The AI Management Crisis: Who Is Actually Steering the Ship?

Six in ten executives now use artificial intelligence to support their decisions, according to Deloitte's 2026 Global Human Capital Trends research. Gartner expects around half of all business decisions to be augmented or automated by AI agents within a year.

Neither number is alarming by itself. The one worth sitting with comes from the same Deloitte research: 57 per cent of organisations operate at low decision-making maturity, with little training in how decisions get made at all.

That combination is the real issue. Tools capable of producing a confident answer to almost any management question have landed in organisations that were never rigorous about how they reached answers in the first place.

The quiet hospital takeover

Very few leaders would say they have delegated judgment to software. The handover happens in smaller steps than that.

A restructure memo gets drafted by a model and lightly edited. Quarterly goals are generated from last quarter's numbers. A market entry option is assessed by asking a chatbot what the risks are. Each step looks like time saved. Together they mean nobody in the building has worked through the reasoning.

Deloitte found that 64 per cent of respondents consider AI-supported decision-making very important to their current performance, while only 5 per cent think they are doing it well. That gap is the shape of the problem. Adoption has run well ahead of any thinking about how these tools should be used.

Board oversight has lagged too. Earlier research cited in the same work found 45 per cent of board members and senior executives said AI was not on their agenda at all, and only 14 per cent discussed it at every meeting.

Why everyone's strategy starts to sound the same

There is now decent evidence that heavy AI use narrows the range of ideas a group produces.

Alwin de Rooij and Michael Mose Biskjaer ran a meta-analysis of 19 empirical studies published between 2022 and early 2026, pooling 61 effect sizes. Their finding was specific and awkward for anyone using these tools in strategy work: individual output can improve in quality while becoming more similar across people. The convergence persisted even after participants stopped using the tools.

Apply that to a market where competitors are all prompting similar models with similar questions. Your differentiation strategy, their differentiation strategy and the strategy of two firms you have never heard of begin to converge on the same handful of moves. The output reads well in each case. It is also interchangeable.

What staff notice before management does

Employees work out very quickly when a performance review, a set of KPIs or a restructure rationale has been machine-generated. The tells are obvious to anyone who has read a few: the specifics are thin, the tone is even, and nothing in it could only have been written by someone who knows the team.

The effect on trust is straightforward. People will accept a hard decision from a manager who owns it. They do not accept the same decision when it arrives with no author, because there is nobody to argue with and no evidence anyone weighed their circumstances.

Australian regulators are now looking at this directly. A John Curtin Research Centre report released in April, backed by the SDA, found the country has no national strategy for regulating workplace AI and warned that unchecked deployment could intensify surveillance, workloads and job insecurity. Co-author Dominic Meagher put the stakes plainly, warning that Australia does not have the luxury of getting this one wrong.

Its recommendations include a review of the Fair Work Act, mandatory human oversight of AI systems, an expert advisory panel inside the Fair Work Commission, and a requirement that employers consult workers before deploying these tools. The underlying principle is that an employer's obligations do not disappear because software made the call.

Where the line actually sits

The organisations handling this well still use plenty of AI. What they have done is get clearer about which decisions carry a name.

Drafting, summarising, modelling, first-pass analysis and pattern-finding across large datasets are all reasonable uses. The distinction worth holding is accountability. If a decision affects someone's job, the allocation of significant capital, or the direction of the business, a person signs it, can explain the reasoning without reference to a tool, and wears the consequences.

That is harder than it sounds, because the tools produce fluent output. Fluency reads as competence, and a well-written memo based on nothing in particular is more dangerous than a rough one based on real knowledge of the market.

The capability gap underneath

Most of this comes down to managers who were promoted for technical or commercial ability and have never been taught how organisational decisions should work. When you have no framework for assessing a strategic option, a tool that produces one instantly is very hard to resist.

The gap is usually in the same places: governance, organisational behaviour, capital allocation and the qualitative side of risk. This is a large part of why experienced managers move into MBA programs at this stage of their career. Melbourne Business School's online MBA covers strategic management, managing people, ethics for managers and data analytics for decision making, which is a reasonable map of what is missing when leaders lean on tools to think for them.

Steering

The question in the title has a straightforward answer in most organisations, and it is worth asking out loud at the next executive meeting. Name the last three significant decisions. Ask who made each one, what they weighed, and what they would have decided differently with better information.

If the answers come easily, the ship has someone at the wheel. If they do not, that is worth knowing now.