Most disruption stories are told after the outcome is already known. Kodak missed digital photography. Blockbuster underestimated streaming. Looking back, the writing was on the wall for both companies.

Yet that is only because the market has already rendered its verdict.

While disruption is unfolding, the picture is far less clear. New technologies often look inferior, niche, unreliable, or strategically irrelevant. Their limitations are easier to see than their potential. That is why even smart, experienced executives struggle to recognize disruption before it becomes undeniable.

Which is what makes the current debate in Europe’s defense industry so interesting.

When Rheinmetall CEO Armin Papperger questioned the strategic significance of low-cost Ukrainian drones, many interpreted the comments as a disagreement about military technology or simply a CEO defending his company’s position.

Perhaps.But disruption stories rarely begin with a debate about technology. They begin with a debate about the economic model.

Kodak’s first digital cameras were inferior to film. Early streaming services offered less content than video stores. The first smartphones looked unimpressive compared with the devices they would eventually replace.

What changed was not the technology first. It was the economics. That is why drones deserve attention. Not because they are superior to tanks, artillery systems, or advanced missile platforms. They are not. But they may be changing the economics of warfare.

In Ukraine, low-cost drone systems have demonstrated an ability to generate battlefield effects that previously required far more expensive assets. A drone costing hundreds or thousands of euros can damage or destroy systems worth millions or incapacitated an infantryman that took months and thousands of euros to train.

Similar patterns have appeared elsewhere. Recent conflicts in the Middle East have shown how relatively inexpensive drones can threaten assets that cost orders of magnitude more to build, operate, and defend.

For years, military value has largely been associated with sophistication: better engineering, greater precision, more advanced technology.

Drones introduce a different dimension. They force planners to think about the cost of achieving the objective.

A tank, an artillery system, and a drone are not competing products. They are competing ways of producing a battlefield effect, achieving the “job to be done“.

When one approach begins achieving meaningful results at a fraction of the cost, attention should follow. Not because the incumbent solution immediately becomes obsolete. But because the basis of competition may be starting to shift.

This is where established organizations often struggle. The challenge is rarely awareness. Kodak knew digital photography existed. Blockbuster knew streaming existed.

The challenge for incumbents is overcoming the gravitational pull of what made them successful.

Successful organizations are designed to invest in proven capabilities. They improve existing products. They optimize existing business models. They become increasingly efficient at serving existing customers.

That logic is rational. It is also why disruption is so difficult to navigate.

Today, Europe’s defense ecosystem is naturally oriented toward highly sophisticated platforms, long development cycles, and procurement systems built around the assumptions of a previous era of warfare.

Signs of tension are beginning to emerge from within that system.

EU Defence Commissioner Andrius Kubilius recently criticized Europe’s tendency to produce what he called “haute couture” weapons – highly sophisticated systems optimized for technical excellence but often difficult to manufacture at scale and in meaningful quantities.

His concern was not that these systems lack capability. It was that the definition of military value may be changing faster than the institutions responsible for funding it.

The same tension appears in discussions about future industrial capacity.

Recent reports about Volkswagen’s defense-related manufacturing discussions have focused on interceptor production. Europe unquestionably needs stronger air-defense capabilities.

But the discussion highlights a broader issue. If the cost of attacking continues to fall dramatically while the cost of defending remains high, the economics of defence will take a central position.

That is not simply a military question. It is a resource-allocation question. And resource allocation is where disruption ultimately becomes visible.

The timing makes the issue difficult to ignore.

Through programs such as SAFE and broader rearmament initiatives, Europe is preparing to commit hundreds of billions of euros to defense capabilities and industrial capacity. The choices made today will shape Europe’s defense portfolio for years, if not decades.

When industries are disrupted, the early signals rarely look dramatic. They often look like isolated anomalies, niche technologies, or debates between people who disagree about the future.

Only later do they reveal themselves as the beginning of something larger. That is why the most important question facing Europe’s defense leaders is not whether drones matter.

The battlefield has already answered that.

The more important question is whether they are paying attention to the same signals that, in hindsight, every disrupted industry wishes it had recognized sooner.

The difference is that this time more is at stake than the future of a company.

This article was originally posted on The Next Web

The post Europe’s defense industry may be facing a Kodak moment appeared first on The Corporate Startup.

This frustration is everywhere.

Every company we have worked with or spoken to had some form of stage-gate process in place. And almost all of them complained about it. They complained about the bureaucracy. They complained about the speed. They complained about the quality of the decisions. Most importantly, they complained about the results.

In many organizations, governance cadence becomes disconnected from learning cadence, to the point where governance itself is often seen as little more than a bureaucratic layer.

Yet stage-gate governance exists for a reason.

Best practice tells us that companies should manage innovation through stage-gate reviews. In fact, the logic behind many modern stage-gate systems can be traced back to NASA’s Technology Readiness Level (TRL) framework — a governance model designed to reduce uncertainty and increase the probability of mission success. Over time, variations of this approach spread across industries and became standard practice for managing innovation.

In practice however, teams work for a predefined number of weeks or months and then present their progress in a formal review meeting where leadership makes a binary decision: progress the project to the next gate or stop it.

But this is also exactly what many people point to when they describe innovation governance as bureaucratic and low-value.

As much as people like to blame stage gates themselves, the problem is not the gates. The problem is twofold.

First problem

First, these meetings often become the only moments when leadership engages with teams, reviews evidence, and makes decisions. Governance becomes episodic, while learning and idea validation happen continuously. Over time, this creates poor outcomes: high cost of failure, low conversion rates, slow decision-making, and long time to market.

