Every year in January, I host the bluegain CxO Luncheon in Davos during the World Economic Forum’s annual meeting. Our team always spends several months intensively discussing our focus. For 2026, we chose the following question to guide our collective actions in the new year and beyond: “What meaningful tomorrow can we shape with the courage to leave yesterday’s recipes behind?”
In my view, it is a theme deeply attuned to the current state of the world. In a time of competing narratives, technology acceleration, and the slow erosion of collective trust we must not fall into the trap of dusting off tried-and-tested concepts and instead act as true ‘leaders of consequence’.
This year, the 2025 Nobel Memorial Prize in Economic Sciences provides a remarkable signal that aligns with this notion. The prize, which was awarded to Joel Mokyr, Philippe Aghion and Peter Howitt, was a recognition of their work around ‘creative destruction‘ as a central driver of economic growth, an idea originally popularized by Alois Schumpeter. The Nobel laureates’ research shows how progress is not a continuation of what exists but a renewal that occurs when outdated structures give way to better ideas, new technologies, and bold capabilities. Their theory makes one idea unmistakably clear: long-term growth favors the courageous. In their models innovation happens when new entrants disrupt incumbents, not because of disloyalty to the past, but because the future requires something else. The conditions for transformation are not technical; they are philosophical. A system renews when it chooses what to preserve, what to evolve, and what to replace. These decisions are rarely comfortable but they are decisive.
I’ve outlined what this means specifically for the 2026 leadership agenda, with a focus on the major topic of AI.
Closing the AI Ambition Operational Reality Gap
There is a massive discrepancy between AI ambitions and operational reality. When companies rush into AI without addressing the fundamentals, the consequences can be devastating. AI does not fix flawed fundamentals it only magnifies them. The sooner companies recognize this and begin to understand their process landscape and their data foundation, the better.
Many companies do not know how their – often undocumented – workflows function. Most workflows were not consciously developed but simply became established over time. Some workflows are even kept together solely by long-tenured staff. As soon as you involve AI agents and co-pilots in these workflows all the hidden chaos in protocols, exceptions, and strange edge cases comes to light. And once the actual processes are revealed, leaders are faced with the question that the organization has been avoiding for years: Can we make decisions quickly enough once the truth becomes apparent?
Most companies realize too late that their processes were never designed to work at this speed. This can be observed in the automotive industry, where established OEMs are under enormous pressure as Chinese full-stack providers launch new models within 12 to 18 months, closely integrate hardware and software, and iterate quickly.
To accelerate, it is important to understand that decisions in companies are made in many places, but rarely where the organizational chart states. That is why many perceive AI as threatening: it enforces a level of transparency and standardization that old habits cannot hide behind.
Winning leaders deal with that clarity and get the unglamorous work done. It is about working with your expert teams to document how your organizational processes work in practice and where decision-making truly takes place, then rethinking and rebuilding your processes end-to-end with clarity and simplicity. Typically 20% of the workflows deliver 80% of the value. The question each organization should be asking is what are you prepared to stop, simplify or rewire in the next 12 months?
Build Your Organization’s Data Foundation
It is crucial to gain a thorough understanding of data quality and the prerequisites for a data lake before starting with your AI cases. If your teams do not know the data it is difficult to estimate the timeline for your AI cases. IT teams are often not fully aware of what data you need for a specific use case and what the business context looks like, which poses a risk to the success of the use case. Therefore make sure each use case solves a specific, usually cross-functional, business problem, integrates specific data sources, and is performed by a team that has both in-depth subject matter expertise and leading IT and digital expertise. Each use case needs a long-term business owner with deep domain expertise, clear requirements, and a high-level business case.
Do not compromise on the fundamental phase of your transformation program. Better data leads to better decisions. Focus on building a solid data foundation, filled use-case by use-case, creating the operational substrate that makes AI possible. Only then begin training your AI model. It is not glamorous work – cleaning, consolidating, and governing data rarely makes headlines – but it determines whether AI becomes an expensive experiment or a competitive advantage.
Turn Sustainability & Circularity Into a Value Creation Engine
Sustainability became a strategic priority in the global business world but recent political developments have led to a downgrade and slowed the momentum. That said, sustainability remains no less critical and should be viewed as a strategic opportunity rather than just a compliance risk.
Sustainability offers transformative approaches to revenue generation and differentiation. Companies can develop new sustainable products and services that appeal to specific customer segments. For example, a company with well-known and long-standing customers could establish a circular economy model for its products, thereby saving resources and costs. When applying circular approaches it makes sense to consider switching to new business models such as anything-as-a-service or outcome-based models, which open new opportunities for monetization and long-term customer relationships. In this way, the integration of ESG [Environmental, Social, and Governance] value drivers into your business model(s) ensures sustainable value creation in the truest sense of the word.
