Rob Borley
3 min read • 16 May 2025
AI is captivating. The possibilities are vast, the demos are dazzling, and the pressure to "do something" is very real. But amid the noise and the headlines, it’s easy for organisations to start in entirely the wrong place. At Dootrix, we believe that the most successful AI transformations begin not with complexity, but with clarity. Not with agents (though they are incredibly cool), but with augmentation.
Not at the end of the journey—but at the beginning.
The Agent Temptation
Generative AI agents are the poster children of the AI era. They talk like humans, act autonomously, and can be deployed across a wide variety of business functions. The pitch is seductive: plug in an agent, connect it to your systems, and let it take care of the rest.
But here’s the truth: AI agents don’t just represent AI capability. They represent AI plus automation plus integration. You're not just teaching a model to respond in natural language. You're giving it objectives. You're connecting it to APIs, data pipelines, and third-party services. You’re essentially turning a tech demo into an autonomous, decision-making component of your business.
This isn’t a bad idea, but it is a terrible place to start.
The Pattern We See (Again and Again)
We’ve seen this pattern play out time and time again in early-stage AI projects:
- The prototype impresses. Internally and externally, there’s enthusiasm. The demo wins awards, garners attention, and sparks optimism.
- Then comes the move to production. Suddenly, real-world constraints emerge: data quality, brittle integrations, model hallucinations, and governance concerns.
- Weeks are lost trying to “fix the data” or work around the lack of robust infrastructure.
- Momentum fades. Interest wanes. The agent ends up on a shelf or worse, becomes an unmaintained liability.
Organisations often conclude that AI didn’t work.
In reality, it was just the wrong starting point.
Begin With Augmentation, Not Autonomy
Instead of diving into the deep end with agentic systems, the smarter approach is to begin with augmented AI. That is tools that enhance human capability without replacing it.
These solutions operate in the "human-in-the-loop" zone. They might help summarise documents, suggest code snippets, generate content drafts, or assist with analysis. The key difference? These tools rely on people to guide, review, and act. They are easier to deploy, require less integration, and generate less risk.
Crucially, augmented AI builds confidence. It gives teams hands-on experience with models. It allows leaders to assess value without taking on undue operational risk. It sets the cultural and technical foundations for more advanced use cases in the future. It inspire; lighting the fire under individuals with a new vision of what is possible.
Integration Maturity Comes Later
At Dootrix, we understand the temptation to go straight for the agent. It feels like progress. But integration maturity, both technical and organisational, doesn’t happen overnight. It’s a capability that has to be earned.
In most cases, the work required to wire up secure access to data, apply robust observability, design constraints and fail-safes, and implement fine-grained access controls is no small task. And this integration debt is often underestimated in the rush to deploy.
That’s why we encourage organisations to build AI muscle slowly and deliberately. Use augmented tools to generate wins. Build trust. Understand where friction lies. Then scale from there.
The AI Journey Has Stages
Adopting AI successfully isn’t about jumping to the most advanced technology. It’s about progressing through the right stages. In our work with enterprises, we often describe it like this:
- Stage 1: Augment – Use AI to support human workflows. No automation. No systems integration.
- Stage 2: Automate – Add workflow automation, but still with oversight.
- Stage 3: Integrate – Connect AI into data systems and APIs.
- Stage 4: Orchestrate – Use agents to make decisions and act autonomously.
Skipping to Stage 4 without mastering Stage 1 is like trying to pilot a plane when you’ve just learned to ride a bike.
The Future Belongs to the Prepared
At Dootrix, we don’t just build AI systems, we help our clients build AI capability. We believe the organisations that will lead in the AI-native future aren’t the ones with the flashiest demos. They’re the ones with the strongest foundations. The ones who started where they were. Who learned, iterated, and scaled deliberately.
So if you’re wondering where to begin, the answer is simple: at the beginning. With real problems. With practical tools. With a clear-eyed view of where AI creates value today and how to earn the right to build the AI systems of tomorrow.
Rob Borley
