QuantumBlack, AI by McKinsey vs Fractal: full comparison for 2026
Quick verdict
QuantumBlack, AI by McKinsey (4.6/5) edges ahead of Fractal (4.5/5) overall. QuantumBlack, AI by McKinsey is the better choice for large enterprises running AI as a CEO-level program. Fractal is the stronger option for consumer and financial firms wanting AI depth from one partner. The right choice depends on the size of your program, your budget, and whether you want the same firm to build what it recommends.
QuantumBlack, AI by McKinsey vs Fractal: head-to-head summary
| Criterion | QuantumBlack, AI by McKinsey | Fractal |
|---|---|---|
| Founded | 2009 | 2000 |
| HQ | London, UK (McKinsey HQ: New York, USA) | Mumbai, India / New York, USA |
| Team size | 1,000+ | 5,000+ |
| Rating | 4.6 / 5 | 4.5 / 5 |
| Primary differentiator | Board-level strategy and change management backed by McKinsey's own AI engineering group | Twenty-five years of AI and analytics delivery behind its strategy work, with its own platforms to build on |
| Pricing model | Project fees set per engagement; rates not published | Project and managed-program fees; rates not published |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Kedro, Vizro, AWS | Cogentiq, Azure, AWS |
| Industries served | Banking & insurance, Healthcare & life sciences, Consumer & retail, Manufacturing, Energy, Public sector | Consumer goods, Retail, Financial services, Insurance, Healthcare, Technology |
QuantumBlack, AI by McKinsey vs Fractal: overview
QuantumBlack, AI by McKinsey
QuantumBlack started in London in 2009 as an independent analytics firm and has been part of McKinsey & Company since 2015. McKinsey says it now has more than 1,000 technical practitioners, plus an R&D group, QuantumBlack Labs, of around 200 engineers, designers, and data scientists (per company website; independently unverifiable). A typical engagement pairs a strategy team that works with the chief executive and board with data scientists who build the first models, so the roadmap and the proof come from one firm. That access to the top of a company is the main reason to hire it. Price is the other side of it: fees follow McKinsey's own levels and are not published.
Fractal
Founded in Mumbai in 2000, Fractal calls itself a pure-play enterprise AI company and runs its US business from New York. It has more than 5,000 employees across 18 locations and listed on India's stock exchanges in February 2026, with TPG and Apax among the selling shareholders. Consulting work starts with use-case discovery and value cases, then moves into data science, engineering, and its own products such as the Cogentiq agent platform. Forrester named it a Leader in its Customer Analytics Services Wave for Q2 2025, according to Fractal's announcement. That history is what you pay for. Few firms have run AI programs for consumer and financial clients this long, although the advice tends to lead into Fractal's own platforms.
Services and capabilities: QuantumBlack, AI by McKinsey vs Fractal
| Capability | QuantumBlack, AI by McKinsey | Fractal |
|---|---|---|
| Readiness assessment | ✗ | ✗ |
| Use-case prioritization | ✓ | ✓ |
| TCO / ROI modeling | ✗ | ✓ |
| AI governance & EU AI Act | ✓ | ✗ |
| Build vs. buy advice | ✗ | ✗ |
| Audit of live AI programs | ✗ | ✗ |
| Change management | ✓ | ✗ |
| Can build what it recommends | ✓ | ✓ |
Frameworks and platforms: QuantumBlack, AI by McKinsey vs Fractal
| Framework / platform | QuantumBlack, AI by McKinsey | Fractal |
|---|---|---|
| EU AI Act | N/A | N/A |
| GDPR | N/A | N/A |
| NIST AI RMF | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | ✓ |
| Databricks | ✓ | ✓ |
| Snowflake | N/A | ✓ |
Pricing comparison: QuantumBlack, AI by McKinsey vs Fractal
| Criterion | QuantumBlack, AI by McKinsey | Fractal |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Strategy & roadmap engagement, Delivery team, Ongoing advisory | Strategy & roadmap engagement, Delivery team, Ongoing advisory |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: QuantumBlack, AI by McKinsey vs Fractal
| Dimension | QuantumBlack, AI by McKinsey | Fractal |
|---|---|---|
| Best company size | Mid-market to enterprise | Mid-market to enterprise |
| Best industries | Banking & insurance, Healthcare & life sciences, Consumer & retail | Consumer goods, Retail, Financial services |
| Best use cases | Setting an enterprise AI agenda that the CEO and board will own., Redesigning operating models and roles around AI at a large company. | Prioritizing AI use cases across marketing, supply chain, and pricing at a consumer goods company., Building customer analytics and personalization models after a strategy phase. |
| Typical project type | Strategy & roadmap engagement | Strategy & roadmap engagement |
QuantumBlack, AI by McKinsey vs Fractal: pros and cons
| QuantumBlack, AI by McKinsey | |
|---|---|
| + | Strategy teams work directly with chief executives and boards, which helps when AI spending needs sign-off at the very top |
| + | Change management and capability-building programs come from the same firm that wrote the strategy |
| + | Its own engineers build the first models, so feasibility gets tested before the roadmap is final |
| + | Maintains open-source tools (Kedro, Vizro) that show real engineering practice behind the advice |
| + | Industry depth across banking, health, consumer goods, and energy |
| - | Fees at McKinsey levels put it out of reach for most mid-market budgets |
| - | The firm that writes the roadmap also sells the follow-on work, so the plan may lean toward what McKinsey can deliver |
| - | Large programs mix partners with junior consultants, so confirm who will actually do the work |
| Fractal | |
|---|---|
| + | Has done AI and analytics work since 2000, longer than most firms on this list have existed |
| + | Strategy hands straight to data science and engineering teams inside the same company |
| + | Named a Leader in Forrester's customer analytics services evaluation (Q2 2025) |
| + | Public since February 2026, so its financials and ownership are disclosed |
| + | Long record with consumer goods and retail clients on demand, pricing, and marketing models |
| - | Strategy work tends to lead into its own platforms and delivery teams, which narrows your vendor choice later |
| - | Governance and EU AI Act advice is less visible than its analytics and engineering work |
| - | Listed in 2026 after years of private equity ownership (TPG, Apax), so check continuity of the team you will get |
Who should choose QuantumBlack, AI by McKinsey?
