If your organization is piloting AI, the headline number is hard to ignore: 95% of corporate AI pilots are failing. It’s tempting to blame the tools. But the truth is more uncomfortable—and far more actionable. Most AI programs don’t fail because the models are weak. They fail because leadership treats AI as a narrow technology rollout instead of an enterprise-level transformation.

In this post, I’ll break down three leadership missteps that quietly sink AI efforts—and how to correct course with practical moves you can implement this quarter. Along the way, we’ll anchor to three essentials for search and strategy: “AI for leaders,” “AI delegation,” and “conflict management with AI.”

1) Stop Treating AI Like an IT Project

When leaders frame AI as an IT initiative, they inadvertently limit scope, budget, and influence. AI touches decision rights, workflows, customer experience, and talent models. That makes it a cross-functional transformation—not a procurement choice.

Leadership move: Stand up a cross-functional steering group with clear decision rights across operations, finance, HR, legal, and IT. Define AI as a capability that enables strategic goals (faster cycle times, better service recovery, improved margins), not as a tool to “install.” This is the essence of AI for leaders: connecting AI’s potential directly to your value chain and KPIs.

2) Build Training That Maps to Real Work

Many teams have had some exposure to AI, yet only a fraction feel adequately trained to use it at work. That confidence gap creates two problems: employees either avoid AI entirely or adopt shadow tools that introduce security and compliance risk.

Leadership move: Design training around actual roles and use cases, not generic demos. Start with three high-leverage workflows for each function (e.g., FP&A narrative drafting, CX escalation summaries, sourcing and vendor analysis). Create repeatable AI delegation patterns that teach people how to brief the system clearly and consistently.

Why it works: Treating prompts as work instructions turns AI into a force multiplier. You shift from “try this cool model” to “here’s how we reduce time-to-decision by 40% in this process.”

3) Fix “Garbage In, Garbage Out” with the RACE Prompt

AI acts like a genius-level intern with zero common sense. It can analyze, draft, and reason—but only within the boundaries you set. Vague asks lead to vague output. That’s why a standard prompt structure is essential. Use RACE—Role, Action, Context, Expectation—to make your delegation crystal clear:

• Role: Who is the AI for this task? Name the perspective and seniority so the voice and approach fit the job.
  Example: “You are a senior customer experience analyst.”

• Action: What do you want done, specifically? Use verbs that describe the work product.
  Example: “Synthesize this week’s top five CX escalations and propose two root-cause hypotheses.”

• Context: What background, data, and audience details matter? Include inputs, constraints, and any known caveats.
  Example: “Use the attached ticket summaries and NPS verbatims; audience is the COO and regional leads; we care about cycle time and refund rate.”

• Expectation: What does “good” look like? Define format, tone, length, success criteria, and guardrails.
  Example: “Return a one-page brief with bullets and a 100-word executive summary; cite ticket IDs; flag any data gaps.”

Standardize RACE across your org. Embed it in templates, knowledge bases, and intake forms so every AI task starts with the same clarity. Leaders who model RACE in their own requests dramatically improve quality, speed, and reproducibility.

Mini example (RACE in action):
Role: You are a procurement analyst.
Action: Compare three vendor proposals and highlight risks.
Context: Use the attached pricing tables; we’re prioritizing time-to-implement over lowest cost; audience is the CFO.
Expectation: Produce a comparison table and a 200-word recommendation; call out data gaps and assumptions.

Managing the People Side: Conflict Management with AI

Adoption introduces friction—between teams, between old workflows and new ones, and sometimes between employees and the technology itself. Conflict management with AI is a leadership competency: you’ll need to surface concerns early, design fair guardrails, and create feedback loops.

Leadership move:

• Clarify what’s changing (roles, metrics, skills) and what isn’t (values, accountability).

• Create shared standards for AI-assisted work (attribution, review checklists, data hygiene).

• Establish escalation paths for issues like data leakage, model bias, and hallucinations.

• Celebrate wins tied to business outcomes, not novelty.

When people see AI reducing toil and improving decisions—and see leaders handling conflicts transparently—resistance drops.

A Simple 30-Day Plan

Week 1: Define two business outcomes where AI can move the needle (e.g., reduce case backlog, accelerate monthly reporting). Form the cross-functional steering group.

Week 2: Map one workflow per function to that outcome. Draft RACE prompt templates for those workflows. Identify required data and approvals.

Week 3: Pilot with a small cohort. Pair every pilot with a review ritual: human-in-the-loop checks, error logs, and quality thresholds.

Week 4: Document lessons learned, adjust RACE templates and guardrails, and roll out targeted training. Publish “golden RACE prompts” and examples for others to reuse.

This plan positions AI for leaders as a business accelerator—not a science project.

The Bottom Line

AI success isn’t about finding the “perfect” model. It’s about leadership that treats AI as a capability, invests in effective training, and masters AI delegation through the RACE framework so the work is clear and repeatable. Pair that with intentional conflict management with AI, and your team can join the 5% that are winning—consistently.

Ready to make that shift?

👉 Free AI Ready Leader’s Cheatsheet: https://renaldo-jones-f71e.mykajabi.com/prompts
👉 AI Ready Leader Starter Kit: https://renaldo-jones-f71e.mykajabi.com/next-step