We are entering an intelligent age shaped by accumulated turbulences – geopolitical shifts, accelerating technologies, and a rising ambiguity that reshapes how decisions get made. In this environment, yesterday’s recipes lose their effectiveness, and the instinct to rely on familiar playbooks becomes increasingly limiting. What organizations need instead is a New leadership posture: one grounded in clarity, guided by a meaningful north star, and strengthened by ‘Mut’– The quiet courage required to step beyond inherited assumptions when the world around us is changing faster than our systems can adapt.

Despite a world of fractured truths, competing narratives, technological acceleration, and the slow erosion of collective trust, we must not fall into the trap of dusting off tried-and-tested concepts. Today, legacy approaches, which look for control and certainty, stretch thinly.

In the face of systemic turbulence and decision fatigue, we especially need to strengthen our entrepreneurial and transformational muscle and act as true ‘leaders of consequence’. Our role is to create clarity for our organization’s bespoke transformation towards our guiding ‘north star’. Peter Drucker knew that innovation is the specific instrument of the entrepreneur. In the intelligent age, new technologies inherently enable new types of innovation and creativity in a domain, rather than simply enhancing traditional methods. Additionally, there is more power outside than inside the organization. Thus, leadership is not about the leader, it is about the ‘ship’ with its blurring borders between inside and outside. It is entrepreneurial in the sense that it shapes an organizational context for people co-creating the intelligent future.

Let’s take a closer look at what this means specifically for our 2026 leadership agenda, with a focus on the major topic of AI: Inspiration for a CEO Leadership Agenda 2026 & beyond

Agentic Enterprise – Close the AI Ambition – Operational Reality Gap to shift from Prototypes to Workflows that drive EBIT Impact

Ignore all the high-flying brochures and CEO guides for a while. They sell a dream – beautiful frameworks, strategic clarity workshops, and reports that show-off on LinkedIn but contain little that is truly actionable. 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. Furthermore, most workflows are designed the way they have been practiced for years. Most workflows were not consciously developed, but simply became established over time. Some workflows are even kept together solely by long-tenured staff with memory and vibes. 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, we as 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?

AI is not just a technological change. It is a test of the speed of decision-making – and 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 counter this, AI-enhanced engineering shortens cycles, and rapid prototyping and integrated manufacturing feedback focus design on manufacturability from day one. 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. The fear is not of the technology itself, but of the fact that AI rapidly exposes ambiguities in operational processes.

• Leadership action: 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 actually work and where decision-making truly takes place. Then rethink and rebuild your processes end-to-end with clarity and simplicity as typically 20% of the workflows deliver 80% of the value. In such a blue field approach, what are you prepared to stop, simplify or rewire in the next 12 months?

Data Backbone: Make sure your teams work on the Data Quality and build your Organization’s Data Foundation on a Use-Case-by-Use-Case Basis

Poor data quality turns insights into noise. Decision-making slows down because no one trusts the results. Reflecting on recent AI and data transformation programs, at the outset the data pools for a specific use case were hardly available with the data maturity required to build an AI case on them. That’s why it is pivotal 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. Since data is often very raw, winning project teams talk to people who have deep business domain expertise and regularly work with the data. 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, your team should start with a broader approach: How does the company handle data, who “owns” the data? Make sure the actual use case work is performed by a team with diverse skills that has in-depth subject matter expertise and leading IT and digital expertise.

For example, when it comes to avoiding unnecessary and costly repairs to equipment, you need a detailed data analysis to distinguish between actual and non-actual problems. You can then label the data and enrich the existing data with additional information, e.g. error codes and the in-depth knowledge of an engineering team, to take data quality to the next level: Which device? Which time period? Which returns and repairs? Which parts were touched? What costs can be saved? Only then can you begin training AI models, e.g., using GenAI to label data and training ML models to predict which device parts may need to be repaired or replaced, using the analysis of the most common reasons for returns for optimal resource allocation and driving EBIT impact.

Last but not least, keep an eye on not loading all data into your data foundation, e.g. Enterprise Lake (House), but ensure that your AI & data transformation program follows a use case-based approach to achieve a single source of truth over time. Each use case solves a specific, usually cross-functional business problem, integrates specific data sources, and should be implemented by a mixed team. Each use case should have a long-term business owner with deep domain expertise, clear requirements, and a high-level business case.

• Leadership action: 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. Start with solid data quality, which usually requires deep domain knowledge of the business area. 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.

Sustainability & Circularity – Make it an integral Part of a Firm’s Operations and turn into a Value Creation Engine

After sustainability climbed the list of strategic priorities in the global business world, recent political developments have led to a downgrade and slowed the momentum for sustainability transformations. However, looking beyond this short-term wave of skepticism, sustainability remains important – perhaps less vocal, but no less critical. This is a good opportunity to rethink our approach and view sustainability as a strategic opportunity rather than just a compliance risk.

Sustainability offers transformative approaches to to revenue generation and differentiation, as 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 up new opportunities for monetization and long-term customer relationships. In this way, the integration of ESG value drivers into the 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, and 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 but 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 to turn ESG from a quiet sales blocker into a lever that secures trust and unlocks growth in competitive markets?

• Leadership action: 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 resource efficiency with cost savings. 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-time event; what matters is your company’s ability to continuously measure impact with the goal of scaling what works and discontinuing what doesn’t, which has the side effect of linking sustainable decisions to financial implications for long-term performance. In this approach, reporting follows programmatic measures and actions, not the other way around.

Next Economic Model – Strike the right Balance between short-term, sustaining Innovations and mid-term radical System Transformation Journeys

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 real promise of AI is to revolutionize businesses rather than to just transform them. We need to stop digitizing the past and innovate for the future. So, in real terms, this means abandoning current business processes and using AI capabilities to help design radically new processes, new operating models, and new business models from scratch. This requires rethinking and rearchitecting the entire value creation fabric of an organization, which also entails changes for your sustainability and especially circularity initiatives. It is about touching the economic system of capabilities, in which – with AI – some capabilities get devalued as AI collapses their learning costs whereas other capabilities become scarce and valuable and with those 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, hence solving a truly relevant problem. With this approach, emerging AI stalwarts and some big tech firms are ploughing in CapEx to fund costs that are yet to be monetized, hence 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, we must 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 different performance/cost ratio on the next technological trajectory. On the other extreme, big bang process replacement across the entire enterprise is an incredibly risking undertaking and involves a huge effort to overcome high levels of structural and cultural inertia. The way out of this dilemma is therefore to find the right portfolio mix of both aspects, which depends on the existence of entrepreneurial leadership and the willingness of customers, employees, and other stakeholders to embrace change.

• Leadership action: Consider a portfolio perspective. The sum of your focus data and AI initiatives should strike the right balance between short-term, 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.

Conclusion

Sometimes, times urge us to set out and move forward, to escape from dependencies, say goodbye to habits, leave well-trodden paths. If leaders can’t change, the organization can’t either.

It starts with us. When we look back with gratitude and yet dare to break new ground, we make new combinations a reality in the Schumpeterian sense. Maybe this is the most critical leadership challenge of our times.

/ About the Author

• Dr. Carsten Linz is the CEO and Founder of bluegain. Formerly Group Digital Officer at BASF and Business Development Officer at SAP, he is known for building €100 million businesses and leading large-scale transformations affecting 60,000+ employees. He is represented on various boards including Shareability’s Technology & Innovation Committee and Social Impact. A member of the World Economic Forum’s Expert Network, Dr. Linz is also author of renowned books and articles who shares his expertise in executive programs at top business schools around the world.