Shadow AI Inventory
Welcome back to Savin on Technology Governance.
When inventory is mentioned, the first thought is usually goods in warehouses, supplies on shelves, or physical counts performed to reconcile inventory records with reality. A Shadow AI Inventory is different. It isn’t a list of goods, assets, or software licenses. It is an inventory of AI usage inside a business, especially AI usage that management may not be aware of. A traditional inventory asks, “What do we have?” A Shadow AI inventory asks, “How is AI actually being used in our business, by whom, with what data, and with what risk?” The discussion below delves into differences between traditional inventories and Shadow AI inventories.
a. The Focus Is Different
A traditional inventory focuses on actual items: products, materials, equipment and other assets; whereas Shadow AI focuses on employee practices and business usage of AI. The issue is not simply whether the company owns a particular AI tool. The issue is whether employees are using AI to perform work, make decisions, draft documents, analyze data, communicate with customers, or support business processes.
With a physical inventory, management usually has some knowledge of the items involved and their approximate quantities. With Shadow AI, the purpose is often discovery. Management may not know which AI tools are being used, how often they are being used, or whether they are being used in sensitive areas of the business.
b. The Content is Different
A traditional inventory includes physical goods, equipment, computers, devices and other business assets. A Shadow AI inventory includes different kinds of information, such as AIs, their features, prompts, business use cases, inputs and outputs, workflows and decisions. These are not characteristics of typical business assets. The difference may be like the difference between computer hardware and computer software.
c. The Basic Unit is Different.
In regular inventories, items and their counts are the basic units. For example, 250 units of Product A in the main warehouse. In a Shadow AI inventory, the basic unit has multiple dimensions, such as
+ AI tool, including version
+ User(s)
+ Business Use Case
+ Business Process
+ Prompt(s)
+ Input Data
+ Output (Type and Nature)
+ Whether or not Output is Reviewed by Humans
+ Risk Level including Impact, Likelihood, Severity
For example, a financial analyst may be using a Frontier AI, such as ChatGPT, Claude, Gemini or other AI, to summarize budget variances using internal financial data where the output is included in management reports and is used to make operational and funding decisions.
d. Visibility is different
Traditional inventories involve items that are visible including records related to purchasing, location, sales and so on. Shadow AI is less visible because it involves behavior. Employees may use AI for a variety of business and personal reasons. Some of the usage may be unofficial. Some uses may be well-intentioned but not formally approved. Some uses may not even be thought of as AI usage, especially when the AI is built into systems already in use. All of which make Shadow AI hard to detect and manage.
e. The Risks are Different
Traditional risks include loss, theft, obsolescence, expiration, miscounts, and un-located differences between physical counts and records.
Shadow AI risks are broader and less tangible. They may include leaking confidential data; exposing customer or employee information; exposing intellectual property; inaccurate or misleading outputs; bias, inequity or unfair treatment; compliance violations; poor documentation; lack of auditability; unapproved decision support.
In a Shadow AI inventory, the risks are more diffused because AI can affect multiple people, systems, data sources, decisions, and business processes.
f. Ownership is Different
In traditional inventories, responsibility is usually clear. The Inventory is owned or controlled by specific departments be they warehouse operations, inventory control, purchasing, production control, asset management or finance.
With Shadow AI, ownership may be unclear. AI usage may be spread across multiple departments including finance, human resources, sales, marketing, operations, legal, customer service, IT, and administration. A department may use AI without formally “owning” the AI. An employee may use AI for business tasks without management approval. A vendor system may include AI features that were never separately evaluated. This creates a challenge: responsibility is both dispersed and unofficial.
g. Controls are Different
Traditional inventory controls usually address purchasing, receiving, storing, issuing, shipping, counting, recording, and reconciling goods. In contrast, Shadow AI controls must answer a different set of questions, including
Is the AI tool approved?
What data may employees enter?
Is confidential information protected?
Are outputs reviewed by qualified people?
Is the AI being used for high-risk decisions?
Are prompts and outputs documented when needed?
Is the AI use legal, ethical, accurate, and appropriate?
Does the organization understand the vendor’s privacy and security terms?
The control objective is not just “Is the item there?” The Shadow AI Control objective is “Is the AI being used responsibly?”
h. Frequency is Different
Physical inventories are usually periodic, potentially annually, quarterly, monthly, or on a cycle-count basis.
A Shadow AI inventory is more dynamic. AI tools, features, vendors and employee practices are changing quickly. New AI tools appear frequently. A new collection of AIs have emerged from China. While the US AIs may be more powerful, the Chinese AIs are cheaper. From a business perspective, is having a cheaper AI better than having the latest most powerful AI? Existing tools and applications add AI features. Employees experiment with new use cases. Business processes change.
What previously took months to develop may now take days or weeks to develop. As a result, Shadow AI inventory is an ongoing process, not a once-a-year exercise.
i. Evidence is Different
Traditional inventory evidence includes physical counts, item records, purchase documents, receiving records, shipping documents, and warehouse records.
Evidence of Shadow AI usage is often indirect. It may come from many sources: Employee surveys, department interviews, workflow reviews, IT logs, browser extension reviews, software usage reports, expense reimbursements, vendor invoices, procurement records, Internal Audit reviews, security monitoring, AI-generated work products.
The evidence is less direct, requiring a broader and more thoughtful discovery process.
j. Value is different
Traditional inventories have items with both direct market value and indirect operational value, such as the ability to manufacture other goods. Also, traditional goods and services have a reputational value where businesses are known based on the quality of their goods and services.
Shadow AI’s value is different. It may improve productivity. It may improve accuracy. It may result in fast analysis. It may result in better communications. It may improve customer service. It may reduce administrative burden. It may result in better knowledge management. It may result in more innovation. It may improve decision making.
Traditional Inventory is about Things; whereas Shadow AI Inventory is about Usage, Behavior, Implications and Impacts. This distinction matters. A business may have excellent physical inventory controls and have little visibility into how employees use AI.
The ultimate question is benefit, value versus risk and potential damage. As with most things, real value comes from attaining benefits while minimizing harm. The lack of visibility can create hidden risks, but it can also hide valuable opportunities for productivity and innovation.
The goal of Shadow AI Inventory is not to stop AI usage but to acknowledge it, to understand its usage and the potential so that business can reap the benefits of AI while reducing potential harm, in one word, managing AI like the business manages other capabilities and challenges.
If you have issues related to IT Technology implementation or utilization, including AI, feel free to email them to us. We are happy to address them in future blog posts.
Contact Information: Jerald Savin, CEO, Cambridge Technology Consulting Group, Inc. Telephone: 1+310-229-8947. Email: jsavin@ctcg.com.