What’s Hot, What’s Hiring, and Where It’s Headed

There is a moment in every transformation journey when organisations must decide: 👉 Will we protect what we’ve built? 👉 Or reinvent what’s possible? On 17 October , the Data Leaders Executive Lounge gathered senior data leaders to explore this very question. Hosted under Chatham House rules, the evening’s theme - “Risk to Reinvent” - brought together sharp minds, bold ideas, and honest reflections on how data leadership is (and must be) reshaping business strategy. Kate Sargent, Chief Data Officer at Financial Times, and Eddie Short, a renowned transformation and AI leader led the conversation. Their perspectives framed a candid discussion about shifting from process-led thinking to data-centric, predictive, and commercially intelligent business models. From Process-Led Legacy to Predictive-by-Design Futures For more than a century, businesses have been organised around process - a model designed for 19th-century manufacturing. But today, 91% of the UK economy is service-based. Yet many organisations still operate as though process is king. This disconnect surfaced repeatedly in the discussion: leaders often can’t articulate what capabilities actually matter to deliver strategy. Instead, they talk in terms of technology platforms - “We need Oracle” or “We need Pega” - rather than customer value or strategic outcomes. The call to action: ✅ Reframe the backbone of the enterprise - where data and AI are the orchestrators, and processes play a supporting role. ✅ Shift from “backward-looking by design” to “predictive by design” architectures - operating models that drive agility, growth, and resilience. The Capability Flywheel & The Intelligent Enterprise Eddie Short shared the evolution of a capability flywheel model developed over 20+ years - integrating people, process, technology, data, and AI to create the Intelligent Enterprise. This approach starts by asking: What must this business excel at to win? How can data and AI supercharge those capabilities ? Many executive teams can’t answer those questions clearly. And if capabilities aren’t defined, a data strategy is destined to be reactive rather than transformational. The Trap of Technology-Led Change One of the most striking points of consensus... Organisations are spending heavily on technology but not transforming. Why? Because technology alone doesn’t solve business problems . A culture of FOMO, vendor pressure, and shiny-object syndrome often leads to tech purchases without clear value articulation. Meanwhile, the real differentiator - execution, adoption, and value creation - gets overlooked. Data Value Over Data Tech: A Necessary Mindset Shift Kate Sargent outlined how the Financial Times is deliberately reframing its approach to data value measurement .  Rather than treating data as an abstract asset, the FT is embedding a “value funnel” into its operating model. This funnel tracks the potential , captured , and realised value of data initiatives, surfacing where value is lost - whether through data quality issues, resourcing gaps, or lack of adoption. The goal? To create a shared understanding of data value across the organisation, linking it directly to strategic and commercial outcomes. This is data not as “back office plumbing” - but as a driver of growth. Case in Point: Reinvention in Retail A real-world example brought the principles to life. A Romanian retailer - Profi - facing stagnant digital performance, shifted from risk avoidance to experimentation: Deployed Azure AI and revamped its digital app to promote bundled meal purchases. Leveraged ChatGPT and Midjourney to rebrand a wine range - from ideation to market in weeks. Result: 50% increase in basket size and repeat purchases , and a £100m uplift in company valuation in under a year. This was data as a commercial engine , not an IT project. Overcoming Cultural and Structural Barriers The conversation turned candid on risk aversion - especially in regulated industries. Many leaders default to compliance-driven, process-heavy approaches, making bold transformation nearly impossible. Key reflections: Too many leaders rely on anecdotes over analytics. Data teams are often pigeonholed into reporting functions, rather than driving strategy. Transformation requires assertive data leadership at the top table. “Stop being the data guy. Be the business transformation leader.” Speaking the Language of the Board Data initiatives fail to resonate at the board level when they are framed in tech-speak. But, when translated into three universal levers the narrative shifts from “support function” to strategic enabler . 1️⃣ Growing revenue 2️⃣ Increasing profitability 3️⃣ Reducing risk This was the evening’s unifying thread: If you can’t articulate the straight line from data to revenue, profit, or risk reduction, you’re wasting your time. Embedding a Data Value Mindset Kate Sargent’s work offers a clear roadmap: Establish a value mindset - shared language, communication assets, and strategic alignment. Capture value systematically - using value calculators and prioritisation frameworks. Close the feedback loop - to learn, iterate, and scale what works. Build literacy beyond the data team - empowering the wider organisation to speak and act in terms of data value. This structured approach aims to make value conversations accessible and embedded into daily business operations - not confined to dashboards. Call to Action for Data Leaders The event closed with a clear mandate for those shaping the future of their organisations through data and AI: ✅ Reframe your role from data manager to transformation leader . ✅ Speak in the language of commercial outcomes. ✅ Challenge risk avoidance with predictive-by-design models . ✅ Experiment fast, prove value, and scale boldly. ✅ Build data value thinking into the fabric of the organisation. As one participant noted: “Risk and performance are two sides of the same data.” What’s Next A heartfelt thank you to our speakers Kate Sargent and Eddie Short, our event sponsors - Cloudaeon - , and everyone who contributed their insights. The Winter Party returns on 20 November 2025 - a festive gathering, in London, and an opportunity to continue these conversations. 📩 If you’d like to be part of the next Data Leaders Executive Lounge, register your interest at Eden Smith.

