AI in the workflow, not in the toolbox: How to achieve AI adoption that actually delivers results

Making AI part of the workflow – not just another tool

It often starts the same way: someone purchases licences, “AI” is rolled out, and suddenly everyone has access to a chatbot. It feels like a major step forward, but in practice it is often just another tool added to the toolbox, while work continues largely unchanged.

Real AI adoption only happens when AI moves into the process itself – when it is given a clearly defined area of responsibility, operates within the same flow as the team, and is measured on outcomes. That is when work truly starts to change. Not because more people are “using AI”, but because the way the work is done is redesigned.

In this article, we focus on that transition: from using AI alongside the work, to letting AI become part of how the work is actually done. We draw on concrete examples such as automated text generation and translation, as well as product data onboarding. One example of this type of AI-first way of working is Onboarder, but the point is broader and applies to far more processes than product data alone.

Below, we summarise the reasoning into five core principles. Together, they describe what is actually required for AI to become a real operational resource – not just another tool in the toolbox.

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1. Work shifts from execution to governance

The first – and perhaps most far-reaching – change that comes with AI in live workflows is not technical, but organisational. When AI is given clear responsibility within a process, the way work is performed changes: how quality is ensured, and where human time creates the greatest value.

When AI is assigned responsibility in a process, a clear role shift occurs:

• From: The team manually handles every individual task (for example, each file, text or correction).
• To: The team defines, governs and continuously improves how AI handles tasks within the process.

As a result, work moves away from “doing the work” and towards:

• Defining what “right” means (such as rules, data models, tone of voice or formats)
• Establishing control points and acceptance criteria
• Identifying deviations and deciding how they should be handled
• Improving instructions, prompts and rules so quality improves over time

This may sound less “hands-on”, but this is where scalability, quality and time-to-market improve for real. It is also where costs level out, risk is reduced, and the organisation becomes less dependent on individuals. The question, then, is: where in your work would a shift from execution to governance make the greatest difference?

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2. Recruit AI rather than installing AI tools

Successful AI adoption requires more than installing AI tools. AI must genuinely become part of the team, the processes and the organisation. A useful way to think about this is to treat an AI-first solution as a new hire.

You do not buy “AI” – you bring a new resource into the team. And just like a new colleague, this requires:

• Onboarding: AI needs to learn how you work, what “right” looks like, and what matters.
• Leadership: AI needs clear boundaries, ongoing feedback and day-to-day follow-up.
• Clear responsibility: AI needs a clearly defined mandate – what it should deliver, where the boundaries lie, and when a human takes over.
• Ongoing development: AI must be followed up so quality is measured, instructions are improved, and manual intervention is gradually reduced.

As with any new hire, someone must own responsibility for how AI is used, developed and followed up. In the beginning, AI is “junior”: it can deliver quickly, but needs guidance and clear direction. Over time, it can become more “senior”, requiring less intervention and delivering more predictable quality. Continuous feedback and development are essential to improve its role in the process.

A common misconception is that this should be “done” immediately. That is like hiring someone and being disappointed after the first week – because they ask questions, need guidance and do not yet deliver exactly as expected. So the question is: would you give up on a new colleague after the first week – or invest in unlocking their real value over time?

3. Embed AI directly into existing workflows

AI adoption is generally higher in the tech sector than in many other parts of the economy. This is repeatedly confirmed by global studies from organisations such as McKinsey, where software and technology companies consistently lead in both the use of generative AI and actual productivity gains.

That does not necessarily mean tech companies are more visionary or “better at AI”. The main reason is far more practical: AI has been embedded directly into the core process.

Coding tools such as Cursor, Claude Code and OpenCode are clear examples. In these tools, AI is given a concrete responsibility: to write code as described by the developer, within the same flow the developer already works in. This allows AI to work effectively because:

• AI operates in the right context (the code, files and structure)
• AI receives clear instructions (what should be built and how)
• Humans take responsibility for governance and quality assurance (review, testing and prioritisation)

The same pattern emerges when AI is embedded into core workflows in other types of organisations and processes. Only when AI works in the right context, with clear instructions and human governance, does it move from being a side-kick to creating real value in the process.

AI adoption, then, happens when AI is built into the tools already in use, given clear responsibility, and measured on outcomes. Not when an organisation “introduces a chatbot”, but when AI actually does the work within the workflow.

So the question is: which tools or workflows in your organisation are equivalent to developers’ coding tools? And how could AI take clear responsibility there?

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4. Ambassadors beat mass roll-outs

There is a crucial difference between spreading access to AI and building real AI adoption. When “everyone gets a licence”, it creates breadth across the organisation, but rarely the depth that creates value. When a team, on the other hand, gets AI working in a live, production-grade workflow, something different happens – real change begins.

When AI is given clear responsibility in a concrete process, several important things occur:

• The team learns AI’s real capabilities and limitations
• They learn to govern and quality-assure output – not just consume it
• They start identifying the next process where AI could take responsibility

This is how AI adoption truly spreads. Not through mass roll-outs or policy documents, but through ambassadors with hands-on experience of managing AI in real workflows. They can demonstrate what works, highlight risks, and show how governance, control and follow-up of AI-driven processes should be designed. Most importantly, they help the organisation understand how AI can become a valuable operational capacity, rather than an experiment on the side.

Make sure AI initiatives also focus on building internal competence and ownership. By allowing capable teams and individuals to take responsibility for AI in core processes, you create ambassadors who can drive both learning and adoption throughout the organisation.

5. Do you have AI in your organisation – or in your workflows?

The difference between having access to AI and extracting value from AI is often less technical than people think. It comes down to how clearly AI is integrated into ways of working, responsibility and follow-up – not how advanced the models or tools are.

If you want a quick way to assess whether you have effective AI adoption, ask yourselves this question:

Can you clearly describe AI’s responsibility within a process, how quality is measured, and what happens when AI gets it wrong?

If the answer is no, you likely have AI as an accessory – a tool that exists, but lacks ownership and impact.

If the answer is yes, you have taken a crucial step: AI has become an operational resource with a clear role, governance and follow-up. That is when AI starts to make a real difference.

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Start where impact is measurable

The fastest path to successful AI adoption is rarely to have the entire organisation start using AI at once. More often, it is about selecting a concrete workflow, giving AI clear responsibility, and designing governance and control that hold up over time.

When a team owns the way of working and uses AI in live production flows, adoption spreads naturally across the organisation. Not as an initiative – but as proof of what actually works.

That is when work shifts from “doing everything manually” to managing a resource that improves with every iteration. And it is at that point that AI begins to deliver real, measurable value.

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Get started today

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