An oilfield services company that started with its own work, not an AI tool.

What the company asked for

The company wanted to understand where AI could help across the business, and where it would not be useful.

Its teams handled very different work, from sales meetings and field reports to parts information and invoice checks. Much of it depended on basic spreadsheets, emails, notes and information passed between people.

So instead of starting with a standard AI course, Jack started by learning how the company worked and what each department needed.

Before the visit: understanding the business

Jack met with the company remotely to hear how the teams worked, discuss their needs and prepare an initial training plan.

That preparation gave the onsite work a starting point. It also left room to change the training once Jack had seen the meetings, processes and day-to-day tasks firsthand.

Day one: observing the work and finding opportunities

On the first day onsite, Jack joined real meetings and spent time with people across departments. He looked for practical opportunities in the work already happening:

• Information typed by hand more than once
• Reports put together from emails, notes or spreadsheets
• Checks done by eye, one line at a time
• Follow-ups that depended on someone remembering

By the end of the day he had identified nine candidate workflows, each tied to a task someone at the company was already doing. He used what he saw to revise the next day’s training, so each department could work on its own processes instead of generic examples.

Day two: hands-on training around each team’s tasks

The morning was for everyone. In the afternoon Jack worked with each department separately to build AI workflows from the ground up.

The starting point was the material people already knew: spreadsheets, meeting notes, field emails, paper records and invoices. The goal was to use AI alongside that material and leave with a practical way to handle a specific task. Examples:

• Sales: turning a weekly meeting recording into a short report, customer notes and a to-do list
• Operations: bringing scattered field emails together into one morning report
• Shop: using a photo of a paper parts sheet to prepare entries for a person to check
• Administration: checking invoices against field tickets before they go out

Each workflow came with a prompt people could reuse and a check to run before relying on the output.

Three rules for using AI

The training included three working rules for every team:

1. Use approved tools. Company information goes only into AI tools the company has approved.
2. Check every number. Any number produced by AI is checked against its source before use.
3. Keep the decision with a person. Sending, submitting, paying and deleting remain human decisions.

What is in use now

Several pilot workflows are now in use, mostly by the sales team. People in other departments also use workflows for their own daily tasks.

After the onsite visit, the company has 30 days of email support for questions that come up as people use the workflows on their own.

What has not been measured

This case describes the work done and the workflows the teams started using. It does not claim:

• Hours saved per week
• Revenue impact or cost savings
• Return on investment
• Which pilots will become standard practice

Those outcomes would have to be measured with the company before they could be reported.