Is AI about to kill the billable hour consulting model?

If or when AI begins to generate effective, consistent research, modelling and strategic insight, the logic of billing for time begins to break down. Ikum Kandola, former PwC GenAI expert and Founder of TheAX.ai, argues that AI is dismantling the billable-hours model, and enabling consultancies to productise their expertise around outcomes rather than time.

For decades, the billable hour underpinned the economics of consulting. Clients paid for time and expertise, and value was measured by how many hours a firm could stack against a project. Scale was achieved by adding people, and profitability grew with utilisation.

AI is now unsettling that logic. Not because it replaces consultants or removes the need for judgement, but because it compresses the time required to produce research, analysis and insight. When work that once took weeks can be delivered in hours, time becomes a far weaker proxy for value.

That shift is no longer theoretical. McKinsey has publicly acknowledged that its internal generative AI tools are saving consultants around 30% of their time. This is not a marginal efficiency gain - it signals a structural change in how consulting work is delivered, staffed and priced.

Uncomfortable questions

Inevitably, this raises uncomfortable questions. If humans remain closely involved in reviewing AI-generated output, doesn’t the billable hour still apply?

To a degree, yes. AI reduces time as a percentage, not as a concept. A task that once took ten hours may now take five. But as long as engagements are rebuilt from scratch and pricing remains tied to hours worked, effort still scales with size. In that sense, the billable hour survives - albeit in compressed form.

The problem is that compression carries a cost. If delivery time is halved, the value and margin of an engagement fall with it unless firms can realistically double their rates. Consultancies are then forced to take on more work simply to stand still, just as clients become increasingly capable of solving parts of their own problems using tools like ChatGPT. For that reason, the long-term viability of the billable hour is now in serious doubt.

Faced with this pressure, many consultancies are turning to productisation. By standardising expertise into tools and platforms, firms can reduce delivery costs, shorten timelines and rely less on large, project-specific teams. Productisation does not solve the revenue challenge on its own, but it does change the cost base in a market where demand is softening and prices are under pressure.

As firms move quickly to adopt AI-enabled approaches, concerns about quality have emerged - from factual inaccuracies to poorly presented outputs. These issues are often blamed on AI itself. In reality, the problem is usually how the technology is deployed.

Consulting was not error-free before AI. When I worked at PwC, templates were reused constantly and outdated data sometimes slipped through. The difference is that those errors were buried inside lengthy decks and legitimised by the hours spent producing them. AI has not created quality problems - it has removed the time buffer that once masked them.

Systemic intelligence

This is where systems matter. When consulting is productised and accelerated by AI, quality depends on clearly defined frameworks, processes and guardrails that govern how insight is generated and reviewed.

Through my work on TheAX, it became clear that quality improves when consultancies define and reuse frameworks such as maturity models, assessments and scoring logic. That structure allows AI to operate within boundaries rather than inventing content in isolation, while enabling benchmarking against past performance and comparable organisations.

Data collection shifts from one-to-one interviews to structured assessments completed by clients in their own time. AI automates collation and first-pass analysis, while consultants retain final judgement. Once this system is in place, time stops scaling with participation. Reviewing insights from a hundred respondents requires effectively the same effort as reviewing one.

When delivery effort becomes fixed, outcome-based pricing becomes viable. Consultancies are no longer charging for assembling analysis, but for what insight enables - better decisions, prioritised investment and reduced risk.

There will always be work that resists productisation, particularly around implementation and complex change. The billable hour does not disappear overnight. But its dominance does.

What is different this time is speed. SaaS platforms now allow consultancies to productise their expertise in hours rather than months - turning frameworks, assessments and insight into repeatable systems almost overnight.

That shift is arriving at exactly the right moment. As margins compress, projects shorten and clients become more self-sufficient, the ability to systemise expertise quickly is no longer a nice-to-have. It is how consultancies protect quality, regain commercial control and adapt to a market where time-based pricing no longer works as it once did.

The opportunity is no longer theoretical. The tools are here, the pressure is real, and the window to adapt is open.