By the end of 2025, most mid sized organisations are already using AI in some form. Customer support teams are trialling copilots, finance teams are extracting data from documents, and product teams are embedding classification or recommendation into workflows. What has changed is not the presence of AI, but the number of business decisions it now influences without being explicitly owned or discussed.
This is where the CPTO role is shifting. The value is no longer in explaining what a model is. The value is in turning AI capability into a clear, ownable business decision, with named accountability, measurable outcomes, and agreed failure handling.
Where translation breaks down in real organisations
The gap between “we are using AI” and “we are making deliberate decisions about AI” tends to show up in a handful of repeatable ways.
One is that AI appears in delivery updates but not in decision registers. A board pack might mention a new triage model or a copilot rollout, but there is no explicit statement of what decision is being influenced and what the organisation is now prepared to own.
Another is responsibility remaining vague. People talk about what “the model” recommended, but nobody can point to a named business owner who is accountable for outcomes once it is in production.
A third is failure modes being left informal. When asked what happens if the model is wrong, biased, or unavailable, the answer is often a shrug wrapped in optimism. Someone will spot it. Someone will fix it. That is not a control.
A fourth is vendor AI being treated as neutral infrastructure. If it is “in the platform”, teams assume governance is handled by the supplier. In reality, the supplier provides capability. The organisation still owns the decision impact, the customer outcome, and the regulatory exposure.
The fifth is the absence of an explicit stance on boundaries. Many businesses can describe what they are experimenting with, but cannot state what they will not use AI for, or what requires explicit approval. Without this, experimentation turns into drift.
These are governance and intent problems, not technical problems. The CPTO is the person who can make them visible and solvable.
If you are not sure if you are missing this type of translation, do any of these examples feel even a bit familiar?
In a claims handling context, a model that triages inbound cases is not “a classification capability”. It is a decision about which customers get a faster route through the organisation and which do not. The board cares about fairness, service impact, escalation, and complaints, long before they care about model architecture.
In finance, document extraction sounds operational, even harmless. But as soon as the extracted fields drive postings, approvals, or payment timing, you have shifted judgement from people to a system. That change needs explicit thresholds, exception handling, and auditability. Otherwise you have simply moved error from a visible process to a hidden one.
In customer support, copilots often start as drafting assistants, then quietly become the default voice of the company. If you do not decide where tone, compliance, and brand risk are owned, you end up discovering “policy” in the form of screenshots shared by customers.
In people processes, AI used for screening or recommending shortlists can change who gets opportunity. Even if a tool is marketed as “assistive”, the effect in practice can be decision shaping, especially under time pressure. That is reputational and regulatory territory, not a productivity tweak.
The translation task is to describe each of these as a decision pathway, then make the pathway governable.
What good CPTO translation looks like
A CPTO who is on top of translation does not report “we deployed a model”. They report what decision has changed, who owns it, and what controls exist.
They can state, in plain language, whether the system is advisory or autonomous. If it is advisory, they describe how human judgement is expected to operate and how that is verified. If it is autonomous, they describe the limits, the escalation rules, and the monitoring cadence.
They attach named ownership to outcomes. Not “IT owns the model”, but “the Head of Claims owns the triage outcomes”, or “the Finance Director owns the extraction driven postings”, supported by technology accountability in delivery.
They make failure explicit. What happens when confidence is low. What happens when behaviour drifts. What happens when the vendor changes a feature. What happens when the model is unavailable at month end. These are agreed in advance, not improvised during incidents.
And they define boundaries. The organisation knows which uses of AI are permitted, which require explicit approval, and which are off limits unless something materially changes. If this sounds a bit like a manifesto, that might not be entirely by chance!
Four recommendations you can apply immediately
First, require every AI use case in production to be expressed as a business decision statement. “This system influences X decision for Y cases under Z conditions.” If a team cannot write that sentence clearly, it is not ready to scale.
Second, assign a single named business owner for each AI influenced decision, with accountability for outcomes. Technology can co own delivery, but outcome accountability must sit in the business.
Third, implement a minimum control set before rollout: threshold rules for low confidence outputs, a documented fallback, a monitoring metric tied to decision quality, not just model accuracy, and an escalation route that is actually used.
Fourth, publish an organisational stance on boundaries. A one page policy is enough to start: where AI is allowed, where it needs approval, and where it is currently prohibited. This alone reduces drift dramatically.
Boards need decisions they can own. The CPTO who can translate capability into intent, accountability, and control enables faster progress because the organisation stops arguing about technology and starts making deliberate choices.
That is the shift for 2026. Not “do we have AI”, but “do we understand the decisions it is shaping?”, and “are we governing them like we mean it?”.
In the next piece, we will look at how organisations can make this visible and discussable at board level by mapping AI usage across data, models, decision impact, ownership, and oversight, turning scattered activity into something that can actually be governed.