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.

The post AI Should Reshape the CEO Role—But Not in the Way You Think appeared first on The Corporate Startup.

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.

The post Lessons from Porsche: Why Strategy Must Be Built to Learn, Not Just to Declare appeared first on The Corporate Startup.

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.

The post Making Metered Funding Work in a World of Annual Budgets appeared first on The Corporate Startup.

Obviously there won’t be one single company that picks only one of the three and they will go for a mix between the three but there is always going to be a question of resource allocation. Where do we focus most of the efforts? To which one of the three options is the opinion of the board the resources that come with that option are going to be skewed.

The first option is optimizing the existing business model by investing in tech that will streamline operations and have a direct impact on the bottom line. Basically there CAPEX and OPEX expenditure for the sake of cost reduction and OPEX optimization. In other words, continuous improvement and digital transformation. This option is the most popular as it is also the most urgent and the one which has the most predictable and short term outcomes. However this is not an option that will impact the top line, prevent disruption or add new revenue streams. This is, if you want the hygiene option. And in some industries like banking, telco, logistics or air travel, this option leads to a zero sum game. 

At the opposite end of spectrum boards have another option: innovation-led growth. Done right, this option creates new revenue streams, adds to top line growth and prevents disruption. But it is risky, has unpredictable and long term returns, hence it’s easy to see why this is not the darling of the options as it is OPEX intensive with unknown upside. 

However there is a third option that boards consider when wanting to grow the company. That is the M&A option. Yes it is resource intensive but it offers the promise (if done right) of predictable returns, adding of new revenue streams and disruption prevention. Also the stock market might not agree with the decision and the stock price will drop immediately after the acquisition is announced like it happened with Bayer when it announced the acquisition of Monsanto seeing its value plunge by $18 billion. 

As you can see the M&A option is not risk free, it’s not a silver bullet for growth, and data shows us that 70 to 75% of deals fail. But to increase the chances of success of an M&A it is advisable to be mindful of the following basic checklist:

• Validate Strategic Fit Against Core Investment Themes. Every acquisition must reinforce your existing strategic direction. Evaluate whether the target aligns with your investment themes, core markets, or future growth vectors. Scrutinize the logic behind the deal—especially the assumptions around synergies. If the integration story feels optimistic or disconnected from reality, challenge it. Be brutally honest: does this deal deepen your competitive advantage, or simply create the illusion of growth?

• Pursue M&A to Accelerate Competitive Strategy, Not Inflate Numbers. Use M&A as a tool to accelerate or deepen your existing competitive edge—not as a mechanism to bulk up revenue or earnings. Strategic alignment must always come before financial engineering. Avoid deals that look attractive on a spreadsheet but offer no clear path to reinforcing your long-term positioning. Opportunistic acquisitions that don’t clearly tie into your core strategy tend to under-perform over time.

• Avoid Complexity That Dilutes Focus and Alienates Customers. Steer clear of acquiring companies with overly complex product portfolios or bloated organizational structures. These deals often result in long, expensive integration processes that confuse customers and distract your teams. Make sure to assess how the acquisition impacts your customer experience—will it improve your capabilities and service, or risk churn? Additionally, audit the target’s ESG profile and brand reputation; you’re inheriting their legacy, for better or worse.

• Don’t Acquire a Peer—Avoid Power Struggles. Acquiring a company of equal size often triggers power struggles that stall momentum and delay value creation. Integration becomes a political battle over leadership, culture, and control. To avoid this, ensure the post-merger leadership structure is defined clearly within the first 90 days. Without clarity, the organization risks descending into strategic paralysis.

• Prioritize Cultural Alignment from Day One. Culture clashes are one of the most common—and costly—reasons deals fail. Conduct a culture diagnostic during due diligence and integrate the findings into your M&A planning. Consider appointing a dedicated culture integration lead, empowered to make real decisions. Without alignment on how people operate and make decisions, no amount of financial or operational planning will save the deal.

• Develop the Integration Plan Early—Not After the Deal Closes. Integration planning should start during the due diligence phase, not after the ink dries. Define clear milestones and timelines so both sides know what success looks like on Day 1, Day 90, and beyond. A deal without an execution roadmap is a strategy in name only.

