Editorial
Executives don’t need mythology – we need a plan we can run this quarter and defend at the board table. The speed and complexity of today’s decisions have outpaced the way most companies operate. When used smartly, AI moves from a strategy to a force multiplier.
The plan is straightforward: Show what AI moved. That means balancing ambition with accountability, innovation with guardrails, and making change feel safer (and faster) for your people.
Table of Contents
- Start With People, Not Models
- The Five "Es" – Separating Talk From Traction
- AI Governance You Can Actually Run
- Bottom-Up Energy, Top-Down Clarity
- Two AI Business Cases to Run This Quarter
- The 30-60-90 Day AI Plan
- What the Best Leaders Do Differently
Start With People, Not Models
Here’s the reality we see across boardrooms and shop floors: the fear of AI is real. If leaders don’t address it head-on — what it means for jobs, careers and identity — people retreat. The result is that the input we need from the front lines goes dark.
Education is the antidote. Teach the “why,” show the “how,” then prove the “win” in small, low-risk ways. When people experience value, momentum follows.
At Walk West, the Raleigh-based digital agency where Donald is Executive Board Chair, the leadership team under CEO Greg Boone made a simple decision: train everyone [Really, we are not kidding here!]. The outcome: 98% of the team completed first-level AI training. Many have a handful of certifications (or more). The rationale is evident: Disruption is on the horizon and Walk West would rather disrupt itself than wait to see what happens in the future.
Clearly, we advocate for education and training as an ongoing foundation for workplace excellence. But, let’s take a few minutes to offer ways senior leaders can start AI work if they are just beginning or still uneasy about the role the technology will have in their industry.
The Five "Es" – Separating Talk From Traction
If you want AI to move the needle on revenue, cost and risk, run these five moves like an operating cadence: Education, Experimentation, Economic Value, Ethics and Execution:
- Education – Build AI literacy across roles. Leaders narrate their own learning curve and get the workforce excited when they do so.
- Experimentation – Form cross-functional “curiosity pods” (IT, data, operations, legal, risk, etc.) to test use cases and share results.
- Economic Value – Define ROI timelines and the metrics that matter. You don’t have to solve the biggest challenges at the get-go. Start small and scale accordingly.
- Ethics – Declare standards for bias, transparency, privacy and data use.
- Execution – Start with low-risk, high-impact pilots. Then test, learn and scale.
Just for fun, let’s add an important sixth “E” – Enormity. We reference enormity, because we realized (from firsthand experience and in our consulting work) that leaders are being asked to do more with fewer resources and — frequently — fewer employees. The 5E system we outline is purposely a guideline with guardrails.
AI is enormous, but leaders should not fall into the fight against time. Our counsel is to approach AI projects and implementations with transparency and caution. Slow and thoughtful will beat out fast and chaotic. Like any experimentation, be deliberate to align resources with outcomes.
AI Governance You Can Actually Run
Ethical guidelines must be the bedrock. Most companies need a lightweight, repeatable AI governance system that fits on a page and survives real-world pressure.
The following two tools can do most of the work:
1. RACI for AI use cases
- Responsible: The use-case owner (often Ops/Product) and the AI/Analytics lead.
- Accountable: The business/P&L leader who owns value and risk.
- Consulted: Security, Legal/Compliance, Finance (benefits validation), Data Steward, Marketing and Internal Communications.
- Informed: Internal Audit and Communications.
2. Stage gates for speed and scale
- Intake & triage (potential value, risk, ROI; spell out the RACI).
- Feasibility (security and data access; architecture choice – prioritize retrieval-augmented generation before any costly model training).
- Pilot (clear KPIs for cycle time, quality, cost; human-in-the-loop review criteria; logging and audit trail).
- Scale (only after results meet targets, controls prove out, users are trained, and Finance validates benefits).
- Run & monitor (ongoing quality/bias/drift monitoring, incidents, periodic re-certification, and a defined kill-switch).
Pair every use case with a one-page Use-Case Card (problem, KPIs, risks, RACI) and a Human Oversight Plan: who reviews, when to stop and how to fix. Create governance standards on day one for anything touching customers, revenue or regulated data.
