What to check before you adopt AI
You want to adopt AI but do not know where to start. Here is what to review before your first AI consultation.
Adopting AI starts not with picking a tool but with "which business problem are we solving." Skip purpose, data and operations and you will just repeat PoCs.
You do not know where to start
Everyone says AI is good, but you cannot tell where in your operations it would actually pay off.
You keep repeating PoCs
The demo works, but it never lands in real operations and quietly fades.
Data and security worry you
There is no standard for how far internal data may be used, or for security and governance.
Adoption becomes the goal
When the goal is the tool rather than a business problem, there is no success metric.
The data is not ready
Scattered, unorganized data lets no AI produce a useful answer.
There is no operating model
No one is assigned to monitor, update and respond to incidents after launch, so it is left to rot.
Confirm purpose, data and operations before the tool and you avoid the PoC trap.
Define the business problem first
Decide "what to improve, by how much" as a metric first. The tool comes after.
Review data and security
Establish which data can be used and how far, with clear governance.
Design for operations too
GIIP designs not just adoption but monitoring, updates and incident response with you.
Pre-adoption checklist
- Is the business problem and its improvement metric defined?
- Is the data you will use organized and connected?
- Is there a security/governance standard for data use?
- Is an owner assigned for post-launch operations and updates?
- Did you agree PoC success criteria and scale-up conditions in advance?
Frequently asked questions
Where do I start with AI adoption?
With the business problem, not the tool. Set "what to improve, by how much" as a metric, then review data and operations.
How do I stop repeating PoCs?
Agree PoC success criteria and scale-up conditions up front, and design for operational adoption from the start.
What makes GIIP's AI adoption different?
GIIP's AI multi-agent team carries operations, monitoring and incident response after adoption — so it does not end at a demo.
Start AI adoption from purpose
It is fine if the use case is fuzzy. We shape the business problem with you first.