award is a significant milestone for La Fosse Academy and validates our core belief that tech should be built by everyone, for everyone. In an industry where women make up just 28% of the global workforce, our commitment to diversity isn’t just talk – it’s reflected in our results, with 53% of our 74 associates trained in 2024 being women. 

Even more importantly, 54% of these women were successfully placed into career-launching tech roles with leading employers across the UK, demonstrating that our model creates genuine, sustainable pathways into the industry. 

Our approach to bridging the gender gap

La Fosse Academy exists to reshape the future of technology by tackling two of the industry’s most pressing issues: the persistent gender and diversity gap, and the shortage of commercial tech skills. Our mission is simple yet transformative – unlock potential by offering free, high-quality training to individuals regardless of background and create a sustainable talent pipeline that truly reflects society. 

Our curriculum focuses on practical, commercially relevant skills over tools, giving associates the flexibility and mindset to thrive in fast-changing tech environments. This approach is particularly impactful for returners, career changers, and those entering tech for the first time – with 55% of our 2024 cohort being career changers, many of whom were women re-entering the workforce or retraining after career breaks. 

Beyond training: Creating sustainable success

What truly sets La Fosse Academy apart is our comprehensive support system. Beyond technical training, we provide vital wraparound support including: 

• Tailored upskilling and development pathways 

• Personalised mentoring from industry experts 

• Professional coaching to build confidence and workplace skills 

• Access to vibrant peer communities for ongoing support 

One of our standout success stories is Leah Thomas, who transitioned into a Data Business Analyst role through our Academy. Leah not only excelled in her technical role but also became an influential member of her company’s Women in Tech Committee, actively participating in major industry events such as Women of Silicon Roundabout and the Global Women in Tech Conference – becoming a role model for other women entering the field.

Read more of our associate stories here

Partnering with forward-thinking employers

Our success wouldn’t be possible without our partnership approach with employers who share our values and commitment to diversity. For example, our collaboration with Leidos UK has already seen 50+ Associates placed into roles, who have since delivered across 46 programmes of work. 

“This award validates what we’ve always believed – that talent is everywhere, but opportunity is not,” says Claudia, Director of La Fosse Academy. “By creating accessible pathways into tech and providing the right support systems, we’re actively reshaping the industry’s future.” 

With over 500 associates trained to date, we’re demonstrating that diverse, inclusive talent strategies aren’t just the right thing to do – they’re essential for building innovative, future-ready tech teams. 

Looking ahead

This recognition reinforces our commitment to continue breaking down barriers and creating a more inclusive tech industry. We’ll keep developing our programmes, expanding our employer partnerships, and supporting more individuals from underrepresented groups to build successful tech careers. 

Want to know more about La Fosse Academy and how we can help your organisation build a more diverse tech team? Visit www.lafosseacademy.com or get in touch with our team today here.

The post La Fosse Academy Wins Best Training Provider at the Women in Tech Employer Awards 2025 appeared first on La Fosse.

The evening brought together mentors, mentees, and aspiring participants to share real experiences, practical insights, and actionable strategies for building mentorship relationships that genuinely work. 

Why mentorship matters now more than ever 

Lucy Kemp, La Fosse’s Director of Brand, opened the evening by highlighting a critical insight from our ongoing research: “Mentoring came up time and again when we asked what would drive change forward. It’s really hard to find a mentor, especially as a woman. You look up and that mentoring layer, that leadership layer, it’s smaller, and it’s getting smaller.” 

The challenges are multifaceted. Beyond the scarcity of senior women in leadership positions, there’s confusion about what type of support people actually need. Is it a mentor who shares experience and wisdom? A coach who helps you reach your own conclusions? Or a sponsor who advocates for you in rooms where decisions are made? 

Most importantly, there’s the fundamental difficulty of asking someone to be your mentor – a conversation that can feel daunting but, as Lucy reminded the audience, “Anyone who has ever been asked to be your mentor will be really flattered.” 

Meet the panel: diverse paths, shared wisdom 

The evening featured three panellists whose varied journeys into tech demonstrate that there’s no single path to success, but mentorship can be transformative regardless of your starting point. 

