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What the major assessments actually show
Forrester’s July 9, 2026 discussion of its Q2 2026 AI Consulting Services evaluation examined 10 providers: Accenture, Bain & Company, Boston Consulting Group, Capgemini, Deloitte, EY, IBM, KPMG, McKinsey & Company and PwC. Forrester separately describes 37 providers in its Q3 2025 AI Consulting Services Landscape. The public article explains the evaluation and differences in provider strengths, but it does not publish full provider-by-provider scores.
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Everest Group’s 2025 global AI and generative-AI services assessment covers 27 providers. It places Accenture, Capgemini, Cognizant, Deloitte, IBM Consulting and TCS in its Leaders category. BCG, EY, McKinsey, PwC and other firms are classified as Major Contenders; Akkodis, Kyndryl, Sonata Software, Stefanini and UST appear among the Aspirants. These categories are useful for broadening a shortlist, not for declaring every provider interchangeable.
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Both assessments evaluate market capabilities and strategies. Neither can predict whether a particular team will deliver your use case, meet your timeline, achieve adoption or control production costs. Treat a category label as a reason to investigate, then require engagement-specific proof.
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How to compare AI consulting firms
A credible proposal should answer the same questions regardless of the logo on the cover. Score the provider against your requirements rather than assigning a context-free ranking.
| Comparison axis | Questions to ask each bidder |
|---|---|
| Business fit | What measurable opportunity is being pursued, and who is the accountable business sponsor? |
| Industry fit | Can the firm show production work in your sector, operating environment and regulatory context? |
| Delivery scope | Can it cover strategy, data and model engineering, deployment, AI operations, change management and security, or will other partners fill gaps? |
| Production readiness | How will a proof of concept become a supported service, and who owns monitoring, incident response and model or agent updates? |
| Governance | What controls cover data rights, explainability, testing, security, bias, compliance, auditability and accountability? |
| People and partnerships | Which named specialists will work on the engagement, how much time will they commit, and which technology partners are dependencies? |
| Economics | What are the deliverables, milestones, acceptance criteria, recurring operating costs, inference assumptions and outcome-linked fees? |
| Proof | Can the bidder provide a comparable reference, describe what went live, show adoption and quantify results? |
Forrester recommends starting with a business opportunity, assembling business, operations, risk and technology sponsors, establishing data and AI-platform foundations, and modeling inference and ongoing AI operating costs before scaling. Those steps should be requirements in your request for proposal, not optional advisory language.
Accenture: broad scale, still requiring use-case proof
Everest Group places Accenture in the Leaders category of its 2025 global assessment. In a separate figure estimating 2024 AI-services market share, Accenture appears in the “above 8%” band. That band is a broad market-position indicator, not audited revenue and not evidence that a particular Accenture team is the best match for your project.
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- Organizations seeking a large provider that can combine business strategy, technology implementation and change management across regions.
- Programs that need several delivery disciplines—data engineering, application integration, security, operating-model design and workforce adoption—in one prime contractor.
- Enterprises that value a substantial bench but can impose clear controls on staffing continuity, subcontracting and partner dependencies.
What to verify before selecting it
- Ask for a named account team and the percentage of work performed by the people introduced during the sales process.
- Request a reference whose data complexity, regulatory exposure and production scale resemble yours, not merely a well-known brand.
- Separate reusable accelerators from custom work, and require acceptance tests for each component.
- Obtain a transparent estimate for cloud, model, data, monitoring and support costs; a large transformation proposal can obscure recurring run costs.
Deloitte: governance and industry context to examine closely
Everest Group also places Deloitte in the Leaders category. Deloitte’s current global AI page describes work spanning generative AI, intelligent agents, AI infrastructure, governance and scaling. It presents examples involving banking, data-center planning, manufacturing and government. Those examples are Deloitte’s own descriptions; they are not independently comparable outcome measurements.
Where Deloitte may fit
- Regulated organizations that need risk, controls, compliance and operating-model questions handled alongside technical delivery.
- Businesses whose initiative crosses functions, such as finance, operations, customer service and workforce change, rather than a single model experiment.
- Teams that want an adviser able to connect an AI roadmap with broader transformation, internal controls and accountability structures.
What to verify before selecting it
- Ask which governance artifacts will be delivered: risk assessments, data lineage, model cards, test evidence, approval gates and incident procedures.
- Clarify whether the proposed specialists have delivered your specific use case in production and for how long the service has operated.
- Test how recommendations change when your data cannot leave a jurisdiction or when a high-risk decision requires human review.
