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Major Consulting Firm AI Positions (2025-2026)

Research compiled March 2026 by Brandon Sneider

The global AI consulting market grew from $8.75B in 2024 to $11.07B in 2025, projected to reach $90.99B by 2035 at 26.2% CAGR. Every major firm has made AI the center of its strategy, revenue, and talent model. This document maps how each firm is positioning itself.

1. McKinsey & Company (QuantumBlack)

Key Reports

“The State of AI in 2025: Agents, Innovation, and Transformation” (March 2025) – surveyed 1,993 participants across 105 nations, June-July 2025
“Unleashing Developer Productivity with Generative AI” (2023-2025 series)
“The Agentic Organization: A New Operating Model for AI” (2025)

Framework: “Rewired”

McKinsey’s signature AI transformation framework maps across strategy, talent, and operational dimensions. Core thesis: organizational plasticity – the willingness to rewrite workflows, structures, talent architecture, and governance around AI – is the real competitive moat.

Key finding: Among 25 attributes tested, workflow redesign has the biggest effect on an organization’s ability to see EBIT impact from gen AI. High performers are 3x more likely to fundamentally rework processes when deploying AI (55% vs. ~18% of other firms).

Key Statistics

88% of organizations deploy AI in at least one function; 72% use gen AI regularly
Only ~1/3 of organizations have begun genuine scaling (rest stuck in pilot/experiment mode)
23% of respondents scaling agentic AI somewhere in their enterprise; 39% experimenting
No more than ~10% of organizations report AI agents as scaled in any single function
Software engineering, manufacturing, and IT report 10-20% cost reductions from AI
Estimated $2.6-4.4T annual impact potential from AI, with software engineering among the largest pools

AI Coding/Engineering Position

More than 90% of all software teams use AI for refactoring, modernization, and testing, saving an average of 6 hours/week
Companies with 80-100% developer adoption saw gains of >110% in productivity
Highest performers saw 16-30% improvement in team productivity, customer experience, and time-to-market; 31-45% improvement in software quality
Documenting code: completed in half the time; writing new code: nearly half the time; code refactoring: nearly two-thirds the time saved
Developers using AI tools are 2x more likely to report feeling happier and entering “flow” state
Software engineers and data engineers are the most in-demand AI-related roles; software engineering functions show expected headcount increases (not decreases)

How They Sell AI Services

QuantumBlack is McKinsey’s AI arm: 7,000+ technologists across 50+ countries, driving ~40% of McKinsey’s entire business
Open-source tools: Kedro (ML framework), CausalNex (causal modeling), Vizro (data visualization)
McKinsey expects 40% of its business to be AI-related in the near future
Internal platform: Lilli (gen AI platform for consultants)

2. BCG (Boston Consulting Group)

Key Reports

“BCG AI Radar 2026: As AI Investments Surge, CEOs Take the Lead” (January 2026)
“The Widening AI Value Gap: Build for the Future” (September 2025)
“AI at Work 2025: Momentum Builds, but Gaps Remain” (2025)
“From Potential to Profit: Closing the AI Impact Gap” (January 2025)
“AI-Enabled Engineering Excellence” (April 2025)

Framework: “Future-Built” vs. “Future-Broken”

BCG’s core framework distinguishes between companies that are “AI future-built” versus “AI future-broken.” Assessed across 41 dimensions of AI maturity. Key principle: the 10/20/70 rule – 10% algorithms, 20% technology, 70% people and processes.

Future-built companies treat AI as a central nervous system, redesign end-to-end workflows, and launch entirely new businesses – not just automate at the margin.

Key Statistics

Corporate AI investment will double in 2026 to reach 1.7% of annual revenues (2x the 2025 increase)
94% of companies will continue investing in AI even without immediate returns
72% of CEOs are now primary AI decision-makers (double from prior year)
82% of CEOs more optimistic about ROI than a year ago
Half of surveyed CEOs believe their job stability depends on getting AI strategy right
90% of CEOs believe AI agents will produce measurable returns in 2026
BCG generated 20% of its $13.5B revenue (~$2.7B) from AI advisory in 2024

The Frontline Adoption Gap

>75% of leaders and managers use gen AI several times/week
Frontline employee regular use has stalled at 51% – the “silicon ceiling”
Trailblazer CEOs (15% of sample) allocate 60% of AI budget to training, upskill 65%+ of workforce

CEO Archetypes

Followers (15%): Recognize potential, cautious investment
Pragmatists (70%): Invest when value/low risk evident
Trailblazers (15%): Complete transformations, 8+ hours/week personal AI training

