How to implement AI in your company the right way

Most guides on how to implement AI in your company stop at a generic checklist: set goals, train your team, measure results. This post walks through the process we run at Galileo14, phase by phase, with the order that works in practice and the mistakes that cost the most. It also covers the questions people ask most when searching for this: how to implement ai in my company and enterprise ai adoption.

How do you implement AI in a company?

You implement it in phases, not all at once. The usual temptation is to decide “we’re moving everything to AI” on a Monday and expect the team to figure it out as they go. That works for the first few weeks, then the same three problems show up: nobody knows who has access to what, spend climbs without a clear reason, and half the team gets far more out of the exact same tool than the other half.

The order that works for us in practice is this:

1. Audit the starting point. What’s already in use, who has access, where sensitive data lives, and which work repeats often enough to justify automating it.
2. Security foundations. Before scaling usage, close the obvious gaps: credentials out of the codebase, permissions scoped by folder or repository, a clear boundary on what each person can touch.
3. Concrete use cases, not “adopting AI” as an abstract goal. A sales pipeline, a content process, a support workflow. Each one with a measurable before and after.
4. A routine the whole team shares, not just the person who already mastered the tool. Document what works and make it easy for everyone else to repeat.
5. Automate what’s already validated. Only once a process runs well by hand does it make sense to let it run on its own.

The most expensive mistake is usually one of sequencing: automating step 5 before closing step 2.

How can I use artificial intelligence in my company?

It depends on whether the question is about one person or about the whole organization, and that’s exactly where enterprise ai adoption gets confused with individual adoption. One person can learn to use Claude in an afternoon: open the chat or Claude Code, describe the task, review the output. An entire company doesn’t learn that way. The challenge is getting the whole team to use it the same way, with the same guardrails, not getting one person to master it.

Day-to-day usage rests on three pieces in practice: clear access (who can use which model, for what type of task), a shared way to store what works (a skill, a template, a tested prompt) so it doesn’t disappear when that person goes on vacation or changes roles, and a regular review of what’s being spent and on what. Without those three pieces, AI use stays limited to a handful of people who figured it out on their own, while everyone else keeps working the same way as before.

How can I integrate AI into my business?

Integrating it means connecting it to work that already exists, not building a parallel process nobody touches after the first week. The most reliable way to do it is to start with a process that’s already well defined (clear steps, runs often, and its output can be checked) and replace or support those steps with Claude, one at a time, verifying each change before moving to the next.

In one of our projects, the sales pipeline was a good candidate because it already existed as four separate manual runs: preparing the client portal, enriching the data, applying the style guide, and assembling the proposal. Each step worked on its own, someone just had to trigger and watch it by hand. Integrating AI there meant chaining those four existing steps into a single routine, without inventing anything new.

How do I implement AI at work?

At the team level, the hard part is closing the gap between who already masters the tool and who doesn’t, more than getting people to use it in the first place. Almost every project we run has that gap: two or three people get a far higher output than the rest with the exact same access. The difference is accumulated hours of trial and error, not talent, and nobody has turned that learning into something the rest of the team can copy.

Closing that gap is more management work than technical work: document what the top performers do, turn it into a template or a skill anyone can run, and review with each team in person which part of their work fits. In our experience, a short weekly session sustained over time closes that gap better than one intensive day of training.

The process in practice: three months, six phases

One client, a marketing agency, moved all its work to Claude in one go, for the whole team, no exceptions. It was the right call, but the setup grew faster than it could be secured: costs climbed, and the gap between the people who had already adopted the new way of working and the ones still catching up kept widening.

We worked with its leadership over three months of weekly work, across six phases:

1. Foundations and security. We audited the entire GitHub organization, secured secrets, and added an automated check that blocks credentials before they reach a public page.
2. Cost and efficiency. We turned rising token spend into a small set of habits (skill scope, model choice, session hygiene) to get the same work done for a fraction of the cost.
3. Shared memory. A simple layer in Supabase where each session writes what it learned and the next one starts with that context, so knowledge doesn’t live only in one person’s head.
4. Subagents. We refactored the most expensive skills to split work across parallel subagents on cheaper models, returning only the summary to the main session.
5. Routines and autonomy. Skills already validated moved from running by hand to running as scheduled and event-driven routines, chained into a single sequence with no manual steps in between.
6. Distribution. We packaged the skills into versioned plugins so a single command updates the whole team, no copying files around in Slack.

The most visible result showed up in creative content: the people already using the new workflow went from a handful of concepts a week to more than thirty per run, with over a hundred published across three markets in a matter of days. The rest of the organization, with the same access but not the same routine, didn’t reach those numbers, and that gap was exactly what we closed in the project’s final weeks.

How long does this take in practice

For a mid-sized team, the full sequence (audit, security foundations, first use cases with a measurable result, closing the gap across the team) usually takes two to three months of sustained weekly work. Less than that and the change doesn’t settle into how the team works. More than that, in our experience, is usually a sign of trying to cover too many use cases at once instead of closing one well before opening the next.