Employees are already using AI without clear guidance or shared expectations
Responsible AI adoption
Move from scattered experimentation to responsible, business-aligned AI adoption.
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Kenna helps leadership teams decide where AI belongs, establish practical guardrails, and create an operating model that supports useful adoption without losing accountability.
Discuss your AI priorities ↗What this solves
Bring one executive agenda to AI opportunity, use, and risk.
Leadership has many ideas but no consistent way to prioritize them
Sensitive information may be entering tools that have not been reviewed or approved
Vendors are adding AI capabilities without adequate business, data, or risk review
Pilots are disconnected from measurable business outcomes and operating ownership
Responsibility for AI decisions, exceptions, and incidents is unclear
Concrete deliverables
Policies, decisions, and operating tools your teams can use.
Each engagement turns AI ambition and concern into clear work products, accountable ownership, and an executable adoption roadmap.
AI use-case inventory and prioritization
A shared portfolio of current and proposed uses, evaluated against business value, feasibility, data needs, and risk.
Current-state usage and risk assessment
A practical view of where AI is already in use, what information is involved, which controls exist, and where decisions are needed.
Acceptable-use and data-handling policy
Plain-language guidance for approved tools, sensitive information, prohibited activity, review requirements, and employee accountability.
Roles, decision rights, and approval workflow
Clear ownership across executives, technology, security, operations, HR, legal stakeholders, and business sponsors.
Vendor and model risk-review criteria
Comparable questions for data use, security, intellectual property, human oversight, performance, monitoring, and exit planning.
Human-oversight requirements
Defined points where people review, approve, challenge, or stop AI-assisted decisions based on business impact and risk.
Pilot selection and governance playbook
Entry criteria, owners, controls, success measures, review gates, documentation, and a disciplined path to stop, revise, or scale.
Employee guidance and adoption planning
Role-based expectations, practical education, communications, feedback channels, and support for responsible day-to-day use.
Monitoring, measurement, and incident escalation
Operational measures, review cadence, issue intake, escalation paths, and ownership for emerging risks or unintended results.
Executive roadmap for responsible adoption
A sequenced agenda connecting policy, priority use cases, enabling capabilities, governance decisions, and investment timing.
Practical governance approach
Governance that moves with the work.
A lightweight lifecycle keeps policy connected to real use cases, evidence, operating decisions, and responsible scale.
Discover
Inventory current use, proposed ideas, data exposure, vendor capabilities, stakeholder concerns, and the decisions already being made.
Prioritize
Compare use cases by business value, readiness, effort, data requirements, risk, and the evidence needed to proceed.
Govern
Set policy, roles, decision rights, review criteria, documentation, human oversight, and escalation before pilots expand.
Pilot
Run a bounded use case with an accountable sponsor, approved data, clear controls, user guidance, and explicit stop-or-scale gates.
Measure
Evaluate business results, user adoption, quality, exceptions, incidents, control effectiveness, and unintended effects.
Scale
Extend only what earns the decision, then strengthen operating ownership, monitoring, training, vendor management, and periodic review.
Framework alignment
Established guidance, translated into practical decisions.
Kenna can tailor its approach using widely recognized AI risk and management-system guidance while keeping the work proportionate to the organization.
NIST AI Risk Management Framework
Use the voluntary Govern, Map, Measure, and Manage functions to connect organizational accountability, context, evaluation, and ongoing risk treatment.
Review the NIST AI RMF source guidance ↗NIST Generative AI Profile
Apply generative-AI-specific considerations when reviewing uses, data, human interaction, vendor capabilities, monitoring, and emerging risks.
Review the NIST Generative AI Profile ↗ISO/IEC 42001 principles
Draw on management-system principles such as policy, accountable ownership, risk-based controls, documented operation, monitoring, and continual improvement.
Review the ISO/IEC 42001 source guidance ↗Framework alignment informs the advisory work; it is not certification, formal auditing, legal advice, a guarantee of compliance, or a promise that all AI risk can be eliminated.
Engagement fit
Start where leadership needs a responsible decision.
Scope can begin with current usage and policy, focus on an upcoming pilot, or establish the cross-functional governance foundation for a broader adoption roadmap.
An initial AI policy and governance foundation is needed
Leadership wants an independent review of current AI use
The organization needs a consistent way to decide which use cases merit investment
An upcoming pilot needs clear ownership, controls, and decision gates
Technology, operations, security, HR, and legal stakeholders need executive alignment
Leadership needs an adoption roadmap that balances opportunity with control
Start a conversation
Make the next technology decision
with clarity.
Tell us what decision you are facing, what is changing, and any timing or operating constraints. Kenna will use that context to frame an initial conversation and determine whether an assessment, focused project, or ongoing advisory relationship is the right fit.