AI implementation services

AI implementation services are the work of getting AI into the systems your business already runs on and into the hands of the people who have to use it. That means five things: preparing the data, connecting the systems, building the workflow, training the team who will own it, and keeping it running after the first month. Strategy is a separate job. Implementation is what happens after somebody decides what to build.

The distinction matters because of where the money goes. Most of what you find when you start looking is built for enterprises. The names that come up first are Accenture, Deloitte, IBM Consulting, Slalom, BCG and The Hackett Group, and their engagements are scoped, staffed and priced for companies far larger than yours. Gartner maintains a whole review category for it (Gartner, Generative AI Consulting and Implementation Services). None of that is wrong. It is just not shaped for a company doing $1M to $50M with one person who would own the automation and no team to hand it to.

This page covers what the work actually includes, what it costs at each scope, the part almost every quote skips, and our own published prices.

The five parts of an implementation, and which one gets skipped

Data preparation. Getting the records the AI will read into a state where reading them produces a right answer. Duplicates merged, required fields enforced, a shared definition for every number a report shows.

Integration. Connecting the systems that are supposed to talk and currently do not. Forms, billing, calendar, CRM. If your conversion tracking cannot be trusted end to end, no automation built on top of it can be either.

Workflow build. The visible part. Lead routing, follow-up sequences, research and enrichment, reporting, handoffs.

Adoption. Training the people who will use it, and naming the person who owns it when it breaks.

Upkeep. The month-four question. Systems drift, vendors change APIs, people leave.

The skipped one is almost always the first. It is unglamorous, it is hard to scope from the outside, and it does not demo well. It is also the one that decides whether the rest holds. In the Modern BizOps GTM Maturity Framework, a method I built for measuring the go-to-market competencies inside a business, this shows up as a data quality problem long before it shows up as an AI problem. You can read the underlying idea at data quality management.

Here is the version of that lesson I learned before AI was the reason for it. At Contactually, a CRM company selling to realtors, I ran customer onboarding for about 100 new small-business accounts a month. The product worked. What decided whether a customer was still there in ninety days was not the software. It was whether their data went in clean and whether one person on their side owned the thing. Rebuilding onboarding around those two facts cut first-90-day churn in half and saved roughly $1M in revenue over six quarters. The technology had not changed at all.

Later, as COO of a digital marketing agency, I was on the other side of it: the person who had to implement business information systems and automation inside a nine-person cross-functional team running fourteen service lines. A system nobody adopts is not a partial win. It is a line item and a grudge.

What it costs, by scope

Prices in this market vary more than they should, because “implementation” covers everything from a single automation to a multi-year enterprise program. The useful move is to price by scope rather than by vendor.

A paid diagnostic. A fixed-fee assessment that looks at your actual stack and tells you what to build and what not to. Often credited toward the work that follows. A provider willing to be paid to tell you not to build something is worth more than one who starts building on day one.

Foundation work. The cleanup above, priced as named items before anything goes on top of it.

A single system. One named automation, scoped, built, documented and handed over.

An ongoing seat. A monthly arrangement. Always ask whether the month buys named deliverables or buys access, because those are different products at similar prices.

Enterprise programs. Multi-quarter transformation work from the firms named above. Different buyer, different budget, different problem.

Six questions that sort providers quickly

1. What exists when you are done, and who on my side owns it? No named deliverable and no named owner means you are buying hours.
2. What has to be true about my data before this works? “Nothing, we handle everything” means they have not looked.
3. Do you price the foundation work separately? A blended number hides whether it is in there at all.
4. What happens in month four? Listen for a number and a process.
5. Is this built on tools I already pay for? Every extra platform is another thing to own.
6. What are your prices? A provider who will not publish them is telling you the number depends on what they think you can pay.

If you are not sure which of those your business would fail on, the free AI Revenue Scan will tell you in about five minutes: take the scan.

Strategy, implementation, and the seat in between

Most of the confusion in this category comes from three different jobs sold under one heading. Strategy work identifies use cases and builds a roadmap, and it is honestly priced by the hour or as a fixed-fee roadmap, because advice is open-ended. Implementation work ships named systems, and it is honestly priced per system, because the deliverable can be named before the work starts. The ongoing seat is a contract resource inside your team, and it should be compared against a hire rather than against a project.

Paying one job’s rate for a different job’s work is the most common expensive mistake in this market. Microsoft’s own adoption guidance makes the same sequencing point from the platform side, which is to start with the business problem and the data before choosing anything (Microsoft, Cloud Adoption Framework, AI strategy guidance).

We have written the two comparisons buyers ask for most: AI consultant vs. AI automation agency and AI consultant vs. in-house. For what the market charges, see how much an AI consultant costs.

Our prices, published

We implement AI automation for B2B go-to-market teams at published prices, foundation first:

• AI Revenue Scan: free. A self-serve readiness read, about five minutes.
• AI Revenue Audit: $2,500. Connects to your actual stack, computes a maturity heat map, and hands you a prioritized automation map with fixed prices attached. 100 percent of the fee credits toward your first build or your first Partner month within 90 days, and it carries a findings guarantee.
• Cleanup Services: $1,500 to $3,000 fixed per item. The foundation work most quotes skip, done before builds go on top.
• Revenue Automation Builds: $2,500 to $6,500 per named system. Fixed price, named scope, a runbook, and your team trained to own it. A second system of record adds $1,000 to $2,000, capped at $6,500.
• Care Plan: $300 to $500 a month per system. Optional, and this is the month-four answer.
• AI Revenue Partner: $2,500 a month. AI Revenue Partner Plus: $8,000 a month.
• AI Team Training: $5,500.