You Don’t Need a Data Scientist, You Need a Business Translator

Australia, Apr 3, 2025

When organisations start talking about AI, one role gets mentioned first almost every time:

“We need to hire a data scientist.”

It sounds logical. AI is data-driven. Data scientists build models. So that must be the first step, right?

Not quite.

In reality, many organisations don’t fail at AI because of a lack of data science talent.
They fail because the people driving the initiative can’t connect business problems to data possibilities.

What they’re really missing isn’t a data scientist — it’s a business translator.

What Is a Business Translator?

A business translator is someone who can:

Understand business strategy, drivers, and operations
Speak the language of data, analytics, and AI (without being overly technical)
Work across departments to extract context, priorities, and pain points
Translate business challenges into AI use cases
Translate AI outputs into real-world decisions or workflows

This is the person who bridges the gap between the business and the data team — and ensures that what’s built is actually relevant, usable, and valuable.

Why the Role Matters More Than Ever

In many AI projects, this is what happens:

The business wants to automate or improve something
The data science team builds a model
The output technically works — but no one knows how to use it
The AI lives in a report or dashboard that never changes anything
The project fails quietly, with no business impact

That failure isn’t a technical one. It’s a translation failure.

Without someone connecting the dots — between intent, data, process, and outcome — even the best model becomes just another underused tool.

What Makes a Great Business Translator?

It’s not just about communication skills. It’s about context, credibility, and curiosity.

A good business translator will:

Ask the right questions early
Challenge unclear value assumptions
Understand how decisions are actually made, not how they’re supposed to be
Know where data lives, and how messy it really is
Bring business stakeholders and data teams into alignment from day one

They become the glue that holds the entire AI initiative together.

This Role Doesn’t Exist in Most Organisations — Yet

The truth is, most organisations don’t have this role clearly defined.
It often shows up informally — in a senior analyst, a forward-thinking business partner, or a project lead who "gets both sides."

But as AI efforts grow, the need for formal business translation capability becomes critical.

At Logicalis APAC, we often play this role ourselves — acting as the intermediary between business leaders and technical teams to define the right problem, scope the right use case, and ensure the outcome is actionable.

And when this role is present, AI initiatives move faster, gain traction, and deliver actual value.

What to Do If You Don’t Have This Capability

You don’t need to rush out and hire a new job title. Instead:

Identify people in your business who understand both operations and data
Upskill them with exposure to AI concepts and use case framing
Encourage IT and business teams to collaborate from day one — not just at handover
Bring in advisory partners who can model the role until your team can own it

Think of this as building a bridge function — one that evolves over time and becomes the foundation for scaling AI across the organisation.

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