Second problem

Second, this setup pushes teams to optimize for the review meeting instead of for learning. Teams spend time preparing presentations, polishing narratives, and managing stakeholders’ perception instead of openly discussing risks, uncertainty, and what they are actually learning.

The goal slowly shifts from “building evidence” to “looking ready” for the gate.

Anyone who has worked on bringing a new product to market knows that ideas do not evolve in predefined intervals. Problems appear early. Assumptions change weekly. New information constantly reshapes direction.

But when governance only happens at review moments and decisions remain binary, learning gets delayed, compressed, and packaged for presentation.

This challenge is becoming even more visible in today’s AI race. Companies are launching hundreds of pilots and proofs of concept, yet many struggle to scale them into real businesses because governance systems cannot keep pace with the speed of learning and experimentation. The issue is no longer a lack of ideas. It is the inability to govern uncertainty at the pace innovation now moves.

The fix is not to remove stage gates and hand innovation teams a blank check. That usually leads to even worse outcomes.

First fix

The first fix is to create a continuous governance cadence between the gates — not by adding more bureaucracy, but by replacing high-pressure review events with smaller, faster, lower-friction decision loops.

This is where Venture Boards become useful.

Unlike traditional gate reviews, Venture Boards are not designed around presentation updates or milestone reporting. They are designed around evidence and learning.

Instead of meeting only when teams believe they are “ready” to progress, or when an arbitrary assigned timebox expires, leadership engages with teams through short, regular working sessions focused on a few simple questions:

• What have we learned since the last meeting?
• Which assumptions were validated or invalidated?
• What are the biggest risks today?
• What evidence do we still need before making a larger investment decision?

This changes the dynamic completely.

Teams no longer spend weeks preparing for one high-stakes presentation. Instead, evidence, uncertainty, and obstacles are discussed continuously and in smaller increments. Leadership stays connected to the real progress of the initiative, while teams get faster feedback and earlier support when problems emerge.

Most importantly, governance starts operating at the same speed as learning.

That does not mean every organization should adopt the same meeting cadence. The right frequency depends on the industry, the maturity of the initiative, and the operating context of the team. A biotech team working part-time on a highly regulated innovation effort may only generate meaningful learning every few weeks or months. A full-time team in retail banking, FMCG, or software may generate actionable insights every few days.

The cadence of governance should therefore not be driven by calendar-based reporting cycles, but by the speed at which teams can realistically generate and validate learning.

Instead of waiting months to discover that a key assumption was wrong, organizations can adjust direction continuously as new evidence appears.

The result is not more governance. In most cases, it is actually less bureaucracy, less politics, and better decisions.

Second fix

The second fix is to introduce a third decision option besides “stop” or “progress”: “persevere.”

This sounds simple, but in practice it requires a significant mindset shift.

I still remember one of our early Venture Building meetings with a large energy company. After every team presentation, leadership instinctively wanted to push the initiative forward to the next stage. The idea of “persevere” initially made no sense to them. If the team was working hard and making progress, why not progress the project?

But over time, through training and repeated reinforcement, leadership started understanding the difference between activity and evidence. A team could be working exceptionally well, generating valuable learning, and reducing uncertainty — while still not having enough evidence to justify progression to the next gate.

Today, that same organization actively uses the persevere option as part of its governance process.

Persevere acknowledges that the team is making progress and generating valuable learning, but that there is not yet enough evidence to move to the next gate. This shifts the focus from defending projects to developing evidence over time.

With these two relatively simple changes, stage gates stop being high-pressure performance events and become milestones that validate learning already discussed continuously. Teams spend less time optimizing PowerPoints and more time optimizing learning. Transparency increases. Decisions improve. Speed goes up. Waste goes down.

And over time, organizations start building something much more important than a governance process: a culture where evidence matters more than politics, learning matters more than certainty, and difficult conversations happen early instead of at the gate review.

A version of this article was initially published on the The Compass blog where I’m a regular contributor.

The post The Problem with Stage Gates Isn’t the Gates appeared first on The Corporate Startup.

Yet when it comes to go-to-market, many organizations revert to a different logic.

Pricing is set. Channels are selected. Positioning is defined. Sales motions are designed. And most of it is still treated as planning rather than experimentation.

The result is a structural mismatch in how companies manage uncertainty. Technical uncertainty is treated as something to be explored through iteration, while commercial uncertainty is treated as something that can be planned upfront. The implicit logic is that product decisions require learning, while commercialization decisions can be planned with sufficient confidence.

This logic rarely gets challenged. Yet it should be, because it directly influences how much value companies ultimately capture from the products they develop.

Unfortunately, the consequences of this logic usually become visible only after launch, when the first commercial results roll in. Companies discover that customers buy differently than expected, that pricing creates friction, that channels underperform, or that partners matter far more than anticipated. By then, key product, commercial, and organizational decisions are already locked in. The issue is not a lack of learning. It is that learning happens later and more expensively than it should, when the cost of pivoting based on new information is already high.

This way of operating is reinforced by organizational design. Product, marketing, sales, and business development are separated into distinct functions with their own budgets, incentives, and metrics. The separation creates clarity, but it also creates a handover point where responsibility shifts from experimentation to execution. The handover itself is not the problem. In many industries, such as pharmaceuticals, medical devices, chemicals, and biotechnology, it is both necessary and appropriate. The problem is what tends to follow it: a shift from testing assumptions to executing a plan.

At that point, many organizations move from structured experimentation in product development into structured execution in go-to-market. 

What is often missing is a comparable discipline of Commercial Learning—the systematic reduction of uncertainty around how a product reaches the market, creates value, and generates growth. Pricing, channels, positioning, buyer behavior, adoption patterns, retention dynamics, and business models are frequently treated as decisions to be made rather than hypotheses to be tested.