Patricia Manley, Head of Agile Delivery at Seven.One Entertainment, brought the perspective of an immigrant woman who has navigated significant hurdles throughout her 12-year UK career. “Being a woman immigrant who doesn’t look the stereotype of a woman in tech – having accents and appearances that were always issues – I went through a lot of hurdles,” she shared. Now, Patricia coordinates mentorship programmes for non-profits and works as both a mentor and mentee, believing that continuous learning is essential at every career stage. 

Leah Thomas, a Data Business Analyst at News UK and La Fosse Academy Associate, represents the growing number of career changers entering tech. Her path from law graduate to tech professional during the pandemic illustrates the “wobbly” journey many take. “I started as a law graduate, wanted to be a lawyer since I was about 15, and completely had a change of mind,” she explained. After googling “innovation, creativity and tech” and finding coding, she discovered La Fosse Academy, demonstrating how the right guidance can accelerate career transitions. 

Kirstie Smith brought 15+ years of marketing experience, alongside her work teaching at Birmingham City University and running networking groups. Her perspective highlighted how experienced professionals can give back while continuing to grow, emphasising that the next generation needs more than Google searches and YouTube videos – they need human connection and face-to-face relationships. 

The difference between coaching, mentoring, and sponsorship 

One of the evening’s most valuable discussions clarified the distinctions between different types of support – understanding often missing in workplace development conversations. 

Coaching, as Patricia explained, involves “going with you on that journey of discovering what you want to do and asking the right questions for you to discover the answers you already have inside yourself.” A coach helps you unlock your own insights through guided reflection. 

Mentoring comes from a place of shared experience. “Mentors are coming from the point of view of ‘this is my experience, this is what I’ve learned,'” Patricia noted. They offer wisdom gained from walking similar paths and can provide specific guidance based on their journey. 

Sponsorship involves active advocacy – someone who talks about you in the right meetings, puts your name forward for opportunities, and uses their influence to advance your career. 

Understanding these differences helps professionals seek the right type of support for their specific needs and career stage. 

What mentees really need: beyond job searching 

While career transitions often trigger the search for mentorship, the panel revealed that women seek guidance across a much broader spectrum of challenges. 

Confidence building emerged as a primary need. “For me, with females, a lot of the time it’s confidence in different areas,” Kirstie observed. “Confidence at networking events, confidence in general, not knowing where to start with something, or dealing with that overwhelm of information.” 

Power skills development represents another critical gap. Patricia identified the transition point where technical expertise alone isn’t enough: “Up to a certain point, we rely a lot on what we know as technical skills – certifications, coding knowledge. But there’s a point where you want to jump to the next level that’s not just about how many certifications you have. It’s the power skills that you lack.” 

Effective communication and leadership capabilities become essential as careers progress, yet these skills are rarely explicitly taught in technical roles. 

Career direction guidance helps when professionals know where they want to go but need help mapping the path to get there. 

Actionable change: Organisations should recognise that mentorship needs evolve throughout careers. Create programmes that address confidence building, power skills development, and communication training – not just technical skills advancement. 

When mentorship relationships don’t work 

Honest discussion about relationship challenges provided practical guidance for navigating difficult situations. The panel emphasised that not every mentor-mentee pairing will be successful, and that’s normal. 

Communication is key. Patricia stressed the importance of having “that feedback conversation” when relationships aren’t working. “Doing it from a place of love, saying ‘I don’t think this is working because of this’ and being honest about needs and expectations.” 

Structured frameworks help. Kirstie noted how having committed timeframes can actually help relationships succeed: “Even if there’s a personality clash, you’re committed to four meetings. Sometimes that structure provides the safety to work through initial challenges.” 

Choose authentically. Leah’s advice was refreshingly direct: “Choose for you, which sounds selfish, but you’re trying to get something from the relationship. Be honest about what you need and whether this person can provide it.” 

Kirstie also introduced a powerful framework for building authentic mentorship relationships, describing how effective mentors can adopt three distinct approaches: acting as gatekeepers who hold information and open doors to opportunities, midwives who help mentees work through challenges using a coaching approach, or fellow travellers who honestly admit when they don’t know something and explore solutions together. “I think the best productive relationship is when you can just totally be honest with your mentee or your mentor in both ways. And you’re both learning, you’re both like, going through that journey together,” she explained, emphasising how vulnerability and mutual learning create stronger, more sustainable mentorship bonds. 

Actionable change: Implement regular check-ins during mentorship programmes and provide clear frameworks for addressing relationship challenges. Create safe processes for changing mentorship pairings when needed, without stigma or blame. 