• Set Measurable Synergy Targets—and Own Them. Define specific, quantifiable targets for both cost synergies and revenue uplift. These should be more than headline figures—they must be owned by accountable leaders with timelines, KPIs, and real consequences for under-performance. Execution discipline is what separates high-performing deals from value-destroying ones.

• Lock in Key Talent Before You Lose Them. People are your most fragile asset post-acquisition. Identify key individuals early and put retention strategies in place—particularly in businesses where IP, client relationships, or institutional knowledge drive value. Communicate clearly about roles, career paths, and what the future looks like. Ambiguity breeds attrition.

• Evaluate IT Integration Complexity and Tech Debt. Technology integration is a common stumbling block. Assess the compatibility and scalability of systems during diligence. Mismatched infrastructure or legacy tech can delay integration and inflate OPEX. Unless there’s a clear modernization path, don’t absorb tech debt that drags down your innovation velocity.

• Define the Brand Architecture Post-Deal. Your brand strategy matters as much as your financial strategy. Will the acquired company be integrated into your master brand, operate as a standalone, or carry an endorsed identity? Confusion here leads to diluted messaging, customer skepticism, and internal misalignment.

• Preempt Regulatory and Antitrust Risks. Regulatory friction can derail even the most strategically sound deal. In regulated industries such as healthcare, finance, and telecom, anticipate challenges early. Conduct a thorough risk assessment and factor in potential delays and concessions. Don’t underestimate the time and cost of navigating regulatory approvals.

To ensure M&A decisions are well-vetted and strategically sound, consider instituting a “Go/No-Go” discipline. Create a deal review committee that includes internal skeptics—such as CFOs, risk officers, or board members—who are empowered to challenge assumptions and rigorously test the deal’s validity before moving forward. In the committee’s first meeting, conduct a “pre-mortem” analysis. Pose the question: If this deal fails, what will have gone wrong? This proactive exercise is invaluable for identifying potential blind spots and mitigating risks early in the process.

This article was originally published on the OUTCOME blog

The post The M&A Checklist for Sustainable Growth appeared first on The Corporate Startup.

Yet fast forward to 2024, and Garmin is not only alive—it’s thriving. The company posted record revenue of $6.3 billion, with its fitness and outdoor segments contributing over $3.7 billion, nearly 60% of its total earnings. So how did a company seemingly killed by technological disruption come back stronger than ever?

Rewinding the story to the company’s golden days in 2008, Garmin shipped 16.9 million personal navigation devices and controlled 36% of the global Personal Navigation Device (PND) market. That year alone, it generated $3.5 billion in revenue—70% of it from car GPS units.

But the timing couldn’t have been worse. In the same year, Apple launched the iPhone 3G, which came preloaded with Google Maps and built-in GPS. Android phones followed shortly after. Consumers no longer needed to spend hundreds of dollars on standalone GPS units—they already had navigation in their pockets.

Garmin’s board first reaction was typical of incumbents: it tried to compete directly. Hence in 2009, it launched the nüvifone, a smartphone resulting from an open innovation partnership with Asus. But the device was underpowered, overpriced, and too late. Needless to say it flopped. Garmin continued down the path of competing head on with Apple and Android for at least 2 more years through other iterations of nüvifone.

However, the writing was on the wall. The car GPS market, Garmin’s bread and butter, was in freefall and the ‘fast follower’ strategy still praised by so many consultants today as a way of dealing with disruption, wasn’t paying off. 

The company was at a crossroads: fade into irrelevance or find a new and fresh direction.

As the nüvifone storywas unraveling, in the background, a small side project from a group of employees was brewing. Garmin had launched its first wearable GPS device—the Forerunner 101—back in 2003. It was chunky, niche, and mostly ignored at the time by the mainstream market with sales amounting for less than $400 mil. from the entire range. 

But in a world where runners, cyclists, and outdoor athletes were starting to track their performance digitally, it held promise.

