DCF Research

Directory · AI consulting

AI consulting firms and companies, compared on public evidence

AI consulting firms help organizations choose, build and run AI systems, from generative AI applications and agents to machine learning in production. Of the 50 firms DCF Research tracks, 47 publish a dedicated generative AI service page on their own site; as of October 23, 2026, the strongest public evidence belongs to DataArt, N-iX and NTT DATA.

Edited by Peter Korpak·Evidence checked ·How we score

Qualifying firms, ranked by evidence score

47 of 50 tracked firms · evidence checked October 23, 2026

  1. 1
    DataArt
    Technology consultancy
    90/100
    Practice page
    Case studies
    3 public case studies
  2. 2
    N-iX
    Engineering partner
    89/100
    Practice page
    Case studies
    3 public case studies
  3. 3
    NTT DATA
    Global systems integrator
    88/100
    Practice page
    Case studies
    3 public case studies
  4. 4
    Slalom
    Technology consultancy
    87/100
    Practice page
    Case studies
    3 public case studies
  5. 5
    Tiger Analytics
    Data & AI specialist
    87/100
    Practice page
    Case studies
    3 public case studies
  6. 6
    Protiviti
    Technology consultancy
    84/100
    Practice page
    Case studies
    3 public case studies
  7. 7
    Wipro
    Global systems integrator
    84/100
    Practice page
    Case studies
    3 public case studies
  8. 8
    Xebia
    Technology consultancy
    84/100
    Practice page
    Case studies
    3 public case studies
  9. 9
    HCLTech
    Global systems integrator
    82/100
    Practice page
    Case studies
    3 public case studies
  10. 10
    Infosys
    Global systems integrator
    81/100
    Practice page
    Case studies
    3 public case studies
  11. 11
    phData
    Data & AI specialist
    80/100
    Practice page
    Case studies
    3 public case studies
  12. 12
    CGI
    Global systems integrator
    79/100
    Practice page
    Case studies
    3 public case studies
  13. 13
    Tata Consultancy Services
    Global systems integrator
    78/100
    Practice page
    Case studies
    3 public case studies
  14. 14
    STX Next
    Engineering partner
    77/100
    Practice page
    Case studies
    3 public case studies
  15. 15
    Accenture
    Global systems integrator
    76/100
    Practice page
    Case studies
    3 public case studies
  16. 16
    Lovelytics
    Data & AI specialist
    76/100
    Practice page
    Case studies
    3 public case studies
  17. 17
    Tredence
    Data & AI specialist
    76/100
    Practice page
    Case studies
    3 public case studies
  18. 18
    Avenga
    Engineering partner
    75/100
    Practice page
    Case studies
    3 public case studies
  19. 19
    Quantiphi
    Data & AI specialist
    74/100
    Practice page
    Case studies
    3 public case studies
  20. 20
    LatentView Analytics
    Data & AI specialist
    73/100
    Practice page
    Case studies
    3 public case studies
  21. 21
    Thoughtworks
    Technology consultancy
    73/100
    Practice page
    Case studies
    3 public case studies
  22. 22
    Capgemini
    Global systems integrator
    72/100
    Practice page
    Case studies
    3 public case studies
  23. 23
    Deloitte
    Big Four
    72/100
    Practice page
    Case studies
    3 public case studies
  24. 24
    EPAM Systems
    Technology consultancy
    72/100
    Practice page
    Case studies
    3 public case studies
  25. 25
    Fractal
    Data & AI specialist
    72/100
    Practice page
    Case studies
    3 public case studies
  26. 26
    IBM Consulting
    Global systems integrator
    71/100
    Practice page
    Case studies
    3 public case studies
  27. 27
    ZS
    Data & AI specialist
    70/100
    Practice page
    Case studies
    3 public case studies
  28. 28
    Grid Dynamics
    Technology consultancy
    69/100
    Practice page
    Case studies
    3 public case studies
  29. 29
    Algoscale
    Data & AI specialist
    68/100
    Practice page
    Case studies
    3 public case studies
  30. 30
    Avanade
    Technology consultancy
    68/100
    Practice page
    Case studies
    3 public case studies
  31. 31
    Cognizant
    Global systems integrator
    67/100
    Practice page
    Case studies
    2 public case studies
  32. 32
    EY
    Big Four
    67/100
    Practice page
    Case studies
    3 public case studies
  33. 33
    ScienceSoft
    Engineering partner
    67/100
    Practice page
    Case studies
    3 public case studies
  34. 34
    Tech Mahindra
    Global systems integrator
    67/100
    Practice page
    Case studies
    3 public case studies
  35. 35
    Data Reply
    Data & AI specialist
    64/100
    Practice page
    Case studies
    3 public case studies
  36. 36
    InData Labs
    Data & AI specialist
    63/100
    Practice page
    Case studies
    3 public case studies
  37. 37
    Innowise
    Engineering partner
    63/100
    Practice page
    Case studies
    3 public case studies
  38. 38
    KPMG
    Big Four
    63/100
    Practice page
    Case studies
    3 public case studies
  39. 39
    Binariks
    Engineering partner
    62/100
    Practice page
    Case studies
    3 public case studies
  40. 40
    Mu Sigma
    Data & AI specialist
    57/100
    Practice page
    Case studies
    3 public case studies
  41. 41
    RTS Labs
    Engineering partner
    57/100
    Practice page
    Case studies
    3 public case studies
  42. 42
    BCG X
    Strategy consultancy
    54/100
    Practice page
    Case studies
    3 public case studies
  43. 43
    Datatonic
    Data & AI specialist
    51/100
    Practice page
    Case studies
    3 public case studies
  44. 44
    QuantumBlack, AI by McKinsey
    Strategy consultancy
    48/100
    Practice page
    Case studies
    3 public case studies
  45. 45
    PwC
    Big Four
    40/100
    Practice page
    Case studies
    2 public case studies
  46. —
    Palantir Technologies
    Platform vendor servicesNot ranked
    61/100
    Practice page
    Case studies
    3 public case studies
  47. —
    Databricks Forward Deployed Engineering
    Platform vendor servicesNot ranked
    50/100
    Practice page
    Case studies
    3 public case studies

