QuantumBlack, AI by McKinsey vs Launch Consulting: full comparison for 2026
Quick verdict
QuantumBlack, AI by McKinsey (4.6/5) edges ahead of Launch Consulting (3.9/5) overall. QuantumBlack, AI by McKinsey is the better choice for large enterprises running AI as a CEO-level program. Launch Consulting is the stronger option for microsoft-centric companies on the US West Coast. 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 Launch Consulting: head-to-head summary
| Criterion | QuantumBlack, AI by McKinsey | Launch Consulting |
|---|---|---|
| Founded | 2009 | 2005 |
| HQ | London, UK (McKinsey HQ: New York, USA) | Bellevue, USA |
| Team size | 1,000+ | 500–1,000 |
| Rating | 4.6 / 5 | 3.9 / 5 |
| Primary differentiator | Board-level strategy and change management backed by McKinsey's own AI engineering group | AI-first operating-model work with Microsoft-based builds and nearshore delivery |
| Pricing model | Project fees set per engagement; rates not published | Consulting fees; rates not published |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Kedro, Vizro, AWS | Azure, Microsoft Copilot, Power BI |
| Industries served | Banking & insurance, Healthcare & life sciences, Consumer & retail, Manufacturing, Energy, Public sector | Healthcare, Retail, Technology, Financial services |
QuantumBlack, AI by McKinsey vs Launch Consulting: 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.
Launch Consulting
Launch Consulting Group was founded in 2005 and is based in Bellevue, Washington, with offices in Buenos Aires and Hyderabad. The Planet Group, owned by Odyssey Investment Partners, bought it in 2022 and later merged its Strive and Future State consultancies under the Launch brand. A managing director of AI, appointed in 2023, leads its "AI first" practice, which covers strategy, data work, and Microsoft-based builds. Its strongest base is Fortune 1000 clients in healthcare, retail, and technology.
Services and capabilities: QuantumBlack, AI by McKinsey vs Launch Consulting
| Capability | QuantumBlack, AI by McKinsey | Launch Consulting |
|---|---|---|
| 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 Launch Consulting
| Framework / platform | QuantumBlack, AI by McKinsey | Launch Consulting |
|---|---|---|
| EU AI Act | N/A | N/A |
| GDPR | N/A | N/A |
| NIST AI RMF | N/A | N/A |
| AWS | ✓ | N/A |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | N/A |
| Databricks | ✓ | ✓ |
| Snowflake | N/A | N/A |
Pricing comparison: QuantumBlack, AI by McKinsey vs Launch Consulting
| Criterion | QuantumBlack, AI by McKinsey | Launch Consulting |
|---|---|---|
| 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 Launch Consulting
| Dimension | QuantumBlack, AI by McKinsey | Launch Consulting |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Banking & insurance, Healthcare & life sciences, Consumer & retail | Healthcare, Retail, Technology |
| 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-first operating-model plans for Microsoft-centered companies., Copilot and Azure AI rollouts after a strategy phase. |
| Typical project type | Strategy & roadmap engagement | Strategy & roadmap engagement |
QuantumBlack, AI by McKinsey vs Launch Consulting: 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 |
| Launch Consulting | |
|---|---|
| + | Focus on operating-model change, beyond tools alone |
| + | Nearshore delivery from Buenos Aires keeps build costs down |
| + | Strong Microsoft stack experience for Copilot and Azure programs |
| + | Years of data platform work for Fortune 1000 clients |
| - | Private equity owned since 2022, with three firms merged under one brand |
| - | Mostly built around Microsoft technology |
| - | Smaller international footprint than the global firms |
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 Launch Consulting?
A typical fit: AI-first operating-model plans for Microsoft-centered companies.
AI-first operating-model work with Microsoft-based builds and nearshore delivery. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare, Retail, Technology, Financial services.
Decision matrix: QuantumBlack, AI by McKinsey vs Launch Consulting
| 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 | 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 Launch Consulting (Not disclosed) |
| You need a large team across many countries | QuantumBlack, AI by McKinsey |
Use case fit: QuantumBlack, AI by McKinsey vs Launch Consulting
| Use case | QuantumBlack, AI by McKinsey fit | Launch Consulting 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-first operating-model plans for Microsoft-centered companies. | Limited | Strong | Launch Consulting |
| Copilot and Azure AI rollouts after a strategy phase. | Limited | Strong | Launch Consulting |
Verdict: QuantumBlack, AI by McKinsey vs Launch Consulting
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.
Launch Consulting (3.9/5) is worth a look if you need copilot and Azure AI rollouts after a strategy phase. If your situation matches that, Launch Consulting is a competitive option.
Related comparisons
QuantumBlack, AI by McKinsey vs Launch Consulting FAQ
Is QuantumBlack, AI by McKinsey better than Launch Consulting?
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. Launch Consulting's strongest advantage: focus on operating-model change, beyond tools alone.
How do QuantumBlack, AI by McKinsey and Launch Consulting differ in pricing?
QuantumBlack, AI by McKinsey's pricing: project fees set per engagement; rates not published. Launch Consulting's pricing: consulting 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 Launch Consulting?
QuantumBlack, AI by McKinsey 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 Launch Consulting?
QuantumBlack, AI by McKinsey's primary differentiator is: board-level strategy and change management backed by McKinsey's own AI engineering group. Launch Consulting's primary differentiator is: AI-first operating-model work with Microsoft-based builds and nearshore delivery. They also differ in team size (1,000+ vs 500–1,000), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Banking & insurance, Healthcare & life sciences vs Healthcare, Retail).
Verify all details directly with each consultant before making a decision.