AI Coding/Engineering Position

Published “From Dev Speed to Business Impact: The Case for AI-Assisted Coding and Generative Engineering” – argues shift from measuring dev speed to measuring business impact
BCG X research: GenAI significantly enhances velocity and capacity savings with mature DevOps
Key caveat: outdated systems and poor DevOps severely dampen GenAI’s impact; 50% of CIOs struggle to quantify GenAI’s impact
Henderson Institute experiment with 480 BCG consultants showed AI elevates aptitude on tasks outside skillset by up to 49 percentage points

AI Revenue and Scale

AI services: ~$2.7B in revenue (20% of total)
Hired 1,000 additional staff in 2024 specifically for AI services
Example client results: EUR250M cost savings by 2026 through AI-transformed marketing; ~95% cost reduction in marketing content generation

3. Accenture

Key Reports

“Reinvention in the Age of Generative AI” (2024-2025 flagship report)
“Technology Vision 2025: AI – A Declaration of Autonomy” (January 2025)
“Technology Vision 2026” (2026)

Framework: “Total Enterprise Reinvention”

Accenture’s core thesis: companies must pursue “Total Enterprise Reinvention” – a deliberate strategy that sets a new performance frontier for the company and, in most cases, the industry in which it operates. Reinventors achieve 2.5x higher revenue growth, 2.4x greater productivity, and 3.3x greater success at scaling gen AI.

Revenue growth gap between Reinventors and the rest expected to increase 2.4x to 37 percentage points by 2026.

Key Statistics

$2.7B gen AI / agentic AI revenue in 12 months ending August 2025; $5.9B in bookings
Q1 FY2026 AI revenues: $1.1B (120% increase YoY)
Planned $3 billion investment in gen AI technology (one-third already deployed)
77,000 AI and data professionals (nearly doubled in two years)
550,000+ staff trained in gen AI fundamentals
AI proficiency now mandatory for promotion
Companies with fully modernized AI-led processes nearly doubled: 9% (2023) to 16% (2024)

Technology Vision 2025 Statistics

69% of executives believe AI diffusion brings new urgency to reinvention
77% believe true AI benefits only possible when built on a foundation of trust
81% agree trust strategy must evolve in parallel with technology strategy

Major Vendor Partnerships (All Active 2025-2026)

OpenAI: Equipping tens of thousands of professionals with ChatGPT Enterprise (Jan 2025)
Anthropic: Accenture Anthropic Business Group, ~30,000 professionals trained (Dec 2025). Claude Code at center of enterprise SDLC
Mistral AI: Multi-year strategic collaboration for European strategic autonomy (Feb 2026)
Snowflake, Microsoft, Google, AWS: Various enterprise AI partnerships

AI Coding/Engineering Position

Positions Claude Code (and other AI coding tools) at center of enterprise software development lifecycle
Framework to quantify real productivity gains and ROI for AI-first development teams
Junior developers “produce senior-level code,” completing integration tasks faster, onboarding in weeks instead of months
Senior developers shift to architecture, validation, and strategic oversight
Gen AI coding assistants are “elevating the role of developer to systems engineer”

4. Deloitte

Key Reports

“State of AI in the Enterprise 2026: The Untapped Edge” (January 2026) – surveyed 3,235 leaders across 24 countries, 6 industries (Aug-Sep 2025)
“Tech Trends 2025: IT, Amplified” (2025)
“Future of Software Engineering: Unconstrained AI Era” (2025)
“2026 Software Industry Outlook” (2025)

Framework: AI Institute + “Ambition to Activation”

Deloitte’s AI Institute is the research arm; their consulting framework centers on moving organizations from “ambition to activation.” The report title itself – “The Untapped Edge” – signals that most enterprises are not yet capturing AI’s full value.

Key Statistics

Worker access to AI rose by 50% in 2025
Number of companies with >=40% projects in production set to double in six months
66% of organizations report productivity/efficiency gains from AI
34% using AI to “deeply transform” their business
85% expect to customize AI agents for their business needs
Today 23% use agentic AI at least moderately; expected to climb to 74% within two years
Only 21% have mature governance models for AI agents
58% already using physical AI; 83% view sovereign AI as strategically important

AI Coding/Engineering Position

AI agents “break traditional patterns and execute across the full software development life cycle”
Engineers shifting from “task execution to intent orchestration”
97%+ of developers have used AI coding tools at work (citing GitHub survey)
Productivity gain from AI coding estimated at US$12 billion in the United States alone
Over next 18-24 months, IT leaders should plan for AI transformation across 5 pillars: engineering, talent, cloud FinOps, infrastructure, and cyber risk
Software engineering shifting from “capacity and productivity” measurement to becoming a “self-compounding capital asset”

Revenue and Investment

Deloitte committed $3 billion through 2030 to generative and agentic AI
AI-related revenue reportedly up ~30% in 2025

