Rethinking AI in Business: From Tools to Transformation
In a recent episode of the Cambridge Executive Business Insights: Rethinking AI podcast, Professor Jaideep Prabhu hosted David Ferne, Vice President of AI at NTT Data Services, for an exploration of how companies can move beyond surface-level applications toward purposeful, scalable AI strategies that unlock broader organisational intelligence and creativity.
Today’s business leaders must ask: what does it mean to design organisations, and work, for a smarter, more adaptive AI future? The episode calls for a reimagining of not only the technology but the very ways we organise, learn and lead.
AI’s journey: from hype cycles to human symbiosis
Every wave of AI enthusiasm brings with it both breakthroughs and cautionary tales. For David Ferne, this has been a personal journey. He describes a career traversing both the “summers and winters” of AI, that is, periods of excitement and apparent dormancy, with his first major turning point coming at a 2021 Microsoft conference. There, a hushed invitation to view an early version of OpenAI’s GPT-3 marked, for him, a watershed moment.
What set GPT-3 apart from the natural language technologies that came before was not merely technical scale but the capacity for nuanced, near-human interaction. Having spent years wrestling with brittle, inflexible natural language processing (NLP) systems, David recalls being stunned by the model’s fluency, adaptability and potential to “do more with less”. It was, in effect, AI that could engage with information, not just data, in a manner previously out of reach.
This encounter shaped his approach at Cognizant and, more recently, at NTT Data Services, where the challenge has become not just technical deployment but wide organisational change: what David describes as the effort to “redefine the social contract” between humans and intelligent machines.
Making AI a business strategy, not a bolt-on
Too many organisations, David cautions, still regard AI as a technical product: such as a plug-in, an app, or a procurement exercise, instead of seeing it as a living part of the business. The result has often been fragmented pilots, isolated proofs-of-concept, and a lack of cohesive transformation. Echoing the way city design is most effective when integrated into the wider urban fabric, he argues that true progress only emerges when firms “bake the AI strategy into the business strategy”, which allows both to evolve together.
The pattern of success, then, is clear. The most forward-thinking organisations don’t set up an “AI strategy” siloed from their overarching plans. Instead, they use AI as an enabler, a catalyst for reimagining products, services and even internal culture. Firms that reap the most benefit see AI not as a ticket to efficiency alone, but as a force for growth: innovation that expands possibilities for their workforce rather than merely shaving costs.
This mindset is critical. “Communicate growth first AI initiatives,” advises David, noting that framing AI as a route to efficiency almost inevitably triggers resistance and fear among employees. By positioning AI as a growth tool, firms enable employees to engage with curiosity, to see themselves as partners in a journey rather than as replaceable cogs.
The leadership imperative: healthy scepticism and distributed ownership
Leaders play a pivotal role in determining whether AI becomes a source of transformation or of anxiety. David Fern distinguishes between emotional scepticism (“let’s not touch this, it’s too risky”) and healthy scepticism – the latter being a pragmatic, thoughtful approach that weighs potential with appropriate caution. The leaders who succeed embed good governance, insist on auditability and encourage experimentation within clear bounds.
But modern leadership, he argues, is not just about the C-suite. The coming era will see “AI leadership” diffused throughout the organisation. Every employee, not just a select technical elite, will need to become comfortable managing teams of AI agents, distributing tasks and coordinating workflows that blend machine and human input. In effect, the skill of “managing the collective” will become as important for frontline workers as it is for traditional managers.
Bridging the void: from pilots to production
The gap between aspiration and reality is perhaps nowhere more visible than in the so-called “pilot to production” chasm. Firms can now swiftly spin up AI pilots in days or weeks, but most struggle to translate those exciting prototypes into reliable, enterprise-grade systems. The technical novelty of today’s AI, with its low barrier to entry, can create a false sense of ease: just as building a parklet is easier than reimagining a city’s transportation, deploying one chatbot does not equal systemic change.
To cross this chasm, firms must build the right underlying platforms, robust governance structures and the “trust layer” that enables broad adoption. “[Trust] is the only enduring moat,” David explains, without it, even the cleverest systems will languish unused. Building this trust involves not just technical measures (audit trails, clear accountability, regulatory compliance), but also transparency and engagement with staff.
The myth of data quality: why information matters more
A familiar refrain in digital transformation circles is the lament over “poor data quality.” Firms fear they cannot embrace advanced AI unless their vast, disparate data stores have been fully cleansed, integrated and standardised. Yet David provocatively suggests this conventional wisdom is outdated (or at least incomplete) in the age of generative AI and large language models.
“Data doesn’t matter. AIs don’t use data, they use information,” he asserts, shifting the focus from raw tables of numbers to the so-called “semantic layer”, the structured, meaningful knowledge extracted from the daily flow of decisions, conversations and actions. Most organisations, he argues, already possess this informational substrate: they make complex choices, price products, and navigate ambiguity every day, even with spotty data. It is this context, not mere data, that AI needs to augment.
In the long run, the “gold” for firms may well be found not in their historic datasets, but in capturing the iterative, collaborative interactions between humans and AI agents as they co-create solutions. The insights gleaned in these exchanges such as heuristics, playbooks, creative “what ifs”, could form a kind of enterprise intelligence, a priceless new resource for adaptation and learning.
Governance as an accelerator, not a handbrake
At the heart of the responsible AI debate lies the question of governance. Conventional wisdom imagines compliance and oversight as drags on pace and innovation, as necessary evils to be applied after the fun of prototyping is done. Ferne turns this logic on its head, proposing instead that governance is “an accelerator” when designed into AI initiatives from day one.
Applying governance retroactively, he notes, often leads to a collapse in the value of proof-of-concept AI, as innovations must be refitted into risk frameworks they were never designed to meet. In contrast, building with governance as a “first class citizen” provides the rails for safe experimentation, catalysing innovation by clearly laying out the “rules of the road.” It is a foundation, not a fence.
Yet one thorny issue remains: accountability. As algorithms make more decisions affecting customers, products and the public, firms must grapple with the question: who is responsible? The current “human-in-the-loop” paradigm strains under the complexity and autonomy of modern AI. Without clear lines of accountability, true trust and thus, widespread adoption, will remain elusive.
Curiosity, creativity and the future of work
As AI continues to mature, the most important shift may not be in the technology, but in the culture of organisations themselves. David dreams of a workplace where an employee’s value is measured not by their mastery of obscure software interfaces, but by their ability to ask the most illuminating questions. “Curiosity will be the single most important skill for the future workforce,” he maintains.
In such an environment, technical knowledge and domain experience count for a great deal, not because they enable more efficient button-clicking, but because they equip people to interrogate problems, imagine alternatives and shape AI’s outputs with creative flair. The tools will take care of the low-level execution; it will be the uniquely human capacity for curiosity, judgment and contextual understanding that confers enduring value.
