How a board should govern innovation it cannot predict (6/6)

Supervisory boards are designed to control risk, ensure accountability, and protect long term value. That logic works well for established businesses whose performance can be forecast and audited, the typical public company or private equity owned shop. 

Transformation initiatives are different. Their outcomes cannot be predicted, only managed probabilistically. 

When both activities are governed identically, boards unintentionally force management to either hide uncertainty or avoid it. Core governance expects reliable plans, budget adherence, and variance explanations. Venture activity produces evolving plans, changing strategy, and learning instead of predictability. So management adapts and presents exploration as execution which leads to late surprises, sudden write-offs, loss of trust. 

A board should never approve innovation projects. They cannot evaluate uncertain outcomes. Instead, they can evaluate whether uncertainty is being reduced responsibly and should ensure capital exposure is bounded, learning velocity is high, and escalation rules are clear. 

A board should only oversee three things:

1. Portfolio exposure
   How much capital is at risk simultaneously
   
   
2. Funding discipline
   Continuation only after evidence
   
   
3. Separation of logic
   Exploration metrics that are not mixed with operational KPIs
   
   

Board should never ever select ideas, approve technical directions, demand detailed forecasts, integrate ventures prematurely. These actions destroy optionality. 

But when governed correctly, downside is limited, upside remains uncapped, and management credibility increases. Transformation becomes auditable without becoming a foregone conclusion. 

No industrial company fails to transform because they lack ideas, talent, or technology. 

They fail because they apply operational governance to exploratory activities. 

Once the board governs uncertainty instead of trying to eliminate it, transformation stops being a gamble and becomes a managed asset class. 

Why industrial startups don't scale like software, and why that matters for corporate strategy (5/6)

Over the last decade, industrial companies have tried to adopt startup methods like agile teams, MVPs, incubator and venture units with the expectation that small teams could rapidly build scalable businesses, just like in software. Yet most industrial digital initiatives plateaued in the same place where successful pilots never became large businesses. The explanation is usually organizational. But the pattern is too consistent to be cultural. 

In SaaS, the sequence was to build product, find users, scale distribution and optimize economics. In industrial systems the sequence is to prove technical reliability in reality, integrate into operations, earn organization trust, and scale commercially. Applying the former to the latter leads to the stereotypical "pilot purgatory', and the product market fit only comes later. 

The Lean Startup thinking has popularized the concept of a minimum viable product. And in fact, the advent of LLMs, vibe coding and agents have made building software MVPs easier than ever. But an industrial MVP still requires safety acceptance, workflow integration, downtime risk, and operator trust. Learning cycles are constrained by operations, not coding speed and the experimentation bandwidth massively narrows. As opposed to software which scales with distribution, industrial solutions scale with deployment capacity from integrators, commissioning, change management, training, and liability acceptance.
Therefore growth is stepwise, not exponential.

Because risk reduces differently in industrial systems, stage gates must evaluate different evidence. Early stages must mitigate technical and operational risk, later stages market and commercial risk. 

Industrial transformation will not be driven by apps on top of factories. It will instead be driven by systems that cross the boundary between software and operations. And the highest value companies in the next industrial cycle will control operational feedback loops, not dashboards. 

Next article (6/6)

Board governance for industrial transformation - managing two business models inside one company.

How to run a corporate venture portfolio without becoming a venture capital firm (4/6)

Operational companies run on annual planning cycles. 

Venture building runs on learning cycles. 

Trying to manage the second with the first results in one of two predictable outcomes. Either the initiatives are frozen by planning, or they run outside governance entirely. 

The solution does not lie in creating a new department, but in implementing a different decision cadence that funds increasingly expensive questions

1. Technical feasibility
   
   
2. Real customer usage (-> viability)
   
   
3. Repeatable deployment (-> product market fit)
   
   
4. Scalable economics

Funding should not be a commitment to success belief, but a purchase of evidence. Capital should only be provided when uncertainty decreases. 

