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AI Strategy Consulting: What It Should Cost, What You Should Walk Away With, and When to Skip It
Every consulting firm now sells an "AI strategy." Some of them are worth every dollar. A lot of them end with a beautiful deck, a roadmap nobody follows, and an invoice for six figures.
I've spent 20+ years building companies, and I've sat on both sides of these engagements. Here's the buyer's guide I wish more executives read before signing: what AI strategy consulting actually is, what a good engagement delivers, what it should cost, and when you're better off skipping it entirely.
McKinsey's 2025 State of AI survey found that 88% of organizations now use AI in at least one business function, but only 39% report any impact on EBIT. Adoption is easy. Getting value is the hard part, and that's exactly what a strategy engagement is supposed to fix.
What AI Strategy Consulting Is
AI strategy consulting helps a company decide where AI will create real business value, in what order, and with what people, data and budget. A good engagement turns "we should do something with AI" into a short list of funded, owned projects with clear success measures.
It's different from implementation. A strategy partner tells you what to build and why. An implementation partner builds it. Some firms do both, and that's often the best setup, because the people who wrote the plan have to live with it.
The term covers a wide range of work, from a two-week workshop to a six-month enterprise program. That range is why prices vary so much, and why you need to know exactly what you're buying.
What a Good Engagement Delivers
At the end of an AI strategy engagement, you should be able to hold these in your hands:
- A prioritized use-case list. Ranked by business value and feasibility, not by what's trendy. Each one tied to a measurable outcome.
- A readiness assessment. An honest view of your data, processes, people and tools, and the gaps that will block each use case.
- One or two pilots, defined in detail. Scope, owner, data sources, success metrics, timeline and budget.
- A build-or-buy recommendation for each priority, including which tools you already own that can do the job.
- A governance starter kit. An AI use policy, data rules and a review process for output quality.
- A 90-day plan with named owners, not a three-year vision.
- At least one workflow moving toward production by the end of the engagement, or a clear plan for the first one in the next 30 days.
What AI Strategy Consulting Costs
Here are the ranges I see in real proposals for US companies.
| Engagement type | Typical length | Typical range | Best for |
|---|---|---|---|
| Workshop or sprint | 1 to 3 weeks | $10,000 to $40,000 | Small and mid-size companies picking a first use case |
| Focused strategy engagement | 4 to 8 weeks | $40,000 to $150,000 | Mid-market companies building a roadmap and pilots |
| Enterprise AI strategy program | 3 to 6 months | $150,000 to $500,000+ | Large organizations aligning many business units |
| Fractional AI leadership | Ongoing | $5,000 to $20,000 per month | Companies that need an owner, not just a plan |
| Hourly advisory | As needed | $200 to $600 per hour | Second opinions, vendor reviews, board prep |
What drives the price: the number of business units involved, how much data and system review is included, whether pilots are built or only designed, and the seniority of the team. Big-brand firms charge a premium for the name, and the people doing the work are often junior.
Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, citing poor data quality, weak risk controls, rising costs and unclear business value. A good strategy engagement should catch every one of those before you spend on a pilot.
Enterprise AI Strategy: What Changes at Scale
For large companies, the strategy problem is less about picking use cases and more about coordination. Five departments are already running their own pilots with different tools. Legal is nervous. IT wants control. Finance wants to know what any of it is worth.
An enterprise AI strategy needs to add:
- An operating model. Who approves, funds and owns AI projects, and how a central team works with business units.
- A shared platform decision. Which models, tools and data layer the company standardizes on.
- Risk and governance. Data privacy, model risk, regulatory requirements and human review.
- A portfolio view. Every AI project tracked against value, cost and status, so leadership can kill the losers.
- Change management. Training and incentives so employees actually use what gets built.
MIT's Project NANDA reported in 2025 that 95% of organizations saw no measurable return from generative AI, and that mid-market companies moved from pilot to full implementation in about 90 days, versus nine months or more at large enterprises. Speed is a strategy problem, not a technology problem.
Generative AI Strategy vs a Broader AI Strategy
Many engagements today are really generative AI strategies: where to use tools like ChatGPT, Claude and copilots for writing, analysis, customer support and internal knowledge. That's a great place to start, because the tools are cheap and the wins are fast.
A broader AI strategy also covers predictive models, automation, data infrastructure and AI built into your products. If your competitive edge depends on data, like pricing, forecasting or personalization, you'll eventually need both. Make sure the proposal is clear about which one you're buying.
An AI Strategy Example
Here's what a good, simple outcome looks like for a 200-person B2B services company:
| Use case | Owner | Metric | 90-day goal |
|---|---|---|---|
| AI-drafted proposal first drafts | VP Sales | Hours per proposal | Cut from 6 hours to 2 |
| Support ticket triage and suggested replies | Head of Support | First response time | Cut by 50% |
| Internal knowledge assistant on policies and SOPs | COO | Questions to HR and ops | Cut by 30% |
Three use cases, three owners, three numbers, one quarter. Plus a one-page AI use policy and a monthly review. That's a strategy a company can actually run.
