The AI consulting market hit $588 billion in 2026. Everyone from McKinsey to a two-person shop that started last Tuesday now calls themselves an AI consulting firm. This makes your evaluation harder, not easier, because the range between “will genuinely transform your operations” and “will burn through your budget and deliver a PowerPoint” is wider than it’s ever been.
I’ve been on both sides of this. I’ve hired AI consultants for enterprise projects. I’ve run the engagements at Dextra Labs. And I’ve evaluated AI consulting firms as part of technical due diligence when investors want to understand what a company’s AI vendor actually delivered versus what the contract promised.
Here’s what I’ve learned about separating genuine capability from well-designed proposals.
The landscape has split into three tiers and most lists don’t tell you this
The global consultancies Accenture, IBM Consulting, Deloitte, McKinsey, have AI practices that are large, well-resourced and expensive. They’re excellent for AI strategy at the board level, governance frameworks and large-scale transformation programmes where the consulting relationship spans years and the budget is measured in millions. If you’re a Fortune 500 company figuring out your enterprise AI strategy, these firms have the breadth.
The mid-tier specialists firms like LeewayHertz, RTS Labs, Neurons Lab and several others sit in the sweet spot for most companies. They combine strategic capability with engineering execution. They can design the solution and build it. Their teams typically have deeper technical specialisation than generalist consultancies and more process discipline than pure development shops.
Then there’s the boutique tier smaller firms that go very deep in specific domains. AI for healthcare. AI for financial services. AI for supply chain. Firms like Dextra Labs sit here, combining deep technical capability in areas like LLM deployment, agentic AI systems and RAG architectures with domain expertise in specific verticals like financial services and enterprise operations. The trade-off is narrower scope in exchange for deeper expertise.
Understanding which tier matches your needs is the first filtering decision and it’s one that most evaluation processes skip entirely.
The evaluation framework that actually works
After watching dozens of AI consulting engagements succeed or struggle, I’ve identified six criteria that predict outcomes far better than proposal quality or reference checks.
Ask for production deployments, not proof of concepts:
The single most important question in any evaluation is: show me an AI system you built that’s been running in production for more than six months. Not a prototype. Not a demo. A system that handles real workload, processes real data and has survived the transition from “cool project” to “operational infrastructure.” Any firm among the top AI consulting firms can build a compelling proof of concept. The firms worth hiring are the ones whose POCs consistently make it to production and stay there.
The follow-up question that reveals everything:
What happened to the POCs that didn’t make it to production? A firm that claims a 100% success rate is lying. A firm that can tell you specifically why certain projects didn’t advance and what they learned from those failures, has the operational maturity you’re paying for.
Evaluate their technical depth on your specific problem:
AI consulting is not a monolithic capability. A firm that’s brilliant at computer vision may have shallow expertise in LLM deployment. A firm that builds excellent chatbots may have never deployed an autonomous agent system. The AI landscape in 2026 spans foundation model deployment, RAG systems, agentic AI, fine-tuning, NLP, predictive analytics and dozens of sub-specialties.
During evaluation, get specific. If you need an enterprise LLM deployment, ask about their experience with context window management, hallucination mitigation and prompt engineering at scale. If you need an agent system, ask about their ReAct loop implementations, tool-use patterns and human-in-the-loop calibration. The technical depth of the answers, not the confidence of the delivery, tells you whether they’ve built what you need before.
Check their integration track record, not just their AI capability:
The most common failure mode in enterprise AI isn’t the model, it’s the integration. The AI system works in isolation but breaks when connected to the CRM, the ERP, the internal databases and the communication systems it needs to operate in context. A firm that builds beautiful AI that doesn’t integrate with your existing infrastructure has built an expensive toy.
Ask specifically: how many of your deployments required integration with legacy enterprise systems? What was the most complex integration you’ve handled? What went wrong and how did you resolve it?
Assess their approach to data readiness:
Good AI consulting firms will tell you uncomfortable truths about your data before they propose a solution. Firms that skip the data assessment and jump straight to model architecture are either inexperienced or optimistic in ways that will cost you later. Your data is probably messier, more fragmented and less accessible than you think. A firm that acknowledges this and builds data preparation into the project plan is a firm that’s done this before.
Look at their governance and compliance capability:
With the EU AI Act in force and AI-specific liability frameworks emerging globally, AI governance isn’t optional for enterprise deployments. Ask whether the consulting firm builds compliance into the architecture or bolts it on after deployment. Ask about their experience with model documentation, audit trails and human oversight mechanisms. A firm that treats governance as an afterthought will build you a system that creates compliance liability.
Evaluate their post-deployment support model:
AI systems aren’t software you deploy and forget. Models drift. Data patterns change. Performance degrades. The consulting firm’s post-deployment support, monitoring, optimisation, retraining schedules, incident response, is as important as the initial build. Ask what ongoing support looks like, what it costs and what happens to the system if you end the consulting relationship.
The pricing conversation nobody wants to have
AI consulting pricing is deliberately opaque across the industry and that serves firms better than it serves buyers. Here’s what you should know.
Strategy and feasibility assessments typically run $5,000 to $25,000 depending on scope. POC builds run $15,000 to $50,000. Full solution development ranges from $40,000 to $200,000 for small and mid-market engagements, with enterprise deployments going significantly higher. Ongoing support retainers run $2,000 to $10,000 monthly.
The variance within these ranges is driven by integration complexity, data readiness, model customisation requirements and the regulatory environment. A straightforward RAG implementation on clean data is a fundamentally different engagement than a multi-agent system integrated with legacy enterprise infrastructure in a regulated industry.
The firms worth working with will give you honest pricing ranges before the first scoping call. The ones that won’t are usually the ones whose proposals come back significantly higher than you expected.
Red flags that should end the conversation
No production references available. A firm that can’t connect you with a client whose AI system is currently running in production either hasn’t achieved production deployments or has lost client relationships, both are disqualifying.
Guaranteed outcomes on complex AI projects. Anyone who guarantees specific accuracy numbers or ROI figures before understanding your data, your systems and your constraints is making promises the technology doesn’t support.
Model-first rather than problem-first approach. If the first meeting is about which model to use rather than which problem to solve, the firm is technology-driven rather than outcome-driven. The best firms are agnostic about the technology until they understand the problem.
No discussion of data requirements. If the proposal doesn’t include a data assessment phase, the firm is either assuming your data is ready (it probably isn’t) or planning to deal with data issues on your budget when they surface during development.
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Making the decision
The right AI consulting partner is the one whose technical depth matches your specific problem, whose engagement model matches your organisation’s maturity and whose honesty about limitations gives you more confidence than their optimism about outcomes.
Start with the problem you’re solving, not the technology you want to use. Evaluate firms on production track record, not proposal quality. Check integration experience as rigorously as AI capability. And never skip the reference calls, but ask the references about what went wrong, not just what went right.
The conversations where a consulting firm tells you what they can’t do are usually more informative than the ones where they tell you what they can.
