Managing Director Americas
“Let’s transform ourselves into an AI-native company. But let’s not use AI to actually aid in the process of transformation itself.”
That seems to be the general philosophy of a good ~50% of what is written about AI transformation. AI transformation has – largely – remained the domain of training workshops, e-learning programs and cascading transformation programs that could have been designed and delivered 10 years ago… let alone 3 years ago.
That strikes me as a bit odd.
Now, a new model is emerging—one where transformation isn’t a slow, reactive process, but a continuous, self-improving system. Because really…
In a world when companies are looking to transform themselves by embedding AI into their business models and processes – why wouldn’t we use AI to help the process of transformation itself?
AI is changing business. But integrating AI into an organization isn’t as simple as plugging in new tools. For AI to deliver real transformation, it must be embedded into workflows, decision-making, and operations at a structural level.
This is where agentic transformation comes in. It’s not just about adopting AI. It’s about using AI to drive the transformation process itself—embedding AI through AI.
Instead of a top-down, human-driven change management approach, agentic transformation leverages AI agents and digital twins to simulate, refine, and guide AI adoption in real-time.
This isn’t just transformation for the sake of transformation. It’s about:
With agentic transformation, AI isn’t just another tool—it becomes a co-pilot in its own deployment, learning, adapting, and ensuring seamless integration.
Let’s break down how this works.
Most organizations today struggle with AI implementation because it’s treated as a one-off initiative.
But AI transformation doesn’t work like traditional technology rollouts
Agentic transformation solves these challenges by making AI implementation dynamic, self-improving, and continuously optimized.
Instead of forcing AI into rigid workflows, companies use AI to build AI-driven workflows—simulating, testing, and adjusting them in real time.
The first step in embedding AI isn’t rolling it out—it’s simulating it.
Digital twins—virtual models of business processes, systems, and AI-driven decision flows—allow companies to:
Instead of guessing how AI will integrate into operations, organizations can see how it will work in action—before committing to full-scale deployment.
Example: AI-powered product recommendation system in a digital twin
Imagine an e-commerce company implementing an AI-driven product recommendation engine to personalize shopping experiences.
Traditional approach
Agentic transformation approach
The agentic transformation approach allows brands to optimize AI-driven personalization before launching, improving customer satisfaction, increasing sales, and minimizing frustration—without using real customers as test subjects.
Once an AI system is deployed, the work isn’t done. AI models drift. Workflows evolve. Adoption hurdles emerge.
Agentic transformation ensures that AI systems stay optimized by embedding AI agents that monitor and refine AI-driven processes in real-time.
How AI Agents guide AI adoption:
Example: AI Agents managing an AI-powered sales forecasting system
Image a company that deploys AI-driven sales forecasting to predict demand.
Traditional approach
Agentic transformation approach
Instead of a static AI tool, the company gets a living, continuously improving AI-powered sales process.
The real power of agentic transformation is that it doesn’t just deploy AI—it makes AI transformation itself iterative and self-correcting.
Instead of implementing AI in a single phase, companies:
Example: AI in Supply Chain optimization
Imagine a logistics company that introduces AI-powered demand forecasting to optimize inventory.
Static AI transformation plan
Agentic transformation approach
Instead of a one-time AI upgrade, the company has a self-correcting, AI-augmented supply chain—continuously improving itself as conditions change.
Most companies adopting AI today are stuck in static deployment cycles.
They launch an AI tool, manually track performance, and make slow adjustments.
But AI itself is dynamic. It learns, adapts, and improves—so the way businesses integrate it must be equally flexible and intelligent.
Agentic transformation allows companies to:
With this approach, organizations don’t just adopt AI. They embed it as an evolving, living part of how they operate.
AI is no longer just a set of tools—it’s becoming a core driver of business strategy.
By 2027 we’ll already see that:
The future of AI transformation isn’t about just using AI. It’s about building a system where AI is continuously embedded, improved, and optimized—by AI itself. The businesses that embrace this shift won’t just keep pace. They’ll define the next era of AI-powered growth and efficiency and create a lasting (always on) competitive edge.
This is agentic transformation—using AI to transform AI, creating a self-improving cycle of intelligence that drives business forward.
The future of transformation isn’t about humans or machines. It’s about humans being elevated by AI that understands and adapts with humans.
Want to learn more about AI Agentic Transformation? Let’s talk!
Managing Director, BOI (Board of Innovation)
[email protected]
At the intersection of strategy, emerging technology and innovation Geoff leads our Americas team to craft AI strategy and build innovative AI-powered solutions. With equal amounts of optimism and skepticism, there’s nothing Geoff loves more than a new problem to solve.
