Comparing AI Consulting: OpenAI, Anthropic, & McKinsey vs Jinba

Summary

• The AI consulting market is crowded with players from OpenAI to McKinsey, but most sell expensive strategies with slow ROI instead of hands-on execution.
• Legacy firms like McKinsey and Deloitte charge high fees (projects often start at $250,000+) and can take over a year to show results.
• Consulting from model makers like OpenAI and Anthropic is biased toward their own ecosystems and isn't vendor-agnostic.
• For regulated industries like banking and insurance, specialist firms like Jinba combine AI strategy consulting with a proprietary workflow platform — delivering production-ready automation in weeks, backed by ~70 enterprise case studies.

The AI consulting gold rush is officially here.

OpenAI has launched a services arm. Anthropic is following suit. McKinsey, Deloitte, and Accenture have been quietly building AI consulting practices worth billions. And every week, a new boutique AI consultancy crops up promising to "transform your business with AI."

If you're a business leader trying to figure out who to trust with your AI strategy, the noise is deafening.

Here's the uncomfortable truth: most of these firms are selling the same thing — expensive slide decks, generic frameworks, and pilots that never scale. The logos are prestigious. The invoices are eye-watering. The results? Often disappointing.

So before you sign a six-figure retainer, let's break down exactly what each of these players actually offers — from the banking/insurance specialists to the Big Four incumbents — and how they stack up.

The AI Consulting Landscape: Who's Playing and How They Compare

1. Jinba — The Banking & Insurance AI Specialist

Jinba is a YC-backed AI consulting and workflow automation firm that works exclusively with banks, insurers, and credit unions. Unlike generalist consultancies that advise across every industry, Jinba has gone deep on financial services — with ~70 enterprise case studies including work with MUFG (Mitsubishi Bank), one of the world's largest financial institutions.

What makes Jinba different from every other firm on this list is the consulting-to-deployment model:

• Free AI strategy assessment to identify high-impact automation opportunities across your operations
• Hands-on consulting from a team that has implemented AI workflows at scale in regulated financial environments
• A proprietary, SOC II compliant workflow platform (Jinba Flow) that consultants use to build, test, and deploy production-ready workflows — not just hand you a slide deck
• On-premise and air-gapped deployment for institutions with strict data sovereignty requirements
• Deterministic, auditable workflows (80% rule-based) that satisfy compliance and regulatory requirements
• Model-agnostic architecture — supports AWS Bedrock, Azure AI, and self-hosted models, so you're never locked into a single vendor

The key advantage? Jinba doesn't separate strategy from execution. When a consulting engagement identifies a KYC processing bottleneck or a contract review workflow, the same team builds and deploys the solution — often in days, not the months it takes traditional consultancies. The platform stays with you after the engagement, so your team continues building without ongoing consulting fees.

Best for: Banks, insurance companies, and credit unions (20,000+ employees) looking for AI strategy consulting that leads directly to deployed, compliant automation — not a strategy deck that gathers dust.

Pricing: Free AI strategy assessment to start; consulting engagements are scoped to outcomes.

2. OpenAI — From Model Maker to Strategic Advisor

OpenAI's move into enterprise services isn't just a product update — it's a strategic repositioning. Through its OpenAI for Enterprise tier and its emerging professional services arm, OpenAI now offers:

• Custom model deployment and fine-tuning
• Dedicated solution architects embedded in client engagements
• API integration support and prompt engineering consulting
• Direct access to frontier models (GPT-4o, o3) before general availability

The catch? OpenAI's consulting is inherently model-centric. Their incentive is to get you deeper into their ecosystem. Objective, vendor-agnostic strategy isn't really on the menu.

Pricing: Enterprise contracts start at $2,000/month for the platform; bespoke consulting engagements are custom-quoted and typically six figures.

2. Anthropic — Safety-First, Now Strategy Too

Anthropic, the AI safety company behind the Claude model family, has begun offering its own professional services layer for enterprise clients. Their pitch centres on:

• Responsible AI deployment frameworks
• Claude integration into customer-facing and internal workflows
• Compliance and governance consulting (particularly strong for regulated industries)
• Red-teaming and AI risk assessments

The catch? Like OpenAI, Anthropic's consulting arm exists to drive adoption of Claude. If your use case is better served by a different model or a multi-model architecture, you won't hear that from them.

