Your Cloud Drive Is Not How AI Learns Your Business

The hard work behind AI transformation starts with how your company stores knowledge and information.

If you’ve tried AI, followed the published playbook, and still don’t find it useful, the problem is likely the way your company has been storing knowledge and information for years.

I have met many teams that are serious about implementing AI. Leaders sign contracts with AI vendors, roll out copilots, send people to conferences and workshops, and ask every function to “try AI in their workflow.” It looks like the company is moving fast with AI. People talk about models, tokens, and new tools in meetings.

Then the leaders come back with the complaint.

“We plugged our data into AI, but the answers are still vague, confusing, or wrong.”

So they start looking for a new model, vendor, or platform.

Most of the time, when AI “doesn’t work,” the model is often not the core issue. The issue is the knowledge and information your company has (or hasn’t) put behind it.

Connecting an AI platform or tool usually takes about 10% of the effort. The other 90% sits in something far less exciting: cleaning up your messy data, making your hidden data visible, and fixing the way information is stored and connected. This is the main bottleneck for adopting and using AI in most companies. People know it is inefficient and sometimes not working, but they do not want to touch it, because fixing it means significant change and a lot of work that nobody wants to own.

The First Mistake: Treating Your Cloud Storage As A “Knowledge Base”

Photo by Solstice Hannan on Unsplash

When people hear the phrase “knowledge base,” most of them still think of a shared folder. For years, the pattern looked the same. Each team created its own shared drive, such as admin, IT, sales, HR, marketing, product, and finance. Everyone filled their own folders with Word documents, Excel files, PowerPoints, PDFs, and images. When new employees joined, they were told, “If you need information, go search the drive.” This was what most companies called a knowledge base.

Now that AI tools are everywhere, the habits have not changed much. Many teams simply take the same files and upload them into an approved AI tool. The documents are messy. The spreadsheets still rely on merged cells and color codes. Key decisions sit in comments or hidden tabs. Acronyms and internal nicknames are everywhere. Only a few people in the company really know what each file means.

When they start using AI, this is what usually happens. They connect these drives to an AI tool, give it permission to read, and expect accurate answers to questions like:

“Explain the difference between our A product line and the B series.”

“Summarize the latest moves from competitor C.”

“Show me everything that happened with customer D in this quarter.”

The model tries to make sense of a dozen different names for the same product, a few different spellings of the same customer, and multiple versions of the same slide. It has to guess which one is right. The answers come back vague or inconsistent. People complain, “AI is not working. I don’t trust the answers. Let’s try a different tool or just go back to the way we work today.”

I have seen this play out in many AI pilots. A team spends months picking a vendor, connects it to the shared drives, runs a few impressive demos, and then hits a wall the moment someone asks a specific business question. Suddenly no one can say which product list is the latest, which customer name is correct, or which competitor code belongs to what. People blame “AI maturity” instead of admitting the basic model of how the business stores knowledge and information was never there.

In reality, most people are feeding AI digital garbage and expecting it to give them intelligent answers back. If a new hire cannot understand your slides and spreadsheets without asking someone to explain them, AI will not understand them either. The biggest misconception is treating AI like a mind reader that can see through hidden information and internal acronyms. It cannot. It only sees what you provide and understands what you’ve made clear and easy to interpret.

The Fix: Making Knowledge and Information Visible So the key question is not “Which AI model should we use?” or “Which AI tool or vendor should we buy?” It is “What are we actually giving the AI model to learn from?”

In most companies, three types of knowledge and information tend to stay hidden.

1. Domain Knowledge This is the experience that lives only in the heads of employees. It includes unwritten rules, small tricks that make processes work, and “how we really handle this client or partner” details. There are also standard operating procedures that people follow but almost never write down, and insider language that everyone uses, but no one defines. However, this type of knowledge is crucial for understanding the nuances of the business, but its undocumented nature makes it invisible to AI systems.

2. Scattered Information The same product description and data might show up in three different PDFs and five Excel files, all with slightly different names. Market definitions vary across teams. Important tables are copied and pasted multiple times instead of linked, so no one knows which version is current. Each person maintains their own file on a shared drive and calls it a source of truth. This duplication and fragmentation make it difficult for AI to identify and integrate relevant information, leading to inaccurate or incomplete insights.

3. Inconsistent Information This shows up as inconsistent product codes, different spellings for competitor names, various shorthand labels for the same customer, and tables that mix formats and categories. One file uses a nickname, another uses the legal name, and a third uses an internal code. While humans can often infer the relationships between these data formats, labels, and categories, AI systems struggle to do so, leading to errors and misinterpretations.

When this hidden knowledge and information stay the way they are, your data model looks more like an overstuffed junk drawer. Things are hard to find and even harder to explain.

So before you think about improving prompt engineering, you have to think about visibility, accuracy, and consistency. You need to bring hidden, scattered, and non-standard knowledge and information into a clean business data model, so AI can actually use and learn from it.

What A Data Model AI Can Use Looks Like If a cloud drive full of files is not enough, then what does an AI-friendly business data model look like?

You can think of it in two layers: the structure that holds everything together and the detailed information that fills it in.

The structure is a set of multi-dimensional tables that define the core parts of your business and how they relate to each other. For example, which products you sell, which customers you serve, which competitors you track, how they are grouped, and how they connect to each other. The content is a set of documents that hold depth and context. These include your reports, SOPs, market analysis, meeting notes, and design decisions. They explain the reasons behind the numbers and capture the parts of the story that a table alone can’t show. AI is good at remembering and reasoning. To reason well, it needs a clear map of how information is connected. The structure provides that map. The content gives it rich knowledge and context to work with once it knows where to look.

