The KNOW AI Enablement Lab - Build the Structure to Stay Ahead
Your Organization Needs a Structure That Keeps Up With AI
AI is moving fast and it is not slowing down. Most organizations are using AI across departments without the committee, decision process, or operating framework to manage it responsibly as the landscape keeps changing. This lab builds that structure.
Designed for organizations with 200+ employees or $50M+ in annual revenue managing operational, financial, customer, or high-consequence data.
20-minute conversation. No pitch. No pressure. We figure out together whether this is the right fit.
What Is Happening Inside Most Organizations Right Now
AI tools and capabilities are changing faster than most organizations were built to handle. Employees are adopting tools, building workflows, and making AI-assisted decisions across departments.
Most organizations do not have the internal structure to manage that activity consistently, safely, or strategically.
Employees are using AI tools without a clear approval path
Departments are making separate AI decisions without coordination
Leaders lack visibility into existing AI tools and workflows
No defined owner exists for AI decisions at the organizational level
Unreviewed AI activity creates exposure undiscovered until something happens
Consistent rules for what data can and cannot be used with AI are missing
Most organizations lack the committee, decision process, and operating rhythm to manage AI responsibly as it keeps evolving. That gap grows larger every time the landscape changes.
This Problem Shows Up in Two Ways
1. Your organization is already dealing with it
AI tools are already active across departments without consistent oversight. Approvals are inconsistent. Leaders are discovering AI activity after the fact. The risk exposure is real and growing. You need structure now.
2. Your organization wants to get ahead of it
Your teams are beginning to use AI and leadership wants to build the right operating framework before problems surface. You want a committee with clear authority, defined rules for use, and a repeatable process that holds as the landscape keeps changing.
Both situations call for the same solution. A functional AI committee, a clear operating framework, and an operating rhythm that keeps your organization current and responsive no matter what changes next.
Why This Matters
AI strategy is an ongoing operational reality that requires ongoing organizational capability to manage well. It is not a project.
A functional AI committee gives your organization a defined owner for AI decisions, a consistent process for evaluating new tools and use cases, a clear escalation path when something goes wrong, and a repeatable operating rhythm that keeps leadership current as the landscape evolves.
Without that structure, organizations react. They discover problems after the fact. They make inconsistent decisions. They slow down legitimate AI adoption while unreviewed activity continues in the background.
With that structure, organizations lead. They make faster and more consistent decisions. They surface and address risk before it becomes an incident. They build the kind of institutional confidence that lets leadership say yes to AI adoption with accountability behind it.
As the team grows and changes, the committee structure keeps the operating framework intact. Institutional knowledge does not walk out the door with any one person. New members can be oriented to the framework and brought up to speed without starting over.
The Regulatory Landscape Is Moving Fast
AI regulation in the United States is no longer a future concern. Laws are already in effect and more are taking effect in 2026. Organizations that do not have a documented AI operating structure may find themselves behind when regulators, auditors, or board members start asking questions.
What is already in effect or arriving in 2026:
Texas HB 149: Effective January 1, 2026 The Texas Responsible AI Governance Act establishes requirements for AI system oversight, risk documentation, consumer protection disclosures, and accountability structures for organizations operating AI in Texas.
Illinois AI Disclosure Law: Effective January 2026 Requires disclosure when AI is used in consequential decisions affecting Illinois residents.
Colorado AI Act: Effective June 2026 Applies to developers and deployers of high-risk AI systems. Requires risk assessments, impact documentation, and governance accountability.
U.S. Treasury AI Risk Framework: Released February 2026 Provides specific AI risk management guidance for financial institutions. Signals regulatory expectations across the financial services sector.
America's AI Action Plan: Federal Level Shapes organizational AI obligations at the federal agency level and signals the direction of broader federal AI requirements.
The organizations that will navigate this environment with the least friction are the ones that already have a committee with defined authority, documented decision rights, and a clear operating framework. The KNOW AI Enablement Lab builds exactly that structure.
What This Lab Is
The KNOW AI Enablement Lab is a facilitated working session series where your leadership team builds a functional AI committee and the operating framework it needs to manage AI responsibly as the landscape keeps changing.
Your cross-functional working group works through the real decisions your organization needs to make. Who owns AI decisions. What rules apply to data use. How new tools and use cases get reviewed and approved. What happens when something goes wrong. How the committee stays current and responsive over time.
The sessions produce working documents your team can use and build on immediately. You leave with a committee that has authority, a framework that has structure, and a 30/60/90-day action plan so the work continues after the sessions end.
Built on the KNOW Leadership Framework™ and informed by the NIST AI Risk Management Framework; used as a practical reference for building safeguards and decision processes.
What Your Team Will Build
Four core work products that give your AI committee the structure, visibility, and operating rhythm to manage AI responsibly over time.
Rules and Safeguards
AI acceptable use rules
Data handling rules
Decision rights map
Visibility and Intake
AI use-case register
Risk register
Shadow AI visibility
Shadow AI routing plan
Measurement and Proof
Governance health scorecard
KPI and KRI starter set with assigned owners
90-day measurement path decision
Run the System
AI incident escalation path
Operating rhythm and cadence
30/60/90-day action plan
*Optional add on tabletop exercises to pressure test triage decisions and response actions.
