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AI is putting pressure on the traditional economics of IT consulting: when tools can speed research, analysis, documentation, prototyping, and some development work, clients may question paying for large teams over long engagements. That does not mean consulting firms are disappearing. The harder question is whether a provider can turn faster work into better outcomes for the client.
Why AI puts pressure on the consulting model
Many consulting engagements have relied on substantial teams working over extended periods. AI tools can reduce time spent on information-heavy tasks, including reviewing documents, analyzing data, drafting materials, and building prototypes. If less labor is needed for those activities, clients may challenge the size, duration, or price of an engagement.
In a September 10, 2026, feature for CIO, Grant Gross reported that some IT leaders are questioning two- or three-year transformation engagements as AI tools assist with work such as cloud modernization and mainframe migrations. Edwin Miranda, founder of konsultora, characterized the risk as pressure on the engagement model: “What AI is attacking is the traditional operating model behind the engagement.”
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →That is an interview-based account of a changing buyer debate, not a quantified study showing that AI has caused industry-wide staffing reductions or that the market has broadly shifted to 90-day projects. Claims about shorter engagements and changing demand should be understood as the views of the people Gross interviewed, not measured market averages.
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What may change in the work—and what still needs people
Work AI can help compress
Miranda says AI can speed research, document analysis, and prototyping. Paul DeMott, CTO at Helium SEO, says clients can run more data analysis with off-the-shelf AI tools and may ask why they should pay premium rates for work they can now do internally. These are their assessments of the changing economics, not independently measured productivity results.
Miranda argues that buyers should look beyond the number of people assigned to a project: “The value moves away from the volume of people assigned to an engagement and toward judgment, architecture, implementation, governance, and measurable business outcomes.” In practice, that shifts the buyer’s scrutiny toward what the team decides and delivers, rather than how many hours it bills.
Work that does not disappear when analysis gets faster
Aelin Golsarry, founder and CIO of AAG Technology Consulting, draws a distinction between speeding up work around a transformation and completing the transformation itself. “AI should make a lot of the work required to get through a transformation faster, but it doesn’t make the transformation itself happen faster.” Organizations still need people to make decisions, change processes, implement systems, and help employees adopt them.
Interviewees also point to expertise in industry context, complex integration, procurement, regulation, governance, implementation, and working within large organizations as continuing sources of value. A specialist or smaller team may fit a focused problem, but scale and organizational reach can matter when a project spans systems, departments, or regulatory obligations.
Why this is not proof that consulting firms will disappear
The CIO feature presents competing views: some observers see AI as a threat to large firms’ labor-intensive model; others expect established firms to adapt and retain advantages in expertise, global delivery, integration, procurement, regulation, and organizational access. The feature does not establish that AI alone caused staffing cuts or that all firms or clients are moving to shorter engagements.
Gross reported that representatives from Deloitte, EY, and Accenture did not respond or declined to comment on AI’s effect on their businesses. Their silence should not be treated as agreement with the interviewees’ predictions. The feature also mentions layoffs at several large firms, but it does not independently establish their causes as AI.
How to evaluate an AI-era consulting proposal
Use the proposal discussion to test whether AI-enabled efficiency benefits your organization, not merely the provider’s margins. Golsarry advises buyers to evaluate the experience of the people doing the work, the outcomes being purchased, and who benefits from efficiency. Her pointed question is: “Do I need 30 consultants, or do I need three people who have seen this problem before and know how to fix it?” That is a useful challenge, not a universal staffing formula.
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- Start with the outcome. Define the business result, decision, implementation milestone, or operational change the engagement must produce. Ask how progress and success will be measured.
- Assess the actual delivery team. Ask who will do the work, what relevant problems they have solved, and how much senior expertise will be involved. Do not infer delivery quality from a firm’s size or its AI claims.
- Test scope and duration. Ask which work AI is expected to accelerate, what still requires human judgment or organizational change, and why the proposed timeline is appropriate. Modular or shorter work can help when the problem is bounded; it is not automatically better for complex transformation.
- Inspect the economics. Request a clear relationship between fees, staffing, deliverables, and outcomes. Ask whether AI changes the proposed effort or price, and how any efficiency gains are reflected in the client’s value.
- Check organizational fit. Determine whether your own staff can make decisions, provide data, change processes, and support adoption. If not, establish which provider responsibilities cover those gaps.
- Account for scale and constraints. For work involving multiple systems, procurement rules, regulation, or many business units, weigh a provider’s integration and organizational capabilities alongside team size and cost.
When a shorter engagement makes sense
Brad Belzak, founder of Acuity Global Partners, says some clients want modular, shorter engagements and describes 90-day sprints as one approach. He also warns: “If your roadmap takes three years, the tools you scoped in month one are obsolete by month 18.” These are interview claims and examples, not evidence that 90 days is a typical or optimal project length.
A bounded sprint can be useful when it answers a specific question, tests a prototype, or delivers a clearly defined phase that informs the next decision. A longer program may still be necessary when implementation depends on complex integration, governance, regulation, or sustained process and adoption work. The right structure follows the problem and the organization’s ability to act—not a blanket preference for short or long contracts.
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What the evidence does—and does not—show
The CIO feature is a reported set of interviews about pressure on consulting economics and possible adaptation. It offers examples and expert opinions, but no market-wide productivity measurement, staffing trend analysis, or proof that AI is the sole cause of consulting layoffs. Belzak’s company, Acuity Global Partners, describes its own advisory positioning; that first-party information is not independent confirmation of wider market trends.
The most defensible conclusion is narrower: AI can make some consulting tasks less labor-intensive, which gives clients a reason to revisit staffing, scope, duration, and fees. Whether that changes a particular engagement depends on the work that remains—especially judgment, implementation, integration, governance, and organizational change.
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