Imagine a product team proposing to launch a product without validating customer needs, testing the concept, or gathering market feedback. Most organizations would reject the idea immediately. Yet many of those same organizations are comfortable launching with largely untested assumptions about pricing, channels, adoption, customer acquisition, or retention. The inconsistency is striking.

This is not an argument against planning, specialization, or handovers. In complex organizations, all three are necessary. Nor is it an argument that go-to-market should always be integrated into product development. In some industries that may be both possible and desirable. In others, it may be impractical or unnecessary.

The real issue lies elsewhere.

Organizations have spent decades investing in Technical R&D. They have methodologies, governance structures, metrics, and review mechanisms designed to reduce uncertainty around desirability and feasibility. Few organizations have built an equivalent capability for Commercial Learning.

What those commercial uncertainties are will vary from product to product. For some, the largest uncertainty may be pricing. For others, it may be customer acquisition, channel effectiveness, retention, partner economics, purchasing triggers, or the viability of a subscription model. The objective is not to create a universal commercialization checklist. It is to identify the commercial assumptions that matter most, prioritize them based on uncertainty and potential impact, and systematically generate evidence before major commercial commitments are made.

Organizations that manage commercial uncertainty well already do this. Before committing to a pricing model, channel strategy, or commercialization approach, they run pilots, test partnerships, analyze competitive dynamics, gather market data, and conduct focused experiments. The objective is not to predict the market perfectly. It is to reduce uncertainty before scaling investments. It is to become a learning organization from one end of the process to the other end.

In this sense, commercialization should be managed much like product development. The output of go-to-market activities should not be plans alone. It should be evidence. Evidence that increases confidence in the assumptions that underpin the success of the products.

The question, therefore, is not when go-to-market begins.

The question is whether organizations have built a Commercial R&D capability that is as rigorous as the one they use for Technical R&D.

In markets where customer behavior, channels, and business models are evolving rapidly, this distinction should not be just theoretical. The companies that outperform will not simply be those that build better products. They will be those that learn faster about how to bring those products to market.

The post The Evolution of Go-to-Market: Building a Commercial Learning System appeared first on The Corporate Startup.

Should AI investments sit under IT because of the infrastructure and security implications? Should strategy own it, given AI’s potential to reshape business models and competitive positioning? Should innovation lead, as the team traditionally tasked with exploring emerging opportunities? 

Increasingly, some organizations are even creating dedicated AI leadership roles (Chief AI Officer) and separate AI functions to centralize responsibility.

While logical on the surface, this is ultimately the wrong question.

The assumption behind the question is that AI is a standalone capability that can be isolated, managed, and deployed as its own function. This is precisely where organizations risk making a familiar mistake. Anyone that’s been around corporate innovation in the past 10–15 years ago knows what I’m talking about.

Around a decade ago, companies had a nearly identical debate about innovation. Faced with pressure to “innovate or die,” many organizations responded by creating innovation departments, appointing Chief Innovation Officers, and building dedicated innovation fictions.

The intention was sound. The outcome, in many cases, was not. Leading to the raise of innovation theater and the fall of countless careers.

By separating innovation from the rest of the business, companies unintentionally turned it into a silo. Innovation teams often became disconnected from the operational realities, customer problems, and profit-and-loss responsibilities that define business priorities – all while giving them a false sense of superiority relative to their peers in the line business. Over time, innovation was increasingly perceived as something adjacent to the business rather than embedded within it. In many organizations, it gradually lost relevance—not because innovation ceased to matter, but because it became someone else’s responsibility.

There is a growing risk of repeating this same pattern with AI.

In an attempt to accelerate adoption and impose structure, organizations are establishing AI centers of excellence, ring-fencing AI budgets, and appointing executives to oversee AI strategy (Chief AI Officer). These moves are understandable through the lens of traditional management practices. However AI introduces unprecedented complexity around data governance, security, model management, compliance, and vendor selection. 

Some degree of coordination is not only useful, but necessary. However, centralization often comes with unintended consequences, a really significant dark side.

When AI is owned by a dedicated team or function, business units naturally begin to outsource responsibility. Rather than building internal capabilities, departments start waiting for the AI team to prioritize their requests. Demand quickly outpaces the capacity of a small centralized group, creating bottlenecks across the organization. 

At the same time, solutions risk being developed further away from the people who best understand the underlying business problems.

The result is a familiar organizational pattern: AI remains strategically important but operationally constrained.

This reveals the false dichotomy at the center of the debate. Organizations are often forced into choosing between full centralization and full decentralization, as if these were the only available models. Neither is likely to succeed (again we have seen this with innovation)!

So here is the conundrum: a fully centralized AI model may create alignment and governance, but it also introduces dependency and slows execution. A fully decentralized model may encourage experimentation and local ownership, but it often results in fragmented technology choices, duplicated efforts, inconsistent standards, and unmanaged risk.

The objective, therefore, should not be choosing one extreme over the other. The real design challenge is building an operating model that enables distributed ownership with centralized coordination (and governance).

This requires reframing the problem entirely.

The most important question is not who owns the AI budget, but how organizations ensure AI adoption happens at scale and as close as possible to where value is created. AI is not a departmental initiative; it is a general-purpose capability with applications across virtually every function. Marketing teams can use AI to improve customer segmentation and campaign performance. Operations teams can optimize workflows, forecasting, and supply chain efficiency. Finance teams can strengthen scenario planning, risk analysis, and decision support. HR teams can redesign talent acquisition, onboarding, and workforce planning.

The value of AI is realized through application, not ownership.

This is why every department should be responsible for identifying, prioritizing, and implementing AI opportunities relevant to its own domain. Business teams are far better positioned to understand where friction exists, where decisions can be augmented, and where automation can create measurable impact.