Organisationally, this demands both top-down and bottom-up transformation. Leaders must set the agenda: “the tone is set by the leaders,” as David notes, but they must also foster a culture of distributed experimentation, empowering employees at every level to shape workflows, try new approaches and co-create with AI. Managing the balance, that is, building structures that both govern and encourage curiosity, will distinguish the “destination employers” of the next decade.
Destination employers and the new value proposition
What will make an employer attractive in the AI age? It will not be mere access to the latest tools, nor rigid certification in a particular platform. The future, David suggests, lies in organisations that develop “AI factories”, structures that nurture curiosity and creativity, moving seamlessly from experimentation to production.
As AI takes over more of the technical heavy lifting, the premium on diverse experiences may rise. Firms may need to encourage staff to broaden their perspectives, to travel, to learn from art and science alike, bringing fresh insight to the problems at hand. In this model, creativity and curiosity are not ancillary to productivity; they are its foundation.
Personal agents and the coming transformation
Looking even further ahead, David Ferne notes “the personal agent revolution” as the next major wave. Whereas much AI today is “data centre” based, operating centrally, subject to enterprise control and governance, the near future will see a proliferation of AI “personal agents” acting on behalf of individuals, running on personal devices, managing mundane tasks, synthesising information, and interfacing with larger systems as needed.
Firms will need strategies for both modalities: robust, centralised agents that govern critical workflows and data, and distributed, highly personalised agents that empower workers directly. Productivity gains will come, not from a single killer app, but from seamless interplay between these modes, all anchored in governance and trust.
The call to action: design for intelligence, design for people
Business leaders today must recognise that AI is not simply a technology. It is a catalyst for reimagining how work is done, how teams organise, and how human intelligence is multiplied, not replaced.
The key is intentionality: “be intentional and strategic about AI right from the get go,” urges Professor Prabhu. Leaders must frame technology as part of a long-term journey, grounded in growth, responsibility and trust. They must think deeply about governance and risk, but also about the curiosity and creativity they wish to encourage.
The future of AI-enabled business is not about building bigger machines, but fostering greater capacity for adaptability, learning and transformation in the people and organisations that use them.
To hear the full conversation and explore these themes in greater depth, listen to the full episode, available wherever you get your podcasts.
Frugal Innovation, Human-Centricity, and Organisational Transformation
Organisations and societies face a crossroads when it comes to Artificial Intelligence. Will the AI of the future be defined by raw computational power, or by its ability to address real human needs, unlock equity, and act responsibly? In the latest episode of the Cambridge Executive Business Insights: Rethinking AI podcast, Jaideep Prabhu, Professor of Marketing at Cambridge Judge Business School, sits down with Mark Bloomfield, founder of Turbulence, to cut through hype and examine how AI can be a lever for thoughtful, resource-efficient innovation.
Framing the AI discussion: from bigger to smarter
An emphasis on purposeful and frugal AI, a theme resonant throughout the episode, asks us to move away from the allure of headline-grabbing, resource-intensive models towards technology that is inclusive, efficient, and deeply connected to human realities. In an era where “move fast and break things” is giving way to “move fast and make things,” the invitation is clear: how can we make technology not just powerful, but meaningful?
Democratisation and the rise of AI as organisational capability
According to Mark Bloomfield, the last three years have seen a genuine democratisation of AI. Tools like ChatGPT and Claude have brought powerful generative models to the fingertips of millions, moving AI out of the realms of technical specialists and into the hands of everyday problem solvers. Yet with this shift comes both opportunity and challenge.
AI is now becoming critical infrastructure for most organisations. No longer the preserve of “nerds in the corner”, its promise stretches beyond automating routine tasks to the full-scale reimagination of processes, growth strategies, and even organisational culture. But this very broadness of potential creates new complexities for leaders: how to sift real value from hype, and navigate the “AI FOMO” that has overtaken boardrooms.
Efficiency versus growth: finding the right balance
A recurring tension in the modern enterprise is whether AI should be prioritised as a vehicle for efficiency, for example for reducing costs and automating workflows, or as a catalyst for innovation and growth. Mark Bloomfield acknowledges that, in a world of quarterly results and investor scrutiny, boards often default to efficiency, encouraged by vendor pitches promising rapid, measurable ROI.
Yet the deeper, longer play of AI is in enabling growth. Generative AI’s capacity for simulation, that is, allowing teams to test, refine, and de-risk ideas virtually, can accelerate innovation cycles and open new frontiers of value. The organisations that will thrive, Bloomfield argues, are those able to pursue both streams: extracting efficiency gains in the short term, while investing resource and attention in the complex, sometimes uncomfortable, work of reimagining their business for the future.
The frugal AI imperative: doing more with less
So what does “frugal AI” look like in practice? Here, the discussion draws on Bloomfield’s background in aerospace and his fascination with nature-inspired optimisation, particularly the famously efficient foraging behaviours of ant colonies. Ants, he points out, learn from each other, coordinate without centralised control, and continually optimise paths with stunning computational thrift.
Translating these insights into the business world, AI becomes a tool not just for brute-force analysis but for de-risking, rapid prototyping, and collaborative problem-solving at scale. For example, creating AI “personas” can allow teams to challenge assumptions, scrutinise ideas, and simulate outcomes swiftly and at low cost, unlocking frugal innovation that might once have required significant investment.
Critically, this approach isn’t about compromise. Rather, it asks executives to find friction points – those high-pain processes or organisational bottlenecks – and target AI interventions where they most readily free up human capacity for creative, higher-value work.
Navigating AI hype, FOMO, and organisational culture
With “AI transformation” now a staple of business magazines and consultancy pitches, leaders often feel enormous pressure to “do something” with AI simply to keep up with the Joneses. The risk, says Bloomfield, is that companies find themselves rolling out tools like Microsoft Copilot or Google Gemini without strategic intent, mistaking box-ticking for value creation.
To counteract this, Bloomfield urges leaders to distinguish between AI curiosity, initial experimentation with new tools, and genuine capability, where AI is embedded in the reimagining of workflows and processes. Language matters here: being “AI-first” is less important than being “problem-first”, putting organisational pain points front and centre and letting AI follow as an enabler.
Equally vital is modelling. Cultural change, Bloomfield argues, happens not just through top-down pronouncements but by leaders at every level demonstrating how they want AI to be used, both intentionally and responsibly, and in the service of organisational learning. When paired with a culture where experiments and lessons are widely shared (avoiding duplication and re-inventing the wheel), this builds trust and helps teams focus on meaningful impact rather than technology for its own sake.
