Transforming to an
AI-first operating model

Arne Van Balen

Partner & Director

Thriving in an autonomous world is not just about adopting new technologies. It’s about changing how we do business.

Leaders need to actively explore and define how AI can redefine their industries, from automating processes to creating entirely new product categories and business models. The future of innovation lies in our ability to envision a world where AI’s potential is fully integrated into the fabric of organizational creativity and growth.

We see three critical areas to focus on in order to become a leader in the increasingly autonomous world:

Why the technology itself isn’t enough to drive change (and wins)

In short; the key to success in an increasingly autonomous world won’t be the technology – but the design of a new operating model to unlock its real value.

The technology behind autonomous engines is advanced, but leveraging it to reduce product launch cycles to just five days involves more than just tech—it requires a transformation of your entire operating model, including production and distribution capabilities. The real bottleneck is necessary human and organizational changes.

Consider the case of Bloomberg: In March 2023, they invested over $10 million to develop a proprietary LLM for financial analysis, only to discover that the next version of GPT released months later outperformed their custom model. This example highlights the challenge for any company trying to surpass the capabilities of broadly accessible, state-of-the-art AI technologies like GPT, which are available globally for a minimal fee.

The key learning here is that it’s not just about the technology. Success lies in reshaping the operating model to integrate and support that technology. We encourage organizations to think of becoming autonomous innovation engines—fusing technology with strategic operational changes.

A holistic approach to AI integration

When discussing transformation, it’s crucial to look beyond individual functions like R&D or HR. Effective AI integration requires viewing the organization and its ecosystem holistically, moving from individual insights to a comprehensive, system-wide approach.

The true potential

The true potential lies in building towards autonomous innovation, leveraging multi-agent systems, and developing a new AI-native operating model that could significantly alter industry dynamics. It’s essential to frame internal discussions not just at the individual level—like improving efficiency—but to consider whether existing functions or processes are even necessary and how these fit into the broader company context.

Transforming operating systems; opportunities and challenges

When done well, creating an AI-driven operating model can provide a significant competitive edge. This advantage stems from the accessibility of powerful technologies and APIs, such as those from OpenAI. However, simply having access to these technologies is not enough. To truly harness AI’s potential, companies must integrate it with their proprietary data and broader operating systems. Without this comprehensive approach, achieving a competitive advantage will be challenging.

While the potential is exciting, this rapid advancement also brings many questions and uncertainties. The complexity of AI technology can be overwhelming, and its fast-paced development often outstrips our understanding. Additionally, there is considerable fear and uncertainty regarding AI’s impact on jobs and the workforce. People are concerned about job security, either because of fear of the unknown, or because they understand all too well what it can do.

Governments are trying to provide guidance and frameworks, but their efforts may lag behind technological advancements. Waiting for regulatory clarity may not be a viable strategy. Instead, organizations must proactively embrace AI and its benefits while addressing the associated risks and fears.

It’s crucial to acknowledge that AI is here to stay. Rather than resisting it, we should embrace the progress it brings. 

While we do not advocate for a fully autonomous setup—recognizing that humans have always been central to every industrial revolution—it’s essential to design AI systems that augment human capabilities. Humans should remain the heroes of this story, guiding and enhancing technological advancements.

By approaching AI with a balanced perspective that values human contribution, we can navigate the complexities and uncertainties of this transformative technology. Embracing AI’s progress with a clear, strategic approach will enable us to leverage its full potential and drive meaningful innovation.

Our vision of the future places humans at the center as the heroes. In the context of an autonomous organization, this means amplifying the power of your people. It’s about getting your colleagues and experts on board and helping them understand their new roles as orchestrators and editors, while machines and agents handle the creation and execution at scale.

How to transform to an AI-first operating model

An AI-first operating model, that will enable you to build a competitive edge with AI, touches upon all areas of the business.

Below, we’ll dive into each of these areas, key principles for each, and the critical actions you need to take.

Strategy, Engine, and the Enablers making out the AI-first operating model


Actively cultivating experimentation, investment, learning and responsible stewardship.

In terms of leadership, it’s essential to cultivate a culture of experimentation, investment, and continuous learning. Share your vision of the autonomous world with your team, explaining how it will benefit them and what their roles will be. Seek a holistic scope for AI integration, which may involve working with external IT environments or agencies to avoid restrictive internal policies.

  1. Create & share your vision: What innovations should you pursue in an Autonomous World? How will the company benefit? How will it create a competitive advantage? What innovations are possible now that were never possible previously?

  2. Seek holistic scope: Run controlled experiments within the span of your control to learn which applications you should focus on – and what you can ignore. If relevant, in an external IT environment to circumvent “catch all” data and AI policies.

  3. Leaders will be becoming product owners: Instead of managing processes, leaders will be responsible for AI-driven products that will automate and improve key workflows and let them operate more autonomously.

On the last point, we believe in hiring and upskilling employees to become product owners. Leaders need to adopt this mindset to drive AI transformation effectively. If you answer yes to any of these, you likely need to adopt a product owner role; 

  1. Are there many repeatable workflows in your business?
  2. Are you responsible for creating competitive advantage?
  3. Do you predict future trends and develop hypotheses in your work?

Embracing this approach to product management will position your organization to harness AI’s full potential and drive meaningful innovation.

Talent & Culture

Upskilling existing talent, attracting and integrating new skills, cultivating AI-first mindsets and behaviors.

When it comes to talent and culture, demonstrating the value of AI is crucial. There will always be skeptics, so it’s important to showcase tangible benefits early on. Maximize exposure to AI by starting with small, manageable projects, even on a personal level.

