From my associate, Grant Tate.
By now, A.I. isn’t new. In fact, nearly nine out of ten organizations report using A.I. in at least one business function. But if you’re looking at your bottom line and wondering why all these fancy tools haven’t revolutionized your revenue or profit yet, you aren’t alone.
We are seeing a massive “value gap” right now. While 88% of companies are using A.I., only about 7% have actually scaled it across their enterprise. The rest? They are stuck in what’s being called “pilot purgatory”. This is that frustrating place where you have lots of experiments and cool demos, but very little impact on how the business actually runs.
So, how do you break out? The companies actually winning with A.I.—the “high performers”—aren’t just using it to save a few bucks or automate boring tasks. They are using A.I. to fundamentally redesign how work gets done.
If you want to move your business from dabbling to dominating, here is a five-step playbook based on what the topper formers are doing right now.
1. Don’t Hire; Upskill
You can’t transform your work if your team doesn’t understand the tools. Instead of trying to hire expensive A.I. experts, look inside your own building. You need to create learning paths for your team.
• For the techies: Teach them about integrating models and data quality.
• For everyone else: Show them concrete use cases in their specific domains, like HR or Sales. The goal is to get your team to stop looking at A.I. as a threat and start seeing it as a lever to fix slow, error-prone processes.
2. Build “Sandboxes” (Because Safety Matters)
People won’t innovate if they’re terrified of breaking something or leaking secrets. You need to define what is allowed and what isn’t.
• Create Policy: Be explicit about what data (like customer info) is off-limits for public A.I. tools.
• Create a Sandbox: Give employees a secure environment where they can experiment with internal data without risking a leak.
• The Golden Rule: Make sure everyone knows that A.I. output must be checked by a human before it goes to a client. No exceptions.
3. Kill the Silos
It is all too common for the marketing team to be streamlining content with A.I. while HR is drowning in manual paperwork, completely unaware that better tools exist. Don’t let A.I. be a private experiment. Set up a monthly “A.I. Council” or a casual “Lunch and Learn” where teams can compare notes, share prompts, and admit what didn’t work. When leaders show up to these meetings, it signals that this is core to the business, not just a side hobby.
4. Focused Pilots Only
Avoid “random acts of digital”—like building a chatbot just because it looks cool. Pick a pilot project that attacks a real friction point, like slow customer service triage or software testing. Most importantly, define success before you start. Ask yourself: “If this works, how do we roll it out to the whole company?”. Without a plan to scale, it’s just a science fair project.
5. Redesign, Don’t Just Optimize
This is the big one. High performers are three times more likely to use A.I. to redesign workflows rather than just automating existing steps. Don’t ask, “How can A.I. help us write this email faster?”. Ask, “With A.I., do we need this email thread at all, or can an agent handle the whole process?”
The opportunity isn’t just to do the old work faster. It’s to do different work altogether.
So, take a broad look at your business. Set some progressive but practical goals, and help your team learn and align with your new vision for A.I. transformation.
Here is the One-Page A.I. Pilot Worksheet. It is designed to be a printable or digital resource that forces clarity before a project begins, ensuring you don’t fall into the “random acts of digital” trap.
The One-Page A.I.Pilot Worksheet
Goal: Move from “cool demo” to real business value by defining the problem, the redesign, and the path to scale.
Part 1: Select the Right Candidate
Don’t start with the tool (e.g., “Let’s use a chatbot”). Start with the friction.
Criteria Checklist: Is the task/processyou want to fix:
• Frequent, repeatable, and well-understood?
• Costly, slow, or error-prone?
• A source of visible pain for customers or employees?
The Problem Statement:
What specific problem are we solving?
Part 2: The”Redesign” Challenge
High performers don’t just automate; they redesign. Before you build, challenge the assumption.
The Old Question:
“How can A.I. help us do this task faster?”
The Transformation Question:
“Do we need this manual step at all, or can an A.I. agent handle the workflow end-to-end with human oversight?”
Your Redesign Strategy: Describe how the workflow changes. Instead of just speeding up the old way, how does this create a new way?
Part3: Define Success
If you don’t measure it, you can’t manage it.
Key Metrics (KPIs)
Examples: Handle time, error rate, NPS, time-to-first-draft, cycle time.
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Part 4: The”Scale-Up” Plan
Avoid “Pilot Purgatory.” If this works, what happens next?
If the pilot meets the success metrics above:
• Which teams are next?
• What trainning will be required for them?
• What technical changes are needed to support the rollout?
Quick Tip: IT, customer service, and marketing are currently the most successful starting points for A.I. deployment. If you are stuck, look there first.