In the years after the iPhone’s debut and the nüvifone fiasco, Garmin nearly doubled its R&D team, growing engineering to over 30% of its workforce. It released rugged, data-rich devices like the Fenix series and Forerunner, capable of tracking not just steps or heart rate, but advanced metrics like lactate threshold, VO2 max, training load, and vertical oscillation. All able to trace their origin to the Forerunner 101.

So when the iWatch was launched in 2015, Garmin was ready. Not only from an engineering and product perspective but also from the perspective of the lessons learned during the initial disruption wave brought on by the iPhone. So rather than compete directly with Apple or Samsung in the mass smartwatch market, Garmin doubled down on specialization. It decided to build the best possible tool for athletes, not the average consumer, starting from the Fenix range. The pivot was clear: Garmin wouldn’t try to be ok-ish for everyone—it would be perfect for someone. 

It also innovated beyond wearables. Garmin’s outdoor and marine divisions developed advanced hiking GPS devices, satellite communicators, solar-powered watches, fish finders, and even in-dash navigation systems for boats and airplanes.

By 2024, Garmin’s decision to focus on the niche of athletes and outdoor adventurers had clearly paid off:

• Fitness revenue reached $1.77 billion, growing 32% year-over-year.
• Outdoor revenue hit $1.96 billion, up 16% year-over-year.
• Garmin now holds nearly 11% of the global smartwatch market by revenue, even if its overall unit market share is small.
• It dominates the premium smartwatch segment (over $500) and is the third-largest smartwatch company by revenue, behind only Apple and Samsung.
• The company’s gross margin reached 58.7%, and operating income surged 46% to $1.59 billion.

Investors have taken notice. Garmin’s stock has nearly doubled over the past year, outperforming peers in both tech and consumer electronics.

Garmin’s comeback story isn’t just inspiring—it’s instructional. Any organization aiming to lead its industry—especially incumbents facing disruption—can draw learn from five strategy lessons in how Garmin executed its strategic pivot and successfully built a new growth curve:

1. As an incumbent, don’t fight the disruptors of your industry head on

Rather than pursuing a ‘fast follower’ strategy or creating a strategy around price competitiveness by being  a “cheaper Apple Watch”, Garmin focused its strategy on an underserved niche market: runners and outdoor adventurers. 

This focus allowed them to deliver far more depth, performance, and customization than generalist competitors. While at the same time freeing up resources.

2. Deprirotize your legacy products until you reach the next S-Curve

Even though GPS devices had built Garmin’s empire in the beginning, the company had to master the discipline of letting those go while they were pursuing the new strategy. 

This strategic humility allowed the company to move into its new S-curve that was presenting a stronger long-term potential.

Once the new strategy was delivering results the company started readdressing the legacy segments, but this time with lessons learned from the new strategic direction.

3. Double down on innovation 

While many listed companies in decline respond impulsively by cutting OPEX and optimizing cost centers such as innovation; Garmin took the opposite route—doubling down on innovation. 

Rather than scaling back, it invested heavily in R&D and leaned into the team that had developed its first wearable, the Forerunner 101. At the same time, Garmin maintained select product lines that had only modest financial impact because they served as valuable sandboxes. These allowed the company to test new technologies, better understand customer behavior, and refine its approach —ultimately helping shape its new direction.

This commitment not only led to the creation of rugged, specialized devices for serious athletes and outdoor adventurers, but also gave Garmin the capabilities it needed to explore and execute a bold new strategic direction. By prioritizing innovation over retrenchment, Garmin didn’t just survive disruption—it used it as a springboard for reinvention.

4. Treat your strategy as an experiment not a roadmap

Instead of treating strategy as a rigid roadmap, Garmin used its R&D and innovation capabilities as a compass—experimenting its way out of crisis rather than trying to plan its way out. During this period, innovation wasn’t just about launching new products; it became a vehicle for learning. Early wearable devices, like those in the Forerunner and Fenix lines, served as real-world testbeds to gauge customer behavior, validate the feasibility of emerging technologies, and refine product-market fit in active lifestyle and performance niches.