- Require measurable adoption and service-level objectives, not only a strategy deck or policy framework.
IBM Consulting: engineering, operations and multi-platform options
Everest Group places IBM Consulting in the Leaders category, and the IBM-licensed version of the assessment describes it as a Star Performer. The profile describes maturity assessments, model development and fine-tuning, LLMOps, governance, and agent orchestration and scaling. IBM’s public AI-consulting description names work with AWS, Google Cloud, Microsoft Azure and OpenAI. These statements establish described scope and partnerships; they do not guarantee that your engagement will use a particular platform, model or team.
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Where IBM Consulting may fit
- Enterprises that need a structured path from AI maturity assessment through engineering, deployment, LLMOps and agent operations.
- Organizations with a heterogeneous technology estate that want a provider accustomed to working across multiple major cloud and model ecosystems.
- Programs where operational ownership, monitoring and controlled model updates matter as much as the initial prototype.
What to verify before selecting it
- Identify the exact cloud, model and orchestration components proposed, and document what happens if a preferred partner changes pricing, access or terms.
- Ask to see monitoring dashboards, evaluation suites, rollback procedures and escalation paths from a comparable live service.
- Clarify what “fine-tuning” means for your data, who owns resulting artifacts and how retraining costs are controlled.
- Confirm that the senior architects who design the operating model remain involved through production handover.
Alternatives worth adding to the shortlist
Alternatives are valuable when they fill a gap in sector expertise, geography, delivery style or economics. They are not automatically cheaper or better. Forrester’s 10-provider set includes Bain, BCG, Capgemini, EY, KPMG, McKinsey and PwC alongside the three firms above. Everest’s broader set adds Cognizant and TCS as Leaders and identifies many other providers as Major Contenders or Aspirants.
| Alternative group | What the published assessments establish | Questions for your evaluation |
|---|---|---|
| Capgemini, Cognizant and TCS | Everest Group places each in its 2025 Leaders category. | Can the proposed local team provide the domain knowledge, architecture and production references your use case needs? |
| BCG, EY, McKinsey and PwC | Everest Group lists these firms as Major Contenders; several also appear in Forrester’s 2026 evaluation set. | How much engineering and operational delivery is included, and which implementation partners are required? |
| Bain, KPMG and other Forrester-evaluated firms | Forrester includes them in its 10-provider Q2 2026 evaluation. | Does the engagement need strategy leadership, implementation depth, or a deliberate combination of both? |
| Everest Aspirants and specialist providers | Akkodis, Kyndryl, Sonata Software, Stefanini and UST are examples in Everest’s Aspirants group. | Could a more focused provider offer stronger hands-on expertise, and can it support your geographic, security and continuity requirements? |
Do not infer that a Major Contender or Aspirant lacks capability, or that a Leader will outperform a specialist. Ask every bidder for the same evidence and score the actual team.
A practical selection process
1. Define one business outcome
Write the problem in operational terms: reduce a measurable processing burden, improve a defined service metric, increase employee enablement or support a specific customer-facing objective. Name the business owner, operations owner, risk owner and technology owner. A vague ambition such as “adopt generative AI” cannot produce comparable proposals.
2. Document the delivery boundary
Describe the use case, users, decisions affected, source systems, data quality, data residency, retention rules, integrations, languages, availability targets and the operational owner after launch. State what is explicitly out of scope. Ask bidders to identify assumptions and dependencies instead of hiding them in a statement of work.
3. Build a balanced shortlist
Invite the three named firms plus alternatives that match your sector, geography and delivery model. A shortlist of four to six serious bidders is usually easier to evaluate than an unbounded market scan. Use the Forrester and Everest categories to discover candidates, then remove any provider that cannot show relevant production evidence or meet mandatory controls.
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4. Demand comparable references
Request at least one reference for a similar use case and one for a similar operating environment. Ask what was delivered, how long it has run, who supports it, what adoption looks like, which targets were missed and what the client would change. Everest identifies experience with similar projects, advanced-AI expertise and industry or domain expertise as explicit sourcing criteria.
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5. Evaluate the actual team
Put proposed architects, data engineers, product owners, security specialists, change leads and service managers in the interview. Record their names, locations, allocation and replacement rules in the proposal. Ask which tasks will be subcontracted or delegated to technology partners and who remains accountable for the integrated result.
