AI engineering practices
Two tracks: AI in your teams' software development, and AI tools for your business lines. Each comes with its own usage rules and checkpoints.
Two tracks · software development (SDLC) · business lines
- Format
- Two tracks, through workshops and hands-on support with your own cases
- Who is involved
- Your development teams and the heads of your business lines
- What you leave with
- Approved tools and usage rules, for the code and for the business
- What comes next
- Team training, or a proof of concept on a specific case
Who needs AI engineering practices
These practices are for organizations whose teams already use AI to produce code or tools, each in their own way, with no common rules set by IT.
- Your developers use code assistants according to their own habits, with no shared rules on tools or repositories.
- Code reviews are backing up, because AI multiplies the changes that reviewers have to read.
- Business teams build their own automations and assistants on the side, out of IT's sight.
- You worry that source code or internal data could leave the organization through a tool nobody approved.
AI in your development lifecycle (SDLC)
Speed up your development teams while keeping control of the code.
- Map the lifecycle: specification, code, tests, review and deployment: where AI steps in, and where a person decides.
- Choose the tools and set the rules: code assistants, repository access, and what data can be sent to them.
- Keep control of the code: human review, automated tests and a change log before anything reaches production.
Equipped business lines
Let ideas and solutions flourish without compromising the organization's security or performance.
- List the business needs: automations, assistants and internal tools that teams want to build or adopt.
- Set up a safe framework: approved tools, data access, and a security check before use.
- Track performance: what the tools cost and the load they put on your systems.
Common rules for AI in software development
For development teams, we start by mapping the lifecycle: specification, code, tests, review, deployment. At each step we note where AI already plays a part, where it could help and where a person must stay the sole decision-maker. The rules that follow cover approved tools, the repositories they can read and the data that can be sent to them. Human review, automated tests and a change log stay in place before anything goes to production, because AI increases the volume of code to check.
For the business lines the starting point is different: we list what teams already want to build, then set up a framework in which they can build it. An approved tool, governed data access and a security check before use replace both a blanket ban and a free-for-all. We also track what each tool costs and the load it places on your systems, so one tool's success does not overload everyone else's.
What you get
An equipped development lifecycle
A map of your lifecycle, from specification to deployment, showing where AI steps in, which tools are approved and where a person reviews and approves before the next step.
Approved tools for the business lines
A list of AI tools cleared for the business lines, with the data access each may have and the security check to pass before anyone uses it.
Verifiable usage rules
Written rules and checkpoints that let ideas circulate without exposing security or performance, with tracking of costs and of the load on your systems.
After AI engineering practices, train and test
Rules only work if teams apply them: training passes them on to developers, to the business lines and to their AI leads. For a specific case, such as an assistant meant for one business line, a proof of concept checks on your data that the tool delivers what it promises before it is rolled out more widely.
Frequently asked questions
Does AI in development mean handing the code over to AI?
No. The first track speeds up your teams while leaving them in control of the code: a person reviews and approves what enters your repositories, tests run before release and every change stays traceable.
Why equip the business lines and not only IT?
The business lines know which tasks are worth automating, and their ideas do not always wait for IT. The second track gives them a framework, with approved tools and data access rules, so those ideas can happen without putting the organization's security or performance at risk.
Do we have to take both tracks together?
No, they can be taken separately. The development track is for your IT teams, the business-line track for the people who want their own tools. The audit shows which to start with.
Which repositories and data can a code assistant see?
That is decided tool by tool, not left to everyone's habits. The development track sets the approved tools, the repositories they can reach and the data that may be sent to them. For the most sensitive code, the choice between a cloud model and a private one is worked out through governance.