Hands-On Executive AI Training

The Engagement

A technology consulting company, one that builds its own software products and provides consulting in a specific industry vertical, brought us in for two things: AI development support from our technical leadership, and something less common, hands-on executive AI training delivered directly to their non-technical staff.

My role is the second part. I sit down with executives, project managers, and other key personnel who have never used developer tools and teach them to use AI developer tools, Git, and AI-assisted workflows to do their actual jobs better. Not a webinar. Not a slide deck. Hands on keyboard, building real things.

Why Executives Need This

Most AI training for business leaders is abstract, “here’s what AI can do, here’s the strategic implications, here’s a framework for thinking about adoption.” That’s fine for board presentations, but it doesn’t change how anyone works on Monday morning.

The gap is practical. Executives who understand AI conceptually but can’t use it practically will always be dependent on someone else to implement their ideas. That bottleneck, the distance between “I want this” and “someone technical needs to build it”, is exactly what these tools are designed to collapse.

When a VP can open the repository in an AI development environment, describe what they need, and produce a first draft of a document, a process, or even a simple application without waiting for engineering bandwidth, the organization moves at a fundamentally different speed.

What the Training Covers

Every session produces a real deliverable, not a practice exercise. The documentation we create during training becomes the client’s actual operational artifacts.

AI Developer Tools

A standardized guide for getting non-developers working with AI inside a repository:

Installation and workspace configuration
How to talk to AI effectively (prompting for business outcomes, not code)
File management and navigation without terminal expertise
Common patterns: drafting documents, organizing information, building templates

Git for Non-Developers

Version control is the capability that makes everything else sustainable. But Git’s interface was designed by and for software engineers. The training bridges that gap:

Why version control matters (and what happens without it)
Core operations: clone, pull, commit, push, explained in business terms
Branch workflows without touching the command line
How to review changes and collaborate through pull requests

AI-Assisted Financial Analysis

The most recent phase moves beyond general productivity into something more specific: using AI to support executive-level financial review.

This means organizing financial statements into a structured folder system in the repository where AI developer tools can index and reference them, then building repeatable procedures that AI can run through for routine accounting and executive review, checking for errors, flagging anomalies, identifying areas for improvement, and surfacing the things that usually require someone to manually comb through line items.

On top of the review workflows, we built an action tracker so that findings don’t just sit in a report. Issues get logged, assigned, and tracked through resolution, with clear handoffs back to other executives or accounting staff. The goal is closing the loop between “we noticed something” and “someone acted on it.”

This is where my own background in operations leadership matters. I’m not just showing someone how to prompt an AI, I’ve sat in the chair where these financial statements land on your desk and you need to decide what to do about them. Understanding what an executive actually needs from a P&L review, what the common errors look like, and what questions the board is going to ask, that context shapes the workflows in ways that a pure AI training engagement wouldn’t produce.

Matching the Environment to the Work

The sessions also cover where to do the work, because different environments make different things inspectable:

AI developer tools in a repository keep plans, source documents, code, and reviewable changes together. They are useful for work that spans files and evolves through repeated planning, execution, and correction.
AI task assistants help with conversational work and tasks that involve connected services. The important question is where the resulting document, decision, or action will live and how someone will review it.
AI spreadsheet assistants work inside the workbook, where the tabs, formulas, and relationships can stay visible. For financial work, that lets the executive inspect the calculation instead of relying only on an answer in chat.

The demonstrations use real tasks so people can judge those differences for themselves. A repository gives multi-file work a durable home; a live workbook keeps its own formulas and controls. The training helps people choose an environment that preserves the context and makes the result reviewable.

AI Licensing & Rollout Planning

Beyond the hands-on sessions, we’re working through the organizational considerations:

Licensing plans for extending AI tools to remaining staff
Which roles benefit most from which tools
Data security and acceptable use boundaries
Measuring productivity impact to justify continued investment

The Approach

The key principle is that training sessions produce documentation, and documentation becomes the training material for the next wave of staff. It’s a self-reinforcing cycle:

Session happens, executive learns a workflow hands-on
Documentation is created, the steps, decisions, and context are captured in Markdown during the session
Guide is refined, the raw session notes become a polished onboarding guide
Next person uses the guide, reducing the need for one-on-one training as the library grows

This means the client isn’t dependent on us forever. The training engagement builds institutional knowledge that compounds over time rather than evaporating when the engagement ends.

Current Status

Active training sessions are ongoing. The engagement has progressed from foundational onboarding (AI developer tools, Git, AI prompting) into applied workflows, financial analysis, executive review procedures, and action tracking. The documentation library continues to grow, and tool demonstrations cover repository work, connected task assistants, and spreadsheet workflows. Next phases include expanding the training to additional staff tiers based on the licensing rollout plan.