6. Test production readiness
Require a delivery plan that includes data pipelines, evaluation datasets, security testing, deployment controls, observability, human escalation, rollback, incident response, model or agent updates and end-of-life procedures. A proof of concept is not a production service until someone owns these activities and their cost.
7. Make governance concrete
Ask for controls covering data access and lineage, privacy, prompt and output logging, explainability where relevant, bias testing, red-teaming, identity and access management, third-party model terms, records retention and audit evidence. Specify approval gates for high-impact decisions and the circumstances requiring human review.
8. Model the full economics
The reviewed assessments do not disclose directly comparable fee ranges, engagement sizes or project durations for Accenture, Deloitte or IBM Consulting. Do not fill that gap with invented market averages. Instead, require each bidder to separate one-time and recurring costs: discovery, engineering, integration, licenses, data preparation, model or inference usage, monitoring, security, support, change management and retraining. Ask for low, expected and high usage scenarios and the assumptions behind each.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors9. Compare proposals on common acceptance criteria
Give every bidder the same target metrics, test data or representative samples, security requirements, timeline assumptions and handover conditions. Distinguish a vendor’s promise from an acceptance test you can run. If an outcome-based fee is proposed, define the baseline, measurement period, attribution rules, client responsibilities and what happens when external factors affect the result.
10. Contract the transition, not just the launch
Specify documentation, training, source-code and configuration access, model and prompt ownership, data deletion, portability, knowledge transfer, service levels, warranty, change control and exit assistance. Make the operating owner a named role before signing. A successful launch without an accountable support model can become an expensive experiment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.An illustrative scorecard
The following weighting is a starting point, not an industry standard. Change it to reflect your risk and outcome.
| Category | Illustrative weight | Evidence to score |
|---|---|---|
| Business outcome and domain fit | 20% | Use-case understanding, sponsor alignment, relevant references and measurable targets. |
| Technical and data delivery | 20% | Architecture, data engineering, model or agent development, integration and testing. |
| Production operations | 15% | Monitoring, evaluation, incident response, updates, support and transition ownership. |
| Governance and security | 15% | Privacy, access controls, auditability, risk management, human oversight and compliance. |
| People and partner model | 10% | Named team, allocation, continuity, dependencies and accountability. |
| Commercial transparency | 10% | Comparable assumptions, milestones, acceptance criteria, recurring costs and fee mechanics. |
| Change and adoption | 10% | Training, communications, workflow redesign, adoption measurement and benefits tracking. |
Score written evidence and interview responses separately. A polished presentation should not compensate for missing references, unclear ownership or unmodeled operating costs.
Common failure modes and how to prevent them
Choosing a category winner
Leader and Star Performer labels summarize an analyst’s market assessment. They do not establish equivalent teams, pricing or outcomes. Prevent this by requiring use-case references and a scored technical demonstration.
Starting with a technology instead of a problem
A preferred model or cloud service can narrow options before the business case is clear. Begin with the process, decision or service metric, then select technology that meets security, quality and cost requirements.
Stopping at the proof of concept
Many pilots omit integration, monitoring, support and change management. Put production gates, an operating owner and a funded run phase in the original plan.
Ignoring inference and operating costs
Usage, context length, retrieval, monitoring and human review can change economics after launch. Require scenario-based cost models before approving scale.
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Accepting a generic governance promise
“Responsible AI” is not a control. Ask for named policies, test methods, approval records, escalation thresholds and evidence that the controls work with your data and jurisdiction.
Overlooking staffing changes
Senior experts may sell the engagement while a different team delivers it. Put proposed personnel, minimum allocation and substitution rights in the contract, and make a leadership change trigger a review.
Comparing incompatible commercial proposals
One bidder may include licenses and support while another prices only consulting days. Normalize scope, assumptions, pass-through costs, acceptance criteria and recurring operations before comparing totals.
What can and cannot be concluded from current evidence
The available assessments support a shortlist that includes Accenture, Deloitte and IBM Consulting, with Capgemini, Cognizant, TCS, BCG, EY, McKinsey, PwC, Bain, KPMG and specialist providers considered where their evidence fits. They do not establish directly comparable prices, project durations, engagement sizes or provider-by-provider Forrester scores. Deloitte’s and IBM’s public pages describe their own offerings, while Everest’s market-share figures are estimates based on its research methods; the IBM-licensed report notes that some provider estimates may be less complete. Keep those limits visible in your decision record.
The strongest buying decision is therefore not “which firm is number one?” It is “which accountable team can prove it understands our opportunity, control our risks, deliver into production and operate the result at an acceptable total cost?”
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