QuantumBlack, AI by McKinsey vs Quantiphi: full comparison for 2026
Quick verdict
QuantumBlack, AI by McKinsey (4.6/5) edges ahead of Quantiphi (4.0/5) overall. QuantumBlack, AI by McKinsey is the better choice for large enterprises running AI as a CEO-level program. Quantiphi is the stronger option for companies committed to Google Cloud or AWS. 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 Quantiphi: head-to-head summary
| Criterion | QuantumBlack, AI by McKinsey | Quantiphi |
|---|---|---|
| Founded | 2009 | 2013 |
| HQ | London, UK (McKinsey HQ: New York, USA) | Marlborough, USA |
| Team size | 1,000+ | 4,000 |
| Rating | 4.6 / 5 | 4.0 / 5 |
| Primary differentiator | Board-level strategy and change management backed by McKinsey's own AI engineering group | Use-case discovery from a top-tier Google Cloud and AWS engineering partner |
| Pricing model | Project fees set per engagement; rates not published | Project and dedicated-team pricing; rates not published |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Kedro, Vizro, AWS | Google Cloud, AWS, NVIDIA |
| Industries served | Banking & insurance, Healthcare & life sciences, Consumer & retail, Manufacturing, Energy, Public sector | Healthcare, Financial services, Insurance, Media, Public sector, Retail |
QuantumBlack, AI by McKinsey vs Quantiphi: 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.
Quantiphi
Quantiphi was founded in 2013, is headquartered in Marlborough, Massachusetts, and has around 4,000 employees. It holds top partner tiers with Google Cloud, AWS, and NVIDIA, according to its own materials, and ISG named it a Leader for AI and machine learning services on Google Cloud in its 2024 Provider Lens report. Its strategy offer is mostly a front end to engineering: use-case discovery and roadmaps that lead into builds on those platforms. It makes most sense when the platform decision is already made.
Services and capabilities: QuantumBlack, AI by McKinsey vs Quantiphi
| Capability | QuantumBlack, AI by McKinsey | Quantiphi |
|---|---|---|
| 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 Quantiphi
| Framework / platform | QuantumBlack, AI by McKinsey | Quantiphi |
|---|---|---|
| EU AI Act | N/A | N/A |
| GDPR | N/A | N/A |
| NIST AI RMF | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | N/A |
| Google Cloud | ✓ | ✓ |
| Databricks | ✓ | N/A |
| Snowflake | N/A | ✓ |
Pricing comparison: QuantumBlack, AI by McKinsey vs Quantiphi
| Criterion | QuantumBlack, AI by McKinsey | Quantiphi |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Strategy & roadmap engagement, Delivery team, Ongoing advisory | Strategy & roadmap engagement, Delivery team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: QuantumBlack, AI by McKinsey vs Quantiphi
| Dimension | QuantumBlack, AI by McKinsey | Quantiphi |
|---|---|---|
| Best company size | Mid-market to enterprise | Mid-market to enterprise |
| Best industries | Banking & insurance, Healthcare & life sciences, Consumer & retail | Healthcare, Financial services, Insurance |
| 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. | AI roadmaps for companies already standardized on Google Cloud., Document and speech AI for healthcare and insurance. |
| Typical project type | Strategy & roadmap engagement | Strategy & roadmap engagement |
QuantumBlack, AI by McKinsey vs Quantiphi: 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 |
| Quantiphi | |
|---|---|
| + | Deep Google Cloud expertise, confirmed by ISG's 2024 Provider Lens report |
| + | Moves from discovery to build without changing vendors |
| + | Large engineering bench for document, speech, and vision projects |
| + | Experience with healthcare and public-sector data |
| - | Advice is shaped by its platform partnerships |
| - | Little board-level or organizational change work |
| - | Partner-tier claims come from its own recruiting material, with ISG's report as the independent check |
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 Quantiphi?
A typical fit: AI roadmaps for companies already standardized on Google Cloud.
Use-case discovery from a top-tier Google Cloud and AWS engineering partner. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Financial services, Insurance, Media, Public sector, Retail.
Decision matrix: QuantumBlack, AI by McKinsey vs Quantiphi
| Your situation | Recommended choice |
|---|---|
| Your board wants a costed, sequenced roadmap within a quarter | Neither lists cost modeling; ask for a sample roadmap |
| 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 Quantiphi (Not disclosed) |
| You need a large team across many countries | Quantiphi |
Use case fit: QuantumBlack, AI by McKinsey vs Quantiphi
| Use case | QuantumBlack, AI by McKinsey fit | Quantiphi 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 |
| AI roadmaps for companies already standardized on Google Cloud. | Limited | Strong | Quantiphi |
| Document and speech AI for healthcare and insurance. | Limited | Strong | Quantiphi |
Verdict: QuantumBlack, AI by McKinsey vs Quantiphi
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.
Quantiphi (4.0/5) is worth a look if you need document and speech AI for healthcare and insurance. If your situation matches that, Quantiphi is a competitive option.
Related comparisons
QuantumBlack, AI by McKinsey vs Quantiphi FAQ
Is QuantumBlack, AI by McKinsey better than Quantiphi?
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. Quantiphi's strongest advantage: deep Google Cloud expertise, confirmed by ISG's 2024 Provider Lens report.
How do QuantumBlack, AI by McKinsey and Quantiphi differ in pricing?
QuantumBlack, AI by McKinsey's pricing: project fees set per engagement; rates not published. Quantiphi's pricing: project and dedicated-team pricing; 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 Quantiphi?
Quantiphi 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 Quantiphi?
QuantumBlack, AI by McKinsey's primary differentiator is: board-level strategy and change management backed by McKinsey's own AI engineering group. Quantiphi's primary differentiator is: use-case discovery from a top-tier Google Cloud and AWS engineering partner. They also differ in team size (1,000+ vs 4,000), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Banking & insurance, Healthcare & life sciences vs Healthcare, Financial services).
Verify all details directly with each consultant before making a decision.