The introduction of sustainable processes can also lead to a better operational and resource efficiency, reducing environmental impact while achieving significant cost savings. For a company in heavy manufacturing this can be achieved by assessing its own resource consumption, such as materials, energy, and water, as well as looking for ways to conserve resources or use new technologies for the resources it needs. Proactively adapting to regulatory changes not only ensures compliance and helps avoid fines, it also enables the company to take advantage of financial incentives such as tax breaks and subsidies. Redefining compliance can also shape daily B2B interactions and help close deals. For manufacturers, especially in resource-intensive industries such as automotive and tire production, the strategic question is clear: How do we turn ESG from a quiet sales blocker into a lever that secures trust and unlocks growth in competitive markets?
Make sustainability an integral part of the value creation fabric of your organization. Identify your company’s ESG-related issues and filter out those where added value and positive impacts on your core business can be created. Drive sustainability transformation as an entrepreneurial program that leads to product and service innovations with revenue growth and cost saving resource efficiency. Use AI and digital technologies as levers for sustainable impact to also achieve a higher return on your innovation investments both financially and non-financially. This is not a one-off. What matters is your company’s ability to continuously measure impact with the goal of scaling what works and discontinuing what doesn’t, linking sustainability decisions to financial implications for long-term performance. In this approach reporting follows programmatic measures and actions, not the other way around.
Innovate For The Future
In a highly controlled business environment the traditional credo is incremental improvement. As a result, large enterprises continue to focus either on incremental process improvement, e.g. through AI- driven data insights or role-based personal productivity tools for users.
However, the true promise of AI is to revolutionize businesses rather than to just transform. We need to stop digitizing the past and innovate for the future. In concrete terms, this means abandoning current business processes and utilizing AI capabilities at the system level to redesign new processes, new operating models, and new business models from the ground up. This requires rethinking and rearchitecting the entire value creation fabric of an organization, which also entails changes for your sustainability and especially circularity initiatives. With AI some capabilities get devalued whereas other capabilities become scarce and valuable, giving you new levers of power to gain a competitive edge for your organization.
This is where startups have the advantage. They can design new processes using radically different effectiveness, productivity and sequencing. Often misunderstood, radical comes from ‘rutex,’ meaning drilling down to the root cause, instead of treating symptoms. With this approach, emerging AI stalwarts and some Big Tech firms are ploughing in CapEx to fund costs that are yet to be monetized, applying a business model lens to rearchitecture value creation, value delivery, and value capture.
Since large companies often focus on change through continuous process improvements, it is important to be careful not to fall into the trap of Clayton Christensen’s innovator’s dilemma, assuming that we are gradually innovating but losing relevance from the customer’s perspective and running the risk of being overtaken by a new market entrant with a superior performance/cost ratio on the next technological trajectory. On the other extreme, big bang process replacement across the entire enterprise is an incredibly risky undertaking and involves a huge effort to overcome high levels of structural and cultural inertia.
Consider a portfolio perspective. The sum of your focus data and AI initiatives should strike the right balance between short-term, close-to-existing-cash-flow initiatives of an evolutionary nature, where you adapt AI to your processes, and more medium-term transformative initiatives of a revolutionary nature, where you bite the bullet – if company policy allows– and rearchitect processes and products, leading the organization on a multi-year journey towards substantial EBIT impact.
It Starts With Us
If leaders can’t change, the organization can’t either. I am deeply convinced that if we elevate the importance of strategic discontinuity, stepping beyond inherited playbooks,with clarity and consequence as leadership traits, we can shape tomorrow together. This maybe the most critical leadership challenge of our times.
This guest essay was written by Carsten Linz, the CEO and founder of bluegain, a company that helps top executives in their challenging mission of transforming established companies by harnessing the power of digital innovation, sustainable practices, and new business models. In nearly 25 years in leadership positions Linz has built several €100 million businesses and led enterprise-wide transformation programs for more than 60,000 employees. Some of his positions include Group Digital Officer at BASF, Business Development Officer at SAP, and Global Head of the Center for Digital Leadership. Linz is a member of the World Economic Forum’s Expert Network on Digital Economy and New Leadership and he is represented on various Non-Executive-Boards in the US and Europe. His articles have appeared in renowned journals and his book ‘Radical Business Model Transformation’ is considered a standard reference in business model and digital transformation literature. In his free time, he teaches in executive programs at leading business schools including the European School of Management & Technology Berlin and Stanford Graduate School.
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