BlogData & AI

AI in data analytics and software development: practical business applications

Oct 22, 20268 min read

At Cloud Levante we ran a LinkedIn poll to find out the main uses of AI at work. The results showed a tie between data analysis (33%) and programming and development (33%), followed by writing and content (17%) and task automation (17%).

33%

17%

Data analysisProgramming and developmentWriting and contentTask automation

Although the poll had limited participation, it gives us a starting point to dig deeper into two applications: using AI to extract useful information from data, and using it to develop software more efficiently.

Let's look at what these technologies actually let you do, how they're used, and what you need to keep in mind to get reliable results.

AI for data analysis: turning information into decisions

Companies generate data constantly through their billing systems, CRM, ERP, and management tools. However, having that information doesn't mean you can actually use it.

AI makes it possible to query large volumes of data, identify patterns, and make forecasts. Here are three of its most useful applications.

1. Querying information in natural language

Getting information out of a database usually requires knowledge of SQL, the language used to run queries, or relying on pre-built reports.

AI can simplify this process by turning everyday questions into structured queries.

For example, a sales manager might ask:

"Which products have seen falling sales over the last three months, and in which regions has the biggest drop happened?"

A system connected to the company's data could identify the information needed, generate the corresponding query, and present the results in a table or chart.

This makes data accessible without needing to know how the databases are organized.

The condition

Correctly defining indicators such as net sales, returns, or comparison periods. If AI misinterprets these concepts, it can generate a technically valid query with incorrect results.

2. Spotting anomalies before they become problems

Anomaly detection models make it possible to identify behavior that strays from the usual patterns. For example:

Finance — spotting unusual amounts or potential duplicate invoices.

E-commerce — detecting an unexpected drop in conversion rate.

Manufacturing — identifying abnormal increases in a machine's energy consumption.

Logistics — flagging routes whose delays exceed the usual values.

Outcome

These models don't necessarily determine what caused the problem, but they help identify where to investigate. To avoid false alarms, it's important to factor in seasonality, promotions, or changes in activity.

3. Forecasting demand and planning resources

Predictive models use historical data to estimate future scenarios.

Imagine a company that needs to decide how much inventory to buy for next month. A model can analyze past sales, promotions, holidays, and replenishment lead times to generate demand forecasts by product.

These estimates can be used to plan purchases and reduce the risk of running out of stock or building up unnecessary inventory.

Keep in mind

Forecasts aren't certainties. They need to be checked against actual results and reviewed periodically to confirm they're still reliable.

AI in programming and development: writing code is only part of it

In software development, AI can help generate functions, investigate bugs, create tests, and understand existing applications. Its value shouldn't be measured only by how much code it produces, but by whether that code works correctly, is secure, and is easy to maintain.

1. Generating code from requirements

Coding assistants can turn instructions into a first implementation. For example:

"Create an API that receives orders, checks that the products exist, validates the quantities, and returns an error when required information is missing."

AI can propose the API's structure, the validations, and the necessary functions.

Verification

This speeds up prototypes and repetitive tasks, but the generated code needs review. Aspects such as permissions, error handling, and data protection must be checked before using it in production.

2. Finding bugs and improving performance

AI can also analyze code snippets and execution logs to identify possible causes of a failure.

Suppose an application takes too long to display a list of customers.

An assistant might detect that the system runs an extra database query for every customer — a problem known as N+1 queries. From there, it could propose a more efficient query that retrieves the related information in fewer operations.

The condition

To confirm the fix works, you need to compare response time and the number of queries before and after the change.

AI helps find and propose solutions. Tests are what let you confirm they actually work.

3. Creating automated tests

An application can work correctly in common situations and fail when it receives unexpected data. AI can help generate tests that check different scenarios.

For example, for a function that calculates discounts, it can propose checks that confirm:

A 10% discount is applied correctly.
Negative percentages are rejected when they should be.
The final amount is never below zero.
Monetary calculations use the right precision.

Caution

These tests make it easier to catch errors when the software changes. However, the expected results need to come from verified requirements. Otherwise, AI could generate an incorrect function and tests that validate that same error.

When data analysis and software development come together

One of the most interesting applications appears when both disciplines are part of the same solution.

Picture a distribution company that wants to improve its inventory management.

Phase 1

Data integration

Sales, inventory, and supplier orders are connected, unifying product information.

Phase 2

Predictive analysis

A model estimates future demand using sales history, seasonality, and other relevant variables.

Phase 3

Application development

A tool is built that compares forecasts with available inventory and suggests replenishment quantities.

Phase 4

Operational integration

The manager reviews the proposals and, once approved, the application can send them to the management system via an API.

Phase 5

Monitoring

Forecasts are compared against actual sales to detect deviations and adjust the system.

The result is an application that connects information, analysis, and operations in a single process.

Not every part needs generative AI: forecasts can be made with statistical models, replenishment rules can be programmed conventionally, and an AI assistant can make it easier to query results.

The key is to use each technology where it actually adds value, without adding unnecessary complexity.

What to consider before applying AI in a company

Before adding AI to data analysis or software development, it's worth checking four things:

Data quality — review incomplete records, duplicates, and inconsistencies between systems. A model can't make up on its own for incorrect source information.

Security — control what information each user can query, and avoid sharing code, credentials, or confidential data with unauthorized external services.

Validation — check the generated queries, measure forecast error, and subject the software to functional and security testing.

Cost and maintenance — assess model usage, the infrastructure required, and the effort of oversight. An AI-based solution isn't always more suitable than conventional automation.

To get started, the most useful thing is to pick a specific process and define how the improvement will be measured.

For example, checking whether an analytics assistant correctly answers predefined business questions, or whether a coding tool lets you expand automated tests without increasing the number of errors.

Conclusion: from using AI to integrating it properly

Data analysis and programming represent two complementary ways of making the most of artificial intelligence.

The first lets you query information, identify anomalies, and make forecasts. The second helps you develop, test, and maintain applications. When the two come together, it's possible to build solutions that don't just display information, but also help put it to use in a company's everyday processes.

That said, the value of AI isn't in generating more answers or more code, but in achieving results that are reliable, verifiable, and useful for a specific need.

At Cloud Levante we help organizations integrate data, artificial intelligence, and software development into solutions tailored to their processes — from preparing the information to putting it into operation.