AI implementation services, from a written plan to daily use

Which businesses need help putting AI to work?

Trying AI and running on it are different jobs. A writing tool on one person’s laptop is a trial. A model that reads every order, inquiry or invoice as it arrives, and puts the result into the system your team works in, is an implementation: it has to fit your systems, your people and the way the work already moves. What that takes depends on the business:

• Established local businesses: one market and an owner who decides, with the work running through email, a CRM or job tool and the accounting software. A plan here stays close to one task and the few systems it touches.
• Multi-location and regional businesses: several sites or areas, a manager layer and a back office with a handful of connected systems. The plan has to show how one change lands at each site, and who answers for it there.
• National businesses: several brands or departments, many integrations, procurement, and legal or compliance review before anything changes. The plan names more systems, more risks and more people who sign off.

We work with businesses across the US, and if a written plan is more than your task needs, we’ll say so on the call. Our AI consulting page covers the rest of our AI work, and the pages on AI consulting for law firms and AI consulting for manufacturers cover what we would build in those fields.

How do I implement AI in my business?

One task at a time. AI implementation is easier to get right as a run of single deployments, each one chosen, planned, built and settled in before the next begins. Whether you hire an AI implementation consultant or keep the work in-house, the order holds:

1. Pick one task: work that repeats, starts as something a person reads (an email, a PDF, a form) and ends in a result someone can check. Our guide to what to automate first helps rank the candidates.
2. Score it honestly: our free AI readiness worksheet rates one task under five headings: the work itself, its data, the systems it touches, the people and the economics.
3. Name the number that should move: hours a week, errors a month or time to reply, measured now, before anything changes, so you can tell later whether it worked.
4. Split the work: decide what the model does (read, sort or draft), what code checks, and what stays with a person.
5. Plan it, then build it in: for anything bigger than one step in one system, write the plan before anyone writes code, then connect the build to your systems through their APIs, so the result lands where your team already looks.
6. Go live with the team, then run it: show people what it does and what it holds back, watch it, and choose the next task once this one has settled.

If you have several candidates and no clear first pick, an AI readiness assessment has us rank them before any plan is written.

What goes into an AI implementation plan?

An AI implementation plan specifies one deployment in full before anything is built: the architecture, the integration specs, the change for your team, the vendor picks and the risks. AI implementation planning is where the expensive questions get answered on paper. Which system holds the record the build trusts? What may it write, and what must it never touch? Who handles the items it can’t finish, and how do they hear about them? Which provider’s model does the reading, and what happens to the data it is sent?

A plan earns its place on a bigger build: several systems, several teams, or a step where a mistake costs real money. For one clear task in one system, the proposal itself can set out what we would build, and the work can start there.

Every plan is written to be built, and compliance sign-off stays with you: the plan names the risks and what the build does about each one, and whether a use of AI meets the rules of your industry is for your own legal or compliance review to decide. Once it is agreed, the plan’s integration specs become the build’s starting point, and our AI integration page covers how a model gets connected to the systems in them.

How should a plan deal with risk?

The US National Institute of Standards and Technology publishes an AI Risk Management Framework, released on January 26, 2023 and intended for voluntary use, and a Generative AI Profile, released on July 26, 2024, that applies it to generative AI. NIST notes that the framework is being revised as part of the White House AI Action Plan. A plan for one deployment keeps to the same habit on a smaller scale: name each risk, say what it would cost, and say what the build does about it. The risks a plan should name:

• Accuracy: how a wrong reading could slip through, and what catches it: a check against your records, a hold for a person, or an approval before anything is saved.
• Data: what the build sends to a model provider, and what that provider’s terms say it does with it.
• Vendors: what happens if a provider changes its prices or retires the model the build uses, and how hard a switch would be.
• Cost: how usage grows with volume, and the cap that stops a surprise bill.
• The team: which steps change for whom, and who answers what the build holds back.

A task where a mistake would be costly can still be built: a person approves the output before anything is committed, and the plan says exactly where. Our readiness worksheet treats a big downside the same way, as a design decision.

How do you get a team to adopt AI?

AI adoption is still the exception among US businesses. The Census Bureau’s business survey found 19.8% of US businesses using AI in any business function as of May 3, 2026, a share that had hovered between 17% and 20% since December 2025, while 20% to 23% expected to be using it within six months. Use rose among firms with at least 20 employees and didn’t change significantly among smaller ones.