The power of informal mentorship 

Some of the evening’s most compelling stories came from informal mentorship relationships that developed organically. Patricia shared how she approached someone she admired: “I saw him behaving amazingly well, and one day I decided to say, ‘Hey, could we meet for half an hour every other week?’ That was amazing because we didn’t set up any agenda, but every time I met with him, I had my questions prepared.” 

This informal approach yielded more learning than her formal company mentorship programme, highlighting how authentic relationships often develop when there’s genuine curiosity and mutual respect. 

Actionable change: Encourage employees to identify and approach informal mentors within and outside their organisation. Provide guidance on how to structure these conversations and maintain ongoing relationships.

Measuring mentorship impact 

The discussion of programme evaluation revealed sophisticated approaches to understanding mentorship effectiveness beyond basic completion rates. 

Relationship continuation serves as a key indicator. As Leah noted: “A really good measure of whether you had a good mentor is if your mentee wants to keep in contact with you afterwards.” 

Goal achievement tracking requires establishing clear objectives at the beginning and checking progress throughout the relationship. 

Structured feedback collection works best when integrated into the mentorship process rather than lengthy surveys at the end. 

Qualitative insights often provide more valuable data than quantitative metrics, revealing the real impact on confidence, career clarity, and skill development. 

Actionable change: Design mentorship programmes with built-in measurement from the start. Focus on relationship quality indicators and goal achievement rather than just participation rates. 

The UNBOUND mentorship programme launch 

The evening concluded with the launch of UNBOUND’s own mentorship programme, designed to address the insights gathered throughout the research and discussion process. 

The programme structure reflects best practices identified: 

• Four-month commitment with one hour per month 
• Careful matching process over six weeks to ensure compatibility 
• Built-in evaluation through retrospectives to assess impact and improve the programme 
• Open access beyond event attendees to create broader community impact 

Participants left with QR codes providing immediate access to applications, emphasising that the programme is designed for anyone committed to meaningful mentorship relationships. 

Key takeaways for building effective mentorship 

The evening’s discussions crystallised into several crucial insights: 

Mentorship is not one-size-fits-all: Different career stages and challenges require different types of support. Understanding whether someone needs mentoring, coaching, or sponsorship is the first step to providing effective guidance. 

Informal relationships often work best: While structured programmes provide valuable frameworks, some of the most impactful mentorship happens through organic relationships built on genuine curiosity and mutual respect. 

Both sides benefit: Effective mentorship provides value to mentors through fresh perspectives, leadership development, and the satisfaction of contributing to someone else’s growth. 

Diversity matters: Mentors don’t need to look exactly like their mentees, but representation and shared experiences can provide unique value and inspiration. 

Communication creates success: Clear expectations, regular check-ins, and honest feedback transform mentorship from a casual relationship into a powerful development tool. 

Looking forward: building a mentorship culture 

The technology industry stands at a critical juncture. As Patricia observed, the leadership layer that should provide mentorship guidance is actually shrinking, making structured programmes and intentional relationship building more important than ever. 

However, the appetite for meaningful mentorship clearly exists. The enthusiasm in the room at The Loading Bay, the quality of questions from attendees, and the immediate interest in joining the UNBOUND programme all demonstrate that people are ready to invest in relationships that create real change. 

The path forward requires both individual commitment and organisational support. Companies must recognise mentorship as a strategic capability rather than a nice-to-have add-on. Individuals must approach mentorship with authenticity, clear goals, and genuine commitment to both giving and receiving value. 

Join the mentorship movement 

UNBOUND’s mentorship programme represents just the beginning of building the support networks women in tech deserve. By creating structured opportunities for meaningful relationships, providing frameworks for success, and measuring real impact, we’re working to ensure that career progression doesn’t depend on chance encounters or personal networks. 

The programme is open to anyone ready to commit to genuine mentorship relationships – whether as a mentor sharing their experience, a mentee seeking guidance, or someone who sees value in both roles. 

Ready to be part of building the support networks women in tech need? 

Apply for the UNBOUND mentorship programme 

Join the UNBOUND community for updates on future events and mentorship opportunities 

Download our Women at Work Blueprint for research-backed insights on what women really need to thrive in technology careers 

This isn’t just about individual career development – it’s about creating an industry where talent thrives through connection, guidance, and authentic support. The conversation has started. Now it’s time to build the relationships that will drive real change. 

