QuantumBlack, AI by McKinsey vs Thoughtworks: full comparison for 2026
Quick verdict
QuantumBlack, AI by McKinsey (4.6/5) edges ahead of Thoughtworks (4.1/5) overall. QuantumBlack, AI by McKinsey is the better choice for large enterprises running AI as a CEO-level program. Thoughtworks is the stronger option for engineering-led companies modernizing software with AI. 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 Thoughtworks: head-to-head summary
| Criterion | QuantumBlack, AI by McKinsey | Thoughtworks |
|---|---|---|
| Founded | 2009 | 1993 |
| HQ | London, UK (McKinsey HQ: New York, USA) | Chicago, 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 | Engineering-practice depth applied to AI strategy, with its tool opinions published in the Technology Radar |
| Pricing model | Project fees set per engagement; rates not published | Time & materials and fixed-scope work; 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, Retail, Healthcare, Public sector, Technology, Energy |
QuantumBlack, AI by McKinsey vs Thoughtworks: 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.
Thoughtworks
Thoughtworks was founded in Chicago in 1993 and has roughly 10,000 staff. Apax Partners took it private in a deal announced in August 2024 that valued the company at about $1.75 billion. Its AI work grows out of software and data engineering, so strategy engagements focus on where AI changes how software is built and how data products are run. In March 2026 it launched an AI joint venture with Teneo, a New York advisory firm, to add a stronger business-advisory side. Its public Technology Radar, a twice-yearly review of tools and techniques, is a free way to judge how it thinks before you hire it.
Services and capabilities: QuantumBlack, AI by McKinsey vs Thoughtworks
| Capability | QuantumBlack, AI by McKinsey | Thoughtworks |
|---|---|---|
| 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 Thoughtworks
| Framework / platform | QuantumBlack, AI by McKinsey | Thoughtworks |
|---|---|---|
| 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 Thoughtworks
| Criterion | QuantumBlack, AI by McKinsey | Thoughtworks |
|---|---|---|
| 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 Thoughtworks
| Dimension | QuantumBlack, AI by McKinsey | Thoughtworks |
|---|---|---|
| Best company size | Mid-market to enterprise | Enterprise |
| Best industries | Banking & insurance, Healthcare & life sciences, Consumer & retail | Financial services, Retail, Healthcare |
| 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. | Planning how AI changes a company's software delivery process., Data strategy for organizations whose data is the main obstacle to AI. |
| Typical project type | Strategy & roadmap engagement | Strategy & roadmap engagement |
QuantumBlack, AI by McKinsey vs Thoughtworks: 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 |
| Thoughtworks | |
|---|---|
| + | Engineering standards are high, and its views on tools are public in the Technology Radar |
| + | Good at AI for software delivery itself, such as coding assistants and test automation |
| + | Data mesh and data product thinking help when the data foundation is the real blocker |
| + | Gives honest build-or-buy calls on tooling |
| - | Taken private by Apax in 2024, so expect cost pressure and leadership changes |
| - | Board-level business strategy is lighter than its engineering work, which the Teneo venture is meant to fix |
| - | Rates and minimums are not published |
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 Thoughtworks?
A typical fit: planning how AI changes a company's software delivery process.
Engineering-practice depth applied to AI strategy, with its tool opinions published in the Technology Radar. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Retail, Healthcare, Public sector, Technology, Energy.
Decision matrix: QuantumBlack, AI by McKinsey vs Thoughtworks
| 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 Thoughtworks (Not disclosed) |
| You need a large team across many countries | Thoughtworks |
Use case fit: QuantumBlack, AI by McKinsey vs Thoughtworks
| Use case | QuantumBlack, AI by McKinsey fit | Thoughtworks 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 |
| Planning how AI changes a company's software delivery process. | Limited | Strong | Thoughtworks |
| Data strategy for organizations whose data is the main obstacle to AI. | Limited | Strong | Thoughtworks |
Verdict: QuantumBlack, AI by McKinsey vs Thoughtworks
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.
Thoughtworks (4.1/5) is worth a look if you need data strategy for organizations whose data is the main obstacle to AI. If your situation matches that, Thoughtworks is a competitive option.
Related comparisons
QuantumBlack, AI by McKinsey vs Thoughtworks FAQ
Is QuantumBlack, AI by McKinsey better than Thoughtworks?
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. Thoughtworks's strongest advantage: engineering standards are high, and its views on tools are public in the Technology Radar.
How do QuantumBlack, AI by McKinsey and Thoughtworks differ in pricing?
QuantumBlack, AI by McKinsey's pricing: project fees set per engagement; rates not published. Thoughtworks's pricing: time & materials and fixed-scope work; 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 Thoughtworks?
Thoughtworks 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 Thoughtworks?
QuantumBlack, AI by McKinsey's primary differentiator is: board-level strategy and change management backed by McKinsey's own AI engineering group. Thoughtworks's primary differentiator is: engineering-practice depth applied to AI strategy, with its tool opinions published in the Technology Radar. 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, Retail).
Verify all details directly with each consultant before making a decision.