The 3 Phases of AI Integration (and why Phase 2 is the goal)

Last week, a CEO told me his company bought 50 ChatGPT licenses. Seven people use them.

That's not unusual. And it's not even the real problem.

The real problem is that he thought those 50 licenses mean his company is now doing "AI". They're not. Using ChatGPT is like learning to drive – a start, but not the same as building a logistics network.

Most companies confuse using AI tools with integrating AI into the business. The first is Phase 1. The second is Phase 2. And the difference is massive.

What we've learned working with dozens of European SMEs: The path to meaningful AI adoption has three phases. But here's the part nobody talks about – Phase 2 is where you need to focus right now, because you can't skip steps, and most companies haven't even mastered Phase 1.

Phase 1: AI as a personal tool

This is where most companies are right now, and it's more important than many think.

Employees use ChatGPT, Claude, or Gemini for emails. Designers try Midjourney for mood boards. Sales teams experiment with Perplexity for research. Some power users even create their own Custom GPTs or build small personal automations.

What does it look like when you've mastered Phase 1?

It's not just about licenses. It's when people in your company are genuinely good at prompting AI tools. They know which tool works best for which task. They share knowledge about effective use cases. Your IT knows what's happening – this isn't "Shadow AI" anymore, it's proper, sanctioned tooling.

The productivity gains are real and measurable. People work faster, write more elegantly, research more thoroughly.

Very few companies have actually mastered Phase 1. Most are stuck somewhere in the middle: Some use it brilliantly, others barely touch it, and nobody really shares what works.

This matters because Phase 1 builds something essential: AI literacy. Your team learns what these tools can and can't do. They develop a sense of when AI helps and when it doesn't.

But here's the thing: Even if you've mastered Phase 1, you're still copying between browser tabs. The moment that employee leaves, the AI workflow leaves with them. It's powerful, but it's not business integration.

Phase 2: AI integrated into infrastructure

This is where AI stops being a separate tool and becomes part of how your business actually runs.

Example 1: Automated lead process

In Phase 1, an employee opens Claude and asks it to research a lead.

In Phase 2, the complete workflow runs automatically:

Lead gets created in CRM → AI researches and enriches data → System creates tailored proposal based on pricing rules → Proposal goes for approval (Human-in-the-Loop) → After approval, automatic sending → Customer replies "too expensive" → System recognizes it and pings Key Account Manager with deal context → Manager approves discount → Revised proposal goes out → Everything logged, status updated

No manual coordination needed. Just intelligent workflows.

Example 2: Market intelligence for R&D

Your R&D department used to fly blind. Critical market intelligence about product requirements was scattered across hundreds of international tender websites. You couldn't hire enough interns to collect and sift through all that, so your database usually sat empty.

Now a process runs every night:

Intelligent agent searches relevant tenders → Extracts technical requirements → Maps them against your product capabilities → Scores opportunities by fit and contract size → Saves prioritized leads into your existing database

Same database. No new tool. No workflow change. Suddenly full of relevant, current, scored data that actually informs product decisions.

That's the difference. Your team doesn't decide to use AI. They just do their job, and AI orchestrates the complex workflows in the background.

Starting with the right quick wins

You begin with a high-value process where you already have decent data. Something that can show ROI in weeks, not quarters.

Automated lead research. Document processing into existing systems. Support ticket analysis and routing. Market intelligence collection.

Finding the right first use case is critical – and this is typically where we come in. We know the patterns from dozens of implementations and quickly see where your biggest potential lies, without you having to experiment for months first.

The goal isn't transformation. It's proof. Your team should see that AI can work behind their existing tools without them having to learn new interfaces or change habits.

Building the foundation

This is the unglamorous part nobody wants to talk about, but it's absolutely essential.

Your data needs work. Different systems use different formats. Information lives in silos. Before you can automate at scale, you need to clean up your data pipelines and make them AI-ready.

We see this again and again: Companies want to jump to the exciting automation, but their data isn't ready. A recent study by MIT's NANDA Initiative found that 95% of AI pilots deliver no measurable financial results. Usually they don't fail because of the AI models, but because of data and implementation.

This takes time. It takes investment. But it's the foundation everything else builds on.

