AI projects can stall even when an organisation has strong technical talent. The missing capability is often someone who can connect a business problem to what AI can realistically do, then translate technical constraints and outputs back into decisions the business can act on. This translator role combines domain understanding, AI fluency, and judgement, and it needs to become part of how you structure AI teams.
The Problem May Be Sitting Between Business And Technology
When an AI initiative stalls, the instinctive diagnosis is often familiar: we need more AI talent. So you hire machine learning engineers, data scientists, and AI specialists. You strengthen the technical team, invest in platforms and give people access to increasingly capable models.
And the project can still stall.
The reason is that building an AI system and identifying the right AI system to build are two different problems. A business stakeholder may say, “Our sales team is spending too much time on unqualified leads.” A technical team could reasonably interpret that as a request for a lead-scoring model based on CRM data.
The model may work exactly as designed. It may even perform well against its technical metrics. Yet six months later, the sales team may still find it largely useless.
Why? Because “unqualified” had a meaning in the sales team’s world that never made it into the technical definition.
This is where the AI talent gap becomes more interesting. You may have plenty of people who know how to build AI systems. What you may lack is someone who understands the business problem deeply enough to determine what should actually be built, while also understanding AI well enough to explain what is technically feasible.
That person is an AI translator.
Why More Engineers Doesn’t Fix A Translation Problem
Consider the same sales example more closely.
Suppose your business analyst does everything right. They spend time with the sales team and discover that “low conversion likelihood” does not mean a prospect who has never engaged. It means a prospect who became unresponsive after a pricing conversation.
That is a perfectly valid business requirement. The development team then looks at the available CRM fields and builds a scoring model using variables such as deal size, engagement history, and time since the last contact.
The model launches on schedule, but it still misses the point.
The important signal, “the prospect went quiet after pricing”, may exist primarily in email conversations and call notes rather than a structured CRM field. The problem therefore requires the system to interpret unstructured text, make a probabilistic assessment, and potentially attach a confidence score to its prediction. A binary “qualified/unqualified” flag may be the wrong output altogether.
Nobody necessarily made a mistake. The business analyst understood the requirement. The engineers built against the data available to them. The problem occurred in the space between those two activities.
An AI translator would recognise that gap before development begins. They would understand that the business distinction matters, know what kind of AI capability could capture it, and identify the data, model behaviour and human review requirements involved.
That is a different skill from requirements gathering. It is the ability to understand what kind of problem you actually have before deciding what technology should solve it.
Bain’s 2025 research provides useful context here. Fewer than 20% of enterprises reported having scaled generative AI efforts in a meaningful way. Bain has linked the difficulty of scaling to issues including weak connections to business outcomes and poorly focused initiatives.
That suggests the AI talent gap is not simply a hiring problem. In many organisations, the harder challenge is connecting AI systems to redesigned business processes and measurable business needs.
What Does An AI Translator Actually Do?
An AI translator sits between domain expertise and technical capability.
The title itself is less important than the function. You may call the person an AI product manager, an AI business analyst, a domain technologist, or something else entirely. What matters is whether they can move confidently between the two worlds.
First, they need to understand the business problem beneath the stated requirement.
That means spending enough time with the people doing the work to understand how decisions are actually made. A claims adjuster may have a set of rules documented in a process manual, but they may also rely on years of experience to recognise an unusual claim. A supply chain planner may appear to follow a fixed planning process while making several judgement calls based on information that never reaches the formal system.
AI translators need to uncover that tacit logic. They then need enough AI fluency to determine which parts of that logic can be automated, augmented, or supported by AI, and where AI is likely to struggle. Finally, they need to translate the technical answer back into business language.
“85% accuracy” is rarely enough for a business leader to make a decision. Accuracy against what? What happens in the remaining 15%? Which errors matter more? When should a human intervene? What does the model need to learn from feedback?
An effective translator turns those questions into something the business can use.
This Is Not A New Name For A Business Analyst
There is an important nuance here.
You do not necessarily need to create a brand-new job title called “AI Translator”.
A business analyst who has developed strong AI literacy can perform the role. So can a product manager with meaningful technical depth and domain knowledge. An engineer who has spent years embedded in a business function may also develop the same capability. The problem appears when everyone assumes somebody else owns the translation.