Yet distributed responsibility does not mean unmanaged responsibility.

One of the common failures in organizational transformation is assuming that simply mandating adoption is enough. From our experience it rarely is. 

We have seen this repeatedly in digital transformation, agile adoption, and innovation initiatives. Declaring AI a priority for everyone without building the underlying system for execution merely creates symbolic alignment.

For distributed AI adoption to work, organizations need several foundational elements in place. 

• First, teams require sufficient AI literacy and capability building. Without a baseline understanding of what AI can and cannot do, ownership becomes performative rather than practical. 
• Second, incentives must be aligned. If business leaders are not measured on AI adoption or operational improvement enabled by AI, other priorities will inevitably dominate. 
• Third, organizations need shared infrastructure, tooling, and access layers that reduce friction and prevent every function from building its own disconnected ecosystem. Finally, governance remains essential—but its role must evolve.

This is where many organizations misunderstand leadership roles such as Chief AI Officer. The issue is not the existence of these positions. In fact, many companies can benefit from strong AI leadership. The problem emerges when these roles become de facto owners of all AI execution. In that model, the organization inadvertently reinforces the idea that AI belongs to a specialist function.

A more effective interpretation of AI leadership is as an enabling layer rather than a controlling one. AI leaders should focus on building organizational capability, setting governance frameworks, defining standards, managing risk, and accelerating knowledge transfer across departments. Their success should not be measured by how much AI they directly control, but by how effectively they enable the rest of the business to adopt and scale it.

This distinction is subtle, but strategically important.

Ultimately, the question of AI budget ownership feels attractive because it offers a simple governance shortcut. It suggests that if the right function controls the budget, the organization will naturally get AI right. Experience suggests otherwise.

AI does not need to be owned as a separate corporate domain. It needs to be embedded into how the organization operates.

Which leads to a far more important question—one that organizations should arguably be asking instead:

How do we make AI everyone’s responsibility without making it no one’s job?

This is a more difficult question because it cannot be solved through org charts or budget lines alone. It forces leaders to think more deeply about operating models, incentives, governance, capability building, and organizational design.

But unlike the original question, it points in the right direction.

Because in the long run, organizations will not generate ROI from their AI projects by deciding who owns AI. They will generate ROI by ensuring AI is embedded where the problems actually are.

The article was originally posted on the OUTCOME Blog.

The post Who Should Own The AI Budget – And Why Is This A Terrible Question appeared first on The Corporate Startup.

Despite the surge in investment, an estimated 80–90% of AI projects fail to deliver a meaningful return on investment. At the same time, companies that rushed to cut costs by replacing people with AI are quietly reversing course—rehiring talent at a higher cost when automation didn’t materialize as expected. And inside many organizations, employees are not working less with AI. They are working more, as new layers of validation, oversight, and coordination emerge.

This is not a tooling problem. It is an execution problem.

Most companies are still applying a traditional software implementation mindset to AI. Define requirements, build a model, deploy it, and expect predictable outcomes. But AI systems don’t behave deterministically. They don’t improve just because you designed them well. And they don’t create value just because they exist.

If you want to understand why AI projects fail—and more importantly, how to make them deliver ROI—you have to look at how they are implemented inside the business.

AI Implementation Mistake #1: Starting with the Solution

“We need AI” is not a strategy. It is a reaction to pressure—board-level, competitive, or internal. Projects that begin with a solution tend to drift because they are not anchored in a clearly defined problem. Without a measurable pain point—lost revenue, operational inefficiency, or missed opportunities—there is no way to evaluate success. The initiative becomes activity without direction.

How to avoid it

Start with a problem that already exists and already hurts. Quantify it. Validate it through direct conversations with users and stakeholders. Only then assess whether AI is the right tool. AI is not the strategy—it is one possible lever.

AI Implementation Mistake #2: Treating AI as a One-Off Delivery

Many organizations still treat AI like a traditional IT project: define, build, launch. The reality is that the first version of any AI system is wrong. Not slightly wrong—misaligned with how the real world actually behaves. What separates successful AI initiatives is not how well they perform at launch, but how quickly they improve after deployment.

How to avoid it

Design for iteration. Build feedback loops that capture real-world usage and feed it back into the system. Allocate time and resources for continuous learning. Start small, test quickly, and scale only when there is evidence of impact. AI rewards speed of learning, not perfection of planning.

AI Implementation Mistake #3: Data Optimism

There is a persistent assumption that the data you have is the data you need. In most cases, it isn’t. Data is often incomplete, biased, outdated, or simply irrelevant to the decision the AI system is supposed to support. This creates a false sense of progress early on and disappointment later.

How to avoid it

Work backwards from the decision. What does the system need to get right? What signals would improve that decision? Treat data as something you actively build and refine—not something you passively inherit.

AI Implementation Mistake #4: Diffused Ownership

AI projects sit at the intersection of multiple teams: data, engineering, product, operations. When everyone is involved, accountability often disappears. The result is predictable. Teams deliver components, but no one owns the outcome. The system exists, but it does not create value.

How to avoid it

Assign a single owner responsible for business impact. Not timelines. Not technical delivery. Outcomes. This person must have both the authority and the incentive to ensure the system works in practice.

AI Implementation Mistake #5: Measuring the Wrong Success

Accuracy is easy to measure. Business impact is not. This is why many AI projects look successful on paper but fail in reality. A highly accurate model that no one uses creates no value. Meanwhile, a simpler solution embedded into daily workflows can drive significant results.

How to avoid it

Define success in terms of behavior and economics. Are decisions faster? Are costs lower? Is revenue increasing? Tie performance metrics directly to business outcomes, not just model outputs.