5. PwC

Key Reports

“2026 AI Business Predictions” (January 2026) – 6 predictions for the year
“2025 Responsible AI Survey” (2025)
“PwC Global AI Jobs Barometer 2025” (2025)
“Agentic SDLC in Practice: The Rise of Autonomous Software Delivery” (2026)

Framework: Responsible AI (RAI) Operationalization

PwC’s differentiator is responsible AI. Three-pillar approach:

Integration: Align IT, risk, and AI specialists with clear responsibilities
Testing & Monitoring: Operationalize AI testing with new tech capabilities
Independent Assurance: Independent assessments for higher-risk/value systems

Key Statistics

60% of executives say responsible AI boosts ROI and efficiency
55% report improved customer experience and innovation from RAI
Nearly half say turning RAI principles into operational processes remains challenging
Agents can do roughly half of the tasks people currently do
Technology delivers only ~20% of initiative value; 80% comes from redesigning work
AI-exposed employees see 4x jump in productivity growth and 56% wage premium

Workforce Shape Predictions

Knowledge work: hourglass shape (strong junior + senior tiers, fewer midlevel roles)
Frontline work: diamond shape (agents replace entry-level, more midlevel orchestrators needed)

AI Coding/Engineering Position

AI influencing every stage of SDLC: requirements, design, coding, testing, deployment, maintenance
AI coding assistants generate routine code, detect errors, modernize legacy systems, translate old programming languages
Smaller teams can deliver more; question shifts from “Do we have enough engineers?” to “What should we build next?”
Engineers becoming “architects of intelligent systems rather than writing every line of code”
Moving toward “agentic SDLC” where governance, measurement, and human-AI collaboration are core design principles

Partnerships

Anthropic: PwC partnered on AI-native finance and life sciences enterprise agents

6. Bain & Company

Key Reports

“Technology Report 2025: AI Leaders Are Extending Their Edge” (September 2025) – 6th annual Global Technology Report
“State of the Art of Agentic AI Transformation” (2025)
“Building the Foundation for Agentic AI” (2025)
“The New AI Stack: Speed, Scale, and Real-World ROI” (2025)

Framework: Four Motions for Agentic AI Transformation

Focus on few business domains – reimagine processes end-to-end for early value
Evaluate architecture for agentic readiness – identify capabilities needed to scale
Reimagine agent experience and access – agents become first-class participants in business operations
Use agentic AI in the transformation itself – reduce effort, control costs, ensure outcomes

Key Statistics

AI leaders achieving 10-25% EBITDA gains by scaling AI across core workflows
$2 trillion in annual revenue needed to fund AI compute by 2030
World is $800 billion short to keep pace with AI compute demand
Global incremental AI compute requirements could reach 200 gigawatts by 2030 (US = half)
5-10% of technology spending could go to foundational AI capabilities over next 3-5 years
30-50% reduction in time spent on content creation in sales and marketing
Most companies remain stuck in AI experimentation mode

AI Coding/Engineering Position

Bain emphasizes that agentic AI is the “top-line story” – agents running complete processes and workflows
Focus on the infrastructure/compute layer more than developer productivity per se
Key message: winners “build momentum with fit-for-purpose solutions rather than waiting for a perfect architectural approach”

7. KPMG

Key Reports

“AI Quarterly Pulse Survey” (8 consecutive quarters of proprietary research, Q1 2024 - Q4 2025)
“AI at Scale: How 2025 Set the Stage for Agent-Driven Enterprise Reinvention in 2026” (Q4 2025 survey, January 2026)
“Global Tech Report 2026” (2026)

Framework: “Agent Orchestrator” Era + Velocity Platform

2026 marks the emergence of the “agent orchestrator” – orchestrated super-agent ecosystems governed end-to-end by strict control systems. KPMG’s consulting approach includes:

Cross-functional AI assessment and opportunity analysis
AI vision and aspiration setting
AI strategy and value assessment
Trusted framework development
Clear AI prioritization and value case design

Key Statistics

AI agent deployment nearly quadrupled: 42% of organizations deployed agents (up from 11% two quarters prior)
By Q4 2025: 26% actively using AI agents (up from 11% in Q1)
Technology departments lead: 95% leveraging agents; Operations: 89%; Risk management: 66%
Average AI investment climbed from $114M (Q1 2025) to $130M (Q3 2025)
System complexity now #1 deployment challenge (surpassing all others)

Technology Platform

KPMG Velocity: Integrates digital transformation tools with AI capabilities, launched mid-2025 across 8 countries
Partnership with Microsoft for AI-powered agents via Azure AI Foundry

AI Coding/Engineering Position

Less emphasis on AI coding specifically vs. broader enterprise agent deployment
Focuses on technology department leadership in AI agent adoption
Emphasis on governance, trust, and platform rigor for scaling agentic systems

Cross-Cutting Themes Across All Firms

1. The Scaling Gap Is the Central Narrative

Every firm identifies the same core problem: widespread AI adoption (~88% McKinsey, 66%+ Deloitte) but a massive gap between experimentation and production value. Only 1/3 of companies are genuinely scaling (McKinsey), and most remain “stuck in experimentation mode” (Bain).