Strategy Partners vs Implementation Partners
| Strategy partner | Implementation partner | Both in one firm | |
|---|---|---|---|
| Main output | Priorities, roadmap, business case | Working systems in production | A plan and the first builds |
| Strength | Objectivity, executive alignment | Speed, technical depth | Accountability for results |
| Risk | Plans that never ship | Building the wrong thing well | Recommending what they like to build |
| Best for | Large, complex organizations | Companies with clear use cases | Most mid-market companies |
If you want a list of firms that actually build, see our guide to AI consulting firms that go beyond strategy decks, our review of the top AI consulting companies, and our list of AI workflow automation and deployment firms. When you're ready to connect AI to your systems, our guide to AI integration services explains what that work really involves.
If you want a starting point before hiring anyone, use these five questions for every possible use case:
- Value: What business number moves, and by how much?
- Feasibility: Do we have the data, process and tools today?
- Risk: What happens if the output is wrong?
- Owner: Who is accountable for the result?
- Speed: Can we see a result in 90 days?
Score each from 1 to 5. Start with the use cases that score high on value, feasibility and speed, and low on risk. That's an AI strategy template most companies can fill out in an afternoon.
Red Flags in an AI Strategy Proposal
- The deliverable is a deck. No pilot, no workflow, no production plan.
- A long discovery phase with no clear end. Six weeks of interviews before anyone touches your data is a warning sign.
- Every recommendation needs their platform. Good advisors recommend the tools that fit, including the ones you already own.
- No named senior people. Ask who will actually do the work, and meet them before you sign.
- No success metrics. If the proposal doesn't define what "done" looks like, you won't know whether you got your money's worth.
- Generic case studies. Ask for an example in your industry, at your company size, that's still running.
If the engagement ends with a deck and not one workflow in production, you bought a book report. The best AI strategy I've seen fit on two pages: three use cases, three owners, three numbers to move, and a 90-day plan. Everything else was decoration.
When to Skip AI Strategy Consulting
You probably don't need a formal strategy engagement if:
- You're a small company with one obvious use case. Build it. A two-day workflow sprint beats a two-month strategy.
- Your data and processes aren't ready. Fix the foundations first, or the strategy will just tell you to fix the foundations.
- Nobody internally will own it. A strategy without an owner dies the week the consultants leave.
Start with our free AI readiness assessment instead. It takes 20 minutes and tells you whether you need a strategy, a builder or just better data. If you need ongoing leadership more than a one-time plan, a fractional Chief AI Officer is often the better buy.
Questions to Ask Before You Sign
- What exactly will I have at the end, and what will be running in production?
- Who are the senior people doing the work, and how much of their time do I get?
- How do you decide build versus buy, and do you earn anything from the tools you recommend?
- Can you show me a recent example in my industry that's still in use?
- What happens if the first pilot fails?
- How will we measure whether this engagement was worth it?
How to Measure Whether the Engagement Worked
Agree on these before the work starts, then check them 90 days after the engagement ends:
- Workflows in production. How many AI-assisted workflows are running in daily use, not just piloted?
- Business metrics moved. Hours saved, response times, conversion rates or costs, measured against a baseline.
- Adoption. What share of the intended users actually use the new tools each week?
- Ownership. Does every live use case still have a named owner?
- Next steps funded. Did leadership approve the next round based on results, not enthusiasm?
If you can't answer those questions, the engagement produced activity, not value.
FAQ
What does an AI strategy consultant do?
An AI strategy consultant helps a company decide where AI can create business value, assesses data and process readiness, prioritizes use cases, defines pilots with owners and success metrics, recommends whether to build or buy, and sets up basic governance. The best consultants also help get the first workflow into production rather than stopping at a plan.
How much does AI strategy consulting cost?
AI strategy consulting typically costs $10,000 to $40,000 for a short workshop or sprint, $40,000 to $150,000 for a focused four-to-eight-week engagement, and $150,000 to $500,000 or more for an enterprise program. Hourly advisory usually runs $200 to $600 per hour, and fractional AI leadership costs about $5,000 to $20,000 per month.
What should an AI strategy include?
An AI strategy should include a prioritized list of use cases tied to business outcomes, a readiness assessment of data, processes and people, one or two detailed pilot plans, build-or-buy recommendations, a governance and AI use policy, and a 90-day plan with named owners and measurable success metrics.
What's the difference between AI strategy and AI implementation?
AI strategy decides what to build, why, in what order and how success will be measured. AI implementation builds and deploys the systems, connects them to data and tools, and runs them in production. Many mid-market companies get the best results from a partner that does both, because the people who wrote the plan stay accountable for delivering it.
How long does an AI strategy engagement take?
A focused AI strategy engagement usually takes four to eight weeks. Short workshops or sprints can take one to three weeks, while enterprise AI strategy programs that align multiple business units often take three to six months. Faster is usually better, as long as the engagement produces a pilot and clear owners.
Do small businesses need an AI strategy consultant?
Usually not. Most small businesses get more value from picking one repetitive workflow, using the AI features in tools they already own, and getting outside help only to connect systems. A readiness check and a short workflow sprint are typically a better investment than a formal strategy engagement.
One Last Thing
The best AI strategy is the one that changes how work gets done in the next 90 days. Buy the outcome, not the slides. Insist on owners, numbers and at least one workflow moving toward production.
If you want a second opinion on a proposal sitting on your desk, or help deciding whether you need a strategy at all, reach out. That conversation is always free at Aiken House.
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