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Distillations in this newsletter: The Bull Case for Strategy; The story of strategic innovation in an energy company; Key concepts in AI for strategists.
STRATEGY DISTILLED:
A monthly concoction of insight, learning and things you might have missed for anyone who works on strategy, works with strategy or just loves strategy.
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This month …
- The Bull Case for Strategy: Why developing expertise in strategy may be the best way to immunise your career against AI-disruption.
- Strategy snippets you might have missed: The story of strategic innovation in an energy company; Key concepts in AI for strategists.
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The Bull Case for Strategy
A short one but a deep one this month (well, I found it deep anyway!) and prompted by Trung Phan’s SatPost from a couple of weeks ago, reviewing an interview with Jensen Huang of Nvidia.
I’m going to set out the bull case for strategy but before we get stuck into that, let’s just be clear about what that means. A bull market is a growing market, typically referring to a market for stocks or other securities and typically lasting months or years. During a bull market, investor optimism and confidence is high. A bear market, by contrast is a declining market, associated with pessimism and negative investor sentiment. Correspondingly, a bull case is a positive or optimistic argument for why a market or stock or technology will increase in value, whereas a bear case argues negatively or pessimistically for why it might decline in value. So, I’m going to set out a positive, optimistic case for why developing professional skills in strategy is a good thing to do. Not just for people who want a job with ‘strategist’ in its title – but for everyone. I want to argue that the value of strategy as a way of thinking and working, and the value of people skilled in strategy, is going to rise hugely over the next 10 years. One implication of this bull case is that if you have any concerns about the future security of the type of work you currently do, invest whatever time you can, right now, into becoming highly skilled and rigorously professional in strategy.
In his interview, Jensen Huang explained that he employs world-class specialists as his direct reports at Nvidia. They are much better at what they do than he is. In order to manage them well, he needs to give them a clear sense of direction – what problem does he want them to solve or what opportunity does he want them to exploit? He also, however, needs to leave sufficient ambiguity in his instructions to give them creative space to operate in. What is the point in employing world-class experts if you tell them how to do their jobs? Giving direction but with room for creativity, he argues, enables innovation.
Huang then goes on to propose that what he is actually doing with his direct reports is good ‘prompt engineering’ (the creation of ‘prompts’ to give direction for tasks you want done) and this is no different from what needs to be done the new world of AI-supported work. He suggests that we all need to become the “CEOs” of the AI agents we work with. This, he surmises, will involve developing a rich and well-informed understanding of problems and opportunities related to your work. This understanding, in turn, will enable you to give clear direction within your prompts. Then you need to know enough about the work involved to specify the necessary guardrails, without over-specifying the work and stifling creativity.
This, to me, seems like a pretty good definition of thinking and working strategically. Direction-setting (strategy) followed by the right guidance on how to take action in the specified direction, which ensures compliance whilst leaving room for creativity (strategic planning).
My bull case for strategy, therefore is that everyone ought to learn how to think strategically. I see that as a core skill for the 21st Century – so much so that I would love to see it incorporated into the educational system for all young people in the later years of their school-education. For those of us who are in work currently, and perhaps concerned about the extent to which our current jobs might be disrupted by AI in the future, I would say hone your skills as a professional strategist. Think strategically. Act strategically. And talk strategically with your peers and leaders. AIs will be taking on more and more of the work that we, as professional knowledge workers, used to call our own. But they are still going to need to be prompted on what work to do and how best to perform it for a long time to come.
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Strategy snippets you might have missed
Strategic innovation in an energy company
Enel used to be Italy’s national electricity utility but, following privatisation in 1999, has become a global utilities company operating across 30 countries. A recent HBR podcast explored its recent transition to a leading green energy pioneer. This involved it making key strategic decisions about which aspects of green energy production and distribution they were going to keep in-house and which they were going to try to out-source to partner organisations. Included in their out-sourcing shopping list was software development and power management and, in pursuit of such partnerships, they launched a website where any supplier could propose solutions to the challenges Enel set out on this website. This was how a six-person spin-out from the University of Delaware, with zero revenue at the time, signed a partnership agreement with Enel for software to “create a two-way connection between an electric car battery and the power grid so that you can not only charge the battery, but you can draw power from the battery to help balance the grid.” As a result of this partnership, Enel discovered that by accessing 5% of the storage capacity of 100,000 Teslas, they could balance the energy requirements of the entire city of Rome!
Key concepts in AI for strategists
With the speed at which AI technologies are developing, it is hard to know which are the key concepts that we as strategists need to understand – so here are two that I’ve recently found useful:
- Accelerated computing: is the use of specialised hardware to dramatically speed up work, using parallel processing that bundles frequently occurring tasks. As an interesting aside, accelerated computing has led to increases in computing power that substantially exceed Moore’s Law over the past 10 years.
- Agentic AI: uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. It’s the type of AI that creates autonomous agents to tackle specific categories of tasks.
If you’ve come across any key concepts in AI that you think strategists need to know about, send them in and I’ll feature them in next month’s newsletter.
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We, at Goal Atlas, work as strategy facilitators. We provide frameworks, tools and hands-on advice and guidance to support your leaders, managers and front-line teams to develop the strategy that works for you.
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