A case in point: fixing real problems with AI
Behind every successful AI journey lies a concrete use case, often in the unglamorous, behind-the-scenes processes that, while “low risk,” soak up time and create churn. Mark Bloomfield recounts his work with a US financial services company, where the temptation was to expect a transformation purely from rolling out an off-the-shelf tool. However, after a costly false start, the company instead focused on a key source of organisational friction: high staff turnover in a particular department.
The root cause? Overburdened HR teams with little capacity for development or meaningful employee support. By deploying AI agents to handle transactional queries, the company dramatically increased HR’s ability to focus on high-impact work, and crucially, made it clear up front that technology was there to augment, not replace, human talent. The result: a dramatic drop in attrition, a more engaged workforce, and a flywheel of receptiveness to further AI-enabled problem solving.
Friction audits and the art of human-centred AI
Central to Bloomfield’s philosophy is the concept of a “friction audit”, systematically identifying points in the organisation which elicit frustration, eye-rolls, or sighs from teams, and targeting these as candidates for AI intervention. In doing so, organisations can rapidly build confidence and showcase ROI, whilst dispelling fears that AI is solely an engine for cost-cutting or job displacement.
The aim, he asserts, is not to subordinate humans to machines, but to ensure that AI removes drudgery, liberating capacity for the sort of uniquely human work such as judgement, creativity, relationship-building, that sustains long-term competitive advantage.
From “AI-enabled” to “problem-native”: a new lens on transformation
Successful AI-adoption, Bloomfield contends, isn’t about attaching technology to every business unit, but about cultivating an organisation that is “problem-native”: one that focusses relentlessly on the issues faced by customers and colleagues, and is open to creatively deploying AI capabilities (and, indeed, any resource) to solve them.
This philosophy requires both humility and bravery. Humility, to revisit processes that “worked” in the analogue era and ask: do we even need to do this anymore? Bravery, to empower teams to take ownership of AI-driven experiments, recognising that the journey may be non-linear and that AI capability itself is evolving at unprecedented speed.
No “point B”: continuous learning and reimagination
If traditional change management describes moving from “point A” to a pre-defined “point B”, AI upends this model entirely. Today’s technology is, as Bloomfield puts it, “the worst it will ever be again.” Organisations must get comfortable not with single, linear transitions, but with continuous cycles of learning, capacity-building, and reimagination.
Pragmatically, this means both top-down and bottom-up mechanisms: leaders modelling (not merely mandating) intentional AI use, and front-line teams empowered, with time and resources, to test and adapt workflows as capabilities evolve.
Personalising AI transformation: Turbulence as a living example
Much of Bloomfield’s advice is grounded in his own experience building Turbulence, where he made the intentional choice to remain a solo founder and leverage AI not to displace people, but to give himself more human capacity for creativity and judgement.
From building AI “non-executive directors” tasked to challenge his own decision-making, to employing AI researchers who summarise troves of insights and data, his approach is not about automating away autonomy, but about reclaiming time for reflection, strategy, and curiosity.
Yet this approach demands discipline: Bloomfield is clear that humans must resist the temptation to fully “outsource” their thinking to AI, building in regular reflection – “AI mental audits”- to ensure that judgement, authenticity, and intuition are preserved.
Scaling beyond pilots: risk, governance, and trust
The journey from successful AI pilot to scaled enterprise capability is fraught with technical, regulatory and often cultural obstacles. A key challenge is risk: as AI systems move from back-office process automation to customer-facing roles, anxieties about bias, hallucination, and unpredictable outcomes multiply.
Bloomfield advocates for a rigorous simulation approach, using generative AI itself to create synthetic customers, simulate board debates or regulator responses, and stress-test new ideas before full-scale rollout. Governance then becomes an enabler, not a roadblock, granting space for experimentation while ensuring responsible oversight as initiatives mature.
Human-centricity to the fore: AI as a productivity multiplier, not a threat
Perhaps the most pernicious myth in contemporary AI discourse is that of mass human obsolescence. Yet in organisation after organisation, including those Bloomfield advises, the evidence runs counter to the dystopian narrative. Human skills remain indispensable: overseeing, refining, and integrating AI solutions in context requires judgement, collaboration, and, above all, curiosity.
This is especially evident in the next generation, as illustrated by Bloomfield’s own children, whose playful interrogations of AI, sometimes to further their own interests, highlight the centrality of curiosity, adaptability, and ethical reasoning for the AI-literate workforce of the future.
Responsible AI, ethics, and the sustainability paradox
As the AI landscape broadens, so too does scrutiny over its impact on privacy, fairness, accountability, and, increasingly, sustainability. Energy consumption, water use, and the extractive toll of massive data centres raise searching questions for a technology sector that aspires to do more good than harm.
Yet Bloomfield is optimistic, pointing to a likely shift away from ever-larger language models towards more efficient, localised approaches: “The phraseology itself contains three assumptions: large, language, model… But if the future is small (smaller models, less resource-intensive) then frugal AI becomes not just an ethical imperative, but ultimately a business one.”
Ultimately, trust sits at the heart of responsible AI. Whether it’s transparency in model reasoning, explainability in automated decisions, or accountability mechanisms for error and bias, organisations must be intentional and proactive if they want to build cultures, both internally and with customers, where AI is welcomed as a partner.
Transitions, empathy, and building for the human future
As the episode closes, Bloomfield offers a reflection apt for a period of discontinuous change: “Change is situational. Transitions are psychological.” Underlying this technological revolution is a human transition, one that demands empathy, warmth, and a shared effort to understand what roles humans will play, and what values must shape the future.
Practical steps for leaders: small, focused, and human-first
For those seeking an actionable starting point, Bloomfield is unequivocal: “Start small. Pick a process or workflow behind the scenes that’s low-risk, high-friction. Be open to how AI can help you solve and reimagine it. Prove the value, and then build the flywheel from there. This builds confidence, trust, and literacy.”
Frugal, effective AI adoption is less about revolution than about a steady accumulation of human-driven wins.
Towards a future of purposeful, frugal, and human-centric AI
The vision emerging from this conversation is neither utopian nor apocalyptic. Instead, it is a call to treat AI as a continuous partner in organisational evolution: a capability to be embedded, scrutinised, and reimagined in perpetuity. Whether through the design of more efficient HR systems, the empowerment of employees to solve friction points, or the refusal to cede judgement and creativity to algorithms, purposeful AI is within our collective grasp.
As businesses, public sector organisations and individuals seek a path through hype, risk, and uncertainty, the lessons from Cambridge Judge Business School’s conversation with Mark Bloomfield are clear: start with real problems, foreground human value, and let technology follow as a capable ally.
