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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteThere is no universal winner among Accenture, Deloitte, and IBM Consulting. The right choice depends on the business outcome, industry, delivery scope, governance obligations, available data, and the team that will operate the system after launch. Forrester’s July 2026 evaluation says provider strengths vary by use case and industry, while Everest Group’s 2025 global assessment places all three in its Leaders category. Those labels indicate market standing, not a guarantee of results for your organization.
Use the comparison below to build a defensible shortlist, then make bidders prove their fit with comparable production references, named delivery staff, a governance design, a transition plan, and a transparent model of ongoing AI operating costs.
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What the rankings actually tell you
Two widely cited assessments provide useful context, but neither selects a provider for a particular buyer.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute- Forrester, Q2 2026 AI Consulting Services Wave: The evaluation covered 10 providers—Accenture, Bain & Company, Boston Consulting Group, Capgemini, Deloitte, EY, IBM, KPMG, McKinsey & Company, and PwC. Forrester’s public article, dated July 9, 2026, says the firms were assessed across strategy, value management, AI operations, data and model engineering, agent development, change management, and security. It also says the providers differ in strengths across operational efficiency, employee enablement, industry work, customer-facing initiatives, and back-office processes.
- Forrester’s separate landscape: Its Q3 2025 AI Consulting Services Landscape profiles 37 providers. That is a broader market map, not a scorecard proving that every provider is interchangeable.
- Everest Group, 2025 global AI and generative AI services assessment: The assessment covers 27 providers using its 2024 annual request-for-information process, provider interactions, client references, and market analysis. It classifies Accenture, Capgemini, Cognizant, Deloitte, IBM Consulting, and TCS as Leaders. BCG, EY, McKinsey, PwC, and others are Major Contenders; Akkodis, Kyndryl, Sonata Software, Stefanini, and UST are among the Aspirants.
Category placement is best used to widen or narrow a shortlist. It does not establish equivalent delivery quality, a guaranteed return, a specific technology stack, or a price level.
#1 Best Overall
Comparison at a glance
| Firm | Independent evidence in the reviewed assessments | Publicly described emphasis | What a buyer still must verify |
|---|---|---|---|
| Accenture | Everest Group Leader, 2025. Appears in the above-8% band in Everest’s 2024 AI-services market-share figure. | Broad enterprise strategy, engineering, implementation, and operations capabilities are represented in the assessments. | Which industry team, architects, partners, and post-launch operators will be assigned; how the proposal connects to a measurable business result. |
| Deloitte | Everest Group Leader, 2025. | Its current global AI page describes generative AI, intelligent agents, AI infrastructure, governance, and scaling, with examples in banking, data-center planning, manufacturing, and government. | Whether the cited examples are comparable in data, regulation, scale, and independently measurable outcomes; who owns production operations. |
| IBM Consulting | Everest Group Leader, 2025; an IBM-licensed version of the assessment also describes IBM Consulting as a Star Performer. | Public materials describe maturity assessment, model development and fine-tuning, LLMOps, governance, and agent orchestration and scaling. IBM names work with AWS, Google Cloud, Microsoft Azure, and OpenAI. | Which cloud, model, and tooling choices are actually proposed; portability, licensing, inference economics, and the named implementation team. |
The market-share figure deserves particular caution. Everest’s report places Accenture and IBM in an above-8% band for 2024 AI-services market share, but says some estimates rely on request-for-information data and its proprietary database and that estimates for certain providers may be less complete. The bands are not audited company revenue shares.
Choose by the business problem, not the logo
A useful request for proposal begins with an opportunity that a business sponsor owns. “Adopt generative AI” is not an outcome. Examples of better-defined opportunities include reducing the time required to resolve service cases, improving a regulated review process, increasing forecast accuracy, or giving employees reliable access to internal knowledge.
Operational efficiency
For high-volume operations, ask bidders to quantify the baseline cost, throughput, error rate, and service-level target. Require a design for process redesign as well as a model or agent. A demonstration that works in a workshop is not evidence that the provider can integrate queues, permissions, exception handling, and human approvals in production.
Employee enablement
Internal assistants and search tools depend on identity, document permissions, retrieval quality, and adoption. Compare how each bidder will measure answer quality, prevent unauthorized disclosure, train users, and retire or update obsolete content. Change management should be a deliverable with owners and milestones, not a paragraph in an implementation plan.
Customer-facing initiatives
Customer applications add brand, privacy, accessibility, and service-continuity risks. Ask for escalation paths, testing with representative conversations, monitoring for unsafe or incorrect responses, and a rollback procedure. A proposal should show how the system hands work to a person when confidence is low or policy requires review.