AI Implementation Mistake #6: Isolating AI Instead of Integrating It

Many organizations make AI a separate initiative—a line item on the agenda, owned by a specific team. This is the same mistake companies made with “innovation” a decade ago. Once AI becomes someone else’s responsibility, the rest of the organization disengages. The result is presentations, pilots, and prototypes—but no real change. AI should not be a standalone topic. It should reshape how core decisions are made across the business—from pricing and hiring to risk and customer experience.

How to avoid it

Stop asking, “What is our AI strategy?” Start asking how AI changes the decisions you are already making. If AI is not embedded into existing workflows and conversations, it is not creating value.

The pattern across failed AI initiatives is consistent. Organizations optimize for building systems instead of validating outcomes. The ones that succeed do the opposite. They focus on real problems, integrate AI into workflows, iterate in the open, and measure what actually matters.

AI does not automatically reduce costs. It does not eliminate work. And it does not reward careful planning as much as it rewards fast, evidence-based learning. The opportunity is real—but so is the discipline required to capture it. If your organization is investing in AI but struggling to see measurable ROI, the issue is rarely the model. It is how the initiative is framed, owned, and executed.

AI is not a shortcut to transformation. It is a test of whether your organization knows how to learn.

This article was originally posted on the OUTCOME Blog

The post Why AI Projects Fail: 6 Mistakes That Kill ROI (and how to fix them) appeared first on The Corporate Startup.

Artificial intelligence is reshaping the global economy at a pace few predicted and even fewer are prepared for. Over the past decade—and especially in the past three years—AI has moved from automating routine tasks to performing activities once considered the exclusive domain of highly skilled professionals. Software development, product design, customer insights, financial modeling, even creative production: AI now tackles these functions with a level of sophistication and speed that fundamentally changes how work gets done. And the velocity of this shift suggests we are still in the early innings.

For business leaders, HR executives, and educators, the implications are profound. Job disruption is no longer a distant possibility—it is happening now. Over recent quarters, companies across sectors have quietly but steadily reduced headcount as automation has proven more cost-effective, more scalable, and often more accurate than traditional labor. This is not a commentary on corporate strategy so much as a recognition of economic reality: when technology reliably performs at a fraction of the cost, organizations have little choice but to adapt.

The question we now face is not whether the nature of work is changing, but how society intends to respond. Two issues deserve urgent attention. First, what responsibility do companies have toward employees whose roles are displaced by automation? Severance packages traditionally focus on financial support, outplacement, or career counseling, but these approaches may not be enough in a world where entire job categories are rapidly disappearing. Second, what skills should we prioritize for future generations—skills that cannot be easily automated and that empower individuals to thrive in a digitally accelerated economy?

One answer sits at the intersection of both challenges: entrepreneurial education. If AI is automating jobs faster than new ones emerge, then the most effective antidote to unemployment is enabling people to create economic value for themselves and others.

Historically, entrepreneurship has served as a powerful counterforce to poverty, stagnation, and structural job loss. Equipping individuals with the mindset and tools to identify opportunities, test ideas, and build solutions may be one of the most sustainable forms of workforce resilience available to us.

Yet entrepreneurial education remains largely misunderstood. Many assume that entrepreneurship is simply about business ideas—and that most people do not have them. In reality, ideas are abundant. They are a “dime a dozen,” as any experienced VC will tell you. People generate ideas for new services, new digital products, community-level solutions, and workplace innovations every day.

What they lack is not imagination but AI awareness. Few truly recognize how dramatically AI has lowered the barriers to entry for starting a venture. Tasks that once required months of labor and a team of specialists—branding, prototyping, customer research, basic software development—can now be done in days or even hours with accessible, affordable AI tools.

The democratization of capability is one of the most underappreciated shifts of our time. A single individual with a laptop now has access to resources once reserved for funded startups or large organizations with dedicated development teams.

AI can draft pitches, conduct market scans, generate design assets, build technical proofs of concept, and support early-stage customer outreach. The entrepreneurial journey still requires creativity, discipline, and resilience, but the cost of trial-and-error learning has never been lower.

However, teaching people to use AI tools is not enough. Entrepreneurship is fundamentally about solving real problems—problems customers value enough to pay for. That requires empathy, validation, and evidence-based decision-making. If entrepreneurial programs focus solely on technology, they risk producing a generation of AI-proficient builders who create solutions in search of a problem.

This is where design thinking and structured innovation methodologies like Lean Startup become essential. They teach individuals to start with user challenges, not technology; to validate assumptions before scaling; and to approach innovation with humility, curiosity, and rigor. These are capabilities that cannot be automated—at least not any time soon. They draw on human insight, emotional intelligence, systems thinking, and the ability to collaborate across functions and cultures.

For CHROs and corporate leaders, entrepreneurial education represents a chance to rethink what a severance package can do. Rather than offering support that simply bridges people to their next job search, companies can invest in programs that help individuals build their own future. Entrepreneurial training—paired with exposure to AI-powered tools—can give displaced employees a chance not only to re-enter the economy but to participate in shaping it.

For educators and academia, the message is equally clear. As AI continues to transform industries, the value of traditional skills will shift. Technical proficiency remains important, but the ability to identify unmet needs, design meaningful solutions, and navigate uncertainty will become the differentiators of tomorrow’s workforce. Institutions that integrate entrepreneurship, design thinking, and AI literacy into their core curricula will prepare students for a world that rewards adaptability and innovation over memorization and routine.

We stand at a critical inflection point. Automation will continue to displace certain types of work, but it also opens unprecedented opportunities for those equipped to seize them. Entrepreneurial education is not simply a “nice to have” addition to corporate training, severance packages, or university programs—it is a strategic necessity. It empowers individuals to transform disruption into opportunity, and it strengthens societies by enabling more people to contribute to economic growth.

