By MatiQ Inc
Our thinking /Working with us on AI adoption
Connect the business objective with implementation and staff preparation.
Bring the business decision into the room
An AI engagement can start with a specific workflow or with a broader change in direction. Both need a clear explanation of what the business wants to improve. Faster handling of a customer request, better access to operational information and less repetitive preparation are different objectives. They need different choices.
Before discussing tools, describe the work as it happens today. Who starts it? Where does information come from? Who checks the result? What happens when the normal process cannot be followed? Those details help determine whether AI belongs in the solution and what else may need to change.
Decide what responsibility you want us to take
We can own the engagement from assessment through implementation and staff training. That includes examining the current situation, documenting gaps and opportunities, agreeing priorities with you and carrying the selected changes into use.
You can also bring a defined engineering project or a training need. An assessment is available when a decision needs evidence; it is not a mandatory first purchase. During the initial conversation, we establish where you are starting and which responsibilities remain with your team.
Agree the checks before implementation
Specify how you will judge a result before selecting an AI model or connecting a tool. Use representative examples of the work, including incomplete information and exceptions. Decide what a person must review and which actions the system is permitted to take.
NIST’s AI Risk Management Framework organizes risk work around governance, understanding the context, measurement and management. Its core guidance includes defining human oversight responsibilities. We use the same practical question in a project conversation: who is accountable when an output is uncertain or a workflow needs to stop?
Include the people who will use the change
Training should follow the actual tools, information and permissions employees will encounter. A demonstration can explain a capability; practice helps people recognize when to trust a result, check a source or ask for help. Agree ownership for support and changes after handover as well.
We are based in Minneapolis and work with organizations on digital transformation, software and AI engineering, and enterprise AI training. Delivery arrangements are discussed around the work and the people involved. Tell us what you want to change, even if the right starting point is not yet clear.