Picture this: a man lounges by his swimming pool as the last of the summer sun sets. In the distance is a town getting ready for evening ruckus. But here, poolside, the only noise is the low hum of the pool’s waterfilter pump accentuated by the occasional rustling of the man turning a page in his book.
That is me on holiday.

Which leads to the next question: what am I reading?

Well no matter what I’m reading this August, the following are worth reading at any point. And because AI is the new digital transformation, I’m adding in my thoughts on that too.

Good Strategy, Bad Strategy - Rumelt Link to heading

The essence of a good strategy is based on a kernel: a specific challenge that is diagnosed.
From that diagnosis you can start working out a focused approach. In other words, which angle of attack will you take to the diagnosed problem.
And lastly, once you have decided on the approach, lay out a set of coordinated actions that together can overcome the challenge.
If you’ve ever heard me speak on building a competitive advantage, there’s no doubt I’ll have mentioned a set of mutually reinforcing choices. This here is the idea behind that. It’s not enough to solve for a challenge. It’s solving for a challenge in a way that is more than the sum of the parts.

These days the world is full of leadership teams looking for an “AI strategy”. Some want to be an “AI First company” or use “AI to transform the business”. Those might make great aspirational statements, but they have a fundamental flaw. They’re not solving a particular challenge.
Those statements are implying that using a tool (AI) is the specific problem. That’s akin to saying we’re a “hammer first” company. And unless you’re in a world of nails, that might not get you very far.

And of course, the argument is that AI is more broadly applicable across the business than the proverbial hammer in search of a nail. But even in that scenario. Is diluting your attention and resources for “AI first” thinking really the best option? My money is still on a focused approach.
Ask yourself: where would leveraging AI really create a competitive advantage? Which set of actions and processes taken together could we exploit better?

In other words: understand systems first, and then add in AI as the leverage on focused action.

Competition Demystified - Greenwald Link to heading

Greenwald argues that most businesses fail because they compete in markets without sustainable competitive advantages. On the other hand, successful companies identify and exploit structural barriers that keep competitors out. There’s a hint of blue ocean strategy in that of course. But tracing the roots back even further you end up, as so often, with Porter’s 5 forces. The key insight here is that by focusing on the “barriers to entry” aspect of the 5 forces you can set yourself apart more than by focusing on the other 4. By building a strong moat, you avoid the strategic collapse that comes from too many competitors chasing operational efficiency for marginal gains.
And ultimately, any strong competitive advantage comes from the interplay of only 3 sources:

Supply costs: usually the result of a proprietary technical advantage in production or process, or economies of scale (see below). In other words, the result of finely honed experience.
Demand: which is about capturing customer habits, rather than product/brand differentiation
Economies of scale: in relative size between competitors, and necessary to capture consumers to exploit the advantage.

Looking at this in light of generative AI initiatives, or really any large-scale transformation, it’s important to understand how this creates a moat. Or alternatively, is it just operational efficiency focused?

The most useful projects are likely those where the company can bring a unique proprietary element. That might be data, inside knowledge thas is poorly captured currently, or other elements that are outside the public domain.
The advantage won’t come from “AI” itself, but rather from the solution to the problem it’s applied to. And through Greenwald’s lens that means:

does it make it harder for new entrants?
Does it make it harder for customers to leave?
And does it build up capabilities in the organization that take years of data and experience to replicate?

Good to Great - Collins Link to heading

Interesting, perhaps, for a book about strategy is the focus it puts on the people. Decide who you want on the bus, and then figure out together where the bus is going. If you select people who are driven, passionate, and competent they are capable of making the best decisions. That will beat anything else in Collin’s view.
With the right people on the bus you focus on “the hedgehog concept”, concentrating on the intersection of existing advantages, sources of revenue and core values of the organization. The latter being a function of selecting the right people to begin with obviously. From that should come a culture of self discipline aka self direction. Think of it as automatic alignment with core values to guide decisions, rather than relying on a bureaucratic regime. And combine that discipline with a flywheel effect of compounding small improvements in the proper direction. As opposed to a doom loop of reacting to outside events all the time.
As far as technology is concerned, technology is not the driver but instead is a tool to enhance core competency.

Taking this back to today’s challenges with AI. The key is to avoid all-out transformation initiatives but instead focus on creating a flywheel. Take a disciplined approach to let small changes compound. Get the right people the right tools to make that happen and focus on small experiments. And the nature of experiments means throw away what doesn’t work and ignore the noise of the outside.

Valuation - Koller Link to heading

This is more a reference manual than a book you’d read cover to cover. I wanted it on here because at heart a company’s value is a function of its ability to generate cash beyond the cost of capital. Everything else is dress up and postponing the inevitable.

If you decouple AI investment from some form of clear ROI calculation you’re certainly at risk of making a wrong decision. It’s one thing making an investment with delayed payoff, even if that is a wide range of possible returns. it’s a whole other making an investment without any idea of the payoff!

Closing thoughts Link to heading

Strategy is always rooted in choices. Making trade offs. And generally finding approaches to problems that are hard to replicate. All while being able to generate more cash than the cost of the investment, over an appropriate timeframe.
In that light, consider how and where AI can help your competitive position, what a focused approach would be and how you can gain momentum.

I hope you go and read these books, in whatever setup makes you most relaxed. Because a relaxed body and mind are best for deep thinking. Enjoy the summer.