How the Cambridge General Management Programme Changed the Way I Lead
This blog, written by Ramesh Sadasivam, highlights the transformative shift from operational management to strategic business leadership. Ramesh participated in the Cambridge General Management Programme in 2026.
For most of my career, I had grown through technical and operational roles within the structural steel detailing industry. Success was measured by delivering projects on time, solving technical problems, improving productivity, and supporting customers. Over time I moved into leading larger teams and eventually became responsible for building a new business unit within my organisation.
As our business grew, I realised the challenges were changing. The questions I faced were no longer purely operational.
How do you build a sustainable business instead of simply delivering projects?
How do you balance growth with profitability?
How do you develop future leaders while maintaining delivery performance?
How do you make strategic decisions when there is no obvious right answer?
Those were the questions that brought me to Cambridge.
Seeing the Business as a System
One of the biggest shifts for me was learning to step back from day-to-day operations and view the business as an interconnected system. The programme covered strategy formulation, business economics, financial interpretation, organisational behaviour, customer value, behavioural decision-making, and leadership under uncertainty. Individually, these were valuable subjects, but together they changed how I evaluate business decisions. Rather than asking “How do we solve today’s problem?”, I began asking:
What capability are we trying to build?
What systems need to change?
What will create value over the next three to five years?
How do today’s decisions affect the organisation tomorrow?
That systems perspective has become one of the most valuable outcomes of the programme.
Moving from Working in the Business to Working on the Business
Perhaps the most important lesson was recognising the difference between managing operations and leading an organisation. Before Cambridge, much of my focus was naturally on execution. After Cambridge, I started investing far more time in building capability rather than simply solving problems. That means thinking about:
Organisational design
Leadership development
Performance management
Customer strategy
Financial sustainability
Market positioning
Innovation and AI adoption
Instead of being the person with all the answers, my role has become creating an organisation capable of finding better answers together.
Applying the Learning Immediately
One of the strengths of the programme was how practical it was. Almost every topic connected directly to challenges I was facing back at work. Shortly after completing the programme, I was promoted to Business Head from Operations Manager of my department, taking responsibility for the overall business unit.
The learning from Cambridge has influenced many of the initiatives we are now developing, including strengthening performance management, creating clearer organisational frameworks, improving operational reporting, building leadership capability, and thinking more deliberately about long-term growth rather than short-term delivery.
It has also changed how I approach decision-making. Rather than relying only on experience or instinct, I now try to combine evidence, structured thinking, and intuition before making important decisions.
The Value of Learning from Others
Another highlight was the people. The diversity of industries, countries, and experiences created discussions that challenged many of my existing assumptions.
Listening to leaders facing completely different business challenges often provided insights that could be applied surprisingly well within engineering and construction services. Those conversations continue to influence my thinking long after the programme has ended.
Looking Ahead
The engineering services industry is changing rapidly. Artificial intelligence, automation, digital collaboration, and changing customer expectations are redefining how value is created.
Technical excellence will always remain important, but future leaders will need to combine technical credibility with commercial thinking, strategic decision-making, and the ability to build adaptable organisations.
For me, the Cambridge General Management Programme was not simply a management course. It marked the point where I began thinking less like an operations manager and more like a business leader.
That shift in mindset is something I expect will continue shaping both my leadership journey and the future growth of our business for many years to come.
Rethinking Artificial Intelligence: Frugality, Sustainability, and Supply Chains
As artificial intelligence (AI) increasingly becomes part of the architecture of business, the conversation is undergoing a quiet but profound shift. Where once the headlines heralded exponential growth and ever-larger models, now, we are asking not just “how much” but “how well”, and crucially, “how responsibly”. A recent episode of the Cambridge Executive Business Insights: Rethinking AI podcast brings these questions to the fore through the lens of sustainability leadership at Specsavers, as Director of Sustainability Munish Datta shares the company’s evolving journey towards frugal, purposeful AI.
AI at the crossroads: from bigger to smarter
“What if the future of AI wasn’t bigger, but smarter?” muses Professor Jaideep Prabhu at the start of the episode, setting the tone for an exploration of AI as a lever for meaningful, accessible, and responsible change. As Datta recounts his own trajectory, from two decades at Marks & Spencer, via the UK Green Building Council, to now bridging corporate ambitions with daily sustainability realities at Specsavers, it quickly becomes clear that AI’s place in this journey is not a matter of luxury or curiosity, but urgent necessity.
Meeting the data deluge: why visibility trumps efficiency
Among the most persistent challenges for businesses with global supply chains is data: its sheer volume, complexity, and fragmentation across continents and vendors. For sustainability teams, the stakes are existential. Precise emissions data, across Scope 1 (direct), Scope 2 (energy consumption), and the notoriously elusive Scope 3 (indirect, upstream and downstream) emissions, is both a regulatory and ethical imperative. As Datta puts it, “the quality of the output is only as good as the quality of the input”.
Historically, such data collection leaned heavily on industry averages or outdated manual reporting. The magnitude and granularity now demanded, however, have outpaced human capability alone. Enter AI. Specsavers employs tools such as Watershed to synthesize massive, messy, and disparate data sets, from invoices and supplier emails to shipment PDFs, into transparent, auditable emissions reports. Rather than a black box, this approach makes supply chain emissions not just visible, but actionable: “Once we’ve built that accurate product level and supplier level data foundation, the real promise then…is that AI will be able to help us accurately forecast what our emissions will look like across our full value chain over the next five to ten years”.
Beyond carbon: traceability and the transition to circularity
But emissions accounting is only the beginning. Traceability, that is, knowing not merely the origin but the full life cycle of every component, is fast becoming the gold standard for a circular economy. To illustrate, Datta spotlights progress in the fashion industry, traditionally seen as a byword for opacity and waste. Platforms such as Tracex, leveraging both blockchain and AI, now offer “factory to shop” digital custody chains, enabling companies to map every step and stakeholder with an unprecedented degree of confidence. Imagine the power, he suggests, if such initiatives were adapted for the complex material flows of optical and hearing devices, with hundreds of global suppliers and highly variable product lifespan.
The result would be a fundamental shift: “We can stop guessing, we can move away from this sort of scattergun approach of many sustainability initiatives and start making really efficient, surgical, data driven decisions that actually move the needle on carbon reduction”. Specsavers is, in Datta’s words, “at the foothills” of this journey, beginning with building robust, accurate data streams before layering in circularity initiatives.
Business resilience in an era of climate risk
For many organisations, the urgency of climate change is no longer an abstract future but a clear and present operational risk. Flooding, extreme weather, and supply chain shocks can threaten healthcare continuity for vulnerable customers dependent on Specsavers’ services. Here too, AI has a quietly transformative part to play. By integrating climate adaptation models that predict exposures to river flooding, wind, heat and water stress down to individual facilities and project out to 2100, AI can enable real-time business continuity planning.
