What does AI implementation include?
Guided by Otto, written by Something Clever Co., updated
A real AI implementation includes more than turning on a tool. It covers finding the work worth automating, connecting AI to the tools you already use, deciding where a person checks the work, testing with real examples, showing your team how it works, and care after launch so it keeps working as your business changes. If a proposal skips the last part, expect it to break quietly later.
Why the word is so fuzzy
“AI implementation” can mean anything from an hour of ChatGPT training to a software project that runs for months. That makes proposals hard to compare. One vendor quotes for setting up a chatbot. Another quotes for rebuilding how your sales process works. Both call it implementation.
A useful way to think about it: implementation is everything needed to get from “we should use AI for this” to “this runs every day, people trust it, and someone is watching it.” Here is what that usually involves.
1. Discovery
Before building anything, someone needs to understand how the work actually happens today. Who does it, in which tools, how often, and where it goes wrong. Good discovery also finds the tasks that are not worth automating, which saves money.
At SCC, this starts with a Free AI Check.
2. Workflow design
Next comes deciding exactly what the AI will do, step by step, and where people stay involved. This includes deciding how much the AI does alone for each job. It can suggest while a person acts, do the work while a person approves, or handle routine work on its own while someone checks in.
3. Connecting your systems
AI is only useful when it can reach your work. That means connecting tools like Slack, Microsoft Teams, Gmail, Google Workspace, HubSpot, QuickBooks, Stripe, Airtable or Notion. Sometimes that is a simple connection through Zapier, Make or n8n. Sometimes it means building something custom, like a CRM shaped around how your team actually works.
4. Sensible checks
This is the part that separates a demo from something you can rely on. Good questions to settle before launch:
- What does each automation need access to, and nothing more?
- Where should a person look at the work before it goes out?
- How will you find out if something stops working?
- If a tool is billed by usage, is there a spending limit?
5. Testing with real examples
Workflows should be tested on real past emails, calls, invoices or requests, including the weird ones. Edge cases are where AI goes wrong, and it is far better to find them before launch.
6. Showing your team how it works
People need to know what the AI does, how to check its work, and who to contact when something looks off. Short and practical beats long and theoretical.
7. Care after launch
After launch, someone has to keep an eye on things, tune instructions, handle tool changes and fix what breaks. At SCC that is our optional monthly care: hosting, fixes when something breaks, small changes, looking for the next thing worth automating, and a real person to contact.
A quick comparison
| Part | DIY with off the shelf tools | Typical one time setup | Setup with ongoing care |
|---|---|---|---|
| Finding the work | You do it | Brief | Includes what not to automate |
| Connecting tools | Simple ones | Yes | Yes, including custom builds |
| Checks before launch | Often skipped | Sometimes | Agreed before launch |
| Showing your team | You teach yourselves | One session | A walkthrough at launch |
| After launch | You maintain it | You maintain it | Someone keeps it running |
When you do not need a full implementation
If you are a small team using ChatGPT or Claude to draft things a person then sends, you do not need any of this. Write a simple policy about what data not to share and get on with your day. A full implementation makes sense when AI will act inside your systems, touch customers or money, or run while nobody is watching.
Questions to ask any provider
- Which tasks did you decide not to automate, and why?
- Where are the human approval points?
- What can each workflow access, and what can it not?
- How do I see what the AI did last week?
- What stops costs from running away?
- Who fixes it when a connected tool changes?
Common mistakes
- Skipping discovery. You end up automating the wrong things.
- Treating launch as the finish line. Workflows drift without upkeep.
- No checks because “it is just a pilot.” Pilots touch real customers too.
- Leaving the team out. People quietly work around tools they do not trust.
- Comparing proposals by price alone. Check what is included after launch.
