QuantumBlack, AI by McKinsey vs Artefact: full comparison for 2026
Quick verdict
QuantumBlack, AI by McKinsey (4.6/5) edges ahead of Artefact (4.3/5) overall. QuantumBlack, AI by McKinsey is the better choice for large enterprises running AI as a CEO-level program. Artefact is the stronger option for european consumer and retail brands, strategy through build. 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 Artefact: head-to-head summary
| Criterion | QuantumBlack, AI by McKinsey | Artefact |
|---|---|---|
| Founded | 2009 | 2014 |
| HQ | London, UK (McKinsey HQ: New York, USA) | Paris, France |
| Team size | 1,000+ | 1,700+ |
| Rating | 4.6 / 5 | 4.3 / 5 |
| Primary differentiator | Board-level strategy and change management backed by McKinsey's own AI engineering group | Data and AI consulting from strategy partners and engineers in one firm, with a wide European base |
| Pricing model | Project fees set per engagement; rates not published | Consulting fees per engagement; rates not published |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Kedro, Vizro, AWS | Google Cloud, Azure, AWS |
| Industries served | Banking & insurance, Healthcare & life sciences, Consumer & retail, Manufacturing, Energy, Public sector | Consumer goods, Retail, Luxury, Financial services, Healthcare, Telecom |
QuantumBlack, AI by McKinsey vs Artefact: 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.
Artefact
Artefact was founded in Paris in 2014 and now employs more than 1,700 people in 31 offices across 25 countries. In 2025 private equity firm Cinven bought a majority stake that valued the business at over €1 billion, and Artefact has said it plans to triple in size by 2030 through hiring and acquisitions. Consulting partners handle AI strategy and data governance while its engineers build data platforms and models, so one firm covers the route from roadmap to production. Most clients are large consumer, retail, and luxury brands. Think of it as a European management consultancy with a much bigger data bench than usual.
Services and capabilities: QuantumBlack, AI by McKinsey vs Artefact
| Capability | QuantumBlack, AI by McKinsey | Artefact |
|---|---|---|
| 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 Artefact
| Framework / platform | QuantumBlack, AI by McKinsey | Artefact |
|---|---|---|
| EU AI Act | N/A | ✓ |
| GDPR | N/A | ✓ |
| NIST AI RMF | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | ✓ |
| Databricks | ✓ | ✓ |
| Snowflake | N/A | ✓ |
Pricing comparison: QuantumBlack, AI by McKinsey vs Artefact
| Criterion | QuantumBlack, AI by McKinsey | Artefact |
|---|---|---|
| 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 Artefact
| Dimension | QuantumBlack, AI by McKinsey | Artefact |
|---|---|---|
| Best company size | Mid-market to enterprise | Mid-market to enterprise |
| Best industries | Banking & insurance, Healthcare & life sciences, Consumer & retail | Consumer goods, Retail, Luxury |
| 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. | Building a data and AI roadmap for a consumer brand selling in several European markets., Setting up data governance before expanding marketing AI. |
| Typical project type | Strategy & roadmap engagement | Strategy & roadmap engagement |
QuantumBlack, AI by McKinsey vs Artefact: 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 |
| Artefact | |
|---|---|
| + | Strategy partners and data engineers work in one firm, so the plan and the build share owners |
| + | Offices in 25 countries help companies running AI programs across several markets |
| + | Runs its own training school (Artefact School of Data) for client teams |
| + | Deep experience with consumer and retail brands on marketing and customer data |
| - | Majority-owned by Cinven since 2025, with an acquisition-led growth plan that may reshape teams and focus |
| - | Clients skew toward large brands, so smaller companies may find engagements heavier than they need |
| - | Less visible in industrial and manufacturing AI than in consumer sectors |
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 Artefact?
A typical fit: building a data and AI roadmap for a consumer brand selling in several European markets.
Data and AI consulting from strategy partners and engineers in one firm, with a wide European base. Minimum engagement is not publicly disclosed. Works best with clients in Consumer goods, Retail, Luxury, Financial services, Healthcare, Telecom.
Decision matrix: QuantumBlack, AI by McKinsey vs Artefact
| 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 | Both cover AI governance |
| 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 Artefact (Not disclosed) |
| You need a large team across many countries | Artefact |
Use case fit: QuantumBlack, AI by McKinsey vs Artefact
| Use case | QuantumBlack, AI by McKinsey fit | Artefact 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 |
| Building a data and AI roadmap for a consumer brand selling in several European markets. | Limited | Strong | Artefact |
| Setting up data governance before expanding marketing AI. | Limited | Strong | Artefact |
Verdict: QuantumBlack, AI by McKinsey vs Artefact
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.
Artefact (4.3/5) is worth a look if you need setting up data governance before expanding marketing AI. If your situation matches that, Artefact is a competitive option.
Related comparisons
QuantumBlack, AI by McKinsey vs Artefact FAQ
Is QuantumBlack, AI by McKinsey better than Artefact?
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. Artefact's strongest advantage: strategy partners and data engineers work in one firm, so the plan and the build share owners.
How do QuantumBlack, AI by McKinsey and Artefact differ in pricing?
QuantumBlack, AI by McKinsey's pricing: project fees set per engagement; rates not published. Artefact's pricing: consulting fees per engagement; 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 Artefact?
Artefact 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 Artefact?
QuantumBlack, AI by McKinsey's primary differentiator is: board-level strategy and change management backed by McKinsey's own AI engineering group. Artefact's primary differentiator is: data and AI consulting from strategy partners and engineers in one firm, with a wide European base. They also differ in team size (1,000+ vs 1,700+), 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.