Instead of reacting to disaster, managers can execute pre-agreed mitigation plans mere hours before a major storm hits – moving critical stock, reinforcing logistics, rerouting deliveries, and ultimately keeping essential health services running. “This shifts us from being reactive…to proactive, which is preventing damage in the first place”. Such integration, Datta contends, is vital for enabling adaptive, resilient supply chains in a volatile climate era.
Tackling supply chain complexity: technology and human connection
One cannot discuss decarbonisation and circularity in supply chains without acknowledging their daunting complexity. Specsavers’ supplier community spans geographies, languages, digital capabilities and cultures. While AI can operate as an “ultra efficient data assistant” to categorise, clean, and flag erroneous information, human oversight remains critical: “The AI does the heavy lifting, but it’s humans, us, that validate it so that we can prove to anyone that the data has integrity”.
Furthermore, the spectre of data bias, “black box” opacity, and systemic errors is never far away. Governance frameworks and new skills, not just for tech teams, but across the sustainability and procurement workforce, are non-negotiable as AI systems embed themselves in decision-making.
The answer, Datta asserts, is not a one-size-fits-all rollout, but patient, iterative supplier engagement. Raising awareness, listening to concerns, and co-developing tailored solutions are essential to ensure every partner whether large or small, digitally advanced or more traditionally run, can come on the journey.
Skills for the new era: humans at the centre of frugal AI
A theme running throughout the episode is the recalibration of roles and skills for a future where AI augments, but does not replace, human expertise. Notable, too, is the scale of transformation: a recent LinkedIn report, Datta notes, identified “responsible AI” as the fastest-growing skill set required of sustainability professionals, with demand up by more than 500% year-on-year.
Inside Specsavers, the imperative is less about headcount reduction and more about capacity expansion: “It’s kind of an all hands to the pump situation…AI is allowing us to survive this workload challenge that we have and do significantly more and very quickly with the same amount of resource, not necessarily less resource”. The new generation of sustainability professionals is evolving from manual data entry clerks to “strategic editors”, prompt engineers, and ethical gatekeepers.
Training is hands-on and highly contextualised. For vendors and internal teams alike, the focus is on using specific, carefully chosen tools (such as Watershed and Neutrino for emissions and manufacturing intelligence) to best effect. Digital literacy is important, but so too is the ability to use, challenge, and improve the systems themselves, and to ensure supplier adoption is an ongoing dialogue.
Navigating the paradox: AI’s own environmental footprint
A less visible but no less important challenge lies at the heart of AI’s promise: its own carbon and resource cost. It’s a striking paradox: “We could be using AI to make our supply chains more sustainable, but in the process we end up increasing emissions or use of energy and water”.
Much of AI’s life cycle impact, as much as 90-95% in some cases, is generated not by initial training, but by the “inference” phase: daily use and querying of models, powered by data centre GPUs. The solution, as Datta and partners at Watershed highlight, is not abstinence but intentional frugality. Principles include:
Right-sizing models: avoiding large, energy-intensive models for trivial tasks.
Context minimisation: keeping prompts specific and streamlined, reducing unnecessary data transmission and compute power requirements.
Location-conscious procurement: choosing data centres and vendors that leverage renewable energy and minimise water use.
Success here is partly a matter of metrics: tracking energy and water consumption (kilowatt-hours per square metre, litres of water used), setting aggressive reduction targets, and favouring on-site renewables wherever possible. The holy grail is regeneration: producing more energy, water or material value than is consumed, and reinvesting it back into wider systems.
From circular supply chains to a regenerative future
So what does a regenerative, intelligent, circular Specsavers look like? The vision is as ambitious as it is practical. In the near future, accurate demand forecasting (optimised by AI) will reduce stockpiles, freeing up working capital for reinvestment into more sustainable solutions. Over time, every material, from rare earths for hearing aids to spectacle hinges, can be tracked, valued not just financially but for their full environmental and social impact. This insight, Datta argues, is the missing piece for fully circular, modular design, new sharing and reuse models, and ultimately less waste.
A regenerative enterprise, then, is one in which “you’re producing more energy than you require and you’re putting it back into the grid or producing more water than you require and putting it back into the sort of water cycle”. Crucially, this vision is not in tension with commercial success – indeed, it is inseparable from it as customers, investors, and regulators alike demand demonstrable, verified progress.
Building supply chains that do more with less
At the heart of Datta’s ultimate recommendation for leaders sits the defining ethos of frugal AI: “It’s using lean, purposeful intelligence to fundamentally change and transform the way we physically manufacture, trace and move products in society…If we can master that and we can build a supply chain that’s not just efficient, but genuinely resilient and sustainable and keeps materials at their highest value for as long as possible, then we can create a more sustainable planet… and I think there is huge business opportunity from a sort of commercial, financial point of view as well”.
A more purposeful intelligence for an urgent future
AI is not a panacea, nor should it be excused from scrutiny for the problems it brings. Yet the journey mapped out by Datta and the Specsavers team offers a quietly revolutionary reinterpretation: one where digital intelligence is harnessed not for its own sake, but in service of real-world resilience, equity, and regeneration.
As sustainability professionals become AI-literate, supply chains become traceable and adaptive, and every partner in the system is engaged as a co-innovator rather than a passive recipient, the possibility emerges for business, and indeed, technology itself, to do more with less. The challenge, as ever, is to turn potential into practice: to remain vigilant to the new problems emerging even as old ones are solved, and to insist that every byte of progress is matched by moral as well as mechanical intelligence.
This blog, written by Ted Takahashi, Vice President of Global Technology and Innovation at Universal Studios Japan, highlights the perspective of an IT executive experienced in driving technology strategy and operational excellence. Ted participated in the Cambridge Advanced Leadership Programme in 2019 and later studied for the Cambridge Executive MBA from 2021 to 2023.
What challenges in your leadership journey prompted you to pursue the Cambridge Advanced Leadership Programme?
Before attending the Cambridge Advanced Leadership Programme, my primary challenge was a growing sense of limitation in how I was approaching leadership in an increasingly complex environment.
Through several years of management experience in the United States, I had become effective at solving operational and tactical problems. However, as my role expanded to include multi-country teams across the US, Japan, and Europe, the nature of the challenges I faced began to change fundamentally. Many of the issues were no longer technical problems with clear solutions, but rather deeply human and contextual challenges involving culture, values, ambiguity, and competing stakeholder expectations.
I became increasingly aware that my thinking relied heavily on experience-driven, high-context, and linear problem-solving approaches. While effective in familiar environments, these approaches showed clear limitations when dealing with organisational change, cross-cultural leadership, and situations where no single “right answer” existed. I felt a strong need to intentionally acquire new frameworks and perspectives to avoid leadership stagnation and to remain effective at scale.
