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Two things are different about this edition.

First: Beyond Strategy is now published in English. Same content, same cadence, just easier to share across borders.

Second: We have news. Together with five other specialist partners, we've launched Modular Alpha, a curated network built specifically for PE value creation. Pre-vetted specialists across tech due diligence, AI transformation, engineering, product, and finance. Deployable at deal speed. No retainers, no conflicts of interest.

The idea is simple: traditional consulting wasn't built for private equity speed. Modular Alpha is. You engage one module or the entire value creation chain depending on what the deal requires. More about the network and the whitepaper is available here.

Most conversations about AI start with disruption. Which industries are at risk, which incumbents survive. That framing misses the more useful question: what does AI actually do to the growth mechanism of a business?

scaleon has mapped 16 business model archetypes into four patterns: Antifragile, Extractive, Resilient, and Fragile. The pattern a business falls into isn't a function of how exposed it is to AI. It's a function of whether it has a self-reinforcing growth core and what AI does to that core. The strategic implications are very different depending on which pattern applies. Read the full piece →

Imagine you're evaluating a company. Solid margins, decent growth, clear product. Competitive market, but the business holds its own.

What's missing from that analysis?

The question of what that company looks like when its closest competitor enters the market in 18 months at 40% lower cost. Not as a startup hypothesis. As a structural fact that AI-native businesses are building toward right now.

AI-native companies don't build more efficiently because they hire better developers. They build more efficiently because certain cost blocks, such as engineering capacity, customer service, content production, data preparation, don't scale linearly with revenue. This shifts the benchmark for an entire market. A portfolio company that ignores this doesn't slowly lose ground. At some point, it competes at a price level its cost structure was never designed for. Up to 80% of exit value can depend on it.

None of these are technical. All of them belong in the investment process.

Where in the P&L is the exposure? Which cost blocks become substitutable when a competitor deploys AI seriously? Which product features turn into commodity within the holding period, not eventually?

What does the status quo actually cost? Not in absolute terms. Relative to the cost base AI-native competitors are building right now. That's a different calculation than a standard benchmark analysis.

Who owns the P&L for AI initiatives post-close? If the answer is the technology team, that's not sufficient. Real EBITDA impact requires an owner in the business unit that captures the benefit.

If you're at SuperReturn International in Berlin (June 8–12), find us at Tiny Space #59.

Deploying Claude across your organization (link to pdf): How Anthropic uses Claude Cowork. A practical guide for deploying Claude Cowork across your business, with use cases, timelines, and lessons from Anthropic and other organizations.

Stefan Benndorf & Dr. Philipp Engelhardt
Founding Partners scaleon

scaleon
Value Creation for Digital Leaders. Build for growth.

The questions this newsletter raises are the same ones our clients bring to us. Which AI initiatives deserve a second round of investment? Where does P&L accountability for digital transformation actually sit? How does technology deployment translate into measurable business value?

scaleon works with CEOs and investors of digital businesses on exactly these questions — in growth strategy, operational management, and transaction preparation.

If anything in this edition is worth a conversation, we'd welcome a direct message.