AI is putting pressure on the traditional economics of IT consulting: if tools can speed up research, analysis, documentation, prototyping and some development, clients have more reason to question the size, duration and cost of large engagements. That does not mean consulting firms are about to disappear. The harder question for buyers is whether they are paying for useful expertise and delivery—or mainly for labor and time.
That tension is the focus of Grant Gross’s September 10, 2026, CIO feature, which reports interviews with consulting and technology leaders. It describes arguments about changing work and buyer expectations, not a measured study showing that AI has transformed the entire consulting industry.
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Why AI puts pressure on the consulting model
Many consulting engagements involve labor-intensive information work: collecting and reviewing material, analyzing data, documenting systems, building prototypes and supporting development. AI tools can assist with parts of those tasks, potentially reducing the time or number of people required for some work. If a client believes more analysis can be done internally or faster, it may challenge a proposal built around a large team working over several years.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Edwin Miranda, founder of konsultora, describes the pressure as a challenge to how engagements are staffed: “What AI is attacking is the traditional operating model behind the engagement.” He argues that value should shift from the volume of people assigned to a project toward judgment, architecture, implementation, governance and measurable business outcomes. These are his assessments of the change, not independently measured productivity findings.
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Paul DeMott, CTO at Helium SEO, likewise says clients can use off-the-shelf AI tools for more data analysis and may question premium fees for work they can perform internally. The feature reports client pushback on scope, duration and cost, but it does not establish that all clients are doing this or that AI alone has caused industry-wide staffing changes.
What AI does not remove from transformation work
Faster analysis is not the same as a completed transformation. Organizations still have to decide what to change, align stakeholders, redesign processes, implement systems and help people adopt them. A tool may accelerate preparation or prototyping; it does not automatically create agreement, execute a complex migration or make a new way of working stick.
Aelin Golsarry, founder and CIO of AAG Technology Consulting, puts the distinction plainly: “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.” Her point is that project speed depends on organizational decisions and implementation as well as the pace of analysis.
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Consultants may also bring capabilities that are difficult to replace with a general-purpose tool: experience in a particular industry, integration across complex systems, procurement knowledge, regulatory expertise, global delivery capacity and the ability to work inside a large organization. The CIO feature presents these as reasons established firms may retain value and adapt, not proof that every large firm has a lasting advantage in every engagement.
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Why shorter projects may appeal—and when they may not
Some interviewees describe clients asking for modular or shorter work rather than committing to a long, fixed roadmap. Brad Belzak, founder of Acuity Global Partners, says some buyers want 90-day sprints and deployable talent able to work with messy data and solve specific problems. He also warns that a long roadmap can outlast the tools chosen at its start: “If your roadmap takes three years, the tools you scoped in month one are obsolete by month 18.” These are interview examples and views, not measured industry averages or evidence that all transformation work should fit a 90-day cycle.
Shorter phases can give a client clearer decision points and room to revise priorities as tools or business needs change. But they are not automatically better. A short engagement can leave the client with recommendations it lacks the people, authority or skills to implement. A complex migration or regulated transformation may still require sustained coordination and specialist support. The useful question is not simply “short or long?” but whether the scope, milestones and client responsibilities fit the work.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to assess a consulting proposal in an AI-assisted market
Ask the provider to explain the work and the value being purchased in concrete terms. The CIO feature’s interviewees emphasize the experience of the people actually delivering the project, the engagement’s scope and duration, and who benefits from efficiency gained through AI.
- Identify the outcome. Define the business or technical result the engagement is meant to deliver, how it will be assessed and what must be true for the client to realize it.
- Meet the delivery team. Ask who will do the work, what relevant problems they have handled and how much of the proposed effort depends on senior judgment versus repeatable analysis or documentation.
- Test the scope and duration. Request milestones and decision points, and ask what can be completed in stages. For a multi-year roadmap, clarify how priorities and technology choices will be revisited as circumstances change.
- Examine the economics. Ask how the fee relates to the outcome, the work required and the use of AI. Golsarry suggests buyers ask whether the provider’s AI use makes an engagement “faster and more efficient for me or simply making the engagement more profitable for them?”
- Account for the client’s part. Establish who will make decisions, supply data, approve changes, implement systems and support adoption. If the client cannot provide those capabilities, a smaller or faster consulting team may not be enough.
- Check where scale matters. For work involving complicated integration, procurement, regulation or coordination across a large organization, ask what capabilities the provider brings in those areas and whether they are needed for this project.
Golsarry frames the staffing question as: “Do I need 30 consultants, or do I need three people who have seen this problem before and know how to fix it?” The numbers are a pointed example, not a benchmark. Use the question to test whether the proposed team matches the problem—not as a rule that fewer consultants always means better value.
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- TRACK REVENUE-GENERATING ACTIVITIES EVERY MONTH Every month gets a dedicated spread to set priorities, track revenue and the activities driving it, and review results against plan, keeping the business moving between quarterly reviews.
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What the reporting does—and does not—show
The CIO feature reports contrasting views: some leaders see AI as a threat to large firms’ labor-heavy model, while others expect established firms to adapt and retain advantages. It does not quantify the effect across the industry, demonstrate that AI caused reported staffing cuts, or show that the whole market has moved to short engagements. It also reports that representatives from Deloitte, EY and Accenture did not respond or declined to comment; their silence should not be treated as agreement with the interviewees’ claims.
The evidence supports a buyer-level conclusion rather than a forecast that consulting will vanish: AI may make some information-heavy work faster and increase scrutiny of staffing, fees and project length. The enduring value of an adviser depends on the expertise, implementation ability and organizational support a specific engagement actually needs.
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