2. Agentic AI Is the 2026 Battleground

All seven firms position agentic AI as the next wave. Agent deployment is surging (KPMG: 11% to 42% in two quarters), but governance lags severely (Deloitte: only 21% have mature agent governance). Every firm is selling agentic transformation services.

3. Workflow Redesign > Technology

Consistent message: plug-in thinking fails. McKinsey finds workflow redesign has the biggest EBIT effect. PwC says technology is only 20% of value. BCG’s 10/20/70 rule. Firms that “rewire” (McKinsey) or “reinvent” (Accenture) outperform.

4. The Frontline Adoption Gap

BCG’s “silicon ceiling” at 51% frontline adoption. Deloitte’s 50% rise in worker access. Every firm acknowledges that leadership adoption far outpaces frontline usage, and closing this gap is essential.

5. AI Coding Is a Proven, High-Value Use Case

Software engineering is consistently cited as one of the most mature and highest-ROI applications:

McKinsey: 90%+ of teams using AI for coding, 6hr/week savings, >110% productivity gains at high adoption
Deloitte: $12B US productivity gain from AI coding alone
Accenture: AI coding tools at center of enterprise SDLC
PwC: Shifting from “enough engineers” to “what should we build”
BCG: Significant velocity gains but only with mature DevOps

6. Massive Revenue Opportunity

Accenture: $2.7B AI revenue, $5.9B bookings
BCG: $2.7B (20% of total revenue)
McKinsey: ~40% of business AI-related
Deloitte: $3B committed through 2030
AI consulting market: $11B in 2025, projected $91B by 2035

Implications for Foley Hoag

The consulting landscape validates the AI coding opportunity. Every major firm identifies software engineering as a top use case. McKinsey’s data (90%+ teams, >110% productivity gains) and Deloitte’s $12B US estimate provide credible third-party validation for AI-in-engineering services.
The “rewiring” framing is dominant. Firms that help clients redesign workflows – not just deploy tools – win. This aligns with a consulting approach that goes beyond tool selection.
Governance is the emerging gap. Only 21% of enterprises have mature AI agent governance (Deloitte). Responsible AI (PwC) and agent orchestration governance (KPMG) are under-served areas.
The scaling gap is where consulting value lives. Every firm says most companies are stuck between pilot and production. The firms making billions are the ones who help bridge that gap.
Vendor partnerships define credibility. Accenture’s partnerships with OpenAI, Anthropic, and Mistral; PwC’s partnership with Anthropic – these signal that consulting firms need demonstrable vendor relationships to compete.
The $11B-to-$91B growth trajectory means new entrants can win. The market is growing at 26.2% CAGR. Specialized, high-conviction players can carve out positions that generalist firms cannot.

What This Means for Your Organization

Seven consulting firms generating billions in AI revenue all agree on one thing: most of their clients are not getting value from AI investments. McKinsey finds only 5.5% of companies drive significant value. BCG puts it at 5%. Accenture says 13%. MIT reports 95% of AI pilots fail to reach production scale. These firms are not publishing this data to discourage you from spending. They are publishing it to sell you the bridge from pilot to production. But the underlying finding is real and independent of their commercial interests: the vast majority of enterprise AI spending is not producing transformational results.

The consensus across all seven firms is that workflow redesign – not tool procurement – is the binding constraint. McKinsey finds it has the single biggest effect on EBIT impact. PwC says technology delivers only 20% of initiative value; 80% comes from redesigning work. BCG’s 10-20-70 rule allocates 70% of effort to people and process. Every firm that makes money helping companies adopt AI agrees that buying the tool is the easy part. Changing how 500 or 5,000 people work every day is the hard part. If your AI budget allocates more to licenses than to training, change management, and workflow redesign combined, it is structured to fail.

Software engineering is the single most validated AI use case across every firm. McKinsey reports 90% of teams using AI for coding with six hours per week in savings. Deloitte estimates $12 billion in U.S. productivity gains from AI coding alone. Accenture places AI coding tools at the center of the enterprise software development lifecycle. This is not a speculative bet. It is the one area where tool maturity, adoption rates, and measurable productivity gains all converge. If you are choosing where to start, start here. If you have already started here, the question is whether you have crossed the threshold from tool distribution to workflow transformation.

Sources

McKinsey

BCG

Accenture

Deloitte

PwC

Bain & Company

KPMG

Market Data

Created by Brandon Sneider | brandon@brandonsneider.com March 2026