QuantumBlack, AI by McKinsey vs Addepto: full comparison for 2026
Quick verdict
QuantumBlack, AI by McKinsey (4.6/5) edges ahead of Addepto (3.8/5) overall. QuantumBlack, AI by McKinsey is the better choice for large enterprises running AI as a CEO-level program. Addepto is the stronger option for manufacturers wanting a small first AI engagement. 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 Addepto: head-to-head summary
| Criterion | QuantumBlack, AI by McKinsey | Addepto |
|---|---|---|
| Founded | 2009 | 2017 |
| HQ | London, UK (McKinsey HQ: New York, USA) | Warsaw, Poland |
| Team size | 1,000+ | 50–249 |
| Rating | 4.6 / 5 | 3.8 / 5 |
| Primary differentiator | Board-level strategy and change management backed by McKinsey's own AI engineering group | Low Clutch entry point ($10,000+) for AI roadmap work in data-heavy industries |
| Pricing model | Project fees set per engagement; rates not published | $50–$99/hr (Clutch band); project-based |
| Min. engagement | Not disclosed | $10,000+ (Clutch) |
| Primary tech stack | Kedro, Vizro, AWS | Databricks, Azure, AWS |
| Industries served | Banking & insurance, Healthcare & life sciences, Consumer & retail, Manufacturing, Energy, Public sector | Manufacturing, Aviation, Logistics, Retail, Financial services |
QuantumBlack, AI by McKinsey vs Addepto: 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.
Addepto
Addepto was founded in Warsaw in 2017 and was acquired in December 2025 by KMS Technology, an Atlanta digital engineering company backed by Sunstone Partners. Its chief executive says 97% of the team are AI engineers (per company website; independently unverifiable). It sells AI consulting and roadmaps for data-heavy industries such as manufacturing and aviation, followed by builds. Clutch lists $50–$99 an hour and a $10,000+ minimum project, so a small first engagement is possible.
Services and capabilities: QuantumBlack, AI by McKinsey vs Addepto
| Capability | QuantumBlack, AI by McKinsey | Addepto |
|---|---|---|
| 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 Addepto
| Framework / platform | QuantumBlack, AI by McKinsey | Addepto |
|---|---|---|
| EU AI Act | N/A | N/A |
| GDPR | N/A | N/A |
| NIST AI RMF | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | ✓ | ✓ |
| Google Cloud | ✓ | N/A |
| Databricks | ✓ | ✓ |
| Snowflake | N/A | N/A |
Pricing comparison: QuantumBlack, AI by McKinsey vs Addepto
| Criterion | QuantumBlack, AI by McKinsey | Addepto |
|---|---|---|
| Minimum engagement | Not disclosed | $10,000+ (Clutch) |
| Engagement models | Strategy & roadmap engagement, Delivery team, Ongoing advisory | Readiness assessment, Strategy & roadmap engagement, Delivery team |
| Rate transparency | Not public | Minimum disclosed |
| Price tier | Mid-market | Accessible |
Target audience comparison: QuantumBlack, AI by McKinsey vs Addepto
| Dimension | QuantumBlack, AI by McKinsey | Addepto |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Banking & insurance, Healthcare & life sciences, Consumer & retail | Manufacturing, Aviation, Logistics |
| 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. | A small readiness check and roadmap for a manufacturer., Predictive maintenance and quality models in industrial settings. |
| Typical project type | Strategy & roadmap engagement | Readiness assessment |
QuantumBlack, AI by McKinsey vs Addepto: 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 |
| Addepto | |
|---|---|
| + | A $10,000+ Clutch minimum allows a small first step |
| + | Team is almost entirely engineers, so roadmaps stay technically grounded |
| + | Experience with manufacturing and aviation data |
| + | Databricks experience for data platform work |
| - | Acquired by KMS Technology in December 2025, and integration may change teams and priorities |
| - | Strategy work is mostly a front end to engineering |
| - | Little governance or change-management work |
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 Addepto?
A typical fit: a small readiness check and roadmap for a manufacturer.
Low Clutch entry point ($10,000+) for AI roadmap work in data-heavy industries. Minimum engagement starts at $10,000+ (Clutch). Works best with clients in Manufacturing, Aviation, Logistics, Retail, Financial services.
Decision matrix: QuantumBlack, AI by McKinsey vs Addepto
| 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 Addepto ($10,000+ (Clutch)) |
| You need a large team across many countries | QuantumBlack, AI by McKinsey |
Use case fit: QuantumBlack, AI by McKinsey vs Addepto
| Use case | QuantumBlack, AI by McKinsey fit | Addepto 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 |
| A small readiness check and roadmap for a manufacturer. | Limited | Strong | Addepto |
| Predictive maintenance and quality models in industrial settings. | Limited | Strong | Addepto |
Verdict: QuantumBlack, AI by McKinsey vs Addepto
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.
Addepto (3.8/5) is worth a look if you need predictive maintenance and quality models in industrial settings. If your situation matches that, Addepto is a competitive option.
Related comparisons
QuantumBlack, AI by McKinsey vs Addepto FAQ
Is QuantumBlack, AI by McKinsey better than Addepto?
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. Addepto's strongest advantage: a $10,000+ Clutch minimum allows a small first step.
How do QuantumBlack, AI by McKinsey and Addepto differ in pricing?
QuantumBlack, AI by McKinsey's pricing: project fees set per engagement; rates not published. Addepto's pricing: $50–$99/hr (Clutch band); project-based with a minimum engagement of $10,000+ (Clutch). 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 Addepto?
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 Addepto?
QuantumBlack, AI by McKinsey's primary differentiator is: board-level strategy and change management backed by McKinsey's own AI engineering group. Addepto's primary differentiator is: low Clutch entry point ($10,000+) for AI roadmap work in data-heavy industries. They also differ in team size (1,000+ vs 50–249), minimum engagement (Not disclosed vs $10,000+ (Clutch)), and primary industries served (Banking & insurance, Healthcare & life sciences vs Manufacturing, Aviation).
Verify all details directly with each consultant before making a decision.