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AI in Consulting: Table Stakes, Token Costs, and the Race to Prove Value

AI in Consulting: Table Stakes, Token Costs, and the Race to Prove Value

AI has become table stakes in consulting. Major firms have built platforms, alliances, accelerators, and proprietary tools around it. For clients, the question is no longer whether advisors use AI. It’s whether they can prove what AI-enabled work actually delivers.

Key Takeaways

  • AI has become table stakes for consulting firms. Differentiation now depends on how deeply AI changes delivery and whether value can be measured.
  • PwC’s 2026 Global CEO Survey found 56% of CEOs saw neither higher revenue nor lower costs from AI, which makes measurement the market issue, not tool access.
  • Enterprise clients with their own AI measurement infrastructure are better positioned to hold any advisor accountable for what AI-enabled work actually delivers.

Key Terms

  • AI in consulting: The use of AI tools, agents, and platforms by consulting firms to deliver client work faster, surface insights, and automate analysis that previously required junior staff hours.
  • Table stakes: The minimum capability required to compete. AI has crossed this threshold for consulting firms: not having it is disqualifying, but having it no longer creates differentiation.
  • AI ROI: The measurable business value generated from AI investment, linking costs to outcomes like hours saved, revenue protected, or decision quality improved.
  • Utilization × Proficiency × Value: Larridin’s measurement framework for AI impact: who’s using AI, how effectively they’re using it, and what business outcomes that use generates.

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The $10 Billion Race

An industry analysis estimates that the Big Four and top strategy firms have collectively invested more than $10 billion in AI initiatives since 2023. PwC announced a $1 billion AI investment. KPMG later described a US$2 billion AI investment tied to its Microsoft alliance. Accenture announced a $3 billion Data & AI investment and a plan to double AI talent to 80,000 professionals. Deloitte announced a $2 billion IndustryAdvantage investment that includes GenAI-enabled accelerators. EY launched EY.ai after a $1.4 billion investment, including its EY.ai EYQ large language model.

The platform layer is visible too. McKinsey built Lilli to search and synthesize firm knowledge. Deloitte rolled out PairD for drafting, coding, and research support. KPMG built KymChat as a protected generative AI environment. These tools are no longer side experiments. They’re becoming part of how consulting work gets scoped, researched, drafted, reviewed, and delivered.

Technology providers are betting on the same shift. At Cloud Next ’26, Google Cloud announced a $750 million partner fund for agentic AI development across consulting firms, systems integrators, software partners, and channel partners. The race is now about which platforms become the infrastructure underneath advisory delivery.

So What Has Changed for Clients?

The competitive differentiator between consulting firms has shifted to how deeply AI is embedded in delivery and whether value can be demonstrated with evidence rather than assertion.

BCG’s 10-20-70 framework puts the issue plainly: about 10% of AI value comes from algorithms, 20% from technology and data, and 70% from rethinking the people component. Firms that changed how their people work are building advantage. Firms that only added tools are facing harder client questions.

The Measurement Gap That Now Defines the Market

PwC found that 56% of CEOs saw neither higher revenue nor lower costs from AI. IBM reports that only about 29% of executives can measure AI ROI confidently. Gartner forecasts worldwide AI spending will reach $2.52 trillion in 2026, while a separate Gartner survey found that only 28% of infrastructure and operations AI use cases fully succeed and meet ROI expectations.

Those figures don’t all measure the same market, but they point to the same operating problem: organizations are spending faster than they’re measuring. Many deployed AI under competitive pressure before they built baselines, quality metrics, and business-outcome tracking.

Consulting clients face that problem from both sides. They need to measure the value of their own AI use, and they need enough visibility to evaluate whether AI-enabled advisory work is actually faster, better, or more valuable.

What Holding AI-Enabled Consulting Accountable Requires

A Harvard Business School and BCG study of 758 knowledge workers on realistic consulting tasks found AI users completed 12.2% more tasks and finished them 25.1% faster on tasks inside AI’s capability frontier. The same research also found worse performance on a task outside that frontier. The lesson is not “AI makes consultants better.” It’s that AI value depends on task fit, baseline measurement, and defined quality metrics.

To hold any advisor accountable for AI-enabled delivery, you need the same infrastructure applied to your own organization:

  • Utilization data: who’s using which AI tools and how often across the engagement team
  • Proficiency data: whether AI-assisted outputs are better, faster, or more accurate than pre-AI baselines
  • Value data: what business outcomes can be attributed to AI-assisted work, not just that work happened

How Larridin Helps

Larridin measures AI utilization, proficiency, and business value across your organization, giving you the data foundation to evaluate any AI-enabled advisory relationship on evidence rather than assertion. Our AI Measurement Framework and AI ROI tracking help enterprise leaders verify productivity claims against their own baseline.

When a consulting firm says AI-enabled delivery is faster and better, you’ll have data to evaluate that claim.

Book a Discovery Call to build the measurement foundation.

Frequently Asked Questions

How are consulting firms using AI in 2026?

Major firms use AI to automate analysis, surface insights from large datasets, accelerate document review, support client deliverables, and give consultants access to internal knowledge. The differentiation in 2026 is how deeply those tools change delivery versus how prominently they appear in proposals.

Is AI a differentiator for consulting firms?

No longer. AI has become table stakes: not having it disqualifies a firm, but having it doesn’t create advantage on its own. The differentiation now is depth of embedding, quality of delivery change, and ability to demonstrate measurable client value.

How do I measure the value my consulting firm delivers through AI?

Start with pre-engagement baselines, defined output quality metrics, and a way to attribute outcomes to AI-assisted work. Without those pieces, productivity claims are hard to verify. Building your own measurement infrastructure puts you in a stronger position to evaluate what you receive.

Why is AI ROI so hard to measure in professional services?

Professional services firms face a structural tension: AI that makes work faster can reduce billable hours under traditional models. The measurement challenge gets harder when firms and clients deploy AI without pre-AI baselines, making before-and-after comparisons unreliable.

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Want to hold your AI-enabled consulting relationships to a measurable standard?

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