Inside one business, adoption comes down to whether the people who do the work trust what the build hands them. What helps:

• Start with a task they’d be glad to lose: the chore people complain about earns more goodwill than a project nobody on the team asked for.
• Ask what they already use: our worksheet asks whether people use AI tools without approval. Quiet use is a risk to manage, and it also points to the tasks people most want help with.
• Show them the holds: a build that says what it couldn’t read earns trust faster than one that guesses.
• Name who answers each hold: one named person, so nothing sits in a shared inbox while everyone assumes someone else has it.
• Keep the manual route open: when the build holds something back, the team can still do that one by hand.

One named owner who can decide matters as much as any of these. Without one, small questions wait for a meeting.

Is AI transformation one big project?

We treat it as a run of small ones. AI transformation is what a business looks like after many single changes: one task moves to a build, then the next, until the way the work runs is different. A program that tries to change every team at once risks ending with a long document and nothing running, while one working deployment shows how your data and your team behave on live work.

The order is the first thing to settle. The first task should be one where the result is easy to see and a mistake is easy to catch, so the team trusts the second one more. An AI strategy and roadmap sets that order: it ranks the uses of AI worth building and puts them in phases. An implementation plan then takes one phase’s deployment and specifies it in full, and the build follows.

Transformation has a running cost, too. Each deployment adds something to look after: model usage, the upkeep of its connections and someone’s time to answer what it holds. A roadmap that adds those up before the builds start is easier to stick to.

Where does the plan end and the build begin?

The plan is a document and the build is working software, so each gets a proposal and a price of its own. Once the plan is agreed, the build takes one of two forms. Where a model reads, sorts or drafts inside systems you already have, it is AI automation: we build the workflow and look after it each month once it runs. Where the work needs its own screens, logins and database, it becomes a custom software build, with the AI steps inside it.

Either way, the proposal for the build sets what it costs and how it runs. The cards below show the one-time setup that US agency price pages and guides show for an AI build at each level, and our AI consulting cost page lines up the market’s ranges for each kind of AI work, from an assessment to the monthly run.

When does a business not need an implementation plan?

Often. A written plan is worth paying for only when the build is big enough to need one. Skip it, or start somewhere else, when:

• The task is one clear step in one system: the proposal can specify it, and the build starts there.
• Rules alone would cover it: when every input arrives in the same fields and nothing needs reading, a Zapier, Make or n8n workflow or a direct API integration does the job without a model, and we’ll say so.
• The tool you pay for already does it: if your CRM or help desk has the feature built in, switching it on and showing the team may be all it takes.
• The task can’t be described yet: if it lives in one person’s head and its data sits in email threads, write the procedure down first. The lowest band of our readiness worksheet says not to buy AI yet, and that is our advice too.
• The work is a decision about people, or a model of your own: we won’t build AI that screens job applicants or makes other significant decisions about people, and we don’t train models.

What does one deployment look like over time?

The example we can show is our work with Alvy Logistics and DSI Transportation, which share AlvyOne, an operations system we built and run with them. Alvy is owned by our co-founder Noah Alvarez, and DSI is an Alvy company. The intake inside AlvyOne began with one action, getting emailed orders into the dispatch system. It was switched on June 29, 2026, built its first orders the next day, and from then on each built job posted to the dispatch team’s chat. Hold cards followed on October 12, 2026: each one says what’s missing from an order the intake won’t guess at, and a dispatcher answers the card or builds that order by hand. Since October 23, 2026, dispatchers can confirm an order’s account in the chat. Alongside it, Alvy’s staff have used a dispatch assistant in their team chat since July 3, 2026; it has handled requests with several steps since October 10, and it asks for a confirmation before any risky change. Through all of it, the intake has stopped at the order, with no driver assigned automatically, and it has kept changing since its first day. The AlvyOne case study has the rest.

What happens after go-live?

Go-live is the middle of the work. Live work turns up cases the test examples missed, and the business keeps moving: a new customer, a new form, a provider that changes its terms or retires a model. So a build needs someone checking it and changing it as those things move.

Looking after it is part of the job: once a build is live, we run it as monthly work, on the scope and price your proposal sets.

If there’s a use of AI you want working inside your business, book a 15-minute call. Bring the task and the systems it touches, and we’ll tell you whether it needs a plan, a build or neither.