What is judgement allocation?

Every organisation using AI has already allocated its judgement. Most did it by default, one tool and one shortcut at a time, and an organisation that has never written the list has answered the question without asking it.

Three questions it settles#

The allocation comes down to three questions, asked before a system is bought rather than after it is running:

• Where should judgement stay human as AI takes more of the decisions?
• Who is able to stop a system, by name, and how would they know they needed to?
• What must the organisation remain able to do on its own, without the tools it has bought?

Each answer is a line on a page with a role against it. A boundary that nobody wrote down still exists. It sits wherever the last convenient shortcut put it.

It belongs to the chief executive#

Ownership of the allocation cannot be handed to the team buying the tools. Deciding which decisions stay human as adoption increases is a decision about what the organisation is, and it sets the risk position, the capability position and the shape of the work for years. The argument is set out in full on who should own AI strategy.

Drift, design and custody#

Three ideas on this site describe the same ground from different sides. Drift is what happens when the allocation is left undone and the boundary moves by itself. Design is choosing it on purpose. Custody is the written result: the list of decisions and capabilities kept by hand, with names against them.

Judgement allocation is the decision in the middle, and the habit of revisiting it each time the tools change.

Where the allocation gets made#

Who else uses the phrase#

The phrase is not unique to this site, and no claim to have coined it is made here. It appears in other writing on AI and organisations, including corporate research that has organised its reporting around human agency and the allocation of judgement. This page sets out how this research uses it, so that the term means one thing wherever it appears on the site.

2 to 8 October 2026: four allocations in one week, one of them written down#

The week’s coverage showed the allocation being made four ways. Utah wrote one down in advance. Eve Bender at MobiHealthNews reported on 5 October a signed agreement under which Nolla Health’s app may issue initial prescriptions for topical acne treatments, with physician oversight stepped down in three stages. First, “two Utah-licensed physicians review each prescription for at least 100 patients before submission”. Then the system submits directly and a physician reviews each case afterwards, weekly, for at least 500 patients over a minimum of eight weeks. Then comes “a monthly retrospective review of at least 10% of enrollment volume”. Anuja Vaidya at Healthcare Dive reported that in all three pilots approved that week the companies “will need written approval from regulators before moving into the later phases that could include less human oversight”. The pilot is contested: MobiHealthNews records that the state’s medical licensing board called in April for an earlier prescribing pilot to be suspended. The schedule is still the practice this page defines. It says who decides, at what volume, and who signs the next step.

A UK research network made the allocation without telling anyone and took it back. Frances Jones at Times Higher Education reported on 5 October that CRANE, hosted at the University of Oxford and funded by the Engineering and Physical Sciences Research Council, had used an AI triage on the 179 proposals to its pilot call, and that almost half “were rejected by the AI, and were not looked at by humans”. CRANE says the process had human sampling and statistical cross-checks. Its statement went on: “we recognise that we were not transparent enough about this approach, and for that we are sorry”. Every application screened out will be re-reviewed and offers already made are suspended. The first report of that call is on whether AI should reject an application before a human reads it.

The UK government postponed one. Its response of 6 October to the National Commission into the Regulation of AI in Healthcare accepts all 44 recommendations, among them the one titled “Clear allocation of responsibility”, and sets up a working group of departments and regulators to make responsibility across the life of a clinical AI tool clear. The response then says: “The group will not determine or redistribute responsibilities.”

Two more sources described the allocation left undone. The Information Commissioner’s Office said on 8 October, opening a call for evidence on agentic AI that closes on 20 November: “the fact AI agents act with autonomy is not an excuse for poor compliance.” It was speaking of data protection law. And Gartner reported on 6 October that in its July survey of 297 chief HR officers “only 24% of leadership teams are aligned on who should be accountable for AI agents”. That is a consultancy’s survey with no method published. It is also the nearest thing to a count this page has found for its opening claim, that most organisations have answered the question without asking it.

9 to 15 October 2026: the allocation nobody can count#

This page says most organisations have made the allocation without asking the question. The week’s reporting suggests many cannot say how many agents the allocation covers. Kim Loohuis at Computer Weekly reported on 13 October that Greg Nelson, chief executive of RSA, had asked a room of security chiefs from twenty of its largest enterprise customers how many AI agents were running inside their organisations. “Not one person could raise their hand,” he said. “We had a bank the other day that said, officially, we have zero AI agents – but unofficially, we have 4,000.” And: “People start agents, but they never end them.” In the same report Rens van Dongen, corporate AI officer at the Dutch railway operator NS, called the human-in-the-loop metaphor dated: “It’s not humanly possible to expect people to step in and judge all these approvals any more.” He argues instead for what he calls a response loop, being alerted and able to intervene when required.

Two surveys put numbers beside the anecdote, both from firms that sell the remedy. SecurityHQ, with Datadog, reported on 15 October from 204 cybersecurity leaders at midsize and large organisations that 76 per cent said their organisation “has formally defined accountability for AI-driven operational or security decisions”, that 45 per cent “have already experienced an incident involving AI agents or automation where accountability was unclear”, and that only 49 per cent “could fully reconstruct every action an AI agent performed over the previous 30 days”. Makenzie Holland at CIO Dive reported on 13 October a Workato survey, run with Censuswide, of 1,001 IT decision-makers: nearly two-thirds agreed that most agents in their organisation lack formal approval from the IT department, almost two-thirds expect an agent to create a public security or compliance incident within eighteen months, and 36 per cent would try to handle such an incident without disclosing it. Neither survey publishes its sampling.

Where the allocation has been made, it is often made at every step. The Wharton and GBK Collective adoption report of 13 October records, in the words of GBK’s Brian Smith, that “Two-thirds of departments say they use AI agents, and only 17% let an agent complete a task without a person approving each step.” His description: “Most companies are sitting in a supervised middle, paying for autonomy and then reviewing every action, and that is where returns are weakest.” Approving every step is an allocation of a kind. It is the one this page calls the default, and van Dongen’s point is that at volume it stops being a decision.

Parliament asked the question of the companies. In a letter of 22 September to OpenAI, published by the Business, Innovation, Science and Trade Committee, its chair Liam Byrne asked: “Who within your company is personally accountable for a decision to deploy a frontier model?” At the hearing on 13 October, as a WIRED report read in syndication records, Anthropic’s Liam Booth-Smith said “The rules should be binding”, OpenAI’s Tom Duff Gordon said “We don’t think basing everything on voluntary commitments for frontier risk is a good long-term plan”, Google DeepMind’s Owen Larter said “Focus on the standards is what we would be doing”, and Meta’s Rob Sherman pointed to existing regulators. Henry de Zoete of the AI Security Institute said of his own body: “What we’re not doing is kite marking.” Asked whether the regime was voluntary, the Cabinet Office’s Cat Little said: “At the moment, yes, it is.” The committee opened an open-ended inquiry the next day. The allocation of judgement between a company and the state is, for now, as unwritten as the one inside most companies.

The limits of a definition#

It cannot tell you which decisions belong on your list. That depends on what your organisation is for, the regulation it works under and the capabilities it intends to keep, and a page cannot know those things. It is a practice to adopt, and the evidence for why it matters is on AI and human judgement.