How Much Does AI Consulting Actually Cost in 2026

The most common question we get on a first call is some version of "what's this going to cost?" And the most common answer in our industry is "it depends" — which is technically true and practically useless. So here are real numbers.

These are 2026 ranges from real engagements. Your situation will land somewhere in here, often closer to one end than the other. The shape of the range matters as much as the price.

Strategy and advisory work

Strategy engagements at the low end run $5,000–$15,000 for a focused roadmap or a maturity assessment. Two to four weeks of work. Output is a written document plus a working session — not a 60-slide deck designed to win the next engagement.

Mid-range strategy work runs $15,000–$50,000 and includes deeper organizational work: AI governance frameworks, compliance posture, vendor selection, board-level reporting structures. Six to twelve weeks. Useful for mid-market companies trying to make AI a real organizational capability rather than a side project.

Fractional Chief AI Officer engagements run $10,000–$25,000 per month for ongoing executive-level AI leadership. Replaces the alternative of hiring a full-time CAIO at $300K+ all-in. Useful if you need senior AI judgment in the room every week but don't yet need it full-time.

What should make you walk away: any strategy quote above $50K that doesn't include implementation. You're paying for a deck.

Automation and workflow work

Single-workflow automation builds run $2,500–$10,000. Things like a CRM lead-routing flow, a sales follow-up sequence, a content distribution pipeline. Two to four weeks. Pays back fast — usually inside 90 days.

Multi-workflow automation engagements run $10,000–$50,000. Full sales operations rebuilds, marketing automation overhauls, customer success workflow systems. Six to twelve weeks. The math here is usually about replacing labor cost — one full-time hire is $80K+ all-in, which makes a $30K automation that does 60% of the same work an obvious yes.

What should make you walk away: a $10K Zapier-based "automation" that has no documentation, no error handling, and depends on the consultant remembering what they built. Durable infrastructure costs more than duct tape, and it's worth more.

Generative content and AI video

Productized generative content engagements — including our flagship Avatar Studio — run $2,500–$5,000 setup + $500–$2,000 per month for ongoing content production.

Custom content engagements (full content systems, multi-channel orchestration, custom video pipelines) run $15,000–$75,000. Twelve weeks-plus. Usually combined with automation work for distribution.

The number that matters isn't the project price. It's the price of the alternative.

The math on generative content is usually labor: a full-time content marketer in 2026 costs $80K-$120K all-in for someone good. A productized AI content system at $24K/year and a fractional content director costs less and produces more — if the system is built right. Built wrong, it produces volume without value, which costs you brand reputation that's hard to repair.

Voice agents

Voice agent setups run $3,500–$15,000 for the build, plus usage-based monthly fees of $200–$2,000 depending on call volume.

The economics on voice agents are usually compelling — replacing missed calls (which is real lost revenue), or replacing a $40K/year receptionist with a $400/month AI agent that runs 24/7. The setup cost amortizes in a few months.

What should make you walk away: a voice agent vendor who can't tell you what happens when the LLM hallucinates a service price or commits the business to something it shouldn't have. Production voice agents need guardrails, not just prompts.

Custom AI software

This is where the range gets wide. Custom AI software runs anywhere from $15,000 for focused single-purpose tools to $250,000+ for production-grade multi-user systems.

Single-purpose tools at the low end: an internal AI research assistant, a custom invoicing system with AI-driven categorization, a document-processing tool. Four to eight weeks. $15K-$50K range.

Production AI applications at the high end: multi-tenant SaaS-style internal platforms, AI-native operations software, industry-specific systems with deep integration. Twelve to thirty weeks. $100K+.

What should make you walk away: a quote that doesn't include hosting, monitoring, ongoing maintenance, and the question of what happens when the underlying foundation models change. Custom AI software is software — and software has total cost of ownership beyond the build.

What governs where you land in the range

Three things drive where in the range your engagement lands.

Scope clarity. The clearer the scope, the lower the price — and the lower the risk of the project ballooning. Engagements with vague scope get priced at the upper end of the range to absorb the risk. Worth investing time in scope clarity before getting quotes.

Industry complexity. Regulated industries (legal, medical cannabis, insurance, healthcare, finance) carry compliance overhead that adds 20-40% to comparable work in unregulated industries. The work isn't different; the documentation, audit trails, and review burden is.

Integration depth. Standalone tools price at the low end. Tools that integrate with your existing CRM, ERP, or industry-specific software (MLS, METRC, policy admin systems) price higher because integration work is genuinely complex.

What you should expect from a real quote

A real quote is written, scoped, and signed. It includes:

• Specific deliverables (not "AI capabilities")
• Fixed timeline with milestones
• Fixed price OR a clearly-bounded time-and-materials structure
• A change-management process for scope changes
• A definition of "done" — what the handoff looks like
• Ongoing maintenance terms (separate from the build)

A quote that doesn't include these is a quote optimizing for the consultant's flexibility, not yours.

If you're trying to figure out where your project lands in these ranges, tell us about it on a free consultation. We'll give you an honest range on the call — including telling you when something is over-scoped or under-scoped relative to what you're actually trying to accomplish.