The call to action is straightforward:

– Business leaders and HR professionals: Reimagine severance as a launchpad for entrepreneurship, not just a runway to traditional employment.

– Educators: Make entrepreneurship and design thinking as foundational as math and writing in the age of AI.

In an era defined by rapid technological change, the most important skill we can teach is not how to perform a task, but how to create new value when old models no longer apply. And that is precisely what entrepreneurship—and entrepreneurial education—makes possible.

This article was originally published on the Horasis blog.

The post Severance Packages & Education in the Age of AI appeared first on The Corporate Startup.

Are we entering the era of the “Algorithmic CEO”? Should CEOs fear for their jobs? Or will they be the “human in the loop” when it comes to decision-making? It’s not a robot replacing leadership — but AI systems quietly shaping which strategic initiatives or directions get funded, which markets get entered, and which bets get killed.

For years, executives have struggled to make data-based, unbiased decisions regarding strategic growth options. Traditional financial metrics paint the picture of what worked. They don’t tell you what’s going to work. So, justifiably without an innovation accounting system in place, leaders default to instinct, politics, or whichever initiative screams the loudest in the room.

AI promises to fix that. It promises to evaluate early signals continuously. To model adoption curves. To stress-test downside exposure. And to compare growth bets across the entire portfolio — all while staying unbiased and unmoved by fancy PowerPoint decks and office politics.

Sounds rational. Efficient. Safer.

But here’s the twist.

Breakthrough, early-stage initiatives (innovation) look terrible on paper in terms of data. They are small. They are inefficient. And they always under-perform relative to the existing core business.

So, if you train an algorithm to help in options planning and decision-making, you are probably going to train it on historical performance data. Most, if not all, LLMs are trained this way. In that case, the CEO’s co-pilot AI will favor incremental improvements over disruptive bets — every day of the week.

However, growing beyond the core and defining the company’s next S-curve requires breaking the pattern of the incumbent business model.

So the real danger isn’t that AI will replace CEOs. The danger is that, with AI in the loop, CEOs will become overly rational — and even more reluctant to invest beyond the core business.

And overly rational companies rarely reinvent industries.

Therefore, we shouldn’t be discussing whether it’s better to have AI augment decision-making or rely on intuition. Instead, we should be discussing how we design decision authority in the AI world and what role AI should play in decision-making if we want our companies to stay relevant in the future.

Here are three things to consider when you bring AI into the boardroom with the hope of improving your decision-making process and helping your company define its next strategic moves:

1. Use AI to rank assumptions, not ideas. Rather than letting AI judge which ideas are “good” or “bad,” use it to evaluate the assumptions underlying each initiative. For example, if a new product idea depends on user adoption doubling in six months, the AI can analyze historical adoption trends, market signals, and competitor data to highlight which assumptions are weak, strong, or uncertain. This approach ensures that the organization retains knowledge about why decisions were made, not just which ideas were chosen. Over time, the company builds a repository of validated and invalidated assumptions, helping future leaders make faster, smarter decisions without reinventing the wheel.

2. Separate activity metrics from impact metrics. AI can track thousands of operational or activity metrics, but the real power lies in connecting those activities to actual business impact: revenue growth, customer retention, or market share expansion. By distinguishing activity from impact, AI retains institutional knowledge about what truly moves the needle. Leaders can revisit this knowledge in future decisions, ensuring that lessons from past initiatives aren’t lost in the noise of busy dashboards or vanity metrics. Over time, this helps the company remember which levers consistently drive success, even as teams and strategies change.

3. Let algorithms inform portfolio balance — but reserve human judgment for asymmetric bets. AI excels at analyzing patterns and optimizing for incremental improvements. It can recommend portfolio adjustments that maximize expected returns based on historical data, ensuring knowledge about past decisions, performance trends, and risk exposure is preserved and leveraged. However, truly transformative, asymmetric bets — entering new markets, developing disruptive products, or reshaping business models — require human judgment informed by experience, intuition, and context. By keeping humans in the loop for these high-stakes decisions, the organization retains the nuanced knowledge that AI can’t quantify, preserving strategic wisdom that might otherwise be lost to pure data-driven optimization.

AI should be used to pressure-test the board’s thinking. It should not define — or limit — the company’s ambition.

The future CEO isn’t data-driven or instinct-driven. They are system-driven. Disciplined in experimentation. Explicit about risk. Accountable for the bets that matter — especially the ones the model, trained on historical data, dislikes.

Article originally posted on the Outcome Blog.

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This moment is being widely read as a clash between heritage and innovation. But the deeper lesson isn’t about electric versus internal-combustion engines. It’s about speed and responsiveness. Disruption today isn’t driven solely by new technologies or new trends. It emerges when the speed of change outside the organization outpaces the speed of adaptation inside it—a sentiment captured succinctly by Jack Welch a few decades earlier in the, now famous, quote: “if the rate of change on the outside exceeds the rate of change on the inside, the end is near.”

Porsche’s challenge wasn’t that electrification was a bad idea. It was that decisions were made on a fixed strategic course long before desirability was validated with real customers. By the time meaningful feedback arrived, billions in CAPEX were already committed—creating strategic lock-in and reducing the company’s ability to pivot.

What happened at Porsche holds a crucial lesson for leaders responsible for growth strategy, innovation, and navigating disruption: long-term success now requires strategy that embraces learning and adaptation, rather than rigid plans declared once and executed without revision.

Why Traditional Strategy No Longer Works

For decades, strategy was treated as a static roadmap—a set of commitments and milestones intended to guide execution over years. But in a world where technologies, markets, and customer behaviors shift rapidly, this model fails because it assumes a level of certainty that simply does not exist. Strategy conceived as a list of actions becomes obsolete almost as soon as it’s finalized.