A typical fit: setting an enterprise AI agenda that the CEO and board will own.
Board-level strategy and change management backed by McKinsey's own AI engineering group. Minimum engagement is not publicly disclosed. Works best with clients in Banking & insurance, Healthcare & life sciences, Consumer & retail, Manufacturing, Energy, Public sector.
Who should choose Fractal?
A typical fit: prioritizing AI use cases across marketing, supply chain, and pricing at a consumer goods company.
Twenty-five years of AI and analytics delivery behind its strategy work, with its own platforms to build on. Minimum engagement is not publicly disclosed. Works best with clients in Consumer goods, Retail, Financial services, Insurance, Healthcare, Technology.
Decision matrix: QuantumBlack, AI by McKinsey vs Fractal
| Your situation | Recommended choice |
|---|---|
| Your board wants a costed, sequenced roadmap within a quarter | Fractal |
| You already run AI that is missing its targets | Neither offers a separate audit; ask for a scoped review |
| Regulators will ask how each AI system is governed | QuantumBlack, AI by McKinsey |
| AI will change roles and processes for many staff | QuantumBlack, AI by McKinsey |
| You want the strategy firm to build the result too | Both can deliver after the strategy |
| Your budget is at the lower end | Compare: QuantumBlack, AI by McKinsey (Not disclosed) vs Fractal (Not disclosed) |
| You need a large team across many countries | Fractal |
Use case fit: QuantumBlack, AI by McKinsey vs Fractal
| Use case | QuantumBlack, AI by McKinsey fit | Fractal fit | Winner |
|---|---|---|---|
| Setting an enterprise AI agenda that the CEO and board will own. | Strong | Limited | QuantumBlack, AI by McKinsey |
| Redesigning operating models and roles around AI at a large company. | Strong | Limited | QuantumBlack, AI by McKinsey |
| Prioritizing AI use cases across marketing, supply chain, and pricing at a consumer goods company. | Limited | Strong | Fractal |
| Building customer analytics and personalization models after a strategy phase. | Limited | Strong | Fractal |
Verdict: QuantumBlack, AI by McKinsey vs Fractal
QuantumBlack, AI by McKinsey (4.6/5) is the stronger overall choice for most AI Strategy Consulting projects. Board-level strategy and change management backed by McKinsey's own AI engineering group.
Fractal (4.5/5) is worth a look if you need building customer analytics and personalization models after a strategy phase. If your situation matches that, Fractal is a competitive option.
Related comparisons
QuantumBlack, AI by McKinsey vs Fractal FAQ
Is QuantumBlack, AI by McKinsey better than Fractal?
QuantumBlack, AI by McKinsey (4.6/5) scores higher overall, but "better" depends on your use case. QuantumBlack, AI by McKinsey's strongest advantage: strategy teams work directly with chief executives and boards, which helps when AI spending needs sign-off at the very top. Fractal's strongest advantage: has done AI and analytics work since 2000, longer than most firms on this list have existed.
How do QuantumBlack, AI by McKinsey and Fractal differ in pricing?
QuantumBlack, AI by McKinsey's pricing: project fees set per engagement; rates not published. Fractal's pricing: project and managed-program fees; rates not published. Any hourly bands shown come from Clutch, not a published rate card, so a scoping call is still needed for a project quote.
Which is better for enterprise: QuantumBlack, AI by McKinsey or Fractal?
Fractal is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each consultant before shortlisting.
What are the main differences between QuantumBlack, AI by McKinsey and Fractal?
QuantumBlack, AI by McKinsey's primary differentiator is: board-level strategy and change management backed by McKinsey's own AI engineering group. Fractal's primary differentiator is: twenty-five years of AI and analytics delivery behind its strategy work, with its own platforms to build on. They also differ in team size (1,000+ vs 5,000+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Banking & insurance, Healthcare & life sciences vs Consumer goods, Retail).
Verify all details directly with each consultant before making a decision.