AI’s Next Advantage Is the Meaning of Data

Moving beyond productivity gains to customer value — the semantic layer and Ontology

What This Article Argues

Organizations are already deploying AI to draft documents, search for information, write code, and handle customer inquiries faster. Real productivity gains from this are already being documented.

But processing work faster doesn’t, by itself, create new value for customers.

Customers feel the difference when AI finds meaning inside company data and turns that meaning into the next action.

• Instead of just answering fast after a problem occurs, AI catches it before it happens.
• Instead of sending every customer the same message, AI offers the help each one actually needs right now.
• Instead of stopping at a status report, AI tells you what to do next.
• Instead of relying only on a staff member’s experience, AI drives consistent, data-backed action.

Here’s the flow in one line:

Adopt AI → Improve productivity → Understand the meaning of data → Make better decisions → Take real action → Value customers actually feel

The hardest part here isn’t getting AI to produce an answer. It’s understanding precisely what your data means, and using that meaning to choose an action that isn’t wrong.

That’s why DB metadata, data dictionaries, semantic layers, and Ontology matter. These aren’t just data-cleanup chores — they’re the foundation that turns an AI’s answer into customer value.

Below is a short summary of the whole argument, followed by a detailed walkthrough of why the meaning of data matters and how to build toward it.

Summary First

Where We’re Headed

Five Key Points

1. AI’s productivity effect is already visible. There are documented cases of AI cutting processing time and raising output in writing, development, and customer support.

2. But productivity gains alone don’t add up to customer value. The value customers feel is created when AI finds meaning in data and turns it into better judgment and real action.

3. The starting point is DB metadata and a data dictionary. AI can only read your company’s data properly once the meaning of tables, columns, codes, units, and dates is spelled out precisely.

4. Reaching customer value requires a semantic layer and Ontology. A semantic layer manages official metrics and calculation logic in one place; Ontology connects business concepts and relationships across systems. Together they keep AI from having to keep guessing when a DB description alone isn’t enough.

5. The closer AI gets to driving customer-facing action, the more validation and control matter. Use approved data and rules, keep evidence for every result, and start with human approval before automating only the workflows you’ve verified.

The One-Sentence Conclusion

Establish DB metadata and a data dictionary as the baseline, then build a semantic layer and Ontology on top of it to turn the meaning of your data into accurate judgment and execution.

The Detailed Case

AI’s First Win Showed Up as “Speed”

Generative AI has already shown clear potential in repetitive knowledge work.

A study of roughly 5,000 customer support agents found that agents using an AI assistant resolved about 14% more customer issues per hour.[1] In a controlled experiment with 95 developers, GitHub found that the group using Copilot completed a given development task 55% faster on average.[2]

These numbers don’t transfer identically to every company and every task — the effect varies by task type and user experience. But the direction is already clear: AI is useful for speeding up work like this:

• Drafting and summarizing documents
• Searching for and organizing information
• Writing code and automating repetitive tasks
• Suggesting support replies
• Writing meeting notes and reports

The value at this stage is mainly doing the same work faster. It cuts internal hours and cost, and frees staff to focus more on judgment and problem-solving.

That’s a real win. But from a customer’s point of view, it may not be enough on its own. What matters to a customer isn’t that the company can write a report faster — it’s whether their own problem gets solved faster, whether they get a more relevant suggestion, and whether an inconvenience or risk gets resolved before it becomes one.

Customer Value Is Created When Data Turns Into Action

For AI to actually help a customer, it has to go beyond answering a question.

Say an analysis flags a customer as high-risk for churn.

Showing that result on a dashboard changes nothing for the customer by itself. Value only appears once the process continues:

1. Identify which customer has the problem.
2. Understand why the problem occurred.
3. Choose the right action for that customer.
4. Notify the responsible person, or have the system execute the action.
5. Confirm the problem was actually resolved.

Customer value shows up only when the chain runs all the way through.

• Catching a likely shipping delay early and notifying the customer
• Spotting a recurring failure signal and acting before service goes down
• Recommending the next feature or product that fits how the customer is actually using the service
• Detecting a churn signal in a contract and providing the right support
• Anticipating a stock shortage and adjusting inventory before an order fails

In other words, the next step in using AI isn’t generating more answers — it’s finding meaning in the data and connecting that meaning to judgment and execution.