The post Building the support networks women need: insights from UNBOUND’s mentorship event 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.

As organisations adapt to this evolving environment, understanding AI’s impact on cyber security recruitment and subsequent roles has become crucial for building robust, future-ready security teams. It’s also vital for a sober and realistic look at what aspects AI are and will be affecting. Let’s explore how AI is reshaping the cyber security workforce and what this means for companies looking to hire top talent in this dynamic field.

AI’s current role in cyber security

AI is transforming cybersecurity by enhancing threat detection and response capabilities. It is capable of analysing huge amounts of data in real time, identifying patterns, and detecting potential threats faster than any human could.

Implications for recruitment

Whilst there are roles in industry that are AI security-specific, the vast majority work with tooling that has AI components, meaning that individuals don’t necessarily need an elevated level of AI expertise. Whilst some knowledge is important, it’s a case of up-skilling existing employees to be aligned with AI approaches rather than having to re-hire for specific skills.

AI limitations in cyber security

While AI is a powerful tool, it is by no means without limitations. AI relies on massive amounts of existing data to learn and can be caught off guard by new, sophisticated threats it hasn’t been trained to recognise. Additionally, AI can sometimes generate false positives, requiring human intervention to determine the real threats.

Areas where human expertise remains irreplaceable

Certain aspects of cyber security still require the critical thinking, intuition, and creativity that only humans possess. For example, strategic decision-making, understanding complex social engineering tactics, and managing the nuances of human behaviour are areas where human insight is essential.

Recruiting for critical thinking and intuition in security roles

To address these gaps, companies must prioritise hiring professionals with strong analytical and critical thinking skills. Cyber security experts who can interpret AI-generated data, make informed decisions, and quickly adapt to unexpected scenarios will continue to be invaluable.

A collaborative future

As AI tools become more integrated into cyber security strategies, the future points toward a collaborative model where human expertise and AI technology work together to create stronger defences against cyber threats.

This partnership involves using AI for routine monitoring, data analysis, and automated responses whilst relying on human professionals for strategic oversight and complex problem-solving. Organisations that can successfully integrate these two forces will be better positioned to defend against both known and unknown threats.

The ideal candidates for this collaborative future are those who can work seamlessly with AI tools, adapt to new technologies, and learn continuously. Look for professionals who demonstrate strong collaboration skills, can bridge gaps between technical and non-technical teams, and have a mindset geared towards pragmatic innovation.

Job market shifts

AI is not only changing how cyber security is practised but also creating entirely new roles at the intersection of AI and cyber security. Positions such as AI Security Specialist, Machine Learning Cyber Security Analyst, and AI-Driven Threat Hunter are emerging, requiring a blend of cyber security knowledge and AI expertise.

Strategies for recruiting these roles and skill sets 

To fill these new positions, companies must adopt innovative recruitment strategies. This might include partnering with educational institutions to tap into new graduate pools with relevant skills, creating internships or entry-level positions focused on AI and cyber security, or upskilling existing employees to meet the demands of these emerging roles.

Partner with a reputable recruiter who specialises in your industry. Whether you’re looking to hire for entry level or leadership roles, joining forces with a recruiter with the clout and capabilities to build bespoke talent solutions will help attract and source the right candidates.

Skills evolution in cyber security

The rapid integration of AI is prompting a shift in the skills required for cyber security professionals. Instead of solely focusing on traditional skills like network security and malware analysis, there is a growing need for knowledge in AI, data science, and machine learning.

How cyber security professionals are adapting to AI-driven industry changes

Professionals are increasingly pursuing certifications and training in AI and machine learning to stay relevant. Upskilling in these areas not only enhances their employability but also prepares them for the evolving challenges in the cyber security landscape.

Recruitment focus: assessing candidates’ ability to learn and evolve

When recruiting, focus on candidates who demonstrate a willingness to learn and adapt. Those who are proactive in acquiring new skills and show a keen interest in AI and cyber security trends are more likely to succeed in an AI-enhanced environment.

Career opportunities in AI-enhanced cyber security

The integration of AI in cyber security is opening up numerous career opportunities and specialisations. From AI Security Consultants to Threat Intelligence Analysts who leverage AI tools, the field is expanding rapidly, providing diverse career paths for professionals.