Ranked by DCF Evidence Score (0–100), which counts verifiable public evidence and does not measure delivery quality. How we score. Every link opens a public page, checked October 23, 2026; outcomes described on those pages are the firms’ own claims.

Platform vendors’ own services teams are shown for comparison and are not ranked.

What does AI consulting include?

AI consulting covers five kinds of work: choosing use cases, preparing data, building and integrating systems, running them in production, and managing risk. Firms sell these separately or together. AI implementation consulting is the build-and-run part, where working software replaces a plan.

WorkWhat the firm deliversGo deeper
Strategy and use-case selectionRanked use cases with owners, a business case and a sequenced roadmapAI strategy versus implementation consulting
Data readinessA source inventory, quality checks, access rules and the pipelines a system will read fromData engineering consulting
Build and integrateA working generative AI application, retrieval (RAG) pipeline or agent connected to your systems, with an evaluation setRAG implementation consulting, GenAI proof of concept
Run in productionDeployment, monitoring, cost tracking, and updates to prompts, models or retrained modelsMLOps consulting
Risk and governancePolicies, security review, documentation and controls mapped to a frameworkNIST AI Risk Management Framework (below)
Value trackingA baseline and a benefit measure that someone other than the vendor can auditEnterprise GenAI ROI

NIST released its AI Risk Management Framework on January 26, 2023 and a generative AI profile (NIST AI 600-1) on July 26, 2024. Both are voluntary. Ask a firm how its controls map to them, or to whichever regulation applies to you.

How firms package AI work

Firms sell different products under the same label. These examples come from the firms' own pages, checked in October 2026, and are not ranked.

PackagingExampleWhat the page lists
Readiness assessmentLatentView: GenAI readinessAn assessment that returns a readiness score across data, platforms, governance and operating models, then use-case mapping and a roadmap
Menu of named solutionsSlalom: AI transformationSix solutions: AI Strategy, AI Office, AI Accelerate Teams, AI Factory, AI Enablement and Agentic Managed Services
Strategy plus an AI operating model you ownThoughtworks: Enterprise AI servicesAI strategy and maturity assessment, production agents, an AI Factory with an operating model the client owns, and data modernization
Firm-built platformProtiviti: Protiviti AtlasAn AI platform the firm uses to build client solutions, including Protiviti GPT

Ask which of these is in your quote.

Which type of AI consulting firm should you hire?

