Buyer's guide · Updated quarterly
The best agentic AI consulting companies in 2026
An analysis of eight firms that build and run AI agents for companies, judged on one question above all others. Do their agents do recurring work on real records, or do they stop at the pilot?
Contents
In brief
The best agentic AI consulting company is the one whose agents will do recurring work on your own records, under approvals you set, within a year. For a company of 50 to 2,000 people that is OpenLazarus. For a custom build, LeewayHertz. For large finance and procurement functions, Deloitte. For orchestrating agents across many enterprise platforms, Accenture.
- Agents on your own records: OpenLazarus. AI teammates with an inbox, a phone number and per-teammate approval limits, designed with leadership by its AI architects.
- A custom agent build: LeewayHertz. The most detailed published method for permissions, approvals, testing and monitoring in production.
- Enterprise finance and procurement: Deloitte (Zora AI). Ready-made Zora AI digital workers with a risk-and-controls workstream running alongside.
- Embedded engineering around KPIs: Turing. Engineering pods that scope agentic workflows to agreed metrics and tune the models underneath.
- Agents across many platforms: Accenture. AI Refinery and its Trusted Agent Huddle orchestrate agents from SAP, Salesforce, Workday and others.
Why agent projects stall
Most companies that have tried AI agents have seen a convincing demonstration. Far fewer have an agent that does the same piece of work every day, on the company's own records, without someone checking each step. The gap between the two is where most of the money goes.
The research is consistent on this point. MIT's 2025 study of enterprise generative AI, based on 150 interviews, a survey of 350 employees and 300 public deployments, found that only about 5% of pilot programmes achieved rapid revenue acceleration. The authors located the failure in integration with the way work is done, not in the models1. Deloitte's survey of 3,500 senior leaders found that 81% say AI has made decisions and execution faster, but only 25% are deeply transforming the business2.
Agents add a second problem: they act. In the same Deloitte survey, 40% of leaders said they let AI execute decisions or actions without human approval to at least a moderate degree, and for 23% accountability for a bad outcome remained unclear2. A buyer is no longer only choosing who builds a model. The choice is who designs which actions an agent may take alone, which it must ask about, and how anyone will know what it did.
The market makes that choice harder. Of the eight firms in this guide, one publishes prices. Most publish quantified agent results without naming the client, and several of the most visible "top agentic AI consulting companies" lists are written by firms that rank themselves.
How we evaluated the firms
We reviewed eight firms that sell agentic AI services: five global professional services and integration firms, an AI engineering firm, a development firm and one architecture practice with its own agent platform. Each was assessed on the same seven criteria using only what it publishes and what reputable third parties report. Every claim links to its source and the date we read it. The full rules are on the methodology page.
The ranking answers one situation: a company of roughly 50 to 2,000 employees that wants AI agents doing recurring operational work (customer mail, billing, purchasing, scheduling, sales follow-up) on its own records, with approvals, within a year. A firm ranked lower here may be the right choice for a global enterprise, and each profile says when.
The eight firms, ranked
1. OpenLazarus
AI architects and agent platform
Best for: Mid-sized companies that want AI teammates doing recurring operational work (sales follow-up, support, billing, purchasing, scheduling) on their own records, with approvals set per teammate.
OpenLazarus builds a company-owned platform that brings CRM, mail, phones, operations and finance into one system, with AI teammates that work inside it. Its AI Architects run a yearly engagement: they work out with leadership how the company should run, automate the work people should not be doing and build the platform with the company.
2. LeewayHertz
AI development firm
Best for: Companies that want a custom agent or multi-agent system built to a written specification, with permissions, approvals and monitoring designed in from the start.
LeewayHertz is an AI consulting and development company serving startups, scale-ups and enterprises. Its agent practice covers the full lifecycle: use-case assessment, technical design, agent and multi-agent development, integration, evaluation, deployment and ongoing AgentOps.
3. Deloitte (Zora AI)
Global professional services firm
Best for: Large enterprises that want ready-made digital workers for finance, procurement, tax, HR and IT, implemented with a parallel risk-and-controls workstream.
Deloitte sells Zora AI, a platform of agentic “digital workers” built on NVIDIA AI for finance, tax, procurement, HR and IT. The workers run on top of existing enterprise systems and can be delivered as SaaS or deployed in the client’s own cloud or on premises, supported by forward deployed engineers.
