QuantumBlack, AI by McKinsey vs Slalom: full comparison for 2026
Quick verdict
QuantumBlack, AI by McKinsey (4.6/5) edges ahead of Slalom (4.1/5) overall. QuantumBlack, AI by McKinsey is the better choice for large enterprises running AI as a CEO-level program. Slalom is the stronger option for north American firms wanting local, on-site AI consultants. 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 Slalom: head-to-head summary
| Criterion | QuantumBlack, AI by McKinsey | Slalom |
|---|---|---|
| Founded | 2009 | 2001 |
| HQ | London, UK (McKinsey HQ: New York, USA) | Seattle, USA |
| Team size | 1,000+ | 10,000+ |
| Rating | 4.6 / 5 | 4.1 / 5 |
| Primary differentiator | Board-level strategy and change management backed by McKinsey's own AI engineering group | Local-office model that puts strategy consultants on-site, with deep cloud partnerships behind them |
| Pricing model | Project fees set per engagement; rates not published | Time & materials and fixed-fee projects; rates not published |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Kedro, Vizro, AWS | AWS, Azure, Google Cloud |
| Industries served | Banking & insurance, Healthcare & life sciences, Consumer & retail, Manufacturing, Energy, Public sector | Financial services, Healthcare, Retail, Manufacturing, Public sector, Technology |
QuantumBlack, AI by McKinsey vs Slalom: 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.
Slalom
Slalom was founded in Seattle in 2001 and has more than 10,000 employees in over 50 offices across the Americas, Europe, and Asia. In August 2026 it hired a former Accenture executive as its chief AI officer. Its AI strategy practice covers operating models, governance, and data strategy, delivered by local teams who work on-site with clients. Partnerships with Microsoft, AWS, Google Cloud, Snowflake, and Salesforce mean a Slalom roadmap usually lands on one of those platforms.
Services and capabilities: QuantumBlack, AI by McKinsey vs Slalom
| Capability | QuantumBlack, AI by McKinsey | Slalom |
|---|---|---|
| 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 Slalom
| Framework / platform | QuantumBlack, AI by McKinsey | Slalom |
|---|---|---|
| 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 Slalom
| Criterion | QuantumBlack, AI by McKinsey | Slalom |
|---|---|---|
| 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 Slalom
| Dimension | QuantumBlack, AI by McKinsey | Slalom |
|---|---|---|
| Best company size | Mid-market to enterprise | Enterprise |
| Best industries | Banking & insurance, Healthcare & life sciences, Consumer & retail | Financial services, Healthcare, Retail |
| 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 and data strategy for a North American mid-size or large company., Governance and operating-model design for a first wave of AI projects. |
| Typical project type | Strategy & roadmap engagement | Strategy & roadmap engagement |
QuantumBlack, AI by McKinsey vs Slalom: 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 |
| Slalom | |
|---|---|
| + | Consultants live in the client's city, which makes workshops and on-site discovery easy |
| + | Covers operating model and governance as well as technology |
| + | Strong standing with Microsoft, AWS, Google Cloud, and Snowflake |
| + | Can staff the build after the strategy without changing firms |
| - | Roadmaps tend to land on its partner platforms |
| - | New AI leadership (August 2026) means the practice direction is still settling |
| - | Less depth on EU regulation than European consultancies |
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 Slalom?
A typical fit: AI and data strategy for a North American mid-size or large company.
Local-office model that puts strategy consultants on-site, with deep cloud partnerships behind them. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Retail, Manufacturing, Public sector, Technology.
Decision matrix: QuantumBlack, AI by McKinsey vs Slalom
| 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 | Both run change management |
| 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 Slalom (Not disclosed) |
| You need a large team across many countries | Slalom |
Use case fit: QuantumBlack, AI by McKinsey vs Slalom
| Use case | QuantumBlack, AI by McKinsey fit | Slalom 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 and data strategy for a North American mid-size or large company. | Limited | Strong | Slalom |
| Governance and operating-model design for a first wave of AI projects. | Limited | Strong | Slalom |
Verdict: QuantumBlack, AI by McKinsey vs Slalom
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.
Slalom (4.1/5) is worth a look if you need governance and operating-model design for a first wave of AI projects. If your situation matches that, Slalom is a competitive option.
Related comparisons
QuantumBlack, AI by McKinsey vs Slalom FAQ
Is QuantumBlack, AI by McKinsey better than Slalom?
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. Slalom's strongest advantage: consultants live in the client's city, which makes workshops and on-site discovery easy.
How do QuantumBlack, AI by McKinsey and Slalom differ in pricing?
QuantumBlack, AI by McKinsey's pricing: project fees set per engagement; rates not published. Slalom's pricing: time & materials and fixed-fee projects; 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 Slalom?
Slalom 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 Slalom?
QuantumBlack, AI by McKinsey's primary differentiator is: board-level strategy and change management backed by McKinsey's own AI engineering group. Slalom's primary differentiator is: local-office model that puts strategy consultants on-site, with deep cloud partnerships behind them. They also differ in team size (1,000+ vs 10,000+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Banking & insurance, Healthcare & life sciences vs Financial services, Healthcare).
Verify all details directly with each consultant before making a decision.