Aim high – Where to place your bets
Forget the endless ideation of isolated AI use cases or the “let’s try a chatbot”. The new game is holistic process redesign across the entire value chain that doesn’t just support, but fundamentally transforms, how value from is created. The smart money is on portfolio management: start with a long list of potential use cases, but ruthlessly prioritize those that provide business value and align with your organizational and technical readiness.
Make it massive – How to scale without breaking Things
Scaling AI is not about sprinkling a few pilots across the organization. It’s about building an “AI factory” – a scalable, repeatable engine for innovation. This means robust development processes, governance, and a relentless focus on reusable patterns.
Embed it – How to scale from an architectural foundation
Scaling AI sustainably means embedding it into the core architecture, not layering it on top. “Embedded AI first” turns platforms like SAP, Salesforce, or Azure into intelligent systems of record, where AI is built into the transactional and analytical backbone.
The foundation: a modern data platform that unifies, governs, and exposes high-quality business data through APIs and semantic layers. On top of that, composable AI services and reusable components enable rapid deployment across use cases.
Plug it in – How to ensure adoption and Integration
The graveyard of failed AI projects is littered with brilliant proofs of concept that never made it past the pilot stage. Why? Because adoption isn’t about technology – it’s about people, processes, and structure. Organizational design, clear roles and responsibilities, and robust governance are non-negotiable. Metrics must go beyond technical performance to measure real business impact and user engagement.
Communication is the secret weapon: from targeted training to change ambassadors, every employee must be activated, not just informed.
Conclusion
What we believe the near future will hold:
- Process-driven AI: Agentic AI will lead to a shift towards AI-first process redesign, leading to greater adoption and integration into the operating model
- Differentiating AI: Companies will increasingly need to connect AI with their business strategies and implement AI in core products / services as well as differentiating capabilities
- Responsible AI: It will become increasingly complex to manage regulatory and ethical questions around AI, leading to an increase in testing and controls (incl. red-teaming for AI)
Don’t just watch AI evolve – lead it. Because in the age of AI, playing it safe is the riskiest move of all.