# Stefan Michel

Stefan Michel writes about business strategy, differentiation, and practical ways for small and medium-sized businesses to explore AI. His examples include how a fastener company can compete by solving customers’ operational problems, and how businesses can investigate AI through focused, three-month projects.

- Profile: https://b2binfluencer.marketing/creators/stefan-michel/
- LinkedIn: https://www.linkedin.com/in/prof-stefan-michel
- LinkedIn followers: 41,224
- Topics: artificial-intelligence, ai-adoption, go-to-market
- Based on 9 dated posts, latest 2026-08-02

## Summary

Stefan Michel is most useful for B2B strategy thought leadership on differentiation and practical AI exploration.

- Best for: A B2B strategy consultancy campaign on differentiation and bounded AI adoption.
- Not for: Consumer brand campaigns needing guaranteed reach or a ready-made sponsorship package.
- Check first: Confirm partnership formats, requirements, rate card, and audience breakdown before contacting.

### Takeaways

- Bossard shows differentiation can come from inventory and engineering support.
- Small and medium businesses can use focused three-month AI exploration projects.
- A 1984 Dell earnings statement offers lessons on timing and route to market.

## Main ideas

- **No market is commoditized, because the margin sits around the product**: Michel's central argument is that a commodity market is a choice rather than a condition. Products that look interchangeable—water, gasoline, salt, screws—still support premium pricing and healthy margins. The difference is what the seller wraps around the product. In the fastener case, they argue that competing on unit price is the wrong battlefield; the real opportunity lies in operational costs buyers cannot easily see or manage themselves, such as inventory and engineering support.
- **When you cannot follow every trend, narrow the scope on purpose**: Michel's answer to small and medium-sized businesses asking how to keep up with AI is to stop trying to follow all of it. Instead, build dedicated teams of explorers, each with one clearly defined focus area and a three-month window to investigate how AI could reshape that part of the business. The narrow scope keeps the analysis deep and the workload manageable, rather than spreading limited resources across the whole landscape. The cycle ends with a decision, not a report: teams reconvene, the organization chooses which use cases to pursue and defines the focus areas for the next round.
- **Timing and route to market decide more than the product**: Michel reads company history as a basis for understanding business-model choices. In the Dell case, they argue that a new direct-to-consumer route to market made the business profitable almost immediately by cutting out several players in the value chain. Timing carries equal weight in their reading: 1984 was the right moment because demand for personal computers was surging and buyers already knew enough about specifications to order from a catalogue or online. They also draw a broader point: a single decision can change the course of a life, a lesson taught through a primary document rather than a success-story summary.

## Work to read

- [Bossard and the case against commodity markets](https://www.linkedin.com/posts/prof-stefan-michel_strategy-differentiation-marketing-activity-7487763913355374593-D5XQ)
- [AI exploration teams for small and medium-sized businesses](https://www.linkedin.com/posts/prof-stefan-michel_how-can-small-and-medium-sized-businesses-activity-7467520747591168000-Ripx)
- [Michael Dell's 1984 statement of earnings and three lessons on timing](https://www.linkedin.com/posts/prof-stefan-michel_entrepreneurship-strategy-startup-activity-7489583652293619712-t9Zs)

## Brand relationships

- **Bossard Group** (disclosed_partnership)

## Partnerships

The posts contain no sponsored-content disclosure, rate card, audience specification, or partnership intake link. One directs readers to a newsletter article and an IMD program page; another tags Bossard colleagues and industry names and describes twelve years on Bossard's board, a governance role rather than a paid engagement. Ask for partnership formats, requirements, and an inquiry route.

## Alternatives

- David Heinemeier Hansson: Choose David Heinemeier Hansson when the co-authored article should speak to founder-led software and independent businesses rather than manufacturers. Both posts argue that small teams can outperform larger ones when they accept constraints, that bootstrapping lets a business avoid the investor race, and that founder leadership keeps a company technically sharp. The Shopify post builds that case around a $6.2 billion Black Friday and the framework contributions the company funds. For a thought-leadership piece aimed at technical founders weighing outside capital against self-funding, these posts give the argument a tested, specific shape.
- Alfred White: Choose Alfred White when a campaign for small-business owners should reframe differentiation as risk reduction. The posts teach tax topics in a plain client-education voice: one uses a Willy Wonka story to argue that experts must walk clients through a process; the other lists five audit risk areas and ends with a concrete next step. The AI-and-differentiation theme would give way to compliance, documentation, and expense-discipline content, delivered as checklists and starting plans readers can act on immediately.
- Todd Gardner: Choose Todd Gardner when a campaign about AI should target SaaS finance leaders, CFOs, and investors rather than manufacturers. One post catalogues red flags from reviewing over a thousand SaaS financial statements, including gross-margin definition, bank reconciliation failures, mixed cash and accrual accounting, and MRR that does not tie to the income statement. The other asks whether AI resembles open source in its effect on application software and argues that service, implementation, and accountability still carry weight. Together, the posts support a co-authored argument about how AI changes software economics and what diligence teams will demand.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/