In today’s boardrooms, the conversation is no longer if AI will reshape work - but how fast. On 15th October , cross-functional business leaders gathered for the first event in the HUMAN + AI Series , a collaboration between Eden Smith and Corndel, designed to demystify AI strategy and help organisations move from intention to meaningful action. This first session was a candid, insight-rich discussion about what it takes to build trust, drive adoption, and enable every part of an organisation to thrive with AI - not just the tech teams. Why AI Success Starts with People Erik Schwartz, Chief AI Officer at AI Expert, opened with a clear message: “AI is only as strong as the leadership behind it.” In a live poll of 27 leaders, most revealed they are still in the early stages of AI adoption. Many have experimented with tools like Copilot, but few have moved into structured implementation. Erik shared powerful case studies where targeted AI initiatives streamlined workflows and delivered measurable business impact. His call to action was simple but potent: Build leadership AI literacy early. Start small but show results fast. Use hackathons and prototype projects to turn theory into momentum. “Put something tangible in front of your executives,” he urged. “AI adoption accelerates when people can see and feel the value.” Embedding Data and AI into Organisational DNA Helen Blaikie shared how Aston University overcame silos, data hoarding, and cultural resistance to create a mature data and AI strategy for 2030. Key pillars of their success: Leadership sponsorship and clear performance measures A robust data governance framework Organisation-wide upskilling (over 600 trained colleagues) A relentless focus on trust and quality By aligning data and AI initiatives directly with business objectives, the university didn’t just modernise - it transformed how decisions are made. The Human Experience of AI Helen Matthews tackled one of the most pressing realities: people’s fears and expectations around AI. 📊 65% of employees fear job loss. 📊 45% resist change. 📊 91% want responsible AI policies. Matthews highlighted how starting with “why” is essential. AI strategy isn’t just about algorithms - it’s about trust, transparency, and storytelling . By mapping workforce capabilities, tailoring training, and leveraging early adopters, organisations can turn anxiety into agency. She also outlined a practical maturity model: start with foundational awareness, tailor training to function, then continuously refine. A particularly resonant insight: use the apprenticeship levy to fund AI learning programs - removing one of the biggest adoption barriers. The Leadership Panel: Turning Insight into Impact A dynamic panel session explored how leaders can practically navigate the intersection of people, talent, and technology. Key insights: Use AI tools to empower employees to self-assess skills and career paths. Start with one well-defined pain point to build trust and credibility. Involve frontline employees early to ensure solutions solve real business problems. Encourage co-creation spaces and flexible policies to adapt fast. The message was consistent: AI adoption is not a spectator sport. It’s a collective, cross-functional effort that demands experimentation, communication, and strong leadership. Top Action Points for Leaders Build AI literacy at the top and cascade it down. Align AI strategy with business objectives - not the other way around. Start small, show value fast , then scale. Invest in data governance, trust, and culture. Equip people to experiment with AI tools and co-create solutions. Communicate, measure, celebrate - repeatedly. This was just Part 1 of the HUMAN + AI Series . The conversations were raw, practical, and inspiring - setting the stage for the next event, where we’ll dive deeper into human capability building and AI readiness at scale.