Pricing: Enterprise tiers begin around $30–$60 per user/month; dedicated services are negotiated separately.

3. McKinsey (QuantumBlack) — The Legacy Giant

McKinsey's AI practice, operating largely under its QuantumBlack brand, is one of the most well-resourced in the world. They offer:

• Enterprise AI strategy and roadmapping
• Data infrastructure and MLOps advisory
• Change management for AI adoption
• Proprietary tools (Lilli, their internal GenAI platform, is now client-facing)

The catch? McKinsey's model is built for Fortune 500 engagement sizes. Engagements routinely start at $500,000+, with multi-year transformation programmes running into the tens of millions. The work is thorough — but you're also paying for the brand, and junior consultants often do the heavy lifting.

Pricing: Day rates for senior partners can exceed $10,000/day. Project minimums rarely fall below six figures.

4. Deloitte AI & Accenture AI — The Big Four Playbook

Both Deloitte and Accenture have made enormous bets on AI services, building dedicated practices with thousands of consultants globally. Their offerings cover:

• End-to-end AI transformation (strategy through to deployment)
• Industry-specific AI accelerators and pre-built use case libraries
• Systems integration with major ERP and cloud platforms (SAP, Salesforce, Azure)
• Workforce reskilling and AI adoption programmes

The catch? Scale is both their strength and their weakness. You get rigour and resources, but customisation is limited. Engagements are process-heavy and slow-moving, often spanning 12–24 months before meaningful ROI surfaces.

Pricing: Similar to McKinsey — project minimums start at $250,000–$500,000, with large transformation programmes running to $5M+.

5. Boutique AI Consultancies — The Wild West

The democratisation of AI tools has spawned hundreds of boutique AI consultancies — often 2–10 person shops offering automation builds, chatbot deployments, and "AI audits." Quality varies wildly.

Some are genuinely excellent: deep specialists who move fast and work closely with clients. Others are glorified prompt engineers charging consultancy rates.

Pricing: Hugely variable. Project-based work ranges from $5,000 to $100,000; retainers from $3,000–$20,000/month.

The Head-to-Head: How They Compare

The pattern is clear: model makers (OpenAI, Anthropic) lock you into their ecosystem. Legacy firms (McKinsey, Deloitte) deliver strategy but move slowly and charge a premium. Boutique firms are hit-or-miss. Jinba is the only firm on this list that combines deep financial services specialisation, consulting expertise, and a proprietary platform that turns strategy into deployed workflows.

Why Consulting + Platform Beats Consulting Alone

The fundamental problem with traditional AI consulting — whether it's McKinsey or OpenAI — is the handoff gap. The consultant delivers a strategy. Maybe a prototype. Then they leave. Your team is left holding a deck and a half-finished proof of concept, with no internal capability to build on it.

The next project? Another six-figure engagement. Another 6-month timeline. Another set of junior consultants learning your business from scratch.

This is why the consulting-plus-platform model is gaining traction, particularly in regulated industries where compliance requirements make handoffs even more painful.

When your AI consulting partner also provides the platform — as Jinba does — the dynamic changes entirely:

• Strategy translates directly to deployment. The same team that identifies your KYC bottleneck builds and deploys the workflow on a platform your team owns.
• Knowledge stays in-house. After the engagement, your operations and IT teams continue building on the platform. No dependency on consultants for the next project.
• Compliance is built in, not bolted on. The platform itself is SOC II compliant, deployable on-premise, and produces deterministic, auditable outputs — not something a consulting engagement can guarantee.
• Iteration is continuous. When regulations change or a new use case emerges, your team adapts existing workflows instead of re-engaging a consulting firm.

This is the model that's replacing the traditional advisory approach at banks and insurers globally — particularly those that have already been burned by $300K+ consulting projects that delivered slide decks instead of working automation.

