Dear colleague,
Most AI risk does not arrive through the technology. It arrives through a decision no one owned, made by a system no one could later reconstruct. Enterprise AI governance should help leadership decide where AI can operate, under what conditions, and with whose accountability, and it should do that at the level of the business use case, not the platform. This guide sets out a practical approach by Stratenity for executives introducing or expanding AI while keeping control of the decisions, the data, and the outcomes.
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Governance effort should scale with consequence
Illustrative · By use case
A single approval process for every application overworks the harmless and under-examines the
consequential.
The appropriate level of control depends on who is affected, what data the system uses, what
actions it can take, and whether an error can be detected and reversed. Illustrative, to show
the principle rather than a measured result.
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About the Stratenity Library
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About Stratenity
Stratenity is an integrated enterprise AI company delivering an
AI-native operating system that turns intelligence into governed, measurable execution.
Stratenity connects knowledge, tools, agents, and human oversight within business operations,
helping organizations coordinate work, act on decisions, and track outcomes, from solo operators
to large enterprises.
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The business decision comes first
A drafting assistant, a customer service agent, and a system that influences eligibility decisions create very different exposures. Governing all three through one approval process creates unnecessary work in the low-stakes cases while overlooking the consequential ones. The unit of AI governance is not the model or the vendor. It is the use.
The appropriate AI governance for any use depends on four questions: who is affected, what information the system can reach, what actions it can take, and whether an error can be detected and reversed. Answer those honestly and the right level of control is usually obvious. Skip them, and control is applied by reflex rather than by consequence.
There is a simple test for whether AI governance is real. Can the organization reconstruct a significant AI decision: the use case, its owner, the evidence reviewed, the restrictions imposed, and the person authorized to accept the remaining risk? Where that record is missing, the first improvement is rarely a new tool. It is a clearer operating process. Establishing that starting point, honestly and quickly, is what the AI Governance Assessment by Stratenity is built to do.
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The unit
The use case
Govern each material use of AI, not the platform it runs on. Exposure lives in the use, not the
technology.
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Every use needs
An owner
A named person who can approve, restrict, or stop the use, and who is accountable for its
outcomes.
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And
A defined purpose
What the use is for, what it may not do, and the standard against which its output is judged.
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Plus
Proportionate controls
Controls sized to consequence. Light where errors are reversible, thorough where they are not.
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Backed by
Evidence
Approval rests on evidence a control operates, not on a self-reported maturity score alone.
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The reconstruction test
Can you rebuild it?
If a consequential decision cannot be reconstructed after the fact, AI governance is not yet in
place.
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Use standards to organize the work
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Standards give you structure. They do not tell you which of your own uses are consequential. That judgment stays with leadership. |
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NIST gives you the frame.
NIST describes its AI Risk Management Framework as voluntary guidance for building trustworthiness into the design, development, use, and evaluation of AI. Its Generative AI Profile adds guidance for the risks specific to generative systems. Useful scaffolding, deliberately non-prescriptive about your context. |
ISO gives you the system.
ISO/IEC 42001 specifies requirements for establishing and continually improving an AI management system: a repeatable way to set policy, assign responsibility, and review whether controls still work. It defines what good management looks like without deciding your risk appetite for you. |
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The obligations are arriving.
The moment AI touches consequential decisions, it invites the same fairness, explainability, and disclosure expectations regulators apply elsewhere, from the EU AI Act to sector rules. A governed, documented use is the defensible path. An ungoverned tool adopted quietly by staff is the exposure. |
Stratenity gives you the approach.
Frameworks describe requirements and controls in general terms. Organizations still have to decide which ones apply to their own activities, at what depth, and with what evidence. Stratenity’s approach connects assessment to implementation priorities and ongoing oversight, so the structure becomes decisions and assigned work. |
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One approval process for all AI
Uniform, and blind to consequence
· The same gate for a
drafting helper and a decision system.
· Heavy process where errors
are trivial and reversible.
· Approval recorded as a
checkbox, not as evidence.
· Maturity scores rise while
real exposures stay open.
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AI governance by use case
Sized to who is affected and what can go wrong
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Control depth set by consequence, not by policy reflex.
• A named owner and defined
purpose for every material use.
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Approval rests on operating evidence, not perception.
• A decision you can
reconstruct, and a plan for when conditions change.
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Six capabilities to examine
Stratenity organizes its AI governance framework around six connected capabilities. The leadership question and the evidence to examine, below, are a practical starting point for discussion, with review depth adjusted to the organization’s AI use and exposure. The AI Governance Assessment by Stratenity is organized around these same six capabilities and returns a maturity perspective from your responses.
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Assess evidence, not just maturity.
A questionnaire reveals perceived strengths and weaknesses. An advisor-reviewed assessment then examines the available evidence and marks where a response is still unverified. Record whether each important control is absent, documented, implemented, or demonstrated through operating evidence, and keep missing evidence visible rather than letting it disappear inside an average score. |
Match control design to consequence.
For agents that can take actions, the review should examine permissions, transaction limits, approval checkpoints, activity records, and the ability to suspend access. A refund an agent can issue, a record it can change, an email it can send on your behalf: each is an action whose control should match what an incorrect one would cost. |
Apply it to a real workflow
AI governance becomes concrete the moment you point it at one system. Consider a customer service agent authorized to draft replies and recommend refunds. The abstract questions turn into specific ones with specific answers.
