QuantumBlack, AI by McKinsey vs Tiger Analytics: full comparison for 2026
Quick verdict
QuantumBlack, AI by McKinsey (4.6/5) edges ahead of Tiger Analytics (4.1/5) overall. QuantumBlack, AI by McKinsey is the better choice for large enterprises running AI as a CEO-level program. Tiger Analytics is the stronger option for enterprises wanting strategy plus lower-cost offshore delivery. 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 Tiger Analytics: head-to-head summary
| Criterion | QuantumBlack, AI by McKinsey | Tiger Analytics |
|---|---|---|
| Founded | 2009 | 2011 |
| HQ | London, UK (McKinsey HQ: New York, USA) | Santa Clara, USA |
| Team size | 1,000+ | 4,000+ |
| Rating | 4.6 / 5 | 4.1 / 5 |
| Primary differentiator | Board-level strategy and change management backed by McKinsey's own AI engineering group | AI roadmap work that feeds directly into large India-based data science teams |
| Pricing model | Project fees set per engagement; rates not published | Project and dedicated-team pricing; rates not published |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Kedro, Vizro, AWS | Azure, AWS, Google Cloud |
| Industries served | Banking & insurance, Healthcare & life sciences, Consumer & retail, Manufacturing, Energy, Public sector | Consumer goods, Retail, Insurance, Banking, Manufacturing, Healthcare |
QuantumBlack, AI by McKinsey vs Tiger Analytics: 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.
Tiger Analytics
Tiger Analytics was founded in 2011, is based in Santa Clara, California, and says it has 4,000+ technologists and consultants, most of them delivering from India (per company website; independently unverifiable). It is privately held. Strategy work usually takes the form of an AI or analytics roadmap that leads into data science and engineering projects run by its own teams. The appeal is cost after the plan is agreed: offshore delivery keeps the build affordable.
Services and capabilities: QuantumBlack, AI by McKinsey vs Tiger Analytics
| Capability | QuantumBlack, AI by McKinsey | Tiger Analytics |
|---|---|---|
| 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 Tiger Analytics
| Framework / platform | QuantumBlack, AI by McKinsey | Tiger Analytics |
|---|---|---|
| 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 Tiger Analytics
| Criterion | QuantumBlack, AI by McKinsey | Tiger Analytics |
|---|---|---|
| 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 Tiger Analytics
| Dimension | QuantumBlack, AI by McKinsey | Tiger Analytics |
|---|---|---|
| Best company size | Mid-market to enterprise | Mid-market to enterprise |
| Best industries | Banking & insurance, Healthcare & life sciences, Consumer & retail | Consumer goods, Retail, Insurance |
| 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. | Analytics and AI roadmaps for consumer goods and retail companies., Demand forecasting and pricing models after a planning phase. |
| Typical project type | Strategy & roadmap engagement | Strategy & roadmap engagement |
QuantumBlack, AI by McKinsey vs Tiger Analytics: 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 |
| Tiger Analytics | |
|---|---|
| + | Delivery costs after the strategy are lower than at US or European firms |
| + | Large data science bench for forecasting, pricing, and marketing models |
| + | Privately held and focused on AI and analytics alone |
| + | Cost modeling is part of how it sizes use cases |
| - | Strategy work is mainly a front end to its delivery business |
| - | Change management and organizational design are not core practices |
| - | Time-zone gaps between India-based teams and US or European clients |
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 Tiger Analytics?
A typical fit: analytics and AI roadmaps for consumer goods and retail companies.
AI roadmap work that feeds directly into large India-based data science teams. Minimum engagement is not publicly disclosed. Works best with clients in Consumer goods, Retail, Insurance, Banking, Manufacturing, Healthcare.
Decision matrix: QuantumBlack, AI by McKinsey vs Tiger Analytics
| Your situation | Recommended choice |
|---|---|
| Your board wants a costed, sequenced roadmap within a quarter | Tiger Analytics |
| 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 Tiger Analytics (Not disclosed) |
| You need a large team across many countries | Tiger Analytics |
Use case fit: QuantumBlack, AI by McKinsey vs Tiger Analytics
| Use case | QuantumBlack, AI by McKinsey fit | Tiger Analytics 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 |
| Analytics and AI roadmaps for consumer goods and retail companies. | Limited | Strong | Tiger Analytics |
| Demand forecasting and pricing models after a planning phase. | Limited | Strong | Tiger Analytics |
Verdict: QuantumBlack, AI by McKinsey vs Tiger Analytics
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.
Tiger Analytics (4.1/5) is worth a look if you need demand forecasting and pricing models after a planning phase. If your situation matches that, Tiger Analytics is a competitive option.
Related comparisons
QuantumBlack, AI by McKinsey vs Tiger Analytics FAQ
Is QuantumBlack, AI by McKinsey better than Tiger Analytics?
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. Tiger Analytics's strongest advantage: delivery costs after the strategy are lower than at US or European firms.
How do QuantumBlack, AI by McKinsey and Tiger Analytics differ in pricing?
QuantumBlack, AI by McKinsey's pricing: project fees set per engagement; rates not published. Tiger Analytics's pricing: project and dedicated-team pricing; 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 Tiger Analytics?
Tiger Analytics 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 Tiger Analytics?
QuantumBlack, AI by McKinsey's primary differentiator is: board-level strategy and change management backed by McKinsey's own AI engineering group. Tiger Analytics's primary differentiator is: AI roadmap work that feeds directly into large India-based data science teams. They also differ in team size (1,000+ vs 4,000+), 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.