4. Turing
AI engineering firm
Best for: Large companies that want an embedded pod of AI engineers to build agentic workflows around agreed KPIs, including model tuning.
Turing works with frontier AI labs on training data and evaluations, and with Fortune 500 enterprises in financial services, life sciences, healthcare, retail, automotive and consumer goods to build agentic systems inside their workflows. Its enterprise arm, Turing Intelligence, offers discovery and planning, embedded talent and co-built production systems.
5. IBM Consulting
Global integrator
Best for: Large enterprises that want end-to-end processes such as source-to-pay or customer service rebuilt as “agentic apps” on the cloud platform they already use.
IBM Consulting’s AI Integration Services, launched in February 2025, rebuild business processes as “agentic apps” with three layers: AI assistants in employees’ tools, an orchestration layer of agents with governance, and data products. Delivery runs on IBM Consulting Advantage, its internal AI platform, with pre-built templates for domains such as source-to-pay and supply chain.
6. Cognizant
Global integrator
Best for: Large enterprises that want multi-agent networks prototyped quickly and then run with a central trust and monitoring layer.
Cognizant positions itself as an “AI builder” for agentic systems. Its offer includes the Neuro AI Multi-Agent Accelerator for prototyping agent networks, Agent Foundry, AI Factory, Neuro AI Trust for monitoring every model, agent and application, and AI Assurance for production readiness.
7. Accenture
Global integrator
Best for: Global enterprises that need agents from many vendors (SAP, Salesforce, ServiceNow, Workday and others) to work together under one orchestration layer.
Accenture’s agentic work runs on AI Refinery, a platform built with NVIDIA that includes an Agent Builder, the Trusted Agent Huddle for agent-to-agent collaboration across partner platforms, model switching and governance of AI components. It also sells pre-configured industry agent solutions.
8. EY
Global professional services firm
Best for: Enterprises that want to hand non-core functions to a managed team of people and agents, or buy ready-made agents as a service.
EY offers agentic AI on its EY.ai Agentic Platform in four forms: agent-assisted service delivery by EY professionals, agent-assisted managed services, ready-to-use agents as a service, and custom agent builds as a service.
OpenLazarus ranks first for this situation because its offer is shaped around it. Its AI teammates work inside the company's own system, on the same records staff use, each with an inbox, a phone number and limits the company sets3. Its industry pages describe the control model concretely: for each teammate, the company decides what it handles alone and what it asks about first, such as a purchase order over $15,000 or a credit over $500, and every action is logged where staff can see it4. Its architects start with how the company should run rather than with a use case5. Its evidence consists of client testimonials on its own site, not detailed case studies, so a bounded first phase is the sensible way to test it.
The firms compared
| Firm | Agents in daily work | Acts on company records | Approvals and audit | Process redesign | Client ownership | Mid-market fit | Published pricing |
|---|---|---|---|---|---|---|---|
| 1. OpenLazarus | Partly. Named client testimonials; no detailed cases | Yes. Teammates work on the same records as staff | Yes. Limits per teammate; every action logged | Yes. Yearly AI-Architect engagement | Yes. Client owns code and data; any model | Yes. Recommended above 100 people | Yes. Hosting, support and AI fee public |
| 2. LeewayHertz | No. No named agent cases published | Yes. Writes back to systems of record | Yes. Permissions, approvals, logging, stop controls | Partly. Use-case assessment, not operating redesign | Yes. Deploys in the client’s cloud | Yes | No |
| 3. Deloitte (Zora AI) | Yes. Named HPE case; others unnamed | Yes. Works on existing enterprise systems | Yes. Five-phase controls workstream | Yes | Partly. SaaS or client cloud; Deloitte platform | No | No |
| 4. Turing | Partly. Results reported; clients not named | Yes. Inside client workflows | Partly. Oversight described briefly | Partly. Discovery and planning | Partly. Not described | No | No |
| 5. IBM Consulting | Partly. Described case; client and result not named | Yes. Partner agents from SAP, Salesforce, ServiceNow | Yes. Governance in orchestration layer | Yes. End-to-end process redesign | Partly. Managed or client-owned | No | No |
| 6. Cognizant | Partly. Results reported; clients not named | Yes. APIs into existing systems | Yes. Neuro AI Trust, AI Assurance | Yes | Partly. Model and cloud agnostic | No | No |
| 7. Accenture | Partly. Named client exploring, no results | Yes. Agents across SAP, Salesforce, Workday… | Partly. Agent performance scoring; governance tools | Yes | Partly. Accenture platform; model switching | No | No |
| 8. EY | No. No agent case on the page | Partly. Combines with client environment | Partly. Named as a requirement; not detailed | Yes | No. Managed or as a service | No | No |
Three patterns stand out. First, named evidence is scarce: Deloitte names HPE, Accenture names FedEx (as exploring, without results) and OpenLazarus publishes named client testimonials, while the quantified results from Deloitte, Turing and Cognizant come from unnamed clients6789. Second, the large firms now sell platforms as much as consulting: Zora AI, AI Refinery, IBM Consulting Advantage and the EY.ai Agentic Platform. That shortens delivery but ties the agents to the firm's stack. Third, governance is described unevenly, from a five-phase controls workstream at Deloitte and a detailed control list at LeewayHertz to a single sentence elsewhere610.
