Most companies are already using AI. Far fewer can explain how that activity will create measurable business value.
One team may be testing a company-approved assistant. Another may use personal AI accounts. A leader may sponsor a pilot while the rest of the organization waits for a policy. The activity is real, but the strategy is fragmented.
In Episode 2 of Where AI Fits, SageCreek AI founders Connor McLeod and Bob Bodily organize the adoption journey into five stages: visibility, ownership, control, focus, and impact.
The stages give leaders a practical way to assess where the company is today and what needs to happen next.
Stage 1: Visibility
AI adoption begins by understanding what is already happening.
Employees may use AI through approved platforms, personal subscriptions, browser tools, meeting assistants, and features built into existing software. Leadership often sees only a portion of that activity.
This hidden use is commonly called shadow AI. It can create data and security risks, but it also reveals unmet demand. Employees are reaching for AI because they believe it can improve part of their work.
A company-wide survey is a useful starting point. Ask which tools employees use, what tasks they support, what data is involved, and where approved tools fail. Follow the survey with interviews focused on recurring workflows.
Visibility should produce a clear map of current behavior. It should also reveal where useful experimentation is already happening.
Stage 2: Ownership
Once the company can see the activity, someone needs to own the next step.
The AI champion should have a clear mandate from leadership. That person becomes the point of coordination for policies, tools, pilots, training, and measurement.
The strongest owners understand how the business creates value. They can work across departments and are comfortable leading change. They also need enough technical judgment to ask the right questions, even if engineers handle implementation.
No single title guarantees success. A COO, CTO, CHRO, or another executive may be a good fit. What matters is the combination of business context, technical awareness, and change-management ability.
In some companies, a cross-functional group will supply those capabilities. One person should still remain accountable for progress.
Stage 3: Control
Control means giving employees clear, useful boundaries.
A company needs approved tools, data-handling rules, security and privacy standards, and a defined way to request new capabilities. Human review should be required when AI output affects customers, financial decisions, legal work, or other high-consequence activity.
The difficult part is balance. If the company clamps down too hard, employees cannot experiment or build confidence. If it provides no guardrails, individual teams make inconsistent decisions about data and risk.
A useful AI policy serves the business. It protects information while making productive work possible.
Small pilots help establish that balance. They give security and business leaders a real workflow to evaluate rather than an abstract technology debate.
Stage 4: Focus
At this stage, the company decides where to invest.
This is harder than generating a list of ideas. AI can touch almost every function, so the challenge is choosing the few opportunities that matter most.
Begin with workflow mapping. Understand what people do today, how long it takes, what it costs, what information it requires, and how the work connects to the business.
Then evaluate where AI, better software, stronger data, or training could change the outcome. The answer will not always be a new AI agent. Sometimes the real need is a cleaner data foundation or a redesigned process.
SageCreek’s AI Opportunity Audit uses executive interviews, workflow analysis, and a technology and data review to surface opportunities. Those opportunities are ranked by their effect on revenue and margin.
Focus turns a collection of experiments into a portfolio with an order of operations.
Stage 5: Impact
The final stage is measurable business impact.
An AI initiative should connect to an outcome the company can observe. That may be revenue, cost, turnaround time, capacity, quality, or customer experience. The metric depends on the workflow.
A project that sounds impressive but has no baseline will be difficult to defend. Leaders need to know what the process looked like before AI and how the new approach performs.
For example, a real estate private equity firm’s comp analysis took 80 analyst hours per park. SageCreek built an agent that brings a park comp back the same day. The value was tied to a specific workflow and a clear before-and-after result. See more client work.
Impact is what separates useful transformation from activity that merely carries an AI label.
The stages work as a sequence
Companies do not always move through these stages in perfect order. A pilot may begin before leadership has full visibility. A policy may appear before anyone has clear ownership. A team may build an impressive tool without deciding how to measure it.
The framework helps leaders identify the missing condition.
If employees are experimenting but leadership cannot see it, work on visibility. If everyone has ideas but no one is accountable, establish ownership. If security concerns stop every pilot, create practical control. If the company has many pilots but little progress, narrow the focus. If projects ship without business results, strengthen measurement.
The next step should solve the constraint that keeps the organization from moving forward.
How to assess your current stage
Leadership can begin with five questions:
- 01Do we know how employees are using AI today?
- 02Is one person accountable for company-wide adoption?
- 03Do employees have clear tools and guardrails?
- 04Have we prioritized opportunities based on business value?
- 05Can we show measurable results from the initiatives we funded?
A weak answer points to the stage that needs attention.
SageCreek’s free AI Readiness Assessment gives businesses a quick view across data and systems, workflows, team skills, leadership, and opportunity.
Build the capability, not just the pilot
A successful AI project should leave the company stronger after launch.
The organization should understand the workflow, own the data, know who operates the system, and have a way to improve it. Teams need training and documentation. Leaders need a review cadence tied to business results.
SageCreek’s method moves from audit to workflow redesign, custom build, and team upskilling. Clients own the code, data, and intellectual property created for them.
If your company has AI activity but lacks a clear path to impact, begin with a free 30-minute AI opportunity call.