Bottom-Up Energy, Top-Down Clarity
AI cannot be a command-and-control rollout. Create curiosity pods, small groups who experiment, document wins and demo to colleagues. Peer-to-peer communication converts skeptics faster than memos.
Your job at the top is to tell the truth about why AI adoption matters. You will make huge strides if you share your own usage (and wins/challenges) openly. If you want belief, show your receipts: where you used AI last week, what worked and what you learned.
Two AI Business Cases to Run This Quarter
1. Assume an AI filter reads your RFP before a human. Run an AI proposal readiness check first.
In many enterprise deals, your response is parsed by a machine before a person deep-reads it. If you don’t understand how the system scores clarity, completeness and risk language, you’re invisible before the conversation starts. Build an internal checklist that forces precise claims, structured benefits and references that are easy for AI scorers to parse.
2. Faster, safer experiments with zero exposure
Take two years of closed-won/closed-lost (or claims) data and run a simulation: “What would we have decided with AI-assisted analysis?” Your team may discover where cycle time could have been compressed, error rates spiked and which signals most correlated with outcomes, all without touching a live process.
The 30-60-90 Day AI Plan
Days 1-30: Set the lanes and lower the fear
- Name an accountable executive sponsor per use case and a small responsible pod (analytics lead + business owner). Publish your RACI and Stage Gates so everyone sees how decisions get made.
- Launch a 60-minute AI literacy session for all managers. Then, record and reuse.
- Pick three low-risk pilots tied to revenue, cost, or risk. Write one-page Use-Case Cards and Human Oversight Plans for each.
- Start a biweekly Show-the-Work – five-minute demos from the pods. Normalize learning in public and get your Comms team onboard to build out the findings across the organization.
Days 31-60: Pilot for signal, not theater
- Run pilots in a sandbox; hold weekly check-ins on KPIs, quality, bias and user feedback.
- Capture “before/after” metrics (cycle time, error rate, and dollars saved/earned).
- Train reviewers on human-in-the-loop standards: what to check, when to escalate, and when to kill.
Days 61-90: Decide, scale or stop
- Move one pilot to limited production with controls. Archive the others and the lessons.
- Publish a one-page “AI Operating Note” to the company: what we tried, what worked, what’s next.
- Budget for year-one enablement: education, governance and selective tooling, so wins are repeatable and safe.
What the Best Leaders Do Differently
The best leaders make AI personal. They connect new tools to individual growth, thinking, “This will make me faster, give me time for strategic work and make me more valuable here and across my career.” That framing turns resistance into curiosity.
Then, they back the AI mindset with training, small wins and visible standards for quality and ethics. To bring out the best in high-performers, the executive can turn to gamification and openly keep score. Then, share the metrics that matter: cycle-time reduction in underwriting or service, hours returned to sales, proposal win-rate lift, and audit exceptions avoided. When people see progress, momentum compounds. Get the competitive juices flowing in a light way that energizes teams while they learn.
AI is already changing how we sell, serve, decide and lead. Most executive teams we meet are piloting the technology somewhere in the business. Many are seeing real operational savings and speed gains, particularly when they pair ambition with governance and invest in their people.
You are trying to figure out AI. Your people are hearing and seeing a lot of positives and negatives about the technology. You may or may not realize the depth, but you do know AI is going to transform your organization. Pull those threads together and it is time to step up. Layer in clarity, empathy and accountability and you will avoid the missteps many leaders are experiencing.
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Editor's Note: Want more advice on how to use AI to deliver results?
- The Human Agency Scale: The AI Strategy Framework Business Leaders Need — To automate or not is no longer the question. The question is: what kind of partnership between AI and people will help this work shine?
- How Businesses Are Turning Generative AI Into Measurable Value — Successful GenAI rollouts share at least one thing in common: the initiative starts by identifying and aligning with a core business goal.
- EZCater's Mark Christianson on Building an AI Mindset to Drive Adoption — EZCater's senior manager, digital workplace and AI strategy discusses their efforts to encourage AI adoption, with the goal of making every team an R&D hub.
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