But the “Meaning” of Data Is Hard to Read From the DB Alone

Suppose a user asks:

“Find customers whose service usage has dropped recently and who are at risk of churning.”

To answer this, AI needs to know:

• Does “customer” mean a signed-up account, or a paying contract customer?
• Does “usage drop” mean logins, feature usage, or transaction volume?
• Compared to what period, and by how much, does a drop count as risk?
• How should a temporary usage dip caused by an outage or holiday be handled?
• When multiple accounts belong to one client company, how should they be grouped?
• How should already-cancelled customers or test accounts be excluded?

AI can’t reliably infer these rules just from table and column names in the database. Without that information, it will construct the most plausible-sounding rule it can.

If a wrong analysis only ends up in a report, a person has a chance to catch and correct it. But once the analysis result feeds directly into a customer notification, a pricing offer, a sales outreach, or a service restriction, the impact of a wrong interpretation of meaning gets much bigger.

The closer AI gets to taking action, the more the accuracy and validation of data meaning matters.

The First Thing You Can Do: Give Your Database a Good Manual

The most realistic starting point is building solid DB metadata and a proper data dictionary.

Metadata is, simply put, information about your data. A data dictionary is a reference that documents what tables, columns, codes, and business terms mean.

For example, it manages things like:

• What each table and column means
• Which data source is the official, authoritative one
• The reference point for dates and the unit for amounts
• What each status code means
• When data was last updated, and who owns it
• Which data should or shouldn’t be used together

When this documentation is solid, AI can find and understand your data far more accurately.

If your data structure is simple and doesn’t involve many calculation rules, a well-maintained metadata layer and dictionary alone can already get you meaningful results. You don’t need to build a large Ontology from day one.

Reaching Customer Value Requires a Semantic Layer and Ontology

Even when every column’s meaning is documented in a data dictionary, AI still has to make judgment calls on its own.

For example, to find “customers at risk of churning,” AI may have to decide, on its own:

• Which customer table to use
• Which data to use to calculate usage
• How to reconcile customer IDs across multiple systems
• What time window and threshold define churn risk
• Which customers and exceptions to exclude

If you leave this judgment to AI every single time, you can get a different answer to the same question on different days.

A semantic layer turns your most-used metrics and calculations into official, fixed rules.

• Official metric: usage decline rate over the last 30 days
• Population: valid, paying contract customers
• Comparison: average usage over the prior 30 days
• Exclusions: test accounts, service outage periods
• Customer join key: contract customer ID
• Risk threshold: usage down 40% or more

This lets AI use an approved metric instead of inventing a new churn definition every time.

Ontology organizes the business concepts and relationships scattered across multiple systems — customers, accounts, contracts, services, usage activity. For example, it makes relationships like “one client company can have multiple accounts,” “an account belongs to one contract,” and “usage activity is tied to a specific service” understandable to a machine.

DB Documentation Is the Starting Point; the Semantic Layer and Ontology Are the Next Step

You don’t have to pick one or the other.

DB metadata and a dictionary are the baseline. To understand customers well enough to drive action, you need a semantic layer and Ontology built on top of that baseline.

The two layers do genuinely different jobs. The semantic layer owns “which number gets calculated with which formula and which data.” Ontology owns “how customers, accounts, contracts, services, and activity relate to each other.” One creates consistency in calculation; the other connects meaning across your data.

So the target structure this article proposes is:

1. Use DB metadata and a dictionary to document the basic meaning of your data.
2. Use a semantic layer to manage official metrics, formulas, and correct data joins.
3. Use Ontology to connect business concepts and relationships across systems.
4. Use validation and access control to make sure these rules actually hold in real execution.

If your data is simple, building a large Ontology first just adds management overhead. On the flip side, if you’re trying to connect data across systems to actually change customer behavior but only provide column-level documentation, AI will keep having to guess at the calculations and relationships that matter most.

The Safeguards You Need as You Move From Answering to Acting

Adopting Ontology or a semantic layer doesn’t make things automatically safe. The rules you define have to actually be wired into the real execution path.

Check before executing

• Did it use approved data and official metrics?
• Did it join the data the correct way?
• Were the customer, time window, and exclusion conditions applied correctly?
• Were privacy and access permissions respected?
• Is there an unusually large query or a dangerous command in here?