Leading thinkers now argue that strategy should not be a fixed plan at all but a living framework for testing assumptions and aligning decisions across the organization. At its core, strategy must articulate the most critical assumptions about the future and then create systems to test those assumptions continuously.

This is the essence of moving from strategy by declaration to strategy by experimentation.

Strategy as a Series of Experiments

Forward-looking companies no longer treat strategic choices as irrevocable commitments. Instead, they break strategy into testable hypotheses—small, evidence-generating experiments that reduce uncertainty and shape strategic direction.

Rather than betting the business on a single, untested vision (as Porsche arguably did with full electrification), companies can run multiple small bets, each designed to reveal whether a strategic assumption holds true in the real world. This model mirrors how industry leaders like Amazon continuously test new ideas in controlled, scalable ways before allocating major resources.

These experiments aren’t tactical A/B tests. They are strategic probes—each one designed to answer a critical question about customer desirability, market response, or operational feasibility of a certain possible growth avenue. Instead of announcing a destination and hoping the journey aligns, leaders declare assumptions, then build organizational processes that systematically validate them.

The advantage of this approach is twofold:

• Faster learning over longer commitments: Experiments generate real data early, enabling organizations to pivot before high costs are sunk into a chosen path.
• Alignment between strategy and innovation: When new initiatives are grounded in tests rather than speculation, innovation outcomes inform strategic decisions and vice versa.

What This Means for Growth, Innovation, and Disruption

For C-level executives and senior strategists, the implications are profound.

First, strategy must be a continuous ‘conversation’, not a one-time ‘declaration’. Traditional plans are too brittle for rapidly shifting environments. Instead, strategy should evolve as insights emerge from experiments that connect markets, customers, and operations more tightly than ever before.

Second, innovation must be connected with strategic intent, not treated as a separate ‘lab activity’. Without that connection, innovation becomes isolated “theater”—interesting but unmoored from the company’s core trajectory.

Finally, leaders must build organizational systems that support both learning and governance. Feedback loops, rapid experimentation frameworks, decision checkpoints, constant strategy review meetings and adaptive planning processes are no longer optional—they are essential components of a strategy that can navigate disruption.

Never Forget the Customer

Underlying all of this is a simple truth: customers decide what is desirable—not internal projections or engineering assumptions. Every strategic hypothesis, experiment, and investment must be tethered to customer response data. When companies ignore this, they risk making bold bets on untested futures.

Conclusion: Strategy for the Speed of Change

Porsche’s experience shows that disruption isn’t about having the latest technology. It is about the speed at which preferences, markets, and competitive dynamics evolve. When an organization’s internal processes are slower than these external shifts, even the strongest brands can lose momentum.

In a world defined by uncertainty, the highest-performing companies will be those that treat strategy as a continuous learning system—where growth, innovation, and disruption are navigated through iterative experimentation, not rigid planning.

This mindset redefines leadership for the modern era. The question for every executive now is not “What is our plan?” but “What assumptions are we testing today?” And “What did we learn yesterday?”

This article was originally posted on the Outcome Blog.

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However, research shows that only about 12% of companies report a strong relationship between their strategy and innovation investments. In other words, most firms still struggle to link where they spend innovation dollars with what their strategy actually prioritizes.

Tightening this link—through mechanisms such as a Venture Board or an Innovation Thesis—helps align growth investments with strategic needs and increases the odds of creating real business impact.

This said, even the most disciplined organizations will encounter moments when opportunities arise outside the boundaries of the current strategy. In these moments, leaders must decide whether to remain rigidly aligned—or make a calculated exception.

Here are three situations when investing beyond your defined strategy may actually be the right move.

Reason 1: Market Opportunism and Agility

Sometimes, innovation teams uncover unexpected insights while exploring a market. In their pursuit of customer empathy and problem validation, they might identify a bigger, more urgent opportunity—one not covered by the current strategic roadmap.

In such cases, it can be wise to act. Investing in an off-strategy idea backed by strong market evidence demonstrates agility and a responsive approach to growth. Not only can this capture emerging value faster than competitors, but it also provides critical data to inform the next strategic cycle. In a world where markets evolve faster than annual planning cycles, strategic agility can be as valuable as alignment itself.

Reason 2: Satisfying Key Stakeholders

Sometimes, the motivation is less about market signals and more about relationships.

Investing in projects that sit outside your current strategy can be a pragmatic move to satisfy key stakeholders—whether that means major partners, regulators, investors, or suppliers. These stakeholders often have influence over the company’s ability to execute its core plan. Making selective, small investments in initiatives they champion can build trust, goodwill, and collaboration capital that pay dividends later.

As long as these investments don’t compromise overall portfolio integrity, they can strengthen the ecosystem that enables strategic execution.

Reason 3: Learning About Emerging Technologies

Occasionally, a new technology emerges that is too early, unproven, or tangential to your current growth strategy—yet too intriguing to ignore.

In such cases, small-scale investments can serve as strategic learning experiments. By funding exploratory initiatives, organizations gain firsthand understanding of how a technology works, its potential use cases, and the pace of its evolution.

This approach builds organizational readiness and prevents being caught off guard when the technology matures and becomes strategically relevant. Think of it as buying a low-cost “option” on future innovation.

True strategic leadership is not about blind adherence to a plan—it’s about knowing when to bend without breaking.

Aligning investments with strategy ensures focus and coherence. But growth often comes from intelligently navigating the gray zones—where emerging opportunities, stakeholder interests, and new technologies challenge the limits of your current direction.