Back-office and regulated processes
Finance, healthcare, public-sector, and other regulated environments need evidence of data lineage, retention controls, explainability appropriate to the decision, bias testing, security testing, and accountability. An attractive model benchmark cannot substitute for an auditable operating process.
Rank #2
Accenture: scale indicators, with fit still to prove
Everest Group places Accenture in its Leaders category in the 2025 global AI and generative AI services assessment. In the same report family, Accenture appears in the above-8% band of the 2024 AI-services market-share figure. These indicators suggest substantial market presence and a broad ability to assemble strategy, engineering, and operational resources. They do not show that Accenture is the best choice for a particular use case.
In a proposal, require the firm to identify the people who will perform the work rather than relying on the scale of its overall workforce. Ask for the percentage of delivery by senior staff, the location and availability of specialists, subcontractor or partner dependencies, and the handoff plan for your own engineers and operators. Request a reference where a comparable proof of concept reached production and remained supported after the initial launch.
Deloitte: governance and sector examples to test in detail
Everest Group also classifies Deloitte as a Leader in its 2025 assessment. 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 company-published descriptions rather than independently comparable outcome measurements. Use them to frame questions, not as proof of a result. Ask what was delivered, over what period, with which data and systems, how many users adopted it, what operating costs were incurred, and what measurable change followed. For a regulated buyer, request the control catalogue, testing evidence, model or agent approval workflow, and a clear assignment of accountability between Deloitte, your staff, and technology vendors.
IBM Consulting: engineering, LLMOps, and multi-cloud questions
Everest Group places IBM Consulting in the Leaders category in 2025, and an IBM-licensed version of the assessment describes it as a Star Performer. The profile describes maturity assessment, model development and fine-tuning, LLMOps, governance, and agent orchestration and scaling.
IBM’s public AI-consulting material names work with AWS, Google Cloud, Microsoft Azure, and OpenAI. That indicates a stated partnership and technology scope; it does not mean every engagement will use all of those platforms. Put the proposed architecture in writing, including model choice, hosting region, data movement, logging, observability, exit options, and responsibility for software or platform charges.
Rank #3
For model-heavy projects, ask how the team will manage prompt and model versions, evaluate regressions, monitor drift or unsafe behavior, control access to sensitive data, and respond when a provider changes a model or price. The answer should include an operating runbook and named roles, not just the acronym LLMOps.
Alternatives worth adding to a shortlist
The two assessments identify credible alternatives across strategy firms, technology implementers, and global service providers. Add them according to the capability gap you need to fill.
| Alternative group | Providers identified in the assessments | How to use the group in sourcing |
|---|---|---|
| Everest Leaders beyond the three named firms | Capgemini, Cognizant, and TCS | Invite one or more when large-scale implementation, industry delivery, or a different commercial model could improve competition. Verify the actual team and comparable references. |
| Forrester-evaluated strategy and professional-services firms | Bain & Company, BCG, EY, KPMG, McKinsey & Company, and PwC | Consider these when business-case design, operating-model change, risk, or executive alignment is central. Confirm how strategy work will become engineered, deployed, and operated systems. |
| Everest Major Contenders and other providers | BCG, EY, McKinsey, PwC, plus other firms in the assessment | Use category status as a prompt for investigation, not a ranking. A specialist may fit a narrow domain better than a larger generalist. |
| Everest Aspirants cited in the assessment | Akkodis, Kyndryl, Sonata Software, Stefanini, and UST, among others | Include a smaller or specialist provider when its domain expertise, delivery economics, or flexibility matches the use case. Demand the same production evidence and controls. |
Do not assume that a firm outside the Leaders category is weak or that a Leader will be the lowest-risk choice. The relevant test is whether the bidder can cover the required strategy, data and model engineering, deployment, AI operations, security, and change management—or clearly identify a partner for the missing piece.
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A buyer-led selection process
1. Define the opportunity and sponsors
Write a one-page problem statement with the baseline, target metric, affected process, constraints, and decision date. Name sponsors from business, operations, risk, and technology. Forrester recommends this cross-functional sponsorship because an AI system can fail through process, control, or adoption gaps even when the model performs as designed.
2. Document data and operating constraints
List the source systems, data owners, quality issues, retention rules, geographic restrictions, required integrations, identity model, and regulatory obligations. State who will own the service after launch. A bidder cannot give a credible schedule or price without knowing whether data is accessible and who can approve production changes.
3. Ask for comparable production evidence
Request at least one reference that resembles your use case in industry, scale, data sensitivity, and operating model. Ask the reference to distinguish the original scope from later additions and to describe adoption, support, incidents, measurable outcomes, and what the client had to provide. Everest Group explicitly identifies similar-project experience, advanced-AI expertise, prior experience with the provider, and industry or domain knowledge as sourcing criteria.