Tools and platforms like Databricks, Snowflake, or Microsoft Fabric can give you strong plumbing, including storage, compute, and catalogs. They are important. But they cannot decide how you name your products, how you group your customers, who your competitors are, or what a “priority” account really means for your sales teams. That work still belongs to your business and your leaders and managers. The platform can carry your data model, but it cannot design it for you.

When you combine these two parts, AI can do more meaningful work. It can follow relationships mapping, pull the right content, and answer questions in a way that reflects how your business operates.

Step 1: Build A Single Source Of Truth You do not have to fix everything at once. You can start by building one simple, but powerful thing: a single source of truth for your core data.

Take the information that matters most in daily decisions, such as product, competitor, and customer lists. Move them out of scattered documents, slide decks, and spreadsheets. Put them into carefully designed tables. Use consistent names for each field. Avoid merged cells. Avoid clever formatting tricks that only make sense to people who created the file.

However, this simple step is the part most teams skip. They often keep the key business information in shared folders because it feels easier to access. The problem is that shared folders are not designed for analysis or decision making. A table with clear rules may feel like extra work, but it makes the information visible and reliable for both people and AI.

Step 2: Use The Same Structure Everywhere Once you have a single source of truth, you start making everything else match it, including the way things are named, how they are categorized, and how the tables are set up.

You decide how products are named, how product lines and series are grouped, and you choose one way to refer to each competitor and each customer. You put internal codes, short names, and full names in the same row. You clean up old labels that no one uses anymore.

You also align formats. If “region” is a field in one table, it should be the same field in another table, not “territory” in one file and “geo” in another unless you have a clear mapping. If dates follow one pattern, you keep that pattern. This type of work sounds boring, yet it clears away the fog that confuses both humans and AI.

At this point, you already start to see the benefits. Reports are easier to find and read. People spend less time arguing about which number is right. AI tools stop mixing up similar information.

Step 3: Write Down Hidden Shared Knowledge and Information The last step is often the hardest. You need to capture the shared knowledge people carry in their heads, but never bother to write down.

For example, your sales team might talk about “A series legacy,” “B plus line,” or “gold customers.” They know exactly what those terms mean, but no one outside the team can explain them. Or your operations team might have a default way of handling certain exceptions that is obvious to them, but not written anywhere.

To make this type of knowledge and information visible, you sit with the people who do the work and ask them to walk through their actual process. While they talk, you capture the terms they use, the decisions they make, and the patterns they follow. Then you turn that into simple definitions, rules, and examples inside your documents and tables.

You do not need a perfect encyclopedia. You just need enough written context for a new hire and an AI model to follow the logic without guessing or filling in gaps on their own.

Start Here: Master Tables That Matter Most If this still feels a bit abstract, let’s make it more concrete. A practical way to start is to build a few master tables that form the spine of your AI business data model: a product catalog, a competitor map, and a customer map.

There are many other master tables you can add later, but these three show up in almost every real business question: what you sell, who you sell to, and who you compete with.

Your AI business data model should not be not a side project. It is a deliberate business decision about how your company describes its products, customers, and competitors.

1. Product catalog: What you sell

Think about asking AI, “Help me explain the difference between our A product line and B series.” For this to work, there needs to be one place where those lines and series are defined.

Your product master table should show:

All current products and their standard names How each product belongs to a line or family Key specs and attributes for every model Relationships such as “successor,” “replacement,” or “bundled with” This table becomes the official family tree of your product offerings. Any document, dashboard, or AI tool that talks about products should anchor to it. When that happens, AI can connect the numbers in an Excel file, the descriptions in a brochure, and the details in a support document, because they all point back to the same product IDs.

2. Competitor map: Who you are compared with

Now think about a question like, “Summarize the latest moves from competitor C and how they differ from us.”

Your competitor master table should gather:

Short names and full names of competitors and partners Common local nicknames or code names used inside the company Key attributes such as region, segment, pricing, positioning, or role in your ecosystem With this map, AI can see that “A Co.,” “A Holdings,” and “Project Alpha vendor” all refer to the same organization. It can then connect public news, external analyst reports, and internal project notes that mention them under different names.

3. Customer map: Who you serve

Consider the question, “Show me all the follow-ups and contracts with customer D in this quarter.”

Your customer master table should list:

Standard identifiers for each customer Short names, full names, and any internal codes Key customer attributes that define them for your business This table acts as the anchor for all business data that touches the customer. Meeting notes, deal pipelines, support tickets, and contracts should all link back to this master list. Once you do that, you get a structured customer network instead of siloed records. AI can then answer questions about a customer’s history, risks, and opportunities with confidence.

A Test For Your AI-Ready Business Data Model

After you do this work, there is a very simple test you can use to check whether your AI business data model is ready for AI.

Imagine a new hire joins your company. For the first week they are not allowed to ask anyone questions. All they can do is read your AI business data model: your master tables and your key documents.

Can this person understand your products, your competitors, and your customers just by using what you have documented? Can they follow the relationship between codes, names, and reports without getting lost?

If they still need to ask, “What does this term mean?” or “How do I read this sheet?” it means your knowledge and information are not visible or connected enough yet. It also means that AI will likely struggle in the same places.

So when your teams say that AI is “not strong enough”, it is worth turning the question around. More often, it is not the AI tool that is weak. It is the AI business data model that is missing.

If you are the one pushing digital transformation in your company, you can be the person who asks where your product, customer, and competitor information lives, who owns it, and whether anyone could understand it without help. When you make that system clear and consistent, you are not just helping AI. You are making it easier for all your people to think, decide, and move faster together as ONE team.

Read the full article here: https://ai.gopubby.com/your-cloud-drive-is-not-how-ai-learns-your-business-4e52af73921b