RA LAB / LEADERSHIP DECISIONS

Why Leaders Win, Fail and Transform

Decisions that changed companies. We examine the evidence and turn the lessons into experiments for Retail, B2B and Service.

All cases ↓

Independent editorial analysis of public sources. Facts, interpretations and hypotheses are distinguished. These are not our project results or claims of affiliation.

When Leaders Fail

Signals, constraints and the cost of a delayed decision.

Leaders Who Transform

How strategy, culture and revenue models change together.

Growth Systems

Recurring value, customer journeys and hypothesis testing.

Human × AI

AI efficiency, quality and human accountability.

Start with this decision / NOKIA

Nokia: when the basis of competition changes

How competition shifts from a strong device to a platform. Explore Nokia’s decisions and turn the lesson into a testable business hypothesis.

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All cases

Select two cases to compare their decisions

Microsoft2014–202602

Microsoft: How Satya Nadella Linked Culture and Strategic Renewal

What changes first: strategy, or the culture that must execute it? Microsoft's renewal suggests that the question is often a false choice.

Leaders Who TransformHuman × AI

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Kolesa Group1996–202608

Kolesa Group: From a Newspaper to a Digital Platform in Kazakhstan

Kolesa Group's story asks whether the product was a newspaper or a way for buyers and sellers to find one another. A durable customer task can survive a change of medium.

Leaders Who TransformGrowth Systems

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Decision comparison

Select two cases to compare their decisions · 0/2

INSIGHT → ACTION

What are you trying to solve?

Move from a company story to a practical route for your business.

SalesLeads arrive, but deals do not close quickly →RetentionCustomers buy once and do not return →Technology transitionWhat is holding business growth back →AI valueHow to choose an AI pilot with measurable value →CostsSales are growing, but profit is not →New business modelHow to enter a market with a clear offer →

Case → insight → constraint → hypothesis → experiment → outcome

How we connect diagnosis to an experiment ↗