Management consulting and IT advisory firms are seeing AI pay back faster than almost any other category right now. Engineering consultancies are seeing almost no movement, and the structural reasons for that matter more than most AI conversations acknowledge.
Management consulting firms report expected 12-month AI returns of 16.2%, roughly double what architecture and engineering firms report (8.3%). The reason is not effort. management consulting work is standardized and repeatable: proposal writing, analysis, reporting, client synthesis. These are exactly the tasks where AI finds traction quickly. Engineering outputs are physical, regulated, and often legally constrained in ways that limit where automation can realistically enter.
If you run an engineering consultancy benchmarking your AI results against IT consulting peers, you are comparing against the wrong group. The opportunity exists, but it sits in bid documentation, project reporting, and knowledge management, not in core technical delivery.
IT consulting sits closer to management consulting here. Expected returns have accelerated sharply, and there is an additional dynamic worth noting: clients are also going through AI transformations, which means AI capability is becoming billable, not just an internal efficiency play.
Leadership and decision-making. Firms using AI as a genuine input to how leadership runs the business, not experimentally but consistently, report EBITDA around 13.5% compared to 8.2% for those that have not applied it here at all. The value is not from the tool itself. It is from faster pipeline synthesis, earlier delivery risk signals, and decisions that are better aligned with what the business can actually execute.
Talent and capacity. Recruitment screening, skills matching, resource planning and capacity allocation: IT and management consulting firms that have moved past pilots and into actual workflows are reducing friction in areas that used to eat significant management time. The firms seeing the biggest gains invested in people genuinely using the tools, through structured training and real change management, not a company-wide rollout announcement.
Client development. consulting firms using AI meaningfully in sales and marketing functions are seeing pipeline volumes that dwarf those of firms still experimenting. The direction is clearest in IT advisory practices, where AI capability has become billable, not just internal. Forty percent of consulting and IT firms now generate revenue directly from AI-related client services, a number that has moved significantly in two years.
Project delivery, with caveats. Firms with AI genuinely embedded in service execution report on-time delivery around 81.5%, noticeably above the 70.8% seen at firms that have not applied it. Project margins follow a similar pattern. The catch is that most firms are still in experimental territory here, and experimental use produces almost no change in delivery performance. The gains live at the implementation depth most firms have not reached yet.
Finance and operations is the consistent laggard. Nearly half of all firms are experimenting with AI in financial workflows, and it is producing almost nothing. EBITDA for firms at that experimental stage is essentially identical to firms not using AI at all. Firms that have reached genuine integration report meaningfully stronger margins, but only around 3.5% of firms are there.
Getting there requires clean, reliable financial data flowing across systems. Most consulting firms do not have that, not because of AI, but because the underlying data infrastructure was never built. Firms frustrated with slow AI returns in finance are often dealing with a data problem that predates their AI investment.
This is also where time-to-ROI stretches. In some cases it has nearly doubled compared with earlier adoption cycles, because making AI useful in financial workflows depends on governance and system integration that was not required for simpler use cases. McKinsey's 2025 research confirms the pattern: use continues to surge but from a value capture standpoint these are still early days, with few organizations experiencing meaningful bottom-line impact.
Three things appear consistently across the IT consulting and management consulting practices that are pulling ahead. None of them are about the technology itself.
Data quality. AI tools are only as useful as the data behind them. Firms with consistent project management and delivery tracking, reliable time capture, and integrated operations convert AI investment into performance improvement faster. Firms with siloed or inconsistent data will not fix that by adding another AI tool to the stack.
Implementation depth. The performance differences are not between firms that use AI and those that do not. They are between firms where AI is genuinely embedded in daily workflows and firms where it is available but optional. Most firms have not crossed that line yet, and that is where most of the ROI gap lives.
People, not platforms. The IT and management consulting practices pulling ahead bought the same tools as their peers. What they did differently was invest seriously in adoption: role-specific training, clear use cases, accountability for usage. They treated it as a change management initiative. Their peers treated it as a software rollout. McKinsey's workplace research puts it plainly: 92% of companies plan to increase AI investments over the next three years, but only 1% of leaders call their organizations mature on the deployment spectrum. The investment is there. The organizational work to realize it is not.
The firms that will be furthest ahead in two years are not necessarily the ones spending most on AI today. They are the ones that are honest about whether the foundations are actually in place: clean data, visible projects, people who are genuinely equipped to work with the tools they have.
The latest industry research shows consistently that the organizations generating real AI returns built those foundations first, and those foundations look a lot like the conditions for strong operational performance in general. For most IT consulting and management consulting firms, that is both the constraint and the opportunity.