The Cambridge ALP stood out as a programme that did not simply offer leadership tools, but instead challenged participants to fundamentally re-examine how they think, decide, and lead under uncertainty. I saw it as a necessary step to recalibrate my leadership approach for the next stage of my career.
How did the Cambridge Advanced Leadership Programme influence your leadership approach?
The most profound impact of ALP was not a single concept or skill, but a transformation in how I view leadership itself.
The programme brought together perspectives from business, technology, politics, psychology, ethics, and global economics, deliberately forcing participants to examine their own assumptions. Engaging with senior leaders from diverse industries and regions gave me a powerful external lens through which I could better understand my own biases, defaults, and blind spots.
One of the most significant shifts was moving away from a “leader-as-problem-solver” mindset toward seeing leadership as the ability to design conditions in which others can adapt, think, and act effectively. ALP helped me recognise how strongly I had relied on “how things should be” thinking, and how such rigidity, while well-intentioned, can unintentionally suppress team performance and learning.
The personal coaching and peer feedback components were particularly impactful. Receiving candid, high-quality feedback from individuals with whom I had no prior working relationship, but with whom I had built trust over the programme, allowed for a level of self-reflection that would have been difficult to achieve in my normal professional environment.
How did the Cambridge Advanced Leadership Programme influence your leadership approach?
Following ALP, there was a noticeable shift in my leadership behaviour almost immediately. My communication became more deliberate and inclusive, with greater emphasis on context-setting and shared understanding rather than implicit assumptions. I also began to place far greater emphasis on delegation and empowerment, focusing less on being the person with the best answer and more on removing obstacles so that my team could operate autonomously.
This shift proved especially valuable during the COVID-19 crisis. Under conditions of heightened uncertainty and speed, my teams began to operate more independently and in parallel, identifying issues and executing solutions with minimal central direction. Concepts discussed in ALP, particularly around adaptive leadership, moved from theory into lived experience.
Beyond my own role, I have actively worked to share what I learned through ALP with my organisation. This has included adopting more coaching-oriented conversations with my leadership team, rethinking how we approach decision-making under ambiguity, and inviting my Cambridge executive coach to conduct sessions with my managers. These efforts helped establish a more reflective and resilient leadership culture within the team.
How has the Cambridge Advanced Leadership Programme influenced your long-term career journey and commitment to continuous learning?
ALP fundamentally reshaped my relationship with learning.
The programme acted as a clear trigger for pursuing further, structured executive education, leading me to enrol in the Cambridge Executive MBA. Through ALP, I experienced firsthand the distinctive value of the Cambridge learning environment, its intellectual rigour, interdisciplinary perspective, and emphasis on reflective leadership. This experience made it evident that continued engagement with Cambridge would be the most effective way to deepen and sustain my leadership development.
The Executive MBA has allowed me to build on the foundations established during ALP, applying more advanced academic frameworks to real organisational challenges while continuing to refine my leadership practice. Together, ALP and the Executive MBA have formed a coherent and progressive learning journey rather than isolated educational experiences.
What began as a short executive programme evolved into what I would describe not as a life‑changing experience, but a life‑amplifying one, deepening the trajectory I was already on while expanding its scope and ambition.
Closing Reflection
The Cambridge Advanced Leadership Programme was not about replacing my prior experience, but about reframing it. It equipped me with a richer vocabulary, broader perspective, and deeper self-awareness to lead in complexity—and it became the catalyst for continued learning through the Cambridge Executive MBA. I am confident that the lessons from ALP and the wider Cambridge ecosystem will continue to inform my leadership for many years to come.
In conversation with Professor Dominique Lauga, Director of the CRO Programme
This article features insights from Professor Dominique Lauga, Professor of Marketing and Programme Director of the Chief Revenue Officer Programme offered by Cambridge Judge Business School Executive Education, exploring how the role of the chief revenue officer (CRO) is evolving as organisations rethink market strategy, pricing strategy and data-driven approaches to growth.
With increasingly complex market dynamics, Chief Revenue Officers (CROs) are at an inflexion point of achieving sustainable growth. The new role is to co-ordinate market strategy, customer insight and operational execution.
Our Chief Revenue Officer Programme examines how organisations can integrate these functions to drive revenue growth in a structured and strategic way. Through a combination of academic insight and executive discussion, the programme helps senior executives and aspiring CROs examine how revenue leadership is evolving in today’s markets.
In this conversation, Professor Dominique Lauga, Professor of Marketing and Programme Director of the Chief Revenue Officer Programme at Cambridge Judge Business School Executive Education, shares her perspective on how revenue leadership is changing and how executives can prepare for this expanding role.
Q: How has data changed the way revenue leaders make decisions, and how does the Chief Revenue Officer Programme help?
Data now shapes many of the decisions organisations make about customers and markets. Organisations can observe how customers interact with products, how pricing strategy affects purchasing behaviour and how marketing channels influence demand.
However, the presence of data does not automatically create insight. Leaders must interpret information and translate it into decisions about product development, pricing strategy or market entry.
This is why revenue leadership increasingly relies on data-driven thinking. Revenue leaders must understand how analytical tools support decision-making and how those insights influence broader market strategy.
In the Chief Revenue Officer Programme, participants examine these questions through discussion, applied frameworks and case studies that explore how organisations interpret data to guide revenue decisions.
Q: How does the Cambridge Chief Revenue Officer Programme support leaders advancing in this role?
The Chief Revenue Officer Programme brings together senior executives, functional leaders and industry experts who are responsible for driving growth within their organisations.
Participants explore how revenue strategy connects marketing, product development, analytics and pricing strategy. The programme draws on insights from world-class faculty at Cambridge Judge Business School, combined with executive discussion and applied learning.
A key element of the programme is the opportunity to learn from global peers. Executives often approach similar challenges from different industry perspectives, and structured discussion allows participants to compare approaches to revenue leadership and refine their own strategies.
Q: Could you share more details on how the CRO Programme helps participants apply revenue leadership concepts to real business challenges?
A defining element of the Chief Revenue Officer Programme is the opportunity to move beyond theory and apply ideas directly to organisational challenges. The programme concludes with an in-person capstone project in Cambridge, where participants work through complex scenarios related to market strategy, pricing strategy and data-driven decision-making.
During this experience, senior executives reflect on how the frameworks explored throughout the programme translate into practical strategic decisions. The capstone project allows participants to test their thinking, refine growth strategies and consider how revenue leaders can build long-term competitive advantage within their organisations.
Q: How does the programme help CROs understand AI and emerging technologies as tools for driving revenue strategy?
