QuantumBlack, AI by McKinsey vs Elder Research: full comparison for 2026
Quick verdict
QuantumBlack, AI by McKinsey (4.6/5) edges ahead of Elder Research (4.0/5) overall. QuantumBlack, AI by McKinsey is the better choice for large enterprises running AI as a CEO-level program. Elder Research is the stronger option for US agencies and firms wanting seasoned data science advice. 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 Elder Research: head-to-head summary
| Criterion | QuantumBlack, AI by McKinsey | Elder Research |
|---|---|---|
| Founded | 2009 | 1995 |
| HQ | London, UK (McKinsey HQ: New York, USA) | Charlottesville, USA |
| Team size | 1,000+ | 170+ |
| Rating | 4.6 / 5 | 4.0 / 5 |
| Primary differentiator | Board-level strategy and change management backed by McKinsey's own AI engineering group | Three decades of applied data science behind its feasibility calls on AI use cases |
| Pricing model | Project fees set per engagement; rates not published | Project fees; rates not published |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Kedro, Vizro, AWS | Python, R, AWS |
| Industries served | Banking & insurance, Healthcare & life sciences, Consumer & retail, Manufacturing, Energy, Public sector | Government & defense, Healthcare, Financial services, Insurance, Energy & utilities |
QuantumBlack, AI by McKinsey vs Elder Research: 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.
Elder Research
Elder Research was founded in 1995 in Charlottesville, Virginia, by data mining author John Elder and has around 170 staff. ManTech, a Carlyle Group portfolio company, bought it in December 2025 to expand its data and AI practice. The firm combines AI strategy and roadmap work with hands-on data science and training, and it builds fraud, waste, and abuse analytics for government agencies. That history matters. When Elder Research says a model won't work on your data, it has usually seen the same problem before.
Services and capabilities: QuantumBlack, AI by McKinsey vs Elder Research
| Capability | QuantumBlack, AI by McKinsey | Elder Research |
|---|---|---|
| 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 Elder Research
| Framework / platform | QuantumBlack, AI by McKinsey | Elder Research |
|---|---|---|
| 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 Elder Research
| Criterion | QuantumBlack, AI by McKinsey | Elder Research |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Strategy & roadmap engagement, Delivery team, Ongoing advisory | Strategy & roadmap engagement, Readiness assessment, Delivery team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: QuantumBlack, AI by McKinsey vs Elder Research
| Dimension | QuantumBlack, AI by McKinsey | Elder Research |
|---|---|---|
| Best company size | Mid-market to enterprise | Startup to mid-market |
| Best industries | Banking & insurance, Healthcare & life sciences, Consumer & retail | Government & defense, Healthcare, Financial services |
| 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. | Feasibility checks on AI ideas before funding them., Fraud and improper-payment analytics for government programs. |
| Typical project type | Strategy & roadmap engagement | Strategy & roadmap engagement |
QuantumBlack, AI by McKinsey vs Elder Research: 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 |
| Elder Research | |
|---|---|
| + | Thirty years of applied analytics make it good at saying which models will actually work on your data |
| + | Training courses for analysts and managers come from the same firm |
| + | Experience with fraud, waste, and abuse detection for public agencies |
| + | Readiness assessments are grounded in hands-on data work |
| - | Bought by ManTech in December 2025, so its focus may shift further toward government work |
| - | Little European presence or EU regulatory work |
| - | Generative AI strategy is a newer area next to its classic analytics |
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 Elder Research?
A typical fit: feasibility checks on AI ideas before funding them.
Three decades of applied data science behind its feasibility calls on AI use cases. Minimum engagement is not publicly disclosed. Works best with clients in Government & defense, Healthcare, Financial services, Insurance, Energy & utilities.
Decision matrix: QuantumBlack, AI by McKinsey vs Elder Research
| 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 Elder Research (Not disclosed) |
| You need a large team across many countries | QuantumBlack, AI by McKinsey |
Use case fit: QuantumBlack, AI by McKinsey vs Elder Research
| Use case | QuantumBlack, AI by McKinsey fit | Elder Research 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 |
| Feasibility checks on AI ideas before funding them. | Limited | Strong | Elder Research |
| Fraud and improper-payment analytics for government programs. | Limited | Strong | Elder Research |
Verdict: QuantumBlack, AI by McKinsey vs Elder Research
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.
Elder Research (4.0/5) is worth a look if you need fraud and improper-payment analytics for government programs. If your situation matches that, Elder Research is a competitive option.
Related comparisons
QuantumBlack, AI by McKinsey vs Elder Research FAQ
Is QuantumBlack, AI by McKinsey better than Elder Research?
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. Elder Research's strongest advantage: thirty years of applied analytics make it good at saying which models will actually work on your data.
How do QuantumBlack, AI by McKinsey and Elder Research differ in pricing?
QuantumBlack, AI by McKinsey's pricing: project fees set per engagement; rates not published. Elder Research's pricing: project 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 Elder Research?
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 Elder Research?
QuantumBlack, AI by McKinsey's primary differentiator is: board-level strategy and change management backed by McKinsey's own AI engineering group. Elder Research's primary differentiator is: three decades of applied data science behind its feasibility calls on AI use cases. They also differ in team size (1,000+ vs 170+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Banking & insurance, Healthcare & life sciences vs Government & defense, Healthcare).
Verify all details directly with each consultant before making a decision.