We helped a company with many departments set up practical AI controls: approved tools, data rules, risk levels, human review, ownership and ongoing monitoring. Teams kept working at the same pace.
Industry: Professional & Business ServicesService: AI & AutomationUpdated
Overview
The challenge
Teams across the company had started using AI for content, analysis, support, automation and day-to-day productivity. The tools helped, but nobody shared a view of which tools were approved, which data could be used or when a person had to review the output.
The company wanted controls that helped teams use AI well, not a large compliance process that people would work around.
Each team picked AI products without a common review.
Staff weren't sure which information was safe to use in each AI workflow.
Higher-risk uses of AI and important decisions had no clear owner.
The solution
We documented where AI was being used, who owned each workflow and what business data it touched.
Use cases were grouped by risk level, each with simple rules for tools, data, human review and approval.
The controls are tied to review cycles, usage feedback and changes in AI workflows, so they stay up to date.
Before and after
Before
Team by team
Each department chose its own tools.
After
Approved options
Teams know which tools and workflows are allowed.
Before
Informal
Staff relied on their own judgement.
After
Clear rules
The risk level of each use case sets the controls it needs.
Before
Unclear
Nobody consistently owned the results of AI use.
After
Named owners
Each important AI use case has a responsible business owner.
Key features
The business can see where AI is used and who owns each use.
Business impact
Clear visibility
Controls scale with how sensitive and important each AI workflow is.
Business impact
Controls that fit the risk
Teams know when AI can act on its own and when a person must approve the result.
Business impact
Clear accountability
Results
More Confident Adoption
Teams can use AI knowing what is allowed and what needs a review.
Before: UnstructuredAfter: Governed
More Consistent Decisions
Higher-risk use cases get stronger controls.
Before: InformalAfter: Risk Based
Better Accountability
Each AI workflow has a clear business owner.
Before: UnclearAfter: Assigned
Faster delivery
Reusing these tested parts kept setup short, so the project could focus on this company's own risk levels and rules and go live sooner.
Our access controls tie each approved AI tool to the users and data its risk level allows.
The workflow layer is where the approved-tool, data and review rules were applied to each team's AI workflows.
Monitoring and feedback loops feed usage data into the regular control reviews.
A tested review step sends higher-risk outputs to a person for approval before anything sensitive happens.
Trust and control
AI tools can only reach the information and actions approved for each user and workflow.
ACCESS CONTROLLEDImportant or unusual cases can go to a person before any sensitive action happens.
HUMAN IN CONTROLKey AI actions and outcomes can be recorded, so teams can check what happened.
TRACEABLERules, prompts, workflows and controls can be adjusted as the way teams use AI changes.
ADAPTABLETeams had started using AI for content, analysis, support, automation and productivity, each choosing their own tools. There was no shared view of which tools were approved, which data was safe to use or when a person had to review the output, and higher-risk uses had no clear owner.
The framework has four parts. A register lists every AI use case, its owner and the data it touches. Risk levels set the controls each use needs. Clear rules cover approved tools, data and human review. Ongoing monitoring ties the controls to review cycles and usage feedback, so they stay current.
Controls scale with risk instead of applying one heavy process to everything. Every use case follows simple rules on approved tools and data, while higher-risk uses get stronger controls and human approval. Teams know what is allowed up front, so they can use AI with confidence rather than working around the rules.
If each department has picked its own AI tools and nobody owns the risk, we can map what's in use, set risk levels and assign owners without slowing the work down.