
From peanut butter to effective policy impact
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We don鈥檛 just react to the world, we model it. We look for patterns, test assumptions and decide where to place our effort. This is an idea that鈥檚 easy to like, but I鈥檝e learned how readily those models warp under pressure. Keep saying yes, keep moving, hope momentum will turn into progress. It often doesn鈥檛.
That was my mistake at . On paper, everything looked promising: strong civic partnerships, direct access to policymakers, visible support from leadership. But I was everywhere and going nowhere.
In academia and policy, I discovered that friction is often mistaken for failure. So, I decided to assume the opposite: that many problems persist because we tackle them at the wrong scale. I began to act as if most problems are local until proven otherwise. In this case, 鈥渓ocal鈥 means close enough to understand, fast enough to learn from, and relevant enough to act on. And if I can act, I鈥檓 not stuck.
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Then I recognised the pattern. I was spreading myself too thinly. Like Yahoo! in the now-famous , I was doing too much, too evenly, and excelling at nothing. The principle that helps individuals focus applies to institutions, too. Strategy isn鈥檛 the sum of everything you鈥檙e willing to do, it鈥檚 the few things you choose to do instead.
Reframing my approach, I stopped asking: 鈥淚s this project worthwhile?鈥 and started asking: 鈥淚s this mine to do?鈥 That subtle shift re-centred my attention from broad ambition to accountable action. I didn鈥檛 build another strategy document; I built a working interface: a bridge between research and decision-making. That meant aligning what the university already had 鈥 our databases, our expertise 鈥 with the needs right in front of me. I mapped internal assets such as and to local priorities: freight emissions, digital inclusion, skills gaps. Then I reshaped research outputs into policy-ready briefs, not for compliance but for consequence.
That shift in mindset helped me see Southampton鈥檚 assets differently. Wind tunnels, data collectives and high-voltage labs viewed narrowly are academic facilities. But seen through a civic lens, they鈥檙e infrastructure for the region. When I started presenting them that way, it changed the conversation. We weren鈥檛 just asking for collaboration; we were inviting joint stewardship. Our partners became co-investors in shared capability, tools with public purpose.
The same logic applies to our AI strengths:, and. These aren鈥檛 just research themes, they鈥檙e tools for governing regional development, if we chose to use them that way.
In both Westminster and academia, it鈥檚 easy to keep adding functions: skills, growth, innovation, civic pride. But good strategy resists this temptation. That鈥檚 the core of David Willetts鈥 recent provocation: value isn鈥檛 something we whisper about in spreadsheets. It鈥檚 something we demonstrate through trade-offs.
That鈥檚 why I now use a simple tool: a one-page What I鈥檓 Not Doing This Year. It started as a personal exercise, distinguishing political compliance from genuine commitment. But it became institutional. It provides the language to make trade-offs visible: turning down funding lines that dilute focus, protecting reflective time for early career researchers, and rebalancing team responsibilities under pressure.
This is what coherence looks like, not doing everything for everyone, but doing the few things that matter most and doing them well. It鈥檚 a form of radicalism that isn鈥檛 nostalgic or defensive: it鈥檚 hyper-focused, place-based and open-eyed.
By building these tools in partnership with the city council and logistics firms, we鈥檙e not merely deploying technology, we鈥檙e shaping the region鈥檚 capacity to meet its net zero carbon goals. We鈥檝e reimagined our as an innovation engine, not a passive landlord. It鈥檚 a place where university researchers and company engineers co-create solutions in real time. And we鈥檝e made our work in , long a research strength, the backbone of how these systems are governed. At the , our ethicists and data scientists work side by side to define the values and constraints that shape what AI should do, not just what it can do. That鈥檚 what coherence looks like, too.
The peanut butter metaphor works because it names something real: the paralysis of vague ambition. I saw first-hand the kind of institutional paralysis that comes from trying to do a bit of everything, spreading efforts so thin that nothing sticks. The cure was a kind of radical focus. And in writing about strategy, I believe the same principle applies: we should speak plainly, say what we mean, and sound like ourselves. An op-ed like this should feel rigorous in thought but resonant in tone. Ideally, it leaves the reader with both an insight and a feeling.
For me, that insight is about focus. If we鈥檙e serious about delivering on grand initiatives, our university鈥檚 much-touted partnership of academia, industry and government, we have to be equally serious about the trade-offs they demand. We have to be willing to name what we won鈥檛 do, to show what we choose not to pursue, and to repeat that message until it becomes cultural muscle memory.
Alistair Sackley is specialist policy officer in the Web Science Institute at the University of Southampton.
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