HolderComm AI Transformation, Digital Strategy & Creative Services

Reflections from the Front Lines of Organizational AI Transformation

By Eric Holder, HolderComm AI Transformation Consultant

Introduction: A Real-World Path into AI Transformation

My path into AI transformation didn’t start in a lab. It started in the practical corners of search marketing and cloud computing—where I saw firsthand how machine learning was reshaping how we surface content, make decisions, and deliver value. From early work with Dell/EMC’s Virtustream and contributions to Forrester’s Cloud Trends 2018, to building GTM systems for platforms like Ivanti and AMI, I’ve had the privilege of seeing AI mature—from buzzword to backbone.

In the years since, I’ve worked across the transformation lifecycle: strategy, product, enablement, operations, and communications—helping teams apply AI in ways that create clarity, unlock scale, and build resilience.

What follows are the lessons I’ve learned along the way.

1. Executive Enablement Isn’t Optional. It’s the Starting Point.

In most organizations I’ve worked with, AI initiatives start with innovation teams or technical leads. But they stall when leadership isn’t directly engaged.

I’ve found the most effective accelerant is a simple one: put executives in direct contact with real tools and real business data. Skip the decks. Pose a challenge. Let them experience the outcome.

When executives feel the potential of AI in their own workflows, buy-in becomes natural—not forced. That early exposure helps set a tone of curiosity and confidence that cascades through the organization.

2. Prompting Is a Core Business Skill—Not Just a Technical One

The best models won’t help if employees don’t know how to interact with them. Prompting has become a foundational skill—just like writing a brief, running a meeting, or interpreting a P&L.

I run compact workshops where participants test poor prompts, then apply a structured framework to improve outcomes. The transformation is immediate—and sticky. When people see the difference they can create, adoption follows.

Prompting isn’t just a technical function. It’s a thinking tool. One that every team—marketing, finance, ops—needs to build into their playbook.

3. Use Case Discovery Is a Ground Game

Strategy decks often outline the big bets—but the most meaningful adoption usually starts with small, high-frequency tasks.

That’s why I focus on empowering “super users” across functions—people who can identify, refine, and demo everyday use cases. A recruiter using AI to summarize resumes. A marketer speeding up creative briefs. A finance lead using AI to reconcile vendor contracts.

These real, repeatable wins create momentum. And they reveal the true shape of transformation—not hypothetical, but operational.

4. Build Around Champions—But Give Them Structure

Champion networks are a smart idea. But without coordination, they burn out. Instead, I help organizations install lead champions—typically senior ICs or managers—who receive structured support from a central enablement team.

This creates a local multiplier effect while keeping the effort aligned with overall governance and strategy. It’s the difference between inspiring adoption and sustaining it.

5. Strategy Needs Structure—Not Just Aspiration

There are now proven frameworks for organizing AI transformation. I often bring in tools like the AI Canvas, AI Radar, and Capability Maturity Model (CMM) to help companies assess readiness, prioritize initiatives, and measure impact.

These tools don’t just provide clarity. They give stakeholders a shared language, which is critical when you’re trying to connect teams from IT, operations, marketing, and compliance.

6. Governance Is the Real Enabler

In my experience, governance is what separates experimentation from execution. And the most mature companies treat it as an enabler, not a barrier.

The best governance models I’ve seen are integrated into workflows, not layered on top:

• Prompting guidelines built into tools

• Metadata tagging and audit trails

• Lightweight review checkpoints

• Embedded principles of responsible AI

Especially when working in regulated environments or on customer-facing systems, these structures create room for experimentation without risk overload.

7. Focus on Capability Expansion—Not Just Cost Reduction

Yes, AI can reduce costs. But its real power lies in expanding what people and processes are capable of. I’ve seen AI help teams:

• Personalize content at scale

• Automate contract reviews

• Accelerate product research

• Deliver better customer support, faster

The common thread: these are not tech projects. They are business capability upgrades—driven by people who know their workflows and now have better tools.

Closing the Loop: From Signal to System

I started in AI by helping people find what they were looking for. Today, I help organizations define where they’re going—and how AI can help them get there.

I’ve worked across industries, alongside leaders and engineers, inside startups and enterprise ecosystems. And while the tools have changed, the most important questions remain the same:

• Are we building habits, not just hype?

• Are we enabling people, not just buying platforms?

• Are we solving for scale, not just the pilot?

If the answer is yes, then the transformation is real.

AI isn’t the next initiative. It’s the next operating system. And like any good system, it needs the right inputs: clarity, structure, and people who know what to do with it.