Here’s a question we put in front of CEOs before an AI and Microsoft Copilot session. Take five seconds and pick your answer:
Which best describes where your company stands on AI today?
Most leaders land somewhere in the middle three. And most are a little surprised by where they land, because they’ve been thinking about AI as a tools decision. Which chatbot. Which license. Whether to turn on Copilot.
The tools are the easy part. Readiness is a leadership decision.
What does AI readiness mean for a business? AI readiness means your organization can adopt AI safely and get measurable results from it. For leaders, that comes down to six things: a named owner, a written policy, data that’s organized and properly secured, a short list of use cases tied to business goals, people trained to use the tools, and a clear way to measure the return.
Because the gaps that stall AI almost never start in the server room.
Cisco’s 2025 AI Readiness Index surveyed more than 8,000 AI leaders and found that only about 13% of organizations are fully prepared to capture AI’s value. The difference between that group and everyone else is mostly about leadership choices: 99% of the most ready companies have a defined AI roadmap, compared with 58% overall, and 91% have a change management plan, compared with just 35%.1
Grant Thornton’s 2026 AI Impact Survey of 950 senior leaders points the same direction. Seventy-eight percent of executives lack strong confidence they could pass an independent AI governance audit within 90 days, and training was identified as the most underfunded area of AI investment.2
If you’re a growth-focused CEO or an operations leader, that probably feels familiar. Your team is already experimenting. Your board or leadership team keeps asking about AI. And you’re not sure whether you’re behind, ahead, or just busy. It shouldn’t be this hard to know where you stand, but without a simple framework, most leaders are guessing.
Use this as a working checklist. You don’t need every box checked to start. You do need to know which boxes are empty.
| Checklist item | The question to answer | TMM stage |
|---|---|---|
| 1. Name an owner | Who is accountable for AI decisions, budget, and results? | Integrate |
| 2. Write the rules | Do employees know which tools are approved and what data they can use? | Secure |
| 3. Get your data house in order | If AI can see everything a user can see, are permissions right? | Operate / Secure |
| 4. Pick two or three use cases | Which workflows would save real time or money in 90 days? | Integrate |
| 5. Train your people | Does every user know how to use the approved tools well? | Innovate |
| 6. Decide how you’ll measure | What number will tell you it’s working? | Integrate |
| 7. Plan for when something goes wrong | Who responds if AI exposes data or makes a bad call? | Secure |
A few of these deserve a closer look.
Name an owner. “Lack of internal expertise or a clear owner” is one of the most common barriers we hear from leadership teams. AI that belongs to everyone belongs to no one. The owner doesn’t have to be technical. They need authority, a budget, and a seat at the leadership table.
Write the rules. If your honest answer to the opening question was “no policy” or “informal,” employees are filling the gap themselves. We covered why that’s a leadership signal, not a rebellion, in our post on shadow AI as a leadership problem. A one-page policy that names approved tools and off-limits data is a strong first step. Our AI governance playbook for CEOs goes deeper.
Get your data house in order. Tools like Microsoft 365 Copilot work from the files, emails, and chats each user already has access to. If a shared folder with salary data is open to the whole company, Copilot will happily surface it. Cleaning up permissions is unglamorous, and it’s the single most important thing you can do before scaling.
Train your people. This is the item leaders most often skip, and it matters most. BCG’s AI at Work 2026 study of nearly 12,000 employees found that only 36% feel properly trained, and just 33% of frontline employees say leadership communicates clearly about AI.3 Licenses without training produce a few enthusiasts and a lot of shelfware. Start with managers, because they set the pace for their teams. Our guide to AI training for employees lays out a practical plan.
Plan for when something goes wrong. Grant Thornton found that only 20% of organizations have a tested AI incident response plan.2 That doesn’t mean slowing down. It means deciding in advance who gets the call.
At Sentry, we guide growing businesses through the Technology Maturity Model (TMM): Operate, Secure, Integrate, Innovate. AI lives in the Innovate stage, and that placement is intentional.
You can’t Innovate on top of shaky ground. AI depends on reliable systems and clean data (Operate), on access controls and policies that protect sensitive information (Secure), and on technology that’s aligned with how your business actually runs (Integrate). Companies that skip ahead tend to buy licenses, see scattered results, and conclude AI is overhyped. Companies that build the foundation first tend to see AI pay off quickly, because it has something solid to work with.
That’s why our AI conversations with leadership teams usually start with a readiness check rather than a product demo. Most second-stage businesses already have more of the foundation in place than they think. The checklist simply shows where to focus first.
Your team stops guessing which tools are allowed. Your data stays where it belongs. The first few AI use cases deliver results you can point to in a leadership meeting, which makes the next investment an easy decision instead of a leap of faith.
Most of all, you move from reacting to AI to directing it. Our post on how AI workflows are changing businesses shows what that looks like once the foundation is in place: hours returned to your team every week, and a business that’s ready for what comes next.
Start with the seven checklist items above. If you have a named owner, a written policy, properly secured data, and at least one defined use case, you’re ready to run a focused pilot. If several items are missing, address those first. You’ll get better results faster.
You need at least a basic one. A short policy that lists approved tools, defines what data can’t be shared, and names who to ask covers most of the risk. You can expand it into a full governance program as adoption grows.
For many businesses already on Microsoft 365, yes, because it works inside the tools your team uses every day. But Copilot is only as good as your data permissions and your team’s training. Review who can access what before you roll it out broadly.
Ideally, a senior leader with authority over budget and priorities, supported by IT or a strategic technology partner. In smaller organizations, this is often the COO, the CEO, or a vCIO (virtual Chief Information Officer) who brings outside strategic guidance.
It depends on your starting point. Many second-stage businesses can close the most important gaps, such as policy, permissions, and a first use case, within one or two quarters with focused effort.
You don’t need to have AI figured out to lead it well. You need an honest view of where you stand, a short list of priorities, and a guide who has helped growing businesses make the same move.
Ready to explore what AI could do for your business? Book a free AI consultation with our team. [PLACEHOLDER LINK]
1. Cisco. “Cisco AI Research: The Most AI-ready Companies Outpace Peers in the Race to Value” (Cisco AI Readiness Index 2025). October 14, 2025. https://investor.cisco.com/news/news-details/2025/Cisco-AI-Research-The-Most-AI-ready-Companies-Outpace-Peers-in-the-Race-to-Value/default.aspx
2. Grant Thornton. 2026 AI Impact Survey Report. Fieldwork February 23 to March 18, 2026. https://www.grantthornton.com/services/advisory-services/artificial-intelligence/2026-ai-impact-survey
3. Boston Consulting Group. AI at Work 2026 (Fourth Edition). June 2026. https://web-assets.bcg.com/e7/c7/00d913744cccb1e4f65bbf54fe86/ai-at-work-slideshow-june-2026.pdf