The BA gathers the requirements. The engineer interprets them. The product manager manages delivery. The business sponsor defines the objective. Each person does their part, but nobody explicitly owns the question: Does the proposed AI solution actually represent the way this business problem works?
That question becomes increasingly important as AI moves from isolated use cases into core workflows.
Why The Translator Role Is So Scarce
The traditional career paths for technical specialists and domain specialists tend to reward depth within one discipline. Engineers build technical depth. Domain experts build contextual depth. Product and business roles often develop coordination and commercial understanding.
The translator sits across these boundaries.
They need enough technical understanding to know the difference between a deterministic rule and a probabilistic model. They need enough domain understanding to recognise when a seemingly minor business detail changes the problem. They also need judgement to challenge both sides when the proposed solution does not hold together.
That combination is difficult to acquire through a standard training course. It is usually built through experience.
You may find it in a domain expert who has deliberately developed technical fluency. You may find it in a technical professional who has spent significant time working inside a business function. Or you may develop it internally by giving selected people exposure to both sides of the organisation.
This is where AI workforce development needs to move beyond generic AI training. Teaching employees how to write prompts or use an AI assistant has value. But organisations also need people who understand how AI changes workflows, how to frame problems for AI systems, how to validate outputs and how to make decisions when the system is uncertain.
BCG’s 2025 research points in this direction, identifying AI fluency alongside systems thinking, problem framing and judgement as increasingly important skills. Its research also describes roles where people guide AI, frame problems, validate outputs and coordinate work across systems.
That skill mix looks remarkably close to the translator role.
What Should CXOs Do Differently?
If you are leading an AI programme, start by looking at the structure of the team, rather than simply counting technical specialists.
Before approving a technical build, ask who owns the translation from business problem to AI solution.
That person should spend meaningful time with the people performing the work. They should understand the current workflow, the exceptions, the informal decisions, and the data that sits outside formal systems.
They should also be able to challenge the proposed AI solution.
Does the available data actually represent the business problem? Is AI the right approach? Should the output be a recommendation, prediction, classification or generated response? Where does human judgement remain necessary? What happens when the model is uncertain?
These questions belong before development, not after deployment.
You should also look at your existing workforce differently. The person who understands the business process deeply may be more valuable to an AI initiative than another technical specialist, provided you give them the opportunity to build the required AI fluency.
This is where AI business skills become important. Your AI workforce needs people who can connect technology decisions with commercial outcomes, process design, and customer or employee behaviour.
That requires deliberate AI capability building, rather than treating AI education as a collection of standalone courses.
Build The Translator Capability, Not Just The Role
The most useful approach may be to build translator capability across selected roles rather than create a single specialist function.
Start with the workflows where AI has a clear business purpose. Give domain experts enough AI knowledge to understand what is technically possible. Give technical teams enough exposure to the business context to understand what their systems are actually being asked to support.
Then create shared ownership around problem framing. The goal is for the business and technical teams to reach the same understanding before development starts.
That can change the quality of the questions being asked. Instead of saying, “Can we build an AI model to score these leads?”, you start asking, “What decision are we trying to improve, what information informs that decision today, and where could AI improve the process?”
That is a much more productive starting point.
The Next AI Talent Question For Leaders
The instinct to hire more engineers makes sense when the bottleneck is technical capacity. But when AI projects repeatedly produce technically sound solutions that fail to create meaningful business value, adding more technical capacity may not address the underlying issue.
So who is responsible for making sure we are building the right thing?
That person may already be somewhere inside your organisation. What matters is the capability.
Your future AI teams will need people who can define the problem, understand the technology, and make sure the two remain connected throughout delivery. Those AI translators may become one of the most important links between AI investment and business value.
How XITE Create Can Help
At XITE Create, we work at the intersection of AI, business processes and technology. Our focus is on helping organisations identify where AI can create practical business value, then translate those opportunities into solutions that fit the way teams actually work.
Our expertise spans AI strategy, use-case identification, AI solution development, and capability building. We can help you assess where your current teams have the skills to take AI initiatives forward, identify the gaps between business requirements and technical execution, and build the capabilities needed to move from experimentation into practical adoption.