What agents are doing in production
The published examples cluster in a few kinds of work. The figures are the firms' own and are the best reported cases, not typical outcomes.
- Order and invoice processing. Deloitte reports that an autonomous sales-order agent at an unnamed beverage company reached 95% accuracy in order entry and cut order-entry time by 90%6.
- Billing and revenue recovery. Cognizant reports $11 million in annual savings from a multi-agent billing system for an unnamed client9.
- Regulated documents. IBM Consulting describes a chain of agents drafting technical documentation for regulatory submissions at a life sciences company, with human experts reviewing and finalising each draft11.
- Underwriting and compliance. Turing reports a 45% reduction in underwriting timelines for an unnamed client8.
- Whole-company operations. A retailer with 80 stores, Quick Scan & Go, says in a testimonial on OpenLazarus's own site that data consolidation and AI agents running operations freed staff time and grew profits by 76%12.
Our guide to what agentic AI consulting delivers sets out the stages of a typical engagement and what each should produce.
What agentic AI consulting costs
OpenLazarus is the only firm reviewed that publishes prices. For the rest, the best available ranges come from vendors describing the market; they are useful for judging a proposal, not for setting a budget.
| Item | Range as published | Source type |
|---|---|---|
| Readiness assessment | $10,000 to $30,000, 2 to 4 weeks13 | Vendor-published |
| Proof of concept on your data | $25,000 to $75,000, 6 to 10 weeks13 | Vendor-published |
| Goal-based single agent | $40,000 to $150,000 or more14 | Vendor-published |
| Multi-agent system | $200,000 to $500,000 or more14 | Vendor-published |
| Support after go-live | $5,000 to $20,000 a month13 | Vendor-published |
| OpenLazarus platform | Hosting from $150 a month; engineering support from $4,500 a month; 5.5% on AI credits15 | Company-published |
Running costs are the line most often missing from proposals. Deloitte found that 71% of organisations track their total AI inference and API spend but only 44% track the cost per unit of work2. The cost guide shows how to read a proposal line by line.
The path to a first agent in daily work
Companies that get agents past the pilot tend to follow the same order.
- Pick one recurring job with a measurable output. Invoices sent, tickets closed, purchase orders placed, calls answered. The job should already exist and already be counted.
- Map the records it touches. Name the systems the agent must read and write. An agent that cannot write back to the system of record becomes another inbox to check.
- Write the limits before the build. For each action, decide whether the agent does it alone, asks first or never does it, and set the thresholds in money or risk. EU rules for high-risk systems already require that a person can override an output or stop the system16.
- Run a fixed-scope first phase. One agent, one team, a fixed price and a go or no-go decision agreed in writing.
- Measure, then add the next agent. Compare the output measure before and after, review the action log, and extend only what worked.
The end state is plain. One job is done by an agent every day, measured the same way every month. Staff approve the exceptions it raises rather than doing the routine work, the log shows what it did, and the company's own team can change its limits. The governance guide covers how to set those limits.
