The playbook of leadership and organization is being rewritten. Agentic AI represents a fundamental shift in how organizations operate – not just faster automation, but intelligent systems that can plan, decide, and act autonomously in pursuit of goals. These agents are already redesigning and orchestrating workflows, redefining roles, and challenging core assumptions about how leadership, trust, and strategy function in companies that spearhead this paradigm.

Progress is not a continuation of what exists but a renewal. Outdated structures give way to better ideas, new technologies, and bold capabilities. Not only since Jospeh Schumpeter and latest Nobel Laureates we know: Long-term growth favors the courageous.

The executives who will thrive in this new landscape aren’t those who simply deploy AI tools. Nor are they those who confuse AI strategy with AI activism. An agent here, a pilot project there, yet another use case. This approach may lead to frantic activism, but it rarely creates a sustainable competitive advantage.

Simply layering AI on top of existing systems often fails to modernize the company. It merely amplifies its weaknesses. Because in reality, processes in many companies are simply not properly documented. They operate differently than the organizational chart suggests. Decisions aren’t made where they’re officially supposed to be made. That’s why AI-driven actionism isn’t enough.

What’s needed instead is strategic discontinuity. The winning leaders are the ones who can reimagine their organizations around a fundamentally different model of agency, where humans and machines collaborate as partners rather than operators and instruments.

And that explicitly does not mean throwing everything that has proven successful overboard. On the contrary: Transformation only works if you also acknowledge what has worked in the past. But at the same time, you must be willing to radically rethink certain areas. Because when companies truly think of AI as native and agentic, it doesn’t just affect individual tools.

It has substantial implications for:

• Business Processes and Workflows
• The Operating model of the Company.
• Individual functions and domains like sales, R&D, HR,..
• And, in the long term, even the business model.

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. And that is precisely the task at hand for business leaders.

How AI redefines Offering Value & Monetization – A Fresh View through the Business Model Lens

Artificial intelligence is reshaping the economics of business in multiple, profound ways. The impact spans cost structures, e.g. one-person unicorns that scale without linear cost growth, revenue models with learning products and hyper personalization, productivity and labor economics with augmented workforce and the shift in skill demand, to name only a few.

By looking through the lens of a business model, you can successfully drive this fundamental shift in how organizations operate in form of a ‘system renew’. It is possible to drive a holistic, innovation-driven transformation—and to do so in a coherent manner. A business model is the distinctive logic of how to create value [back-end], deliver value [front-end] to customers and capture value [monetization mechanics] for the organization and other stakeholders.

In the past, we have seen product companies struggle with the transformation towards service-orientation, first with services wrapped around their products, and then second the shift to services as the core of new offerings. This was particularly challenging for companies that had successfully operated with a hardware, product-centric mindset for decades. The next shift, still hiding a bit in plain sight, is the transformation from stable offerings, e.g. ‘products,’ to dynamic outcomes. These offerings are characterized by their ability to act proactively, adapt, and improve over time through sensing, connectivity, and feedback loops. Once that happens, product excellence stops being a finish line and becomes a moving system.

Make AI Operational –Transform Value Creation, Delivery, and Capture in Automotive Today

When it comes to AI in the automotive industry, one could argue that capital is moving away from the hardest end-state bets of self-driving vehicles. Instead, we witness a strategic shift toward nearer-term driver assistance capability stacks that can scale inside real economics. GM’s decision to stop funding Cruise’s robotaxi development and refocus on personal-vehicle assistance is a clear signal. The same applies for Mercedes, which is moving away from Level 3 for independent self-driving vehicles despite they have been the first to implement the concept successfully in their S-Class, but did not meet with sufficient willingness to pay.

Mercedes’ E-Active Body Control demonstrates how AI is driving a new logic of value creation with tangible short-term benefit. Mercedes E-Active Body Control is an example of a continuously ‘learning product’ based on the accumulation of usage data, whose value increases over time. Sensors on the vehicle’s wheels transmit the data to the cloud, where every other vehicle equipped with this technology can access this digital road condition report in advance. This real-time digital mapping of road conditions then allows the car’s springs and dampers of every other Mercedes car with the same body control to be adjusted with pinpoint accuracy to achieve the most comfortable ride possible.

This case exemplifies how you can translate the concept of strategic discontinuity into operating reality. Following signals stand out as design cues for how value creation, delivery, and capture are being rewired right now.

Value Creation: Activate, Update, Monetize – Intelligent In-Vehicle Services Driving Data-Powered Growth

This new generation of intelligent onboard services can be activated or deactivated, updated, and upgraded while the vehicle is in operation. With over-the-air updates, we are currently seeing an expansion of the publisher/subscriber architecture in the field of automotive electronics, which enables pull- or push-data-integration and -delivery directly into the vehicle. Data is becoming a core production factor with the help of i.e. Retrieval Augmented Generation. Learning loops are replacing linear processes and turbo-charging organizational learning. The Automotive Tier 0,5/ Tier 1-Supplier’s backend must ensure that the activation and deactivation and billing of these services work properly. It also handles the accounting for which services were purchased or subscribed to, translating this – via a billing logic – into invoices.

Value Delivery: From Learning to Earning – AI-Powered Personalization Requires New Backend Capabilities

AI changes, how value is delivered. AI-driven services enhance the personalized driving experience. You can purchase AI-supported features. i.e. when the vehicle has learned which configurations and subsystems are relevant for a specific driver. For example, the car learns your personal  relaxation mode that enables driving with as little stress and energy consumption as possible. For Tier 1 and Tier 0.5 suppliers, this means a significant change in terms of the differentiating features they must deliver on the backend.