Many CROs are asking similar questions today. How will AI influence customer behaviour? What does it mean for pricing strategy, market strategy and the way organisations make data-driven decisions?
The Chief Revenue Officer Programme addresses these questions through AI-focused live online sessions. In these discussions, senior executives explore how emerging technologies are shaping revenue leadership and influencing the strategic decisions that drive sustainable revenue growth.
Q: How does the CRO Programme help leaders develop the right mindset?
Revenue leadership requires leaders to think beyond individual functions. The CRO role focuses on how organisations create value for customers and translate that value into growth.
This perspective requires curiosity about how different parts of the organisation interact. Leaders also need to remain adaptable because technologies, markets and customer expectations continue to evolve.
For many organisations, the CRO now plays an important role in shaping long-term revenue direction. The Chief Revenue Officer Programme helps leaders examine how this role is developing and how organisations can approach revenue strategy in a more integrated and structured way.
For executives interested in strengthening their approach to revenue leadership, the Chief Revenue Officer Programme offered by Cambridge Judge Business School Executive Education explores how organisations can integrate analytics, pricing strategy and market strategy to drive revenue growth in complex markets.
Professor Dominique Lauga is the Programme Director of the Chief Revenue Officer Programme, a collaborative programme between Cambridge Judge Business School Executive Education and Emeritus.
The 9 to 12-months Chief Revenue Officer Programme is a multiformat executive programme delivered through online learning, live online sessions and an in-person capstone experience in Cambridge. It equips senior executives with the frameworks and tools needed to lead revenue strategy in complex and data-driven markets while also providing opportunities for peer learning and networking with other leaders responsible for growth.
Independent Directors in 2026: More Distant, More Accountable, More Important
This blog, written by Vivek Menon, Research Operations Director at G-Research, shares reflections from his experience as part of the inaugural cohort of the Board of Directors Programme at Cambridge Judge Business School. Drawing on discussions with faculty and peers, it explores how AI, regulation, and increasing information accessibility are reshaping the role of the independent director and raising expectations of modern governance.
How AI, regulation, and the information revolution are reshaping the non-executive role
These reflections have been shaped by my experience as part of the inaugural cohort of the Board of Directors Programme at Cambridge Judge Business School. Through discussions led by Programme Director Simon Learmont and a highly experienced faculty, alongside a diverse group of peers, I’ve had the opportunity to test many of these ideas against both academic perspectives and lived experience.
There’s a quiet shift happening in boardrooms — subtle, but significant.
AI tools can now review regulatory filings, analyse financial statements, monitor press coverage, track ESG metrics, and even synthesise employee sentiment — all in a fraction of the time it once took. Tasks that previously required teams of advisers can now be supported, at least in part, by well-designed prompts and connected data sources.
That shift has implications for the independent director or Non-Executive Director (more commonly referred to as NED in the UK)
When information was expensive
For many years, the NED role has rested on an implicit understanding. You attend a series of meetings each year, prepare carefully, ask thoughtful questions, and contribute experience and perspective. In return, you receive a fee, professional standing, and the opportunity to shape governance at a strategic level.
Information asymmetry between executives and non-executives was always part of that model. NEDs were, by design, a step removed from day-to-day operations. They relied on management reporting and board packs to understand what was happening inside the organisation. That distance was recognised — and largely accepted.
When problems emerged, it was often reasonable for a director to say they had relied on the information presented and the assurances given.
That context is evolving.
When information becomes accessible
One of the more interesting themes explored during the programme is how governance expectations tend to evolve not in step changes, but through gradual reinterpretation of what “good” looks like in practice. Faculty sessions highlighted how legal and regulatory frameworks rarely move as fast as technology, but expectations of director behaviour often do.
In that context, the availability of AI-enabled analysis doesn’t just improve efficiency — it subtly raises the baseline of what constitutes reasonable diligence.
If public records, regulatory actions, tribunal outcomes, sentiment data, and media archives can be reviewed quickly and at low cost, expectations begin to shift.
The legal standard has long been framed around what a “reasonably diligent person” would do. As the tools available to support diligence improve, it’s likely that interpretations of reasonable diligence will evolve as well. Regulators and courts tend to adjust expectations over time, especially as new capabilities become mainstream.
Recent legislative developments — from the Senior Managers and Certification Regime to the Economic Crime and Corporate Transparency Act and sector-specific reforms — suggest a broader trend toward clearer personal accountability in governance roles.
The direction of travel appears consistent: expectations are rising.
The modern NED paradox
This tension between distance and accountability was a recurring topic in our cohort discussions. Many participants brought current board experience across sectors, and a consistent theme emerged: while access to data has increased, proximity to organisational reality has, in some ways, decreased.
What became clear through these exchanges is that effective oversight increasingly depends not just on information access, but on how deliberately directors choose to bridge that distance — through questions, triangulation, and judgement.
At the same time, the operating environment has changed.
Hybrid working, geographically dispersed operations, and digital-first business models mean many NEDs are physically further from the front line than in previous decades. Organisational culture can be harder to observe directly. Informal signals are easier to miss.
Yet personal accountability has not diminished. Directors continue to face regulatory scrutiny, reputational exposure, and, in certain circumstances, personal liability.
This creates a tension: greater distance from daily operations, alongside heightened expectations for oversight.
It’s not necessarily a broken model — but it may be one that requires recalibration.
The rarely discussed risk–return balance
There is also a practical consideration that deserves more open discussion.
A typical FTSE 250 NED fee may sit in the £30,000–£70,000 range. For that, a director commits time, reputation, and legal responsibility. The role is intellectually rewarding and professionally meaningful — but it also carries exposure under company law, potential regulatory investigation, and reputational risk.
Executives, by contrast, usually have deeper operational insight, larger teams, and greater direct control over outcomes — alongside materially higher compensation.
This isn’t to suggest the NED role is unattractive. Many pursue it for precisely the right reasons: contribution, challenge, and stewardship. But the risk–return balance is rarely examined explicitly, and perhaps it should be.
Structural patterns in appointments
The programme also prompted reflection on board composition. One of the more valuable aspects of the Cambridge experience has been exposure to a cohort with varied professional backgrounds — finance, technology, health care, public sector, and beyond.
That diversity of perspective is not always fully reflected in boardrooms today, and it reinforced the idea that future governance effectiveness may depend as much on cognitive diversity as on traditional measures of experience.
Board appointments still tend to flow through established personal networks and executive search channels. Progress on diversity — in background, thought, and experience — is visible, but gradual. Many boards continue to draw from familiar corporate pathways.
That familiarity brings advantages: experience, credibility, and shared language. It can also create shared blind spots.
At a time when businesses are becoming more technologically complex and socially scrutinised, there may be value in broadening the range of perspectives around the table — particularly those comfortable with Data, AI, Cyber and Digital risk.