Most companies have steering committees, project reviews, and budget committees to evaluation progress against plan. In contrast, a venture forum evaluates risk versus evidence and is characterized as follows

• Frequent meetings (6-10 weeks)
  
  
• Very small group 
  
  
• Authority to stop initiatives
  
  
• No project ownership
  
  
• Compares initiatives against each other
  
  

This forum manages allocations, not execution. It is making kill decisions based on transparent stop conditions: 

• No repeatable customer usage
  
  
• No technical feasibility
  
  
• No economic faith
  

Because the conditions are agreed beforehand, stopping is not political. And the organization can celebrate disciplined termination more than heroic persistence. 

This requires a clear delineation of roles and responsibilities. Leadership controls the funding continuation, the strategic direction, and the portfolio size. The teams control the solution, the iterations and the learning speed. Corporate burden is reduced and outcomes are improved. 

The role of the executive changes from approving projects to continuously reallocating capital. 


All of this may sound like software venture building, but industrial initiatives behave differently:

• hardware dependencies
  
  
• pilot purgatory
  
  
• deployment friction
  
  
• long validation cycles
  
  

and so the operating model must adapt. 


Next article (5/6)

Why industrial ventures are structurally different from software - and why most corporate playbooks fail here 

Innovation culture doesn't exist - governance and incentives do (3/6)

When industrial innovation initiatives stall, the diagnosis is almost always cultural: risk aversion, silo thinking, lack of entrepreneurial mindset. 

This diagnosis is not wrong, and companies respond predictably by launching training programs, incubators, intrapreneurships, innovation days et cetera et cetera et cetera. And yet the same patterns reappear where promising initiatives expand too early, weak ones linger, and transformational ones rarely scale.

If behavior does not change after repeated cultural interventions, the cause is probably structural rather than psychological. Managers optimize for what is rewarded - approval, budget adherence, and avoiding visible failure - and thus produce optimistic forecasts, broad roadmaps and incremental proof. Clearly this is rational behavior, and the organization punishes the alternative. 

This governance creates a doom loop: An upfront business case plan is created with massive amounts of detail and the team commitments to such plan. But then the evidence contradicts the plan and the team is forced to defend rather than adapt. The funding continues to protect prior decisions, and terminations happen late and are expensive. It is the ultimate irony that nobody chose this insane path, but the governance enforced it. 

There is a better way. Instead of asking people to think like entrepreneurs, the corporation should adopt a system with three mechanisms which reward and entrepreneurial behavior:  

1. Mile stone funding focused on progress
   
   
2. Predefined kill criteria that make stopping a successful outcome
   
   
3. An independent decision forum to separate continuation decisions from project sponsors
   

An organization will always behave rationally within the rules it is given.
The leaderships job is not selecting ideas, it is designing decision environments.

Most companies understand they need a portfolio logic, but few understand how to operationalize it without losing control. 


Next article (4/6)

How to actually run a corporate venture portfolio

Every industrial company already runs a venture fund - just without the rules (2/6)

If innovation outcomes cannot be predicted upfront, funding decisions have to stop being project approvals and become probabilistic bets. At that moment, the management domain changes from operational planning to portfolio allocations. 

Yet most companies never acknowledge the transition. They still ask each initiative to justify itself individually, even though statistically only a minority of ventures will succeed. In this obviously paradoxical world, they demand certainty from activities whose economics are defined by uncertainty. 

In an industrial world, machines are expected to perform as planned. Variation is noise. Deviation is undesirable. Contrast that with new technology ventures where most create little to no value, and only very few lead to disproportionate outcomes. And if returns concentrate in very few successes, evaluating initiatives individually guarantees systematic underinvestment in winners and overinvestment in mediocrity. 

Projects are being evaluated by expected ROI, NPV and payback periods. Those metrics are appropriate when variance is small. But with high uncertainty, the expected value does not equal the average outcome and managers unintentionally end up favoring safe outcomes, resulting in incremental projects being passed and transformational ones being failed. 