Attracting talent by highlighting innovative career paths and growth opportunities

To attract the best tech talent, emphasise the exciting opportunities available in this evolving field. Showcase the potential for career growth, involvement in cutting-edge projects, and the chance to work with groundbreaking technologies that define the future of digital security.

Is AI going to replace cyber security?

While AI is revolutionising the field of cyber security, it is unlikely to replace human experts entirely. AI enhances threat detection and automates routine tasks, but it still lacks the critical thinking, intuition, and creativity that only human professionals can provide. The future of cyber security lies in a collaborative approach where AI and human expertise work hand in hand to create robust digital defences.

So, is AI going to replace cyber security experts? The answer is no – AI will not replace them but rather empower them, allowing professionals to focus on complex problem-solving, strategic decision-making, and managing sophisticated threats that AI alone cannot handle.

As AI continues to reshape the cyber security landscape, organisations must adapt their recruitment strategies to build teams that leverage both human insight and AI technology. Understanding these evolving roles and skill requirements is crucial for effective talent acquisition.

Ready to future-proof your cyber security recruitment strategy? Partner with La Fosse, an award-winning tech recruitment agency, offering specialist tech & IT talent solutions, and secure the professionals who can navigate the complexities of an AI-enhanced future while maintaining strong human expertise.

The post Redefining cyber security talent acquisition in the age of AI appeared first on La Fosse.

Developing young talent speaks to my personal values and, as a leader, it’s where I’d love to continue to take the business. Providing an equitable starting line for young people to begin their careers is front and centre for me, and La Fosse Academy offers just that – we train and develop the next generation of junior talent, with a focus on improving DEI in the industry. It speaks directly to our vision of helping create a world where talent is recognised regardless of background and lived experience.  

That equitable starting line was one of the reasons I was attracted to the fast-paced world of the recruitment sector in the first place. Irrelevant of your background, upbringing, education, financial backing, or societal beliefs, there’s a home for everybody who has the right skill set – it’s what makes our industry a vibrant and energetic place for your career.  

This sadly comes with its downsides, with varying levels of quality and consistency for our customers and candidates, but I truly believe there’s exceptional talent in our sector, delivering real value to businesses and the wider economy. The numbers speak for themselves, with the UK recruitment market now estimated at £140 billion (a £20 billion increase on 2019), highlighting the importance we play in continued growth.  

One of the most rewarding parts of my job is hearing about the experiences of the rising stars we’re developing at La Fosse Academy. I’ve recently been speaking to some of our current and graduated Associates, alongside some of our key Academy customers.  

Harry de Blaby was part of the first cohort at La Fosse Academy. He completed his placement with C. Hoare & Co., was taken on permanently, and has recently been promoted to Delivery Manager. Harry went from a deckhand on luxury yachts to signing up to the Academy and subsequently beginning his tech career in London.

Harry says of his experience: “The Academy set me up with all the skills I needed. Even going into a role that I wasn’t technically trained for, I still had the technical, industry-relevant base knowledge to understand what an API was, how a system hangs together, DevOps processes, and all the must-haves to develop a successful career.” 

Harry’s mentor and line manager, CTO Chris Loake, says the qualities he’s looking for in entry-level talent are “a general aptitude to learn, to problem-solve, to apply skills to shifting context”. Chris says he believes good leadership is about not walking past problems but addressing them and finding solutions. By identifying junior talent to provide diverse thinking and problem-solving, Chris and his team have unlocked future potential that now contributes to the private bank’s current and future success.  

Sophie Hebdidge, our Academy Director, said recently, “The Academy is special because it’s different. We worked backwards when we designed the programme, asking our customers, “What would you benefit from in terms of junior tech talent joining your business?”. We’re not only training people in specific skills or tech – we’re teaching them the underlying techniques they need to be able to succeed in the workplace. We took this approach because technologies continue to evolve, and it allows our Associates to follow the career path they feel most inspired and empowered by.” 

Some of our Associates have found themselves exceeding their own expectations. Zahra Mahmood is 18 months into her placement at the Department of Transport and has not only developed her technical capability but has gone above and beyond with her wider professional skills. Most notably, she won a nationwide hackathon and attended a presentation at 10 Downing Street. Zahra credits the Academy for giving her the opportunity to stretch herself, and she’s now looking forward to a successful career in the civil service as a result.  