Keep the result and its evidence together

• What data was used?
• Which formula and criteria were applied?
• How did AI interpret the question?
• What parts of the result are uncertain?
• What action was actually taken?

Automate high-stakes execution in stages

Start by having AI recommend an action and a person approve it. Expand the scope of automatic execution only for repeated workflows where accuracy and impact have been thoroughly verified. Keep stronger validation and approval steps around decisions involving pricing, contracts, service restrictions, or sensitive information.

What the Evidence Outside Our Company Shows

There’s evidence that providing business meaning and data relationships can improve the accuracy of data queries.

• Snowflake reported plain Claude 3.5 Sonnet averaging 57% across four datasets, versus Cortex Analyst — which includes a semantic model — averaging 78%.[3]
• A 2026 arXiv preprint reported that adding business-semantics documentation improved accuracy by 17–23 percentage points across three models, on 100 questions over a single retail dataset.[4]
• On Spider 2.0, which covers realistic enterprise data work, an o1-preview-based coding agent’s success rate topped out at 21.3%.[5]

These numbers come from different datasets and evaluation conditions, so they can’t be used directly as an accuracy forecast for your own company. But two directions are clear:

1. Even AI that performs well in a clean environment struggles with complex enterprise data.
2. Providing business meaning and official relationships reduces how much AI has to guess.

How to Actually Get Started

1. Pick questions where customer value is high

Choose questions you can turn into real action, not just questions that are easy to answer.

• Who is likely to churn?
• What signals appear before a recurring failure happens?
• Which customers need what kind of support or offer?
• Can we reduce order failures or shipping delays proactively?

2. Document the meaning of the data each question needs

Review the metadata and dictionary for the relevant databases, and fill in the official data source, units, code values, date conventions, ownership, and the correct join method.

3. Build the semantic layer and Ontology

In the semantic layer, define the formulas and exclusion conditions for customer metrics, usage, and risk signals. In Ontology, define the relationships among customers, accounts, contracts, services, and usage activity. Build outward from the business area with the highest customer value rather than trying to cover the entire company at once.

4. Test judgment and execution on a small scope

Prepare 30–50 real business questions with known correct answers and measure accuracy. Start by having AI recommend the next action to a staff member, and automate only the workflows where effectiveness and safety have been confirmed.

5. Measure success by customer outcomes

Don’t just track document-writing time or AI usage volume.

• Did customer problem-resolution time go down?
• Did you prevent outages and inconveniences ahead of time?
• Did churn or order failures decrease?
• Did the relevance of recommendations and customer satisfaction improve?
• Did wrong judgment calls and unnecessary outreach decrease?

AI’s ultimate performance should be judged by the change it creates for customers — not by how many answers it generated.

Conclusion — Better Action, Not More Answers

Using AI to speed up writing, search, coding, and support is a good starting point, and it delivers real productivity gains. But if you stop there, AI stays a tool that does existing work faster.

The next step is to find the meaning of customers and business inside your company’s data precisely, and connect that meaning to better judgment and execution.

A realistic path looks like this:

Establish DB metadata and a dictionary as the baseline. Build a semantic layer on top to unify official metrics and calculations, and build Ontology to connect business concepts and relationships across systems. Then carry that meaning system through to validated execution, so customers experience a real result.

What we should be aiming for isn’t an AI that produces more answers. It’s an AI that genuinely understands a customer’s situation and helps take the right action at the right moment.

References

[1] E. Brynjolfsson, D. Li and L. R. Raymond, Generative AI at Work, NBER Working Paper 31161. Based on data from roughly 5,000 customer support agents, agents using an AI tool resolved about 14% more issues per hour on average.

[2] GitHub, Research: quantifying GitHub Copilot’s impact on developer productivity and happiness. Results from a controlled experiment on a specific JavaScript task with 95 developers.

[3] Snowflake, Agentic Semantic Model Improvement: Elevating Text-to-SQL Performance, Cortex Analyst official documentation.

[4] M. Rumiantsau and I. Fokeev, Semantic Layers for Reliable LLM-Powered Data Analytics, arXiv preprint, 2026.

[5] Z. Lei et al., Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows, ICLR 2025.

Keep Reading

• Why AI Doesn’t Understand Company Data
• Ontology
• Enterprise AI