4. Compare named teams and dependencies
Require résumés or role profiles for the engagement lead, solution architect, data and model engineers, security specialists, change lead, and operations owner. Record which roles are employees, subcontractors, or technology partners. Make replacement rights and minimum staffing part of the contract if continuity matters.
5. Test governance before approving a pilot
Ask bidders to show controls for data governance, explainability, testing, security, bias, compliance, and accountability. Define who can approve a use case, who can stop it, how incidents are reported, and what evidence is retained. Include adversarial testing, access reviews, red-team or abuse testing where appropriate, and a documented rollback path.
6. Require a production and transition plan
The plan should cover environments, deployment gates, monitoring, model or agent evaluation, alert thresholds, incident response, support hours, disaster recovery, and knowledge transfer. Specify the operational owner and acceptance criteria for each stage from discovery through production. A proof of concept is not complete until the service can be run safely by the agreed owner.
7. Model the full economics
Forrester recommends modeling inference and ongoing AI operating costs before scaling use cases. Require assumptions for model calls, tokens or other usage units, storage, retrieval, data transfer, observability, security tooling, human review, support, retraining or re-evaluation, and platform licenses. Separate one-time consulting fees from recurring costs and show how costs change with volume, latency, and user growth.
8. Compare proposals on common assumptions
Give every bidder the same data description, volumes, service levels, acceptance tests, and implementation milestones. Everest Group highlights attractive, value-aligned pricing and flexible value-based commercial models as buyer considerations. If a bidder proposes outcome-linked fees, define the baseline, measurement method, attribution rules, caps, and what happens when your organization delays a dependency.
Questions to put in the RFP
- Which measurable business result is this design intended to change, and who signs off on the baseline?
- What comparable system has your proposed team delivered into production, and may we speak with the client about adoption, incidents, support, and results?
- Which capabilities are performed directly by your team, and which depend on cloud, model, data, software, or security partners?
- What data leaves our environment, where is it processed, how is it retained, and how can we export it if we change providers?
- How are model quality, hallucinations, bias, prompt attacks, permissions, and regressions tested before release and during operation?
- What monitoring, alerting, human review, and rollback mechanisms are included in the base scope?
- Who owns the prompts, evaluation sets, pipelines, documentation, and operating runbooks at the end of the engagement?
- What assumptions drive the implementation schedule, and what is the recovery plan if data access or integration work is late?
- Show one complete five-year cost model, including inference and other usage-based charges, support, upgrades, and expected volume changes.
- Which named personnel are committed, how much of their time is reserved, and what substitution rights do we have?
Commercial and evidence limits
Comparable public fee ranges, engagement sizes, and project durations for Accenture, Deloitte, and IBM Consulting are not established in the reviewed material. Any number in a vendor proposal should therefore be treated as a scope-specific offer, not a market rate. Compare the assumptions and acceptance criteria rather than forcing different scopes into a single headline price.
Forrester’s public article describes its evaluation but does not publish the full provider-by-provider scoring detail. Deloitte’s and IBM’s pages describe their own offerings. Treat those pages as statements of available scope and partnerships, then validate them through references, demonstrations using representative data, and contract language.
Common selection mistakes
Choosing the highest category label
A Leader or Star Performer label can help establish that a firm is active in the market. It cannot tell you whether the proposed architecture fits your regulations, data, operating model, or budget.
Buying a pilot without a destination
A pilot with no production owner, acceptance metric, security review, or cost model often becomes an isolated demonstration. Define the next gate and the evidence required to pass it before work begins.
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Data access, integration, permissions, process redesign, training, monitoring, and support frequently determine whether value appears. Ensure they are priced, staffed, and tested rather than left as “client responsibilities” in an appendix.
Comparing logos instead of teams
The individual partner, architect, and operators assigned to the engagement affect execution more directly than the firm’s global headcount. Lock the key roles and escalation paths into the statement of work.
Underestimating recurring costs
Inference, storage, retrieval, observability, human review, and model changes can grow with usage. Require a sensitivity analysis for low, expected, and high volumes before approving scale.
A practical decision rule
Select the provider that can demonstrate the strongest combination of business fit, relevant industry delivery, end-to-end production capability, governance, named talent, and transparent economics for your specific opportunity. If two firms remain close, run a tightly scoped, paid discovery with identical success criteria and require each to produce the same artifacts: a data and architecture map, risk register, evaluation plan, production backlog, operating model, and five-year cost model.
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Accenture, Deloitte, and IBM Consulting all have credible evidence of market standing, and the assessment results justify including them in an enterprise shortlist. The decision should turn on what each proposed team can prove for your use case—not on a generic ranking.
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