Match the firm type to the decision in front of you. Strategy consultancies suit board-level direction, large integrators suit programs that touch many systems, technology consultancies and specialists suit building, and a platform vendor's own team suits work that sits wholly on its platform.

This is DCF Research's grouping of the firms it tracks, with a judgment about fit. It is a reading aid, not a measured result.

Firm typeUsually the right choice whenCheck
Strategy consultanciesThe question is where AI changes the business and who must actWho builds afterward, and whether the advice is tied to a platform
Big Four networksAI work must sit alongside risk, audit, tax or regulatory programsWhether the team that pitches is the team that delivers
Global systems integratorsMany systems, countries or legacy platforms are involved and you need delivery capacity at scaleNamed senior staff, and how much work is subcontracted or offshore
Technology consultancies and data and AI specialistsYou need a working system built and a team that stays close to the codeDepth on your cloud and platform, and references for production systems
Engineering partnersYou have a clear specification and need engineers, often as an extended teamWho owns architecture and quality decisions
A platform vendor's services teamThe work sits entirely on one vendor's platform and needs access to its engineersWhether alternatives were considered, since the vendor sells the platform

Our AI consulting buyer's guide walks through the buying process, and the ranking table at the top of this page includes firms from each group.

Should you start with strategy, a proof of concept or a build?

Start with strategy only if you cannot name three use cases with owners and data. Start with a time-boxed proof of concept when you have one credible use case and data to test it. Go straight to a build when the use case, data and success measure are already proven.

SituationStart withRequire
No agreed use cases, or sponsors who disagree on prioritiesA strategy engagementRanked use cases with owners, data readiness and a first-90-days plan
One credible use case and accessible dataA proof of conceptWritten success criteria and an evaluation set before work starts, plus a budget and plan for production
A use case already proven on a prototypeA production buildAcceptance tests, monitoring and run costs inside the scope

Many proofs of concept stop there. Gartner predicted in July 2024 that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs or unclear business value. Our GenAI proof-of-concept guide covers how to scope one so that it can lead to production.

How much does AI consulting cost?

Estimate: a generative AI proof of concept with two ML/AI engineers, a data engineer and half an architect for eight weeks costs about $193,000 at federal ceiling-rate medians, and a 26-week production build with five people about $863,000. Commercial rates for the same roles are often higher.

Scenario (DCF Research estimate)Hours × GSA median hourly rateEstimateAt 25th to 75th percentile rates
Proof of concept, 8 weeks. 2 ML/AI engineers and 1 data engineer full time, 1 data architect half time640 × $187 + 320 × $149 + 160 × $158 = $119,680 + $47,680 + $25,280$192,640$155,040 to $244,160
Production build, 26 weeks. 2 ML/AI engineers, 2 data engineers and 1 data architect, all full time2,080 × $187 + 2,080 × $149 + 1,040 × $158 = $388,960 + $309,920 + $164,320$863,200$702,000 to $1,081,600

The rates are role medians from GSA CALC+: machine learning and AI engineer $187 an hour (230 records), data engineer $149 (838 records) and data architect $158 (913 records). They are fully burdened ceiling prices on U.S. federal contracts, not commercial quotes, and commercial rates for the same roles are often higher. The hours are our assumptions: 40 hours a week for each full-time person. The last column reprices the same hours at the 25th and 75th percentile GSA rates for each role.

The estimates leave out model usage, which the model provider bills separately, usually per token (see the OpenAI and Anthropic pricing pages), plus cloud services, licenses, partner or principal time and your own staff's time.

Use the table to test a proposal. Ask for hours by role and compare them with the closest scenario. A large gap is not automatically wrong, but the firm should be able to say whether it comes from seniority mix, scope or margin. Our AI consulting pricing guide covers pricing models and what drives cost.

How do you evaluate an AI consulting firm?

Ask for evidence you can check: a dedicated service page, named case studies with dates, credentials in the vendors' own partner directories, and a reference for a system still in production. Then test how the firm will measure quality, handle your data and hand the system over.