What Your Organization Will Have Within 30 to 60 Days
A functional AI committee with defined authority and an operating rhythm
Clear rules for what AI use is allowed and what is not
Defined decision rights with named owners
A documented intake and oversight path for new tools and use cases
A process for identifying and addressing unreviewed AI activity
A defined escalation path for AI-related incidents
A repeatable operating cadence that keeps the committee current as the landscape evolves
A committee structure that sustains institutional knowledge through staffing changes and internal growth
A 30/60/90-day action plan so implementation does not stall
Within 30 to 60 days, leaders can measure
Intake path utilization
How many new AI tools go through the review process before going live
Risk register activity
Whether the register is being updated on schedule
Shadow AI trend
Whether unreviewed AI activity is decreasing over time
Escalation response time
How quickly flagged issues get addressed
Intake adoption rate
How much AI activity is going through the formal path versus around it
Who This Lab Is For
This lab is a strong fit for mid-market organizations where AI is already in use and leaders need visibility, clear rules, and faster decisions. Most organizations we work with have 200+ employees or $50M+ in annual revenue, but the better qualifier is consequence.
If ungoverned AI activity would cost your organization real money, operational continuity, or leadership confidence, or if you want to build the right structure before the landscape forces the issue, the conversation is worth having.
The Readiness Call is where we figure out whether the lab is the right fit or whether a different starting point makes more sense for where you are right now.
Employees are using AI tools without a clear approval path
Leaders lack visibility into existing AI tools and workflows
Tool approvals are inconsistent across departments
Departments are making separate AI decisions without coordination
You handle confidential, financial, customer, or operational data
You are evaluating AI vendors or automation initiatives
You want to build the right AI operating structure before problems surface
Typical participants include COO, VP or Director of Operations, CRO or Risk leaders, Compliance leaders, IT leadership, HR leadership, and department managers or department representatives.
Who This Lab is Not For
You want someone to write policies without leadership involvement.
You want to ban AI completely (which is unlikely to happen).
You want software implementation or tool selection.
No decision-makers can participate.
You are looking for a one-time lecture.
Format and Investment
- Four virtual sessions.
- 120 minutes each.
- Scheduled over four to six weeks.
- Cross functional participation required.
Investment is discussed during the Readiness Call based on your organization's size, scope, and current AI landscape.
Readiness Check
If you can check two or more of these, the Readiness Call will be worth your time.
Employees are already using AI tools but no clear owner exists
Leaders are unsure what data can be used with AI
Tool approvals are inconsistent across departments
Departments are making separate AI decisions without coordination
You would not know what to do if an AI related incident occurred today
You want to build a formal AI operating structure before problems surface
FAQ
What will we have at the end of the lab?
Your organization will have a functional AI committee with defined authority, a clear operating framework, and a repeatable operating rhythm so leadership stays current and responsive as the AI landscape keeps changing.
The committee is supported by four core work products built during the sessions: AI acceptable use and data rules, a decision rights map, an AI use case register and risk register, and a governance health scorecard. You will also have a vendor intake framework, an incident escalation path, an operating cadence for your committee, and a 30/60/90-day action plan with named owners.
Everything is produced at version one during the lab, usable immediately and designed to improve over time.
How is this different from AI training or a policy writing service?
Training teaches concepts. Policy writing produces documents. This lab produces decisions. Your cross-functional team works through the real questions your organization needs to answer; who owns AI use cases, what data can and cannot be used, what gets approved and what gets escalated and leaves with work products that reflect those decisions. You build it together so the people who have to own it actually understand it and can use it.
Who needs to participate for this to work?
The lab requires a cross-functional working group of five to eight people. At minimum you need representation from risk or compliance, IT or security, operations, and at least one business function or frontline perspective. An executive sponsor attends a pre-lab briefing and receives a recap after each session. The working group attends all four sessions. Without cross-functional participation the work products will not reflect the full picture of your organization's AI activity and the decision rights will not hold.
Do you record sessions or require sensitive data?
Sessions are not recorded by default. If your organization wants a recording for internal use that can be discussed during the Readiness Call. You are not required to share sensitive data, proprietary systems information, or confidential records during the lab. The work is built from what participants know about their own operations.
What drives scope and investment?
Investment is based on organization size, the number of departments involved, and your current level of AI visibility going into the lab. These factors affect the complexity of the working sessions and the depth of the work products that need to be built. The Readiness Call is also where we confirm whether the lab is the right fit or whether a different engagement makes more sense for your situation. Investment is discussed once we have that picture. There is no pressure to commit during that conversation.
Build the Structure Your Organization Needs to Stay Ahead of AI
Whether your organization is already navigating unreviewed AI activity or you want to build the right operating framework before problems surface, the KNOW AI Enablement Lab gives your leadership team the committee, decision process, and operating rhythm to manage AI responsibly as the landscape keeps changing.
The Readiness Call is 20 minutes. We talk through what is happening and figure out together if the lab is the right fit.