Funding innovation outcomes requires a mental shift from funding initiatives to funding learning paths where capital is released to answer progressively expensive questions: 

1. Is it technically possible? 
   
   
2. Does anyone need it? 
   
   
3. Will they pay? 
   
   
4. Can it scale? 
   
   

Because success is concentrated, funding decision must compare options, not plans. Persistence without evidence of progress and customer traction destroys returns, killing projects early increases returns. The purpose of an innovation portfolio is not to avoid failure, but to make failure cheap enough so that success becomes more likely. 

Once innovation behaves like a portfolio, the organization needs new rules: 

• Who decides continuation? 
  
  
• When is stopping success? 
  
  
• How independent can the teams be? 
  
  
• What replaces annual budgets?

Those questions are governance questions, not innovation questions.


Next article (3/6)

Why culture programs fail and governance determines behaviors
 

Digital initiatives don’t fail — capital allocation does (1/6)

Industrial innovation companies have more innovation activity than ever - accelerators, labs, venture arms, partnerships - yet the economic impact remains marginal. Yet, years later, most executives struggle to point to measurable impact on growth or valuation. 

The usual explanations are cultural: risk aversion, legacy thinking, slow decision making. But those explanations don't survive scrutiny. The same organizations successfully execute billion dollar plant expansions and platform transitions. 

The real difference is more mundane: Industrial innovation inside corporations is financed like a product development program, economically is behaves like a venture investment. And it is this very mismatch that determines the outcome long before the technology reaches maturity. 


The vast majority of executives are being told they are running a portfolio, but in reality they are managing a bunch of projects and apply a project logic consisting of fixed budgets and predefined deliverables where success is measured against plan and deviations are branded as failures. Often corporate innovation projects are expected to prove they work even before funding.

Venture logic of staged capital, frequent pivots, and unknown outcomes is fundamentally different where success is measured by learning and termination is success if risk is removed. The ventures are funded to discover whether something works.

The symptoms are only to well known and are directly related to the lack of stage gates and kill criteria. Because companies require certainty upfront, business cases are inflated, teams optimize for approval and not truth, cancellations get pushed off, and scaling happens either too later or never. 

The most common questions I have encountered is "how do we make innovation succeed in my company?".

The better question would be "how should we govern investments with unknown outcomes"?

What Industrial Leaders Should Do Now About Humanoid Robots

In the recent interview Elon Musk gave on the Cheeky Pint + Dwarkesh podcast, he laid out the path forward for Tesla’s Optimus humanoid robot. 

That path also translates into five practical implications for industrial leaders who want to utilize humanoids

• First, map your 24/7 tasks.
  Humanoids will enter where operations are continuous, repetitive, and stable. Start where uptime matters and variability is low.
  
  

• Second, identify hand-complexity tasks.
  If a job requires dexterity but limited judgment, it is a prime candidate. Fine manipulation without deep contextual reasoning is the early sweet spot.
  
  

• Third, redesign future lines to be robot-friendly.
  Clearances, modular stations, standardized interfaces, digital twins. The factories that win will not retrofit blindly; they will architect for humanoid coexistence.
  
  

• Fourth, invest in simulation capability.
  Sim-to-real transfer is the gating constraint. Organizations that build internal simulation pipelines now will compress deployment cycles later.
  
  

• Fifth, secure exposure to critical components.
  Actuators, precision gears, motors, rare materials. Manufacturing scale — not AI demos — will determine who ramps first.

Points one, two, and three squarely fall into the hands of industrial leaders.
Point five is a selection criterion for their humanoid robotics providers.

The hardest problem for humanoids to be fully adopted is closing the sim-to-real gap and the scale required.

Musk was explicit about the magnitude of that challenge:

“For the robot, what we’re going to need to do is build a lot of robots and put them in a kind of Optimus Academy so they can do self-play in reality… We can have at least 10,000 Optimus robots, maybe 20–30,000, that are doing self-play and testing different tasks.”

Industrial leaders should ask themselves now: Which of our production systems are humanoid-ready - and which ones are structurally not?