The AI Audit

The CTO of a $200 million manufacturing company got a simple question from his board last month: "What AI tools is the company currently using?"

Fifteen seconds of silence. Then: "Well, we have that ChatGPT enterprise license. And I think the marketing team bought something for content. Beyond that..." He shrugged.

A leader responsible for $12 million in annual technology spending couldn't tell his board how the organization was using artificial intelligence.

Most executives today face the same problem. They approved AI budgets. They championed digital transformation. When pressed for specifics about their AI footprint, they can’t answer definitively.

The shadow IT problem has evolved into shadow AI. And most companies are flying blind.

The New Shadow IT Crisis

Remember when marketing bought its own CRM without telling IT? When the sales team started using their own lead gen tools? When every department had its own cloud storage solution?

Shadow IT felt manageable back then (kind of). Worst case, you had some data silos and integration headaches.

Shadow AI carries different risks.

Individual contributors are signing up for AI writing tools with corporate credit cards. Marketing teams are feeding customer data into third-party AI platforms. Sales reps are using AI to analyze prospect emails. Finance is experimenting with AI-powered forecasting models. The CFO is dropping the company's financials into ChatGPT (!!).

Each decision seems harmless in isolation (except the CFO’s bungling of financials). Together, they create a compliance nightmare.

Take data governance. Most companies spent years building frameworks around where customer data lives and who can access it. AI tools blow through those guardrails in seconds. It happens every day when people upload a customer list to an AI platform for analysis, export conversation transcripts to train a chatbo and/or feed CRM data into an AI forecasting tool.

Where does that data go? How long is it stored? Who else has access? Most AI tool providers can't answer those questions clearly. Your employees certainly can't.

The Real Cost of AI Blindness

The immediate problem is financial waste. Companies are paying for redundant AI subscriptions across departments. Three different content generation tools. Two customer service chatbot platforms. Multiple AI analytics solutions that analyze the same datasets.

The deeper problem is strategic. You can't optimize what you can't measure. You can't scale what you can't see. You can't govern what you don't know exists.

AI spending without oversight becomes random acts of automation. Teams solve local problems with AI tools that create global complications. Marketing builds an AI-powered lead scoring system that conflicts with the sales team's AI prospecting tool. Operations implements AI inventory management that fights with the finance's AI demand forecasting.

Each team thinks its AI solution is working. And they're probably right, for their specific use case. The company pays the price through duplicated efforts, inconsistent outputs, and competing systems.

The Audit That Never Happens

Most companies approach AI governance backwards. They write AI policies before they understand their AI reality. They debate ethical frameworks while teams are already using AI to make customer-facing decisions. They plan AI strategies without knowing which AI tools are already embedded in daily operations.

A proper AI audit reveals three things executives hate to discover:

First, your AI spend is probably 40% higher than you think. The $50,000 you approved for the enterprise ChatGPT license represents maybe half your actual AI expenditure. Factor in all the individual subscriptions, API usage, and one-off tool purchases, and you're looking at serious money.

Second, your data is in more places than you know. Every AI tool your teams use creates a new data endpoint. Customer information, financial projections, operational metrics—all flowing through systems you may not even know exist.

Third, your teams are making AI-powered decisions you haven't approved. That sales forecast your board reviewed was partially generated by AI. Those customer service responses? AI-assisted. That financial model? Built with AI-powered analysis.

You're already an AI company. You just don't know it yet…and you may not want to be an AI company yet.

A Framework for AI Visibility

Before you can govern AI, you need to see it. Before you can optimize it, you need to measure it. Before you can scale it, you need to understand what's already working.

Start with the three-layer audit.

Layer One: Financial Tracking 

Pull credit card statements and expense reports for the past six months. Search for payments to obvious AI companies—OpenAI, Anthropic, Jasper, Copy.ai, Notion AI. Also search for subscription services your teams might be using for AI capabilities. Zapier, Monday.com, HubSpot…many SaaS tools now include AI features that your teams are paying for and using.

Calculate your total AI spend. Most executives underestimate by at least 60%.

Layer Two: Tool Discovery 

Send department heads a simple spreadsheet with three columns: Tool Name, Primary Use Case, and Monthly Cost. Give them one week to audit their teams. Don't ask for justifications or business cases yet. Just get visibility.

You'll be surprised what surfaces. AI-powered design tools in marketing. AI writing assistants in legal. AI data analysis platforms in finance. AI scheduling tools in operations. Your organization is probably using 3-4x more AI tools than you think.

Layer Three: Decision Mapping 

This is the hard part. For each AI tool, ask:

• What decisions does this influence?

• Customer communications?

• Financial projections?

• Operational planning?

• Strategic analysis?

Map the decision flow from AI input to business outcome. You'll discover your organization is already running on AI-powered insights. The question now becomes whether you want to manage that reality or let it manage itself.

The goal of an AI audit is not to eliminate AI usage. Teams are using these tools because they work. The goal is to eliminate waste, reduce risk, and create alignment.

Start consolidating redundant tools. If three departments are paying for similar AI capabilities, negotiate an enterprise contract that serves all three. You'll save money and gain oversight.

Establish data governance for AI tools. Not bureaucratic approval processes—clear guidelines about what data can go where, and simple tools to track usage. Most AI platforms offer enterprise features for data residency and access controls. Use them.

Create feedback loops between AI experiments and business strategy. The AI tool the marketing team discovered might also solve problems in operations. That sales AI platform might generate valuable insights for product development. You can't connect the dots if you can't see the dots.

The Bottom Line

Every company is becoming an AI company. The question is whether you're doing it intentionally.

Right now, your teams are using AI tools to write emails, analyze data, generate content, and make predictions. They're feeding your proprietary information into third-party systems. They're making decisions based on AI outputs you've never seen.

That's not necessarily bad. But it's definitely expensive. And it's definitely risky.

An AI audit gives you three things:

1. Visibility into your actual AI spending

2. Understanding of your AI-powered decisions

3. Control over your AI future.

Most executives wait until something goes wrong to audit their use of AI. A data breach. A compliance violation. A major strategic misalignment.

The smarter move is to find out what you're already doing with AI before the board asks.

Because they will ask. And "I think the marketing team bought something" isn't an answer that any board member wants to hear.

If you're ready to understand your organization's actual AI footprint, grab some time on my calendar, and let's chat.

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