AI Consulting Cost: 20 to 200 Employees

The first question every CEO, COO, and managing partner asks me is the same one: "How much does this cost?"

It makes sense. You've been thinking about AI for months. You've read the articles, listened to the podcasts, and probably sketched out what it could look like in your own operation. The moment you consider actually hiring someone to build it, the question lands.

One construction company I audited was spending $78K a year on manual reporting. A $15K dashboard build eliminated it. That's the kind of math this guide is built around.

The reason you're asking is usually one of three situations.

Your data is scattered across Excel, email, QuickBooks, Google Drive, and WhatsApp. Nobody has a single source of truth. You've bought tools, a CRM here, a BI platform there, and they sit disconnected. You need someone who treats data as the starting point, and you need to know what that costs.

Your team is using ChatGPT and other AI tools on their own. You don't have a policy. You don't know what's exposed. You know it's a risk and you know someone needs to govern it. You don't yet know whether it's a $10K problem or a $100K problem.

You got quoted $400,000 by a Big 4 firm. They described a twelve-week engagement with a team of consultants. You want to know whether that's your only option, or whether there's a smarter way to structure the investment.

All three buyers are asking the same underlying question. What will it actually cost to get from where we are to where we need to be?

This post is the honest answer.

The price of AI consulting depends almost entirely on which of three models fits your company. Most mid-market companies start with one and move to the next.

Model 1: AI Ops Audit. $5,000 to $10,000 (2 to 4 weeks)

This is the entry point. It's built for CEOs who say, "I know we need to do something about AI, but I don't know where to start." Full scope on the AI Ops Audit page.

An AI Ops Audit is a diagnostic. I map four things. Your data (where it lives, how it flows, where the quality issues are). Your tools (what you've bought, what your team found on their own, what's exposed). Your processes (where you're losing time and money). Your team's AI usage (what's governed, what's shadow). You get a decision document, not a slide deck, with a clear recommendation on what to build first and why.

What's included:

• Complete inventory of all AI tools in use (sanctioned and unsanctioned)
• Data readiness assessment (can your data support AI, or does the foundation need work?)
• Process efficiency map (your top 10 time and cost drains, rated by automation potential)
• Shadow AI risk report (what data has been entered into public tools, what's at risk)
• Prioritized roadmap (quick wins ranked by impact)
• 60-minute executive briefing with the founder, not a junior analyst

Who chooses this: CEOs who need clarity before they commit budget. Family offices evaluating operational improvement. Construction companies trying to see their real efficiency gaps. Legal firms asking whether AI can meaningfully reduce admin overhead.

The math: $5K to $10K for a diagnostic that costs Big 4 firms $50K to $200K. If you move forward with implementation, this fee credits toward the build. If you decide to execute internally, you have a clear roadmap.

Model 2: AI Foundation Build. $10,000 to $50,000 (4 to 16 weeks)

This is the implementation. It starts with the audit findings and builds the systems. Full scope on the AI Foundation Build page.

An AI Foundation Build takes your priorities from the audit and ships a production-ready system. Not a prototype. Not a pilot. Something that runs and works. It might be a consolidated data layer and live dashboard. It might be a private AI assistant trained on your internal documents. It might be a WhatsApp agent for lead qualification. It might be a governance policy and team training. What gets built depends on your operation and what will move the needle fastest.

What's included:

• Phase 0: Audit findings (or a fresh diagnostic if you skipped the audit)
• Phase 1: System design and architecture (data layer, integrations, AI layer, governance)
• Phase 2: Build and testing
• Phase 3: Deployment and team training
• Phase 4: Handoff documentation and 30-day support

You work directly with me, not a team of junior developers. This is production work and it ships.

Cost drivers:

• Company size (20 people ≠ 150 people)
• Data complexity (clean data in one system ≠ scattered across six)
• Number of integrations (one system ≠ five disconnected tools)
• Scope of automation (one process ≠ five critical workflows)

Who chooses this: Companies that did the audit and said, "We know exactly what we need." Construction firms moving off Excel into a real dashboard. Financial services companies consolidating their data layer. Manufacturers building a private AI system for their operations. Legal firms automating document review and client communication.

The math: $10K buys a small, focused build (one data consolidation, one dashboard, one governance policy). $50K buys a larger implementation (multiple integrations, custom automation, extensive training).

Model 3: Retained AI Operations. $5,000 to $12,000/month (Ongoing)

This is fractional AI leadership. It's built for companies that need ongoing AI strategy, governance, tool selection, and optimization but don't need (or can't justify) a full-time Chief AI Officer at a $200K to $250K salary.

Retained AI Operations means I'm on your team. I sit in on leadership meetings. I review new tool selections before the team adopts them. I monitor your AI spending. I adjust your strategy as the market shifts. I handle governance updates. I'm the person your CFO calls when the CEO asks, "Should we buy this AI tool?"

What's included:

• Monthly strategy session with leadership
• Vendor and tool evaluation and approval
• Governance policy updates and enforcement
• Performance monitoring and optimization
• Industry updates and competitive intelligence
• Direct access for urgent questions

Who chooses this: CEOs who finished a build and realized they need ongoing guidance as AI tools evolve. Financial services firms managing regulatory exposure and AI risk. Construction companies scaling operations. Family offices building out their authority footprint.