Start small with synthetic testing or simple applications of AI to show quick wins. As you gather more data and experience, you can scale up to more complex projects, such as creating digital twins of buildings or entire systems. These digital twins can simulate various scenarios, providing valuable insights and facilitating better decision-making.

By gradually integrating AI and showcasing its benefits, you can build a culture that embraces innovation and leverages technology to drive continuous improvement and efficiency.

  1. Transforming by doing, not talking: Identify tangible demonstrations of value that can help the organization transform one innovation and proof point at a time – building organizational belief and de-risking by incubating transformation from part of the business to many

  2. Maximize exposure: (starting with yourself): Make it a habit to use GenAI on different use cases and inspire others. For $25/month, you can create your own first agent as a custom GPT.

  3. Think big, start small: Humans are geared to fear the unknown. Allow people to move from their comfort to their learning zone, rather than pushing them into their panic zone.

Processes & Business Model

New business processes, new ways to work with suppliers and ecosystem partners, and new ways to monetize data and IP.

In terms of processes and business models, partnerships are crucial. Identify where your company can gain the most significant benefits and focus your efforts there. A key principle is to view risk and compliance teams not as obstacles but as essential partners in mitigating risk.

  1. Ecosystem development: Cultivate the right partnerships, data sharing and monetization strategy to enable shared success (e.g. Kraft Heinz’s joint venture with NotCo)

  2. Cost-Benefit Analysis: Conduct detailed analyses to understand which use cases are most valuable to explore, both existing repetitive work as well as those that are not feasible today

  3. Maximal liability: Redefine risk management by working with the principle of maximal liability (rather than the aim of eliminating risk). E.g. Which use cases are possible without the use of confidential data?

Having open conversations with your risk and compliance teams is vital. They are the true experts in limiting risk and can provide valuable guidance. By working together, you can develop strategies that balance innovation with safety, ensuring that your initiatives are both groundbreaking and secure.

Data & Technology

Intelligent use of external and internal data, fueled by proprietary ‘co-pilot’ tools that support new processes.

When it comes to data and technology, it’s essential to ensure your IT architecture is API-driven. Without this foundation, integrating advanced systems will be challenging. Focus on identifying data sets that can provide a competitive edge. For instance, NotCo uses its data to innovate its products, sell to competitors, and collaborate with ingredient providers, tapping into a massive global market.

  1. Set up API-driven architecture: The use of external tech such as LLMs, APIs, and data sources will continue to expand. Establish policies to effectively leverage these resources. For internal use, each department will have its own agents that can be utilized by the entire company.

  2. Define which proprietary data truly drives competitive edge: What is the data that no one else has? Leverage that data to substantially grow your Total Addressable Market? And can you get paid by competitors or other ecosystem players?

  3. Experiment with multi-agent systems: AI agents are the future of artificial intelligence – and they’re becoming more popular as AI technology continues to advance. Many systems setups can be put together without coding. 

On the last point, experimenting with multi-agent systems can seem complex, but it’s achievable on a modest budget. Start with a knowledge base, adding as much or as little data as you have. Combine this with specific frameworks, even simple prompts, to create AI agents tailored to specific tasks, like design or merchandising.

Different agents can be linked to various language models (LLMs) based on their capabilities, whether it’s OpenAI, Gemini, or others. Define the necessary outputs—text, video, or voice—and use tools like Cassidy or Dust to enable these agents to communicate and collaborate.

Crucially, maintain a human feedback loop. Human oversight ensures the AI’s outputs are continually refined and improved. This collaboration between humans and AI is vital for achieving optimal performance and making the most of your AI systems.

Net positive impact

New approaches to managing risk, security, privacy, sustainability and safety to ensure a net positive impact.

When it comes to data and technology, it’s essential to ensure your IT architecture is API-driven. Without this foundation, integrating advanced systems will be challenging. Focus on identifying data sets that can provide a competitive edge. For instance, NotCo uses its data to innovate its products, sell to competitors, and collaborate with ingredient providers, tapping into a massive global market.

  1. AI ethics policy: Draft an AI ethics policy ensures responsible use, emphasizing transparency, fairness, and accountability, while addressing bias and privacy protection.

  2. Code-in sustainability: Often viewed as crucial by leadership but merely a nice-to-have in projects, what if it were ingrained in every decision by default, with AI using it as a core criterion?

  3. Remember: people are the heroes: AI benefits should benefit your current workforce, so implement practices for upskilling, job transition support, and collaboration between AI and human workers. Prioritize training and development to integrate AI seamlessly.


The real competitive advantage in transforming lies not just in having the right technology, leadership, or data, but in how seamlessly these components synchronize. This integration is where companies can truly develop a competitive edge.

We are here to support you in each of these areas, but the most important step is to take the initiative and begin this journey yourself. Embracing this proactive approach will position you and your organization to thrive in the autonomous era.

We’ve developed a model with several companies that focuses on strategic alignment, alongside nurturing talent and culture, refining tech stacks, optimizing processes, and establishing effective structures and governance. This approach not only leads to superior products and quicker market entry but also drives cost efficiencies and a distinctive competitive edge.

Our focus as a company is to help clients achieve this harmony. By ensuring that technology, leadership, and data are not just present but working in concert, we guide organizations towards creating a cohesive and powerful operating model. This synchronization is essential for unlocking the full potential of an AI-native operating model and driving sustainable competitive advantage.

Keen to learn more or discuss how we might help you? Drop us a note!