This iterative approach allowed Garmin to de-risk its strategic pivot. By the time the company fully committed to the fitness and outdoor wearables market, it had already gathered enough insight and market validation to move with confidence. In essence, Garmin didn’t leap blindly into a new growth curve—it prototyped its way there.

5. Focus on your strengths not your weaknesses

Garmin didn’t abandon what it knew—it redirected its GPS expertise into specialized markets where precise navigation still mattered. 

Its core technology became the backbone for growth in aviation, marine, cycling, and wearables. From flight decks to bike computers and rugged smartwatches, Garmin extended its strengths into high-value niches where it could lead, not follow.

Garmin’s story is a powerful reminder that companies don’t die from disruption—they die from denial. When smartphones upended its market, Garmin didn’t double down on dying products or try to imitate Apple. It looked inward, found what it was uniquely good at, and asked, “Who really needs this?” Then it built for them—and built well.

The result? A tech company that was once left for dead is now one of the most respected, profitable, and durable brands in wearables and outdoor technology.

In a world obsessed with mass appeal, Garmin proved the power of specialization.

This article was originally posted on the OUTCOME blog.

References

https://www.cnbc.com/2020/10/06/how-garmin-survived-the-iphone-and-started-growing-again.html
https://d3.harvard.edu/platform-digit/submission/garmins-navigation-to-a-new-business-segment-fitness-wearables/
https://www.forbes.com/sites/alexknapp/2016/09/14/how-garmin-mapped-out-a-new-direction-with-fitness-wearables/
https://techsoda.substack.com/p/from-recovery-to-reinvention-jay
https://www.garmin.com/en-US/newsroom/
https://www.youtube.com/watch?v=EpXhY085yfQ
https://www.garmin.com/en-US/newsroom/press-release/automotive/2010-Garmin-Asus-nvifone-A50-An-Android-Smartphone-with-More-Location-Technology-Than-Any-Other-Smartphone/
https://pbeckman.substack.com/p/garmins-40b-pivot
https://www.garmin.com/en-US/newsroom/press-release/corporate/garmin-announces-fourth-quarter-and-fiscal-year-2024-results/
https://www.garmin.com/en-US/p/231/

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But in fast-changing markets, rigid strategies can quickly become outdated. And worst, they can hamper the very thing they were designed for, growth.

Customer preferences evolve, competitors shift, and assumptions made during planning can unravel in real time. This is why forward-thinking companies are embracing a more flexible approach to strategy — one built not around big bets and rigid plans, but around continuous experimentation.

Instead of committing to a single master plan, these companies are building strategy as a series of experiments. This approach enables them to learn faster, adapt quicker, and make smarter decisions grounded in evidence, not just vision.

The Shift from Strategic Planning to Strategic Experimentation

A traditional business strategy assumes that we can predict what will work. An experimental strategy starts with the opposite assumption: we don’t know yet, and that’s okay — as long as we can learn quickly about the general direction we want the company to go on. 

This approach shift is central to modern strategy and in particular to innovation strategy. New ideas, connected with the general strategic direction the company wants to go on — whether they involve entering a new market, launching a product, or changing a pricing model — are treated as hypotheses. Teams test these ideas and the most critical assumptions behind, gathering data, and informing the strategic direction based on real-world feedback.

Think of it like running a series of mini-experiments within your broader strategic framework. Each experiment helps reduce uncertainty, validate (or invalidate) assumptions, and guide better decision-making. It’s strategy by discovery, not just declaration.

A Real-World Example: Amazon’s Experimental DNA

One of the most prominent examples of this approach is Amazon, a company that has embedded experimentation deep into its strategic growth process.

Whether testing new features in Prime Video, piloting a strategic market entry in grocery retails through Amazon Go stores, or refining its product recommendation engine, Amazon constantly runs controlled experiments. These initiatives aren’t rolled out as massive, fully-funded launches. Instead, they start small — often within what Amazon calls “two-pizza teams” — autonomous groups empowered to innovate and test independently. Only if the results from the experiments are positive, the company decides to double down on that larger strategy. 