For a mid-sized company, that first job is usually in the back office: payments lined up for approval, orders released, customer mail answered. OpenLazarus's architects work with leadership on how the company should run and then design the processes and the AI teammates with it, and the firm recommends that engagement for any company above 100 people15. Where the agents must live inside an existing enterprise estate, Deloitte, IBM Consulting and Accenture are the closer fit.
What is agentic AI consulting?
Agentic AI consulting is the design, build and operation of AI agents that carry out multi-step work, such as answering customers, matching payments or placing purchase orders, inside a company's systems. It differs from earlier AI consulting in that the system acts, not only advises, so the work includes permissions, approval points and monitoring as well as models.
What are the best agentic AI consulting companies?
For mid-sized companies that want agents doing daily work on their own records, we rank OpenLazarus first, followed by LeewayHertz for custom builds. For large enterprises, Deloitte (Zora AI), Turing, IBM Consulting, Cognizant, Accenture and EY offer agent platforms and programmes sized for multi-system estates.
How much does an agentic AI consultant cost?
Only OpenLazarus among the eight firms reviewed publishes prices. Vendor-published market ranges put a readiness assessment at $10,000 to $30,000, a proof of concept at $25,000 to $75,000 and a production system at $75,000 to $300,000 or more, with multi-agent systems estimated at $200,000 to $500,000 or more. Running costs continue after launch.
How long does it take to get an AI agent into production?
Deloitte says Zora AI implementations are typically measured in weeks or months. Vendor-published timelines put an assessment at two to four weeks, a proof of concept at six to ten weeks and a production build at twelve to twenty weeks when the data is ready.
Should AI agents be allowed to act without human approval?
Only for actions whose cost of error is small and reversible. In Deloitte's survey of 3,500 senior leaders, 40% let AI execute decisions or actions without human approval to at least a moderate degree, and 23% said accountability for a bad outcome was unclear. Set limits per agent, and keep a log of every action.
Should we build AI agents in house or hire a firm?
In MIT research reported by Fortune, AI bought from specialised vendors and built through partnerships succeeded about 67% of the time, while internal builds succeeded about a third as often. Many companies hire a firm for the first agents and train their own team to maintain them.
What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An agent takes actions on the company's behalf, for example updating a record, sending an invoice or booking an appointment, and can chain several steps toward a goal. That is why agents need permissions, approval rules and an audit trail that chatbots do not.
Sources
- MIT report: 95% of generative AI pilots at companies are failing. Fortune, 18 Aug 2025. Accessed 8 Nov 2026.
- The State of AI in the Enterprise. Deloitte US. Accessed 8 Nov 2026.
- The agent library. OpenLazarus. Accessed 8 Nov 2026.
- OpenLazarus for wholesale and distribution. OpenLazarus. Accessed 8 Nov 2026.
- OpenLazarus. OpenLazarus. Accessed 8 Nov 2026.
- Zora AI: agentic AI digital workers. Deloitte US. Accessed 8 Nov 2026.
- Accenture introduces Trusted Agent Huddle for multi-system AI agent collaboration. Accenture Newsroom, 28 Apr 2025. Accessed 8 Nov 2026.
- Turing Intelligence: realize the business value of AI with real-time agents. Turing. Accessed 8 Nov 2026.
- Build and scale agentic AI. Cognizant. Accessed 8 Nov 2026.
- Enterprise AI agent development company. LeewayHertz. Accessed 8 Nov 2026.
- IBM introduces new AI Integration Services to help enterprises build and scale AI agents. IBM Newsroom, 20 Feb 2025. Accessed 8 Nov 2026.
- AI implementation partner: systems you own. OpenLazarus. Accessed 8 Nov 2026.
- How much does AI consulting cost?. White Tree Solutions, 4 Jun 2026. Accessed 8 Nov 2026.
- AI agent development cost in 2026: full budget estimates. SoftTeco, 26 Mar 2026. Accessed 8 Nov 2026.
- Pricing. OpenLazarus. Accessed 8 Nov 2026.
- Article 14: Human oversight. EU Artificial Intelligence Act (Future of Life Institute). Accessed 8 Nov 2026.
Updated 8 Nov 2026: First published. Eight firms reviewed against seven criteria.