It’s also worth acknowledging that executive success does not automatically translate into effective non-executive contribution. The shift from decision-maker to challenger requires a different mindset. Some make that transition seamlessly. Others find it more nuanced.
An analogue structure in a digital world?
While businesses are embracing AI and real-time analytics, board processes often evolve more slowly. Governance codes update periodically; board composition shifts incrementally.
There’s nothing inherently wrong with deliberate change — stability has value. But the contrast between the speed of operational transformation and the pace of governance reform is becoming more noticeable.
NED’s increasingly sit at the intersection of these two worlds.
Calibrated scepticism
If information is becoming abundant, the scarce resource may be judgment.
The directors likely to thrive in this environment may not be those who simply consume more data, but those who practise what might be called calibrated scepticism.
Not cynicism. Not blind trust.
But the disciplined habit of asking:
What might we be missing?
What assumptions are embedded here?
If this narrative were incomplete, what signals would we expect to see?
That might involve cross-referencing internal engagement data with external employee reviews. Noticing patterns in litigation provisions. Requesting clarity on insurance wording rather than headline limits.
These aren’t acts of distrust — they are expressions of stewardship.
Evolving the NED role
The traditional image of a respected figure attending quarterly meetings and offering periodic wisdom may no longer capture the full demands of the role.
Future NED’s may need to be:
More proactive investigators — supplementing board materials with independent, technology-enabled research.
More skilled questioners — focusing on substance rather than performative challenge.
More sophisticated risk thinkers — considering second- and third-order effects that don’t always appear neatly on a risk register.
More comfortable dissenters — prepared, when necessary, to document concerns clearly and constructively.
More reflective portfolio managers — understanding how multiple board roles interact in terms of time, sector exposure, and reputational risk.
This is less about working harder and more about working differently.
Reflections from the Cambridge programme
A consistent takeaway from the programme has been that the NED role is less about static best practice and more about evolving judgement.
Faculty emphasised that governance is ultimately a human system — shaped by incentives, behaviours, and the willingness to challenge constructively. Tools and frameworks matter, but they do not replace the need for directors to exercise independent thinking.
Perhaps most valuable has been the opportunity to step back and examine the role itself: not just what NEDs do, but how they prepare, how they calibrate their involvement, and how they remain effective as the context shifts.
That reflective space is increasingly important in a role that is often performed alongside other professional commitments.
The opportunity
None of this diminishes the importance of the independent director. If anything, it elevates it.
Boards are operating in more complex, regulated, and scrutinised environments than ever before. Organisations need directors who combine experience with curiosity, authority with humility, and confidence with thoughtful challenge.
The role is not becoming obsolete. It is becoming more demanding — and arguably more meaningful.
Why this matters
This isn’t an argument that the NED model is broken. It’s a reflection that the environment around it is changing quickly.
High-quality independent directors remain one of the most effective checks and balances in modern organisations. When they operate well, governance strengthens, strategy sharpens, and risk is better understood.
But expectations are shifting. Tools are evolving. Accountability is tightening. And the mindset required may need to evolve alongside them.
The directors I most respect share a quiet but consistent trait: a willingness to pause, to question, and to express discomfort when something doesn’t sit right — even if consensus is leaning the other way.
In a world increasingly shaped by AI-generated insight and accelerating information flows, that human capacity for thoughtful, calibrated scepticism may be more valuable than ever.
If there is one thing the Cambridge programme has reinforced, it is that the future of the NED role will not be defined solely by regulation or technology, but by how individual directors choose to interpret and respond to a changing environment. The real question is whether our governance structures, appointment processes, and preparation pathways are evolving quickly enough to support it.
That may well be one of the defining boardroom conversations of this decade.
Vivek Menon, Research Operations Director at G-Research
Note on the author
Vivek Menon is Research Operations Director at G-Research, a quantitative research and technology firm based in the United Kingdom. He has over 35 years of experience in global financial services and research operations, including senior executive roles in banking and governance across Europe and Asia. His interests focus on governance, psychological safety, and the organisational conditions that shape voice, silence, and long-term performance.
He is a member of the inaugural cohort of the Board of Directors Programme at Cambridge Judge Business School. He also developed the Board Career Framework (tonedornot.co.uk), a practical tool designed to support both aspiring and experienced independent directors in thinking more deliberately about board readiness and development.
The views expressed here are his own and are not written on behalf of any organisation.
Inspiring Leader Insights: Driving Impact Through Leadership
This blog, written by Nathaniel Hancock, Public Sector Strategist, shares insights from his experience completing the Cambridge General Management Certificate of Achievement, during which he completed the following Executive Education programmes: People & Organisational Effectiveness Transformational Leadership, Strategic Decision-Making for Leaders and Creating High Performance Teams.
Nathaniel has been on an inspiring learning journey and we extend our huge congratulations to him on this outstanding achievement. This post is part of the Executive Education Inspiring Leader series.
Nathaniel Hancock, GMCA Alumnus
Before joining our programmes, what were the biggest challenges you faced in your industry?
After two decades working in the American public sector, one challenge that stands out is helping others “remove the friction for connection,” as Executive Coach Jamesina Sainsbury teaches in the CJBS People and Organisational Effectiveness programme. For civil servants, the need to modernise must constantly battle with bureaucratic inertia; motivation often stems from a desire for public service rather than from notions of profit or competition; and performance management can be hamstrung by powerful tendencies towards risk aversion. These are but three areas where the public arena sometimes faces dilemmas that differ from those of the private sphere.
After completing the programmes, what has had the biggest impact on you, your team, or your organisation?
Thankfully, the world-class faculty and speakers at CJBS possess centuries of collective experience listening to and carefully advising executives from private industry and government alike. Because of this (and due to their exceptional presentation and facilitation skills), the professional development gained from my General Management Certificate of Achievement courses at CJBS far exceeded my expectations and translated directly into mission outcomes.
As a leader, what skills do you believe will be most valuable for professional and organisational growth in the coming year?
In addition to the focus on breaking down communication barriers at work, my CJBS experiences highlighted the need to (1) balance efficiency with innovation, and (2) never underestimate the power of the organisation’s underlying culture to shape mindsets, processes, and outcomes. Returning to the office armed with these indelible impressions resulted in the launch of new communications approaches and strategies to increase historical analysis on the one hand and enhanced future investment on the other. I cannot thank the trusted CJBS faculty and course developers enough for their investment in our success. Bravo!
Embark on a transformative journey with the General Management Certificate of Achievement (GMCA) and move from functional specialist to confident, strategic leader. Create a bespoke learning pathway by choosing from 40+ programmes and complete 10 days of intensive study flexibly over two years. Learn more >