This model allows Amazon to try hundreds of ideas connected with possible strategic moves at once without jeopardizing the core business. Some ideas fail quickly and cheaply. Others gain traction, get refined, and then the company doubles down on their underlying strategic intent. 

Case in point, AWS, which started as an OPEX optimization initiative which was then rolled out to a small ‘beachhead’ audience to see if the company can actually play in the ‘cloud storage space’ at scale, and today accounts for about 20% of Amazon’s revenue. 

Back in the day, entering the ‘cloud storage space’ was a massive strategic undertaking by Amazon, that was informed by numerous small scale experiments. It’s a clear demonstration of how an experimental mindset connected with a strategic intent can drive continuous business growth while mitigating risk.

Why Experimental Strategy Works

Building strategy through experimentation doesn’t mean you abandon long-term thinking. It means you reach long-term goals through smarter, faster learning cycles. There are several key advantages to this approach:

• Reduced Risk Through Small Bets: Instead of betting big on unproven initiatives, you make smaller, reversible investments. This limits downside and allows failure to be informative rather than destructive.
• Faster Time-to-Learning: You move quickly from idea to insight. Experiments generate real data, reducing the lag between planning and learning.
• Adaptability in Uncertain Environments: As markets evolve, so can your strategy. You’re not locked into a rigid plan — you’re constantly adjusting based on what you learn.
• Culture of Curiosity and Evidence: Teams become more comfortable asking “What if?” and backing decisions with data. This cultural shift supports long-term strategic innovation.
• Connect innovation with strategy : Taking this approach strengthens the link between strategy and innovation—a connection that is currently weaker than ever, with only 12% of companies reporting a strong alignment between the two functions. This disconnect is a key factor contributing to the decline of many corporate innovation arms.

How to Apply an Experimental Approach to Strategy

Adopting an experimental approach to strategy doesn’t mean throwing away your existing processes. It means adapting and improving what you already do. Here’s how to get started:

1. Break Down Strategic Direction into Testable Assumptions

Don’t just assume a new strategy will work. Identify what must be true for your strategy to succeed. Write these in the form entries in an Innovation Thesis and then.

2. Run Minimum Viable Experiments

Design tests around those Innovation Thesis items in the form of investments in teams and ventures. Encourage teams to use prototyping techniques and methodologies such as design thinking and lean startup. The goal is to learn fast about the underlying assumption of the ideas which in term are the underlying assumptions of your strategy. The goal is not to launch perfectly developed businesses – that’s a collateral if it ends up happening.

3. Build Feedback Loops Through Your Governance Process

Hold regular check-ins with your investments—don’t wait until the end of the quarter, a major decision point, or a new funding request. Maintain a consistent meeting cadence to track and record progress. These ongoing updates provide validated learnings that inform your overall strategy. While in some cases, there may not always be significant update from one meeting to the other, holding these sessions at a regular cadence reinforces a culture where strategy is informed by experimentation

Alongside the insights gained, keep a record of the investment and effort involved. This information will be valuable when planning larger moves in that strategic direction. This data can be extrapolated to guide decisions if you choose to double down on that path.

4. Update Your Strategy

Determine how often to meet with your broader leadership team to review and refine your strategy and innovation thesis. While there may not always be significant updates, holding these sessions at a regular cadence reinforces in your leadership circle a culture where strategy is informed by experimentation not subjective opinions. 

The appropriate frequency should be guided by the learning velocity of your innovation teams and the pace of change in your market. For instance, a pharmaceutical company may meet less frequently than one in retail banking. 

From Rigid Strategy to Responsive Innovation

The future of strategy isn’t about locking into a single path. It’s about building the capacity to adapt, learn, and innovate continuously. By shifting from rigid plans to strategy as a series of experiments, companies position themselves to thrive in uncertain environments.

This approach isn’t just for digital-native companies or tech giants. Organizations in retail, manufacturing, financial services, and even nonprofits are using experimentation to drive smarter, more resilient strategies.

In a world where change is the only constant, the most strategic thing you can do may be to stop trying to be right from the beginning — and start committing to learn faster than anyone else.

This article was originally posted on the OUTCOME Blog

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