The 6 Questions to Ask Any AI Consulting Partner

Before you commit to any firm — incumbent giant or nimble newcomer — put these questions on the table:

1. Are you vendor-agnostic? If a firm is primarily funded by or affiliated with a model provider (OpenAI, Anthropic, Google), their recommendations will inevitably skew toward that ecosystem.
2. Do you build, or just advise? Strategy without execution is a very expensive document. Confirm your partner can take ideas from whiteboard to production.
3. What does success look like in 90 days? Any credible partner should be able to articulate near-term, measurable milestones — not just a transformation vision.
4. Who actually does the work? At large firms, senior partners sell the engagement and junior analysts deliver it. Know who will be sitting in your meetings.
5. How is your pricing structured? Be wary of open-ended day-rate models. Outcome-based or fixed-scope engagements align incentives far more effectively.
6. Can you show me work you've done for a company like mine? Case studies from Fortune 500 clients are irrelevant if you're a 200-person SaaS company. Ask for comparable references.

The Bottom Line

The arrival of OpenAI and Anthropic's consulting arms is a watershed moment — it signals that AI implementation has matured from a technical experiment into a professional services category in its own right. That's validation for every business that's been on the fence about investing.

But validation isn't a reason to overpay — or to settle for generic advice.

McKinsey brings brand prestige and deep resources — if you're a global enterprise running a decade-long digital transformation, that may be worth the premium. OpenAI and Anthropic bring model-depth and early access — if you're already committed to a single model ecosystem, their services arms make sense. Boutique firms can be excellent for targeted, high-trust engagements.

But if you're a bank, insurer, or credit union looking for AI consulting that leads directly to production-deployed automation — not a strategy deck that sits in a drawer — the specialist approach wins. Jinba combines deep financial services expertise (~70 case studies), hands-on consulting, and a proprietary workflow platform that turns strategy into deployed, compliant workflows in days.

Book a free AI strategy assessment →

Frequently Asked Questions

What is the main difference between AI consulting firms like McKinsey and OpenAI?

The primary difference lies in their focus and business model. Legacy firms like McKinsey offer high-level, strategic advice, often resulting in expensive reports with long implementation timelines. Model makers like OpenAI provide services centered on driving adoption of their specific AI ecosystem. A third category — specialist consulting firms like Jinba — focuses on specific industries (banking and insurance) and combines strategy consulting with a proprietary platform that deploys working automation, not just recommendations.

How much does AI consulting cost?

AI consulting costs can range from $5,000 for a small project with a boutique firm to multi-million dollar transformation programs with legacy giants like McKinsey or Deloitte, where project minimums often start at $250,000. The cost depends on the firm's prestige, project scope, and engagement model. Execution-focused firms often offer more accessible, outcome-based pricing that provides a faster return on investment.

Why is a vendor-agnostic AI strategy important?

A vendor-agnostic strategy is crucial because it ensures you are using the best possible AI model for each specific business problem. Relying on a consultant tied to a single vendor (like OpenAI or Anthropic) limits your options and may lead to a less effective or more expensive solution. Being vendor-agnostic allows for a flexible, future-proof AI architecture that prioritizes your business outcomes, not a vendor's sales targets.

How long does it take to see ROI from an AI project?

The time to see a return on investment varies dramatically. With large, traditional consulting engagements, it can take 12-24 months before meaningful ROI is realized due to long discovery phases and a focus on strategy over implementation. Specialist firms that combine consulting with a deployment platform can compress this timeline significantly — Jinba's consulting-to-deployment model, for example, typically delivers production-ready workflows in days, with measurable efficiency gains in under 90 days.

What should I look for in an AI partner for a regulated industry like finance or insurance?

For regulated industries, you need a consulting partner that prioritizes compliance, security, and auditability. Key criteria include deep experience with financial services regulations, the ability to deploy solutions on-premise or in secure air-gapped environments, and certifications like SOC II. Look for firms with verifiable case studies in your specific industry — not generalists who claim cross-industry expertise. The partner should deliver deterministic, auditable AI workflows, not black-box models.

Do I need an AI consultant if I already have a technical team?

A strong internal team doesn't eliminate the need for specialist consulting — it changes what you need from a consultant. Rather than paying a generalist firm to provide basic AI expertise your team already has, look for a specialist that brings deep industry knowledge (specific use cases, compliance patterns, deployment architectures for your sector) and a platform your team can build on after the engagement ends. The ideal consulting relationship is one that builds lasting internal capability, not ongoing dependency.

Ready to see what AI consulting looks like when strategy meets execution? Book a free AI strategy assessment with Jinba →