The most successful organizations treat these exceptions not as distractions, but as strategic experiments that inform and evolve their next wave of innovation.

Staying aligned with strategy ensures disciplined growth, but remaining flexible ensures relevance and resilience. The goal isn’t to avoid off-strategy investments entirely—it’s to make them intentionally, with clear hypotheses, measured risk, and a line of sight back to long-term strategic value.

The post Three Reasons To Keep Investing In Ideas Not Aligned With Strategy appeared first on The Corporate Startup.

But while metered funding works well in the startup ecosystems, applying it inside large corporations presents a conundrum. Most enterprises still operate on annual budget cycles—designed for predictability and control, not agility. This creates structural friction: innovation programs may release funds incrementally, but once projects are a point of graduating to business units, they collide with rigid budget gates.

When Annual Budgeting Collides with Innovation

Consider a global life sciences company we worked with. Its central innovation accelerator embraced metered funding through a structured Idea Lifecycle Framework with four stages of development. The central team financed early exploration (stages one and two), but projects entering later stages had to secure sponsorship from business units.

One team rapidly validated its business model and received funding to move into stage two. However, when it was ready to scale further, it faced a barrier: no business unit had budget capacity until the next annual cycle. The result was a year-long delay. The market happened to remain stable, so the team’s evidence from early stages held up—but the company lost a year of potential revenue. In a less forgiving market, the delay could have invalidated their work entirely, or killed the idea altogether.

This is not an isolated case. Across industries, many promising innovation projects stall after proof-of-concept, not because they lack evidence, but because they don’t fit the company’s financial operating system. The consequence is frustrated teams, missed market opportunities, and higher overall costs of innovation.

If corporations want to capture the benefits of metered funding, they must adapt their budgeting practices. Here are four ways to reconcile the two systems.

1. Decentralize Innovation

When business units are responsible for driving their own innovation initiatives, they become active stakeholders rather than passive recipients of projects handed off by a central team. This decentralization allows units to integrate innovation priorities into their financial planning from the outset, ensuring continuity of funding as projects advance. It also accelerates alignment between emerging ideas and the units best positioned to commercialize them.

The challenge is that decentralization can fragment innovation if not guided by a clear innovation strategy. To counter this, companies should establish shared principles and evaluation criteria—while empowering BUs to allocate a portion of their budgets to innovation directly. Some organizations set a fixed percentage of revenue or operating expenses aside for BU-led innovation. The key is balancing autonomy with alignment.

2. Involve Business Units Early

No innovation project should progress without the early and active involvement of at least one sponsoring business unit. Too often, projects complete proof-of-concept phases only to discover there is no BU willing—or financially able—to take them on. By engaging business leaders at the ideation or prototyping stage, teams can anticipate downstream requirements and embed them in budget assumptions.

This approach does more than solve budgetary issues: it ensures stronger market relevance. Business units bring customer relationships, distribution channels, and operational know-how that can de-risk scaling efforts. In practice, this means the central innovation team should require BU sponsorship before advancing a project beyond early validation stages. Projects without clear BU alignment should not move forward, however promising they may appear on paper. Companies like Unilever, Haier and P&G are known to use this approach.

3. Elevate Innovation Governance

The central innovation function should not have to compete with business units for funding on an ad hoc basis. Instead, it should report directly to the CEO, CFO, or board, and have access to a discretionary innovation budget. Such a fund can be deployed across the full lifecycle—from early exploration to scaling—bridging the financial gap between annual cycles and ensuring that high-potential projects are not left waiting for the calendar to turn.

This structure elevates innovation to a strategic priority, signaling executive commitment. It also creates accountability at the top: leadership must decide whether evidence is compelling enough to warrant additional funding. In doing so, the company applies the same rigor to innovation investments as it does to other capital allocation decisions, while preserving flexibility. This is the preferred approach by brands like DuPont, 3M and PepsiCo

4. Adopt Rolling Budgets

Finally, organizations should move toward rolling budget models, aligned with the principles of “beyond budgeting.” Unlike traditional annual cycles, rolling budgets allow leaders to reallocate resources dynamically in response to evidence, customer feedback, or changing market conditions. For innovation, this flexibility is critical: it ensures capital can flow to promising initiatives at the pace of discovery, not the pace of corporate accounting.

Adopting rolling budgets is not easy. It requires cultural change, systems upgrades, and finance leaders willing to rethink decades of practice. But companies experimenting with hybrid models—keeping annual budgets for core operations while applying rolling principles to innovation—are already reaping benefits. For instance, a European bank we studied created a rolling innovation fund within its digital division, which allowed it to accelerate fintech partnerships without waiting for the next fiscal year. The result was faster go-to-market and improved competitiveness in a crowded space.

Leading the Change

Making metered funding work in corporations is not only a question of process but also of leadership and culture. CFOs play a central role in bridging innovation and finance, ensuring that governance structures support agility without sacrificing accountability. Innovation leaders, in turn, must present evidence clearly and consistently, building trust that projects merit the next tranche of funding. Both must champion a culture where decisions are driven by data and customer insight, not hierarchy or politics.

The lesson is clear: no matter how much energy companies put into training, upskilling, or shifting mindsets, innovation will remain slow and costly unless the operating system itself evolves. Metered funding offers a powerful mechanism to accelerate learning and reduce risk—but to unlock its full potential, companies must reimagine how budgeting and innovation intersect.

For organizations serious about innovation, the call to action is straightforward: treat funding as a strategic lever, not a bureaucratic hurdle. Begin with pilots, build evidence, and gradually scale new budgeting practices. The companies that master this balance will not only innovate faster but also outpace competitors in translating ideas into sustainable growth.

This article was initially published on the Outcome Blog.

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