Scaling with expertise and building internal competence

Now you can build the bigger automations. Document processing. Intelligent workflow routing. Systems that actually learn from your business patterns.

This is where external expertise makes the difference: MIT's research shows 67% success rate with external partnerships vs. only 33% with internal builds.

Why? Because we've already learned the hard lessons. We know how to do context engineering. How to design AI systems that don't hallucinate on critical data. How to build workflows that don't waste millions of tokens. How to build systems that get better over time through careful collection of examples and feedback loops.

We do this every day, and we're still learning better ways.

But just as important: Through our collaborative approach, internal champions emerge almost automatically. Your people learn by doing, understand the systems, can maintain and extend them. You can't outsource that – you need internal knowledge and internal capabilities that grow over time.

Why Phase 2 should be the focus now

Only 20% of German companies actively use AI, according to Bitkom's February 2025 report. And when they say "use AI", they usually mean ChatGPT or similar tools – not actual integration into their systems.

But 82% plan to increase their AI budgets. The market here is growing from €10 billion this year to over €32 billion by 2030.

This growth isn't coming from companies becoming "AI-native". It's coming from companies doing Phase 2 well.

A mid-sized machinery manufacturer needs efficient processes, not an AI-driven business model. A law firm wants to review contracts faster, not become a legal-tech platform. A logistics company wants optimized routing, not transformation into a data company.

Phase 2 delivers what most European SMEs actually need: working solutions that produce measurable results, integrate into existing systems, and can be maintained by their own team.

Phase 3: AI-Native Operations

This is the vision you see in keynotes. AI-first thinking in every initiative. C-level strategy revolving around AI capabilities. New roles like "AI Product Manager."

Companies where the question isn't "Should we use AI for this?" but "How do we design this around AI from the start?"

Will everyone eventually need Phase 3? Probably. The future is almost certainly AI-first across all industries.

But here's the thing: That future is still vague and dubious. Nobody really knows which technology will win or what the landscape looks like in five years. So it makes very little sense to plan too precisely for Phase 3 right now.

More importantly: You can't even sensibly think about what Phase 3 looks like for your company before you've completed Phase 2. You need the foundation. You need the data infrastructure. You need the internal knowledge of what AI can and can't do for your specific business.

Reality check: Almost nobody is actually at Phase 3 right now. Not even us at DoryAI, and we build AI systems professionally. We're solid in Phase 2 – using AI heavily in our operations, but still learning and building rather than running on a fully AI-native model.

Google is Phase 3. Netflix. Maybe a handful of other tech giants.

For everyone else? Phase 3 is the future. Phase 2 is where the work happens now.

Where do you actually stand?

Phase 1: Your employees use ChatGPT, Claude, or Gemini in browser tabs. Some do it well, others barely touch it. There's inconsistent knowledge sharing. AI is a personal productivity tool, not a business system.

Phase 2: AI works in your actual business systems. When someone does their normal job in their normal tools, AI makes them more effective in the background. You can measure ROI in reduced time, fewer errors, or faster processes. You're building internal capabilities to maintain and extend these systems.

Phase 3: You design business processes from the ground up with AI at the center. You hire AI specialists as core staff. Your competitive advantage explicitly depends on your AI capabilities.

Most companies reading this are somewhere in Phase 1 and think they're further along. Some try to jump to Phase 3 because it sounds impressive. Very few do Phase 2 well.

Bottom line

Gartner has a 5-level AI Maturity Model. McKinsey has their framework. Deloitte has theirs. All are designed to help assess where you stand and move you toward "transformation."

Our framework is different. It helps understand where the focus should be right now.

For most European SMEs, that's Phase 2. Not because Phase 3 isn't important – quite the opposite – but because you can't skip steps. You need the foundation. You need the internal knowledge. You need the data infrastructure.

And that takes time. Smart investment. External partners who've done it before. Internal teams that learn by doing and can increasingly take over themselves.

In Phase 1, you develop AI literacy. Phase 3 is where the future is headed. But Phase 2 is where the actual work happens now – and where companies either build competitive advantages or fall behind.

The question isn't "Are we doing AI?" The question is "Are we building the foundation that actually matters?"