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What a practical review asks
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Before expanded use
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1
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Scan · establish the start The AI Governance Assessment by Stratenity gives a maturity perspective in 8 to 12 minutes. |
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2
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Assess · verify the evidence An advisor review tests available evidence and marks what remains unverified. |
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3
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Workshop · agree priorities A leadership session sets ownership, sequence, and the risk leadership will accept. |
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4
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Activate · assign the work A living activation plan connects each priority to an owner, action, and evidence of completion. |
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5
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Monitor · keep it working A review cadence checks that agreed controls still operate as conditions change. |
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By use case
Control depth follows consequence, so effort lands where exposure actually sits.
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Evidence-based
Approval rests on demonstrated controls, not a self-reported score.
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Reconstructable
Every significant decision can be rebuilt: owner, evidence, restrictions, sign-off.
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Living, not static
A plan the team updates and scales, not a report that sits on a shelf.
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Turn findings into a 90-day plan
A useful assessment leaves leadership with decisions to make and work to assign. Each priority should specify the exposure being addressed, an accountable owner, the action required, its dependencies, a target date, and the evidence that will demonstrate completion. The sequence below is a recommended starting structure, adapted to the findings and delivered as a living activation plan, alongside leadership slides, for the team to initiate, update, and scale.
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Days 1–30 · Establish visibility
Identify material uses, assign owners, prioritize exposures, and set interim restrictions where
needed. Get the consequential uses named and under an accountable owner before anything else.
Evidence: initial inventory · named owners · priority risk register ·
documented interim decisions
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Days 31–60 · Put controls into the workflow
Define intake, approval, vendor review, access controls, and escalation responsibilities, and
move the priority controls into daily operating routines rather than policy documents.
Evidence: reviewed control designs · sample approval records · training and
communication materials
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Days 61–90 · Test and set a cadence
Exercise an incident scenario, review exceptions, and inspect evidence from selected uses.
Confirm the process holds under pressure, then set the recurring review that keeps it honest.
Evidence: control review results · remediation log · leadership review ·
next-quarter priorities
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Track evidence of progress.
Choose a small set of measures with clear definitions and owners: the share of in-scope uses with a named owner and current risk review; the share of higher-risk uses with documented approval; overdue remediation actions; and time to triage a reported incident. Set targets after the baseline. A rising maturity score alone does not prove a control is effective. |
Decide how it will be supported.
Some teams can execute the plan internally with templates, guidance, and an agreed review cadence. Others need help designing policies, configuring approval workflows, preparing evidence, or supervising vendors. The assessment should make that distinction explicit, so leadership can assign internal capacity and buy additional support only where it is genuinely needed. |
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The rest of the detail is a conversation,
not a download.
Which of your uses are consequential, what evidence already exists, and where a control is missing is specific to your roles, data, and jurisdictions, and it is the part we would rather get exactly right with you than flatten onto a page. Tell us where AI is already in use, and we will show you precisely how AI governance would sit around it. |
Plain English
Govern the use case, not the tool. Exposure lives in what a system does, who it affects, and whether an error can be undone, not in the platform it runs on.
A single approval process for everything overworks the harmless uses and misses the consequential ones. Size control to consequence instead.
The honest test of AI governance is reconstruction: for a significant decision, can you show the use, its owner, the evidence, the restrictions, and who accepted the remaining risk?
Standards like the NIST AI RMF and ISO/IEC 42001 give you structure. They do not decide which of your own uses matter. That judgment, and the evidence behind it, stays with leadership.
An assessment is only useful if it ends in assigned work: owners, actions, dates, and evidence, held in a living 90-day plan the team can update and scale.
If AI is already operating in your business, the safe path is to make each material use owned, proportionate, and reconstructable, starting from where you are today. Then let us help you close the gaps.
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Build a clear view of your current AI governance position and a practical plan for what to do next. Stratenity combines an initial scan and assessment with a three-hour leadership workshop, prioritized recommendations, an implementation roadmap, and an AI governance activation plan. The starting fee applies to a defined assessment scope; final pricing reflects the number and complexity of AI uses, business units, jurisdictions, stakeholders, and review requirements. Scope and deliverables are agreed before work begins.
The Library is a subscription you read, download, and build with
Velora to tailor deliverables to your organization, moving from insight to activation in 24
hours (features vary by plan); this engagement is advisor-led
assessment and a leadership workshop scoped to your organization. The plan includes a Read seat;
Download-tier extractions and Consulting Portal access are separate.
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Happy to send the full guide and map a first engagement to your priorities.
Sincerely,
Dodi
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Establish your starting point.
The Stratenity online AI Governance Assessment introduces the six capabilities and provides a maturity perspective based on your responses. The page estimates 8 to 12 minutes to complete. Use the results to identify questions for leadership and areas that merit deeper review. It is a starting perspective and does not independently verify control effectiveness. |
This is an executive guide grounded in published AI governance standards and in Stratenity’s advisory practice. It presents a practical approach; it is not a claim that every organization faces the same obligations or risks. The commercial offer summarized here reflects the package specified for this guide.
The workflow example and the exposure comparison are illustrative, used to show the principle rather than a measured result, and do not represent a specific Stratenity client outcome. The assessment provides a maturity perspective; it does not independently verify that a control is effective. Certification, independent assurance, and legal opinions are outside the scope of the engagement described.
Grounded in Stratenity’s frameworks: the Stratenity Execution Model (SXM) and the Stratenity Foundational Model (SFM). Sources reviewed September 26, 2026.
[1] NIST (2023). AI Risk Management Framework (AI RMF 1.0), and (2024) Generative AI Profile (NIST AI 600-1).
[2] ISO/IEC 42001:2023. Information technology, Artificial intelligence, Management system.
[3] European Union (2024). Regulation (EU) 2024/1689 (the EU AI Act).
[4] Stratenity Advisory (2026). AI Governance. stratenity.com
[5] Stratenity Advisory (2026). AI Governance Assessment and Library plans.
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