Innovation leaders rarely fail because they lack ideas. They fail because they commit resources before they understand which assumptions carry the greatest risk and what evidence would justify the next decision. INNOVATION& helps executives, boards, founders and innovation teams decide what to start, fund, pivot, stop, defer or scale when certainty is unavailable. Start with the area closest to the decision you are facing.
Innovation Decisions
How should leaders evaluate initiatives, allocate innovation capital and distinguish activity from meaningful evidence?
Explore articles on innovation portfolios, governance, evidence thresholds, funding decisions and the choice to start, pivot, stop or scale.
Business Model Evidence
What must be true for a business model to create customer progress, attract demand and become financially viable?
Explore business model innovation, Jobs to Be Done, customer evidence, pricing, willingness to pay and business model validation.
AI & Innovation
Where does AI genuinely extend expertise, improve decisions or enable new value—and where does it merely make average output faster and cheaper?
Explore AI strategy, automation bias, expertise, accountability and AI-assisted innovation.
Continuous Business Model Innovation Cases
How do companies repeatedly adapt their value proposition, revenue logic, capabilities and market position as conditions change?
Explore chronological business model innovation cases and the decisions behind them.
PS: Looking for support with a real decision?
Explore keynotes, workshops and advisory for leadership teams making consequential innovation bets.
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INNOVATION& is not designed to help you consume more ideas. It is designed to help you make better decisions under uncertainty.
Some free resources for you:
You do not need to read the archive chronologically. Begin with the question closest to the decision you are facing.
Are we solving the right problem?
Start here when a team is already discussing products, technologies or features, but the progress the customer is trying to make remains unclear.
Begin with why early customer research should happen before commitment. It explains how concepts, budgets, and internal narratives can turn research from discovery into justification when it starts too late.
Why Jobs to Be Done Matters More in the Age of AI examines why faster functional output does not eliminate the emotional, social and contextual dimensions of customer choice.
For an applied industry case, read The Plant Does Not Need More Data. It Needs Better Decisions. It shows how industrial companies can mistake connected products and dashboards for customer value when the real job is improving operational decisions.
Its paid companion, Decision Memo: The Plant Does Not Need More Data. It Needs Better Decisions., provides a strategic commitment test for suppliers deciding whether to move from connected products toward decision infrastructure.
Which assumptions are carrying the opportunity?
Start here when a founder or innovation team has a strong vision but has not made the conditions beneath it sufficiently visible.
Examine how premature company-building turns assumptions into facts. The article shows why teams can acquire budgets, roadmaps, roles, and a shared identity before market evidence has justified greater commitment.
The Founder’s Forward Bias examines the tension between the conviction required to create a different future and the disciplined doubt required to recognize when that future is not emerging.
Why Startups Really Fail: Looking at the Root Causes moves beyond visible symptoms such as running out of money and examines the deeper assumptions about problems, buyers, demand, business models and ecosystems that often fail much earlier.
To examine the opportunity as a connected system, read why business model validation must test the whole system. It connects customer progress, switching, revenue, delivery, capabilities, and economics instead of treating one positive signal as validation.
Read these with one question in mind:
What would have to be true for our preferred future to become real—and which of those conditions have we actually tested?
What does the evidence really support?
Start here when a team has produced interviews, prototypes, experiments, research summaries or pilot results, but the decision has not become clearer.
Innovation Automation Is Here. Now Sponsors Must Raise the Evidence Standard explains why AI can make innovation artifacts cheaper without making customer evidence stronger. It separates faster production from genuine uncertainty reduction.
For AI-assisted discovery specifically, Synthetic Users Are Useful. Synthetic Customers Are the Problem. shows why simulated feedback can sharpen hypotheses but cannot establish demand.
Then examine how AI exposes OKRs that reward output instead of outcomes. It shows why stage-appropriate evidence must define progress when reports, prototypes, and analyses become cheap to produce.
This path is especially relevant when a project arrives at a governance gate with a polished story, impressive activity and no clear answer to:
What did we learn that should change the funding decision?
For the governance implication, examine why innovation decisions must be judged separately from their outcomes. Luck and hindsight can make disciplined decisions look weak while making poorly reasoned decisions appear justified.
Is AI improving the strategy—or protecting it from scrutiny?
Start here when AI investment is being justified through productivity, efficiency, adoption or faster output.
Decision Memo: AI Is Not a Productivity Tool. It Is a Strategy Test. examines whether AI strengthens the current operating model, exposes its limits or merely helps the organization avoid questioning it.
The central question is not only whether AI makes work faster.
It is whether the faster work still matters.
This distinction is useful before funding another assistant, copilot, agent, automation initiative, or organization-wide AI program.
Should we stop—and what should survive?
Start here when an initiative is being closed, has lost funding or has reached the point where continuation is difficult to justify.
Before closing the initiative, examine when weak traction stops being ambiguity and becomes evidence. The article helps distinguish a difficult signal that can still produce learning from repeated effort that mainly preserves interpretive freedom.
The Ruins of Innovation examines why organizations frequently stop innovation projects without preserving the knowledge, technical assets, relationships and capabilities those projects created.
It challenges the binary language of success and failure.
A stopped initiative may still have reduced future uncertainty, exposed an invalid assumption, developed reusable capabilities or prevented a much larger commitment. That value exists only when the organization can identify and preserve it.
Read it before the project disappears into a final presentation and the people who learned the most are dispersed.