Although there are numerous individual success stories, and we’ve now helped over 200 Associates start their careers in tech and transformation, not everything is rosy. The market is competitive, and the economic backdrop is challenging. As a result, customers in every sector are battling for business and facing the question of where to allocate hiring budgets.  

Whilst many customers buy into the idea of solving junior tech talent shortages and improving long-term DEI shortfalls, the reality is it requires a solid support structure, an attractive employee value proposition, a stable management team, and a consistent flow of good talent. None of these challenges are easy for us to solve, but we’re excited about what 2024 has in store for us, our Associates, and our Academy customers.     

(If you’d like some further reading, I thought this article by McKinsey made some interesting points. And if you’d like to talk to me about any of the topics in this blog, please do reach out as I’d love to hear from you.) 

The post Young talent – The equitable starting line 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.

The post The future of ITSM: Four trends to watch appeared first on La Fosse.

During a lively React Native Leadership roundtable event, facilitators Henry Moulton and Harry Jell from Yonder welcomed mobile specialists from a variety of UK organisations to outline the pros and cons of these challenger frameworks and predict what the future holds for mobile tech.

Consider the customer experience

Over the past decade, mobile apps have developed from gimmicks and games to fundamental tools essential for everyday life in a modern world. As such, usability and customer experience are key when designing, building, and deploying mobile apps, keeping the consumer journey at the forefront of development considerations.

It’s important to think about target market; if your mobile and web apps are equally utilised, the customer experience needs to align across both. If one is clearly more favoured, consider how your mobile framework will influence the development of your dominant platform and affect the usability of subsequent platforms.

Updates are imperative

Access to the latest mobile version is vital, especially in cases such as banking where innovation often comes hand-in-hand with security. App stores across both iOS and Android are the gatekeepers when it comes to updates. Although Apple’s App Store has historically had a stricter app review process, Google’s Play Store has caught up with a similar review process and guidelines.

One of the advantages of React Native is that it offers Over The Air (OTA) updates, which allows app developers the choice of not having to go through the review process for updates that don’t materially change the functionality or product, for example, an update to fix a bug.

The talent challenge

Hiring for a specific tech stack is often problematic; niche skills mean the talent pool is certainly smaller, and the inevitable competition for top candidates drives remuneration and benefits packages to the high end of the market.

Successful React Native teams are often made up of typescript engineers and iOS and Android engineers, who are able to combine their complementary skill sets to benefit building with React Native. Moving to mobile is a paradigm shift; it’s not considered web technology, so expecting an already established web team to adapt is unrealistic.

If you’re delivering features across multiple teams, consider how those teams will interact with each other. As with any complex project, establishing a structure that results in efficiency is key, whilst also ensuring clear communication and performance measurements for each area of responsibility.

Looking to the future

The future of tech has been debated and predicted by experts across the industry – virtual reality, artificial intelligence, further alignment with the Internet of Things – but all of these share a commonality; the future of tech will depend on delivery speed. Consumers already want immediate interaction, and apps that put agility at the top of the features list will come out on top with the next wave of development.

Frameworks that can handle rapid rendering and dynamic user journeys are a consideration for the next generation of capability. As we discussed at the beginning of our event, increasing the quality of the customer journey will be the key consideration for the future of mobile technology.

A quick look at mobile frameworks

React Native

Pros:
– Always a package for what you need, saving time
– Large community to tap into
– Can push updates and new features relatively quickly

Cons:
– Original architecture can cause performance issues
– Route through the web is questionable
– Upgrading is a big problem

Flutter

Pros:
– Great toolchain makes process more efficient
– Cross-platform development allows for flexibility
– Strong support from Google

Cons:
– More competition for talent
– Dart is a unique language
– Smaller community of specialists

For help building your mobile, digital, or development team, or to find out more about our talent solutions, contact Jacob Brown.

The post React Native Leadership Event: Key takeaways appeared first on La Fosse.

Poor data literacy 

Referring to a lack of understanding surrounding the role of data analytics across a business, poor data literacy is often a result of low visibility or engagement. Other teams are unclear about the work that data analytics teams put in, which can be difficult to demonstrate to those without a deep technical comprehension or a direct connection to the impact it has.  

Building successful infrastructure commonly goes unnoticed; it’s only when there’s a failing or weakness within that infrastructure that the wider team feels the absence of good data.  

“It’s the role of the data analysts to break down projects, present specific metrics, and connect the work they’re doing with individuals and teams across the business to bring a greater understanding and, therefore, appreciation for data professionals. They’ve got to be their own champions.”  