Value Capture: From Static One-Time-Sale to Dynamic Outcome Monetization in Ecosystems

Finally, new value capture mechanisms come into play. AI enables novel monetization models such as usage- or outcome-based pricing or subscriptions for intelligence with AI- or Agent-as-a-Service models. As an offering becomes continuously optimizable, the unit of value shifts from what is sold once to what performs over time – and monetization naturally evolves toward usage, performance logic, and value sharing in the ecosystem. In the vehicle ecosystem, this could be data monetization for an insurance company to calculate insurance premiums based on real-time risk predictions rather than static profiles. In nutshell, once your offering is dynamic, you are no longer pricing a product – you are pricing a continuously improving outcome.

This example illustrates how AI enables transformational leaders to bring about a strategic shift and translates it into operational practice regarding value creation, value delivery, and value capture.

Shift from Ownership to Outcomes: Build Resilient Circular Business Models with AI

Sticking to the theme, changing the subject. Although we see sustainability being deprioritized globally, the topic also presents strategic entrepreneurial opportunities rather than merely a compliance risk. This is particularly true for circularity because – when done right – it becomes an integral part of the organization’s value fabric. Circular business models and AI are a very natural fit because both are about maximizing value over time instead of one-time transactions.

Lets take repair management as an example, which is appealing because it is close to the heart of the inner loop – unlike recycling, remanufacturing, and refurbishment. From a value creation perspective, circular models build long-term resilience across your supply chain against resource scarcity, supply chain disruptions, and market shifts. In terms of value delivery, circularity drives the shift from selling products to offering product-as-a-service models like the Swapfiets bike services focusing on performance rather than asset ownership including on-site repair services. And from a value capture perspective, in outcome-based business model companies even take on an end-to-end-process and operate it on customer’s behalf like Signify’s ‘pay-per-lux’ model for Schiphol airport. Monetizing products at the end of their lifecycle through refurbishment, reconditioning, or recycling can also have a significant financial leverage effect, as refurbished or repaired items can be sold for up to 70% of the original price, even with a lower level of warranty. This has a significant financial leverage effect from reusing depreciated assets.

AI acts as the ‘intelligence layer’ of circularity. It makes products visible, predictable, optimizable, and monetizable over time. Without AI, circular models are complex and costly. With AI, they become scalable and economically viable.

Recommendations for CEOs – 5 Levers that make Strategic Discontinuity Actionable:

For leaders, strategic discontinuity represents a subtle shift in focus: from preserving the mechanics of the past to architecting the renewal of the system. Moving beyond the illusion of incrementalism allows you to invest your leadership effort where it can truly compound in the capabilities that will define relevance in the era of agentic operating models.

1. From North Star as messaging to North Star as a decision filter that reallocates capital

A North Star only earns its name when it changes what gets funded, what gets measured, and what gets stopped. The practical move is to translate direction into a small set of non-negotiable design choices – the kind that survive trade-offs, e.g., monetize performance over units, build lifecycle loops, own the data-to-decision pathway. If it doesn’t alter governance and resource allocation, it stays philosophy.

2. From Transformation Initiatives to a Capability Architecture that runs end-to-end

The real unit of transformation is not the initiative; it’s the capability chain: sensing/data → intelligence/model → workflow → decision rights → outcome measurement. Treat capabilities like products: assign accountable owners, run roadmaps, manage adoption, and measure outcomes. This is how ‘impossible’ outcomes become operational rather than inspirational. And consider that with AI some capabilities get devalued whereas other capabilities become scarce and valuable, giving you as a leader new levers of power to gain a competitive edge for your organization.

3. From Sustainability Programs to Inner-Loop Economics that compound Margin

The strategic circularity play is lifecycle control: repair, refurbishment, redeployment – where value retention is highest. Designed well, inner loops convert depreciation into value capture, harden supply resilience, and strengthen legitimacy in procurement and stakeholder expectations. This is about business model transformation, not a sustainability add-on.

4. From Pricing Ambition to Building a Value-Proof Layer

In a world of AI-driven and circular offerings, you are no longer selling products – you are monetizing continuously improving outcomes. Usage-based and performance-based monetization models only work when value can be proven continuously. As this shift only works if value can be measured and not assumed, establish a ‘value-proof layer’ as a core capability of your business model. This means embedding instrumentation, attribution, and auditability directly into your products, services, and platforms from day one. What strategic control points are in your hand to monetize the growing value stream?

5. From courageous intent to strategic abandonment that creates space for renewal

Courage becomes measurable when stopping is formal, not emotional. Create a discipline for subtraction: which legacy incentives, channel dependencies, product complexities, and governance bottlenecks must be removed to make room for the next model. In discontinuity, what you stop determines how fast the new capabilities can compound. In simple terms: What are you prepared to stop, simplify or rewire in the next 12 months?

Renewal is not a motivational concept. It is discipline. It is to make renewal operational – capability by capability – until the ‘impossible’ becomes a byproduct of your steering with coherence. Start with what is necessary, then expand into what is possible, and you will be surprised how quickly the boundary of the impossible moves.

This keynote was delivered by Carsten Linz as part of the bluegain CxO Luncheon during the World Economic Forum Annual Meeting at Davos – 2026.

/ About the Speaker

• 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.