The math: $5K per month for a smaller company with simpler AI operations. $12K per month for a larger company with multiple tools, multiple teams, and deeper strategic complexity.

The table below compares your primary options. A fractional CAIO, a Big 4 firm, a boutique AI agency, a full-time AI hire, and a DIY approach.

Inside any engagement model, the actual price depends on four core factors.

Factor 1: Company Size. A diagnostic for a 25-person company costs less than one for a 200-person company. More departments, more systems, more processes to map, more stakeholders to interview. The deliverable is the same in both cases. A clear picture and a roadmap. The price difference is usually inside the published range ($5K for small, $10K for large), not a dramatic jump.

Factor 2: Data Complexity. This is the biggest cost driver. One construction company had 15 Excel tabs, a QuickBooks instance, and WhatsApp conversations. High chaos, low complexity. Five main systems and a clear consolidation path. A different company had data in QuickBooks, Salesforce, a homegrown ERP, three databases, a legacy AS/400 system in maintenance mode, and institutional knowledge sitting in three people's heads. Same conceptual problem. Very different cost. The integration work is exponentially harder.

Factor 3: Number of Systems to Integrate. Each integration multiplies complexity. One system into one dashboard is linear work. One system into six is not. API work, field mapping, reconciliation, conflict resolution. If your entire operation runs on a single ERP, the implementation is straightforward. If it spans Salesforce, NetSuite, HubSpot, Zapier, Power BI, and a legacy accounting system, the build is more involved.

Factor 4: Industry Regulations. Financial services need compliance and audit trails. Healthcare needs HIPAA. Legal firms need privilege and confidentiality. Manufacturing has supply chain documentation requirements. Regulated industries cost more to build for. The core AI work isn't different. The governance, audit trail, and security layer is.

If you're building a business case, here's a realistic Year 1 budget for a mid-market company moving from "we're thinking about AI" to "we've shipped one major system and tightened our governance."

Conservative scenario (small, focused build):

• Audit: $5,000
• Build: $15,000
• Retained operations (6 months): $30,000 ($5K/month)
• Year 1 total: $50,000

Moderate scenario (medium company, multi-system build):

• Audit: $8,000
• Build: $35,000
• Retained operations (6 months): $42,000 ($7K/month)
• Year 1 total: $85,000

Comprehensive scenario (larger company, multiple builds, full governance implementation):

• Audit: $10,000
• Build: $50,000
• Retained operations (6 months): $60,000 ($10K/month)
• Year 1 total: $120,000

Now the ROI frame.

One construction company saved 8 hours per week through process consolidation and automation. 8 hours × $50/hour (loaded labor cost) × 52 weeks = $20,800 per year. The build cost $25,000. Payback in 14 months.

A financial services company eliminated 65 manual hours per month of reporting across their wealth management team. 65 hours × $60/hour × 12 months = $46,800 per year. The build cost $40,000. Payback in 10 months.

A legal firm eliminated 12 hours per week of document search and retrieval. 12 hours × $75/hour × 52 weeks = $46,800 per year. The build cost $30,000. Payback in 7.7 months.

The pattern is consistent. If you automate the real pain points, not hypothetical AI use cases, but processes that currently waste 8 to 15 hours per week per person, ROI usually exceeds cost inside 6 to 9 months.

When you present this to your CFO or your board, you need to reframe the question.

The question isn't, "Should we spend $50K to $85K on an AI consultant?"

The real question is, "How much are we losing right now because our data is scattered, our processes are manual, and we don't know where we actually stand operationally?"

One CEO told me, "I realized I was burning the equivalent of $50K every quarter on inefficiency. I just wasn't seeing it as a line item."

Here's a concrete framework.

Step 1: Identify your largest manual process. What's one thing your team does repeatedly that takes 8+ hours per week? For construction companies, it's usually project status reporting and cost reconciliation. For financial services, it's quarterly reporting and data reconciliation. For legal firms, it's document search and matter reconciliation. For distribution companies, it's inventory reconciliation and order fulfillment reporting.

Step 2: Calculate the cost of that process. How many people touch it? How many hours per week does it consume? What's the fully loaded hourly cost for those people? Example. One construction company had three people spending 10 hours per week on project status. 30 hours total. At $50/hour fully loaded, that's $1,500 per week, or $78,000 per year.

Step 3: Ask, "What if we reduced that by 80%?" If you consolidated the data and built a live dashboard, could that 30-hour process become a 6-hour process? Most of the time, yes. That's $62,400 per year in labor freed up. The build costs $30,000. Payback in 5.8 months.

Step 4: Present to your board. "We're spending $78,000 per year on a manual process that would cost $30,000 to automate. If we're 70% successful, we hit payback in 8 months and we get better operational visibility in the meantime." That's a business case. Not "we need AI." Instead, "we'll eliminate this specific $78K per year cost."

The pricing in this post is honest. It's based on real engagements across construction, legal, financial services, distribution, and professional services. Most of my clients started exactly where you are. The audit gave them clarity. Many also reduce out-of-pocket cost using R&D tax credits and grants. Confirm eligibility with your CPA.

You can take the free assessment and see which layers of the AI Operating System your company needs. If you'd rather talk directly, book a 30-minute call. Either way, you'll have clarity before you commit to anything.