If the business isn’t digital-first, it can be hard to properly present the benefits of data analysis across the many different functions it serves. Begin with a statement of intent or direction: are we answering a specific question or solving a specific problem? Who will be affected by the outcome? Are we building something new or further exploring something that already exists?   

Utilising tools such as dashboards can help others to visualise objective data and why it’s such an important component, and demonstrating tangible outputs resonates more with colleagues who are less technically minded, with less understanding of the full data journey.   

Risk intervention takes an alternative approach, outlining the possible negative outcomes associated with unavailable data or insight. Whilst many would rather promote positive effects, scare tactics are effective in driving home the importance of good data.  

Simplifying concepts can be a great help when communicating value; something that may seem straightforward to the data team isn’t necessarily accessible to all. Using clear steps and outcomes allows others to recognise how data is having a measurable impact on their projects and the business as a whole.   

Stakeholder buy-in  

Support from stakeholders is key for any business function, but when it comes to elements like data science that are still considered ‘new’, it’s even more challenging to get buy-in.  

It’s important to have allies within the leadership team who not only understand the impact of the work, but also communicate this effectively to the wider team. These senior stakeholders want to see real economic value, so using metrics that demonstrate a cost or time saving, or result in increased efficiency and output, can be instrumental in getting the buy-in that the data team needs.  

“Whole business concepts are difficult to present – breaking down the data points into specific blocks and mapping projects using those blocks makes them easier to digest.” 

Case studies and storytelling are other tools that help stakeholders to visualise outcomes, demonstrating real-life examples of how and where data analysis has resulted in a success story.  

When it comes to the more senior stakeholders, putting a flagship product at the centre is often an easy win. Being able to pinpoint areas for improvement with the products that the business relies on generates interest, especially if there are tangible outcomes to the changes being made.  

It’s also important to actively promote the data team overall. Building relationships that instil a culture of collaboration, being present, open, and involved makes a big difference with attitudes towards the data function.  

Those relationships also establish trust with your stakeholders; facilitating conversations and mutual respect between the different parties means there’s a greater chance that everyone involved will feel they’re working towards a shared goal. Show flexibility with regard to others’ needs and ask for input.  

Skills gaps 

“Lots of people are technically great, but don’t have commercial understanding and awareness, can’t sit with stakeholders, or drive strategy and growth. Then, on the other hand, you find that most technical people want to stay technical, not be collaborative or client-facing.” 

Whilst the skills-gap piece is experienced across most sectors at some point, placing for roles within data can present a specific challenge as a result of the technical and soft skill solution requirement. Those with technical expertise are not always equipped or motivated to manage stakeholders and propose strategy, and those with more understanding of the business perspective can be lacking in technical know-how.  

Consider attracting candidates from alternative talent pools outside of data – those with different experiences, different passions, and different skills who can contribute to diversity of thought and approach to work. Those with a numerical mindset, but with the presence and confidence to communicate strategically with wider teams, often perform well.   

It’s also important to remember that teams are made up of individuals. There will still be a place for a singularly technical individual and a singularly strategic individual; it’s about ensuring the balance across the division is correct. Team members who want to stay on the individual contributor path provide stability and longevity that should be highly valued.   

“It’s about attitude over aptitude – you can teach technical skills, but you can’t teach curiosity, or how to think, or how to approach problems.”  

AI and the future of data 

AI is already widely considered the next innovation in tech, and its impact on the data landscape is unquestionably an important factor in that.  

Building, maintaining, and adapting foundational systems will be a key element of its usability and success; trust in data warehouses will be fundamental, as will understanding the layers of capability and the effect AI application will have on different platforms and functions.   

“AI tries to mimic humans, but humans are wrong all the time, so will it become normalised that AI is also wrong?” 

 The ability to map, analyse, and report on huge amounts of data at speed will potentially change the way these teams work. But as with any new technology, the capabilities and possible failures have not yet been fully applied or explored. Using AI alongside other analytics tools, creating a blended model, could give data analysts the best of both worlds.  

Thanks to our event facilitator Kelly Freeman, Head of Data at World of Books Group, special guest Leo Pape, Co-founder at Point Sigma, and to all our attendees. 

To find out more about La Fosse’s total talent solution, or for information on upcoming events, contact Kayla Usswald. 

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