Agentic transformation: using AI to embed AI

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?

Agentic transformation - what it is

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:

? Simulating AI-driven workflows before rolling them out

? Continuously refining AI processes based on live feedback loops

? Ensuring AI models, automation, and decision systems stay aligned with business objectives

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.

How AI can be used to drive its own adoption (very meta)

Most organizations today struggle with AI implementation because it’s treated as a one-off initiative.

• A new AI tool is introduced
• A manual process for adoption is designed
• A fixed roadmap is created for roll-out

But AI transformation doesn’t work like traditional technology rollouts.

?? AI models evolve—they need continuous updates, fine-tuning, and retraining.

?? Workflows change—AI-assisted decision-making requires real-time adjustments.

?? Adoption challenges emerge—employees need to trust and understand AI outputs.

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.

1. Using 'digital twins' to prototype AI workflows

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:

? Test AI models in a simulated environment before real-world deployment

? Predict how AI will impact workflows and decision-making

? Identify potential risks, inefficiencies, or employee resistance points

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 customer support in a digital twin

Imagine a company rolling out AI-powered customer service chatbots to replace parts of its human support team.

A traditional approach might involve:

? Launching the chatbot directly into production

? Fixing issues only after customers complain

? Slowly iterating based on real-world failures

With agentic transformation, the process is different:

? An AI-powered digital twin simulates the chatbot interacting with real customer conversations

? AI agents analyze failure points—where human intervention is still needed

? Optimizations are made before customers ever interact with the system

This allows companies to fine-tune AI adoption before it impacts real operations—making integration smoother, faster, and more effective.

2. AI Agents as real-time transformation orchestrators

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

• Real-time monitoring → AI agents track AI model performance, flagging issues before they become major problems.
• Continuous feedback loops → AI agents analyze user interactions and suggest workflow improvements dynamically.
• Adaptive process refinement → Instead of waiting for quarterly reviews, AI-powered transformation adjusts in minutes, not months.

Example: AI Agents managing an AI-powered sales forecasting system

A company deploys AI-driven sales forecasting to predict demand.

Without agentic transformation:

? The AI model is trained on historical data but isn’t continuously updated.

? Sales teams must manually adjust forecasts when market conditions shift.

? The system eventually becomes outdated and unreliable.

With agentic transformation:

? AI agents monitor forecast accuracy daily—detecting when predictions start deviating from real-world sales.

? Real-time feedback loops adjust the AI model dynamically—ensuring continuous learning and adaptation.

? AI-generated insights flow directly to human sales strategists—so they can make data-driven decisions without manual intervention.

Instead of a static AI tool, the company gets a living, continuously improving AI-powered sales process.

3. Refining AI Workflows as they evolve

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:

?? Start small, test AI-driven workflows, and refine based on AI-powered insights

?? Use reinforcement learning to optimize AI models continuously

?? Ensure AI adoption remains aligned with strategic business goals

Example: AI in Supply Chain Optimization

A logistics company introduces AI-powered demand forecasting to optimize inventory.

A static AI transformation plan might involve:

? Deploying an AI model trained on historical data

? Relying on periodic manual reviews to update the model

? Struggling when unexpected disruptions (e.g., supply chain shortages) occur

With agentic transformation:

? A digital twin models the AI-driven supply chain—testing different demand scenarios dynamically

? AI agents continuously monitor real-time logistics data—adjusting forecasts instantly

? Automated process refinement ensures that AI workflows remain optimized over time

Instead of a one-time AI upgrade, the company has a self-correcting, AI-augmented supply chain—continuously improving itself as conditions change.

Why This Approach Matters

Most companies adopting AI today are stuck in static deployment cycles.

They:

? Launch an AI tool → Manually track performance → 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:

? Embed AI in ways that evolve naturally, rather than through rigid rollout plans

? Ensure AI workflows remain optimized over time, instead of slowly degrading

? Let AI monitor AI—creating self-improving, continuously learning business systems

With this approach, organizations don’t just adopt AI. They embed it as an evolving, living part of how they operate.

Where This Is Headed

AI is no longer just a set of tools—it’s becoming a core driver of business strategy.

By 2027 we are already seeing that:

?? 60% of enterprises will use digital twins to model AI transformations before deployment (Siemens)

?? AI-powered feedback loops will replace static change management in 50% of Fortune 500 companies (Deloitte)

?? Organizations with AI-driven transformation systems will adapt faster than competitors

The future of AI transformation isn’t about just deploying 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.