Lost in Translation: Why Your AI Plans Need a Translator

What an AI Business Translator does, and how to tell if your organisation needs one

The vendor demo went well. The screen filled with dashboards, the presenter talked about integrations and tokens, and everyone nodded.

Afterwards, each person left the room with a different story. The operations manager heard a fix for the Friday backlog. The finance lead heard a cost. The frontline team heard that their jobs were about to change, and nobody told them how. Three months later, the licences are paid for and the backlog is still there.

Nothing was wrong with the tool. The organisation had no one translating.

A role that has been here before

The idea of a translator between business and technology is older than generative AI. In 2018, McKinsey authors Nicolaus Henke, Jordan Levine and Paul McInerney described the “analytics translator” in Harvard Business Review as a new must-have role. They wrote that translators play “a critical role in bridging the technical expertise of data engineers and data scientists with the operational expertise of marketing, supply chain, manufacturing, risk, and other frontline managers.”

Generative AI has widened that gap. Tools like ChatGPT, Claude, Gemini and Copilot now sit on every desk, yet decisions about how work should change still sit with managers who are not technologists. The technology arrived in every department at once. The translation did not.

An AI Business Translator fills that gap. The role does not code or build models. It makes sure the right AI is applied to the right business problem, then leads the change management that gets people to use it.

What the translator actually does

The work runs in two directions.

From business to AI. The translator starts with the organisation’s strategic goals, then finds the workflows where AI could save time, cut cost or open up new ways of working. Each opportunity becomes a clear brief for the vendor or IT team. In my practice, that brief is the Opportunity Play: one page covering the goal, where the work happens, the simplest fix, the people affected, the story of the change, and how success will be measured.

From AI to business. The translator explains what the tools can and cannot do, in plain operational terms leaders can decide on. Then comes the part that is easiest to underestimate: helping teams understand the change, trust it, and build the new workflow into their routine.

Why the story matters as much as the system

BCG’s guidance to leaders is to put 10% of AI transformation effort into algorithms, 20% into technology and data, and 70% into people and processes. The tool is the smallest part of the job.

That 70% is where translation happens, and it depends on a shared story. Leaders talk about strategy. Staff talk about workload. Technologists talk about systems. Customers talk about their experience. Without a common language, each group builds its own version of the change, and those versions rarely match.

This is why I lead change with narrative. I use a Story World approach: one shared story of the change, set by leadership, with room for staff, customers and partners to add their own experience. When every stakeholder can find their place in the same story, translating between business and technology becomes a shared task.

Five signs you need a translator

Use this as a quick check. If two or more are true, translation is your missing piece:

1. Your team uses AI tools, but no workflow has changed.
2. You have bought an AI tool that few people use.
3. Staff are anxious about AI, and leaders have not addressed it directly.
4. Your board or funders are asking for an AI plan you cannot yet write.
5. Your vendor and your managers describe the same project in different words.

In social service organisations, add a sixth: you want AI to free up staff time, and you need to decide which parts of the work depend on a person.

Translator, vendor or someone inside?

You have options, and the simplest one that works is the right one.

Some organisations can grow a translator internally: a manager who understands the business, is curious about AI, and has the time and mandate to lead. If you have that person, back them.

If you do not, a fractional AI business analyst can play the role from inside your team without the commitment of a full-time hire. Either way, keep your vendor. The vendor builds and runs the system. The translator makes sure it solves the right problem and that your people use it.

Where to start

Before you buy another tool, answer five questions: what problem you are solving, which workflow will change and who owns it, what your people gain and fear, what story you will tell, and how you will know it worked in 90 days. My free guide, The Question Before the Tool, walks through each one.

Who in your organisation is translating AI today, and do they know that is their job?

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