AskA strong answerA warning sign
Show a system you built that is still runningA named client or reference who will take a call, and what changed after launchCase studies with no client, date or architecture; pilots only
How will we know it works before launch?An evaluation set built with your users, agreed thresholds and a test reportQuality judged by demos; no acceptance criteria
Which models and platforms do you propose, and why?A written comparison that includes cost and data location, and what switching would takeOne vendor's stack proposed before use cases are agreed
What happens to our data?Where it goes, whether providers retain it or train on it, and who can access itAnswers deferred to a provider's terms nobody has read
Who owns the outputs?Written ownership of code, prompts, evaluation sets and any fine-tuned weightsBroad reuse rights reserved by the firm
Who does the work?Named people, roles and hours, with rules for substitutionSenior names in the pitch and a junior team in delivery
What will it cost to run?A usage and infrastructure estimate with its assumptionsFees quoted for the build only
How is risk managed?Controls mapped to the NIST framework or to the regulation that applies to youA generic responsible-AI slide

Case-study outcomes on firm websites are the firm's own account, and DCF Research has not verified them. Credentials can be checked in the vendors' own directories: see our pages on Snowflake partners and Databricks partners, and each cloud provider publishes its own partner finder.

What are the red flags in an AI consulting proposal?

Watch for no baseline to measure against, no evaluation plan, case studies without a named client or date, a demo presented as the deliverable, and agent labels on ordinary automation. Gartner expects many generative and agentic AI projects to be abandoned or canceled.

  • No baseline. Without a measured starting point, no benefit can be audited later. Our enterprise GenAI ROI guide shows how to set one.
  • A promised percentage improvement before anyone has examined your data.
  • A demo as the deliverable. Scope that leaves out data work, integration, security review or run costs.
  • Production deferred to a later phase with no budget or owner.
  • Agent washing. Gartner forecast in June 2025 that over 40% of agentic AI projects will be canceled by the end of 2027, and estimated that only about 130 of the thousands of vendors claiming agentic AI are real, because many relabel chatbots and robotic process automation. If a proposal centers on agents, ask which steps the system decides on its own and which a person approves.
  • Lock-in by design: proprietary components with no code, prompt or evaluation-set handover.

Our RFP template turns these checks into vendor questions.

Frequently asked questions

How is this list ordered, and can a firm pay to rank higher?

The order comes from the DCF Evidence Score (0 to 100), computed only from public evidence: partner-directory credentials (30 points), capability pages on the firm's own site (25), public case studies (25), industry pages (10) and rate or review transparency (10); it measures how much evidence a firm publishes, not delivery quality. Rankings cannot be bought, and a clearly labeled Featured placement, which no firm pays for today, never changes a score or rank. DCF Research receives no referral fees.

How is generative AI consulting different from machine learning consulting?

Generative AI work mostly builds applications around existing foundation models: retrieval, prompts, evaluation, guardrails and integration. Machine learning consulting trains models on your own data, so labeling, features and retraining take more of the effort. Many programs need both.

Do cloud providers' partner badges show a firm is good at AI?

They show the firm met that provider's program requirements, which differ by provider and change over time. They do not show independence from the provider or results on a use case like yours. Read the requirements on the provider's own partner pages and ask for references.

Who are the top AI consulting firms?

There is no objective top ten. The table orders firms by how much verifiable public evidence they publish, not by delivery quality, so use it to build a shortlist of three to five firms that match your platform, industry and size, then compare references, staffing plans and a paid pilot.

What should an AI consulting contract cover?

Scope and acceptance criteria, ownership of code, prompts and evaluation sets, how your data is handled and by whom, named staff with substitution rules, a run-cost estimate, and what the firm hands over at exit. Pay against accepted deliverables rather than elapsed time where you can.

Sources

  1. GSA CALC+ ceiling rates (role medians, accessed October 2026) — U.S. General Services Administration, 2026buy.gsa.gov
  2. Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025 — Gartner, 2024gartner.com
  3. Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027 — Gartner, 2025gartner.com
  4. AI Risk Management Framework — National Institute of Standards and Technology, 2023nist.gov
  5. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1) — National Institute of Standards and Technology, 2024nist.gov
  6. API pricing — OpenAI, 2026platform.openai.com
  7. Pricing — Anthropic, 2026anthropic.com
  8. GenAI Readiness Assessment and Consulting Services (checked October 2026) — LatentView Analytics, 2026latentview.com
  9. AI Transformation: 6 Solutions for Enterprises (checked October 2026) — Slalom, 2026slalom.com
  10. Enterprise AI Services (checked October 2026) — Thoughtworks, 2026thoughtworks.com
  11. Protiviti Atlas (checked October 2026) — Protiviti, 2026protiviti.com

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