The Risk to Reinvent

Christa Swain • October 17, 2025

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.


By Christa Swain December 3, 2025
Executive Summary: AI, Ethics, and Human-Centred Design Our recent Leaders Advisory Board event - designed in partnership with Corndel - featured three engaging sessions that explored how AI impacts human cognition, customer experience, and fairness. Here's what we learnt: 1. Think or Sink – Are We Using AI to Enhance or Reduce Cognitive Ability? Speaker: Rosanne Werner , CEO at XcelerateIQ & ex Transformation Lead at Coca-Cola Roseanne opened the day with an interactive and thought-provoking session, firmly positioning AI: “AI should be your sparring partner, not your substitute for thinking.” Her research revealed a striking insight: 83% of people using LLMs couldn’t recall what they wrote, compared to just 11% using traditional search . The message? It’s not about avoiding AI, but using it in ways that strengthen thinking , not outsource it. Roseanne explained how our brains form engrams - memory footprints that enable creativity and critical thinking. Over-reliance on AI risks weakening these pathways, reducing retention and problem-solving ability. She introduced the Mind Over Machine Toolkit , six strategies to use AI as a thinking partner: Provide Context First – Frame the problem before asking AI. Use AI as a Challenger – Stress-test ideas and uncover blind spots. Iterative Co-Creation – Collaborate, refine, and evaluate. Document Your Thinking – Keep reasoning visible. Reflective Prompts – Support reflection, not replace judgment. Sparring Partner – Test assumptions and explore risks. Roseanne summed it up with a simple rule: use Sink for low-value, repetitive tasks, and Think for strategic, creative decisions. 2. Designing Chatbots with Human-Centred AI Speaker: Sarah Schlobohm , Fractional Chief AI Officer Sarah brought a practical perspective, drawing on experience implementing AI across sectors - from banking and cybersecurity to rail innovation. She began with a relatable question: “Who’s been frustrated by a chatbot recently?” Almost every hand went up. Through a real-world example (redacted out of politeness), Sarah illustrated how chatbots can fail when designed with the wrong priorities. The chatbot optimised for deflection and containment , but lacked escape routes , sentiment detection, and escalation paths - turning a simple purchase into a multi-day ordeal. “Don’t measure success by how well the chatbot performs for the bot—measure it by how well it performs for the human.” Sarah introduced principles for better chatbot design: Human-Centred Design – Focus on user needs and emotional impact. Systems Thinking – Consider the entire process, not just chatbot metrics. Escalation Triggers – Negative sentiment, repeated failures, high-value intents. Context Awareness – Detect when a task moves from routine to complex and route accordingly. The takeaway? Automation should remove friction from the whole system - not push it onto the customer. 3. Responsible AI and Bias in Large Language Models Speaker: Sarah Wyer , Professional Development Expert in AI Ethics at Corndel “When we create AI, we embed our values within it.” She shared her journey tackling gender bias in large language models , from GPT-2 through to GPT-5, and highlighted why responsible AI matters. AI systems reflect human choices - what data we use, how we define success, and who decides what is fair. Real-world examples brought this to life: facial recognition systems failing to recognise darker skin tones, credit decisions disadvantaging women, and risk assessment tools perpetuating racial bias. Even today, LinkedIn engagement patterns show gender bias! Sarah made the point that simple actions - like testing prompts such as “Women can…” or “Men can…” - can reveal hidden disparities and spark vital conversations. To address these issues, Sarah introduced the D.R.I.F.T framework , a practical guide for organisations: D – Diversity : Build diverse teams to challenge bias. R – Representative Data : Ensure datasets reflect all user groups. I – Independent/Internal Audit : Test outputs regularly. F – Freedom : Create a culture where employees can challenge AI decisions. T – Transparency : Share processes without exposing proprietary code. Wrapping up the final session - before we opened the floor to panel questions and debate - Sarah created the opportunity to discuss how we address AI bias within our organisations by stepping through the DRIFT framework. Shared Themes Across All Sessions AI is powerful, but context matters . Human oversight and ethical design are critical . Use AI to augment thinking , not replace it. Measure success by human outcomes , not just automation metrics. We've had such great feedback from this event series - especially around the quality of speakers and the opportunity to have meaningful conversation and debate outside of functions. Definitely more in the events plan for 2026! If you'd like to be part of the conversation please navigate to our LAB events page to register your interest .
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