Of course, human judgment remains essential when it comes to keeping business data accurate and reliable, but when workers report that they spend at least a quarter of their week on data management, and better automation can save them more than 6 hours a week, there is a clear reason for businesses to explore how AI can reduce the manual work involved in data management (while keeping employees in control of the process).

As businesses handle growing volumes of information, the pressure to process it faster without compromising accuracy will only increase.

So, where does AI fit into data management? What challenges come with using it in everyday workflows, and how can it make things easier while keeping employees and customers in control?

That's what we'll explore in this article.

The first challenge: Integrating AI into existing data management systems without creating more work

Over the years, companies have built their workflows around systems that their teams know and rely on, so introducing new technology without considering how it fits into those processes can create additional work instead of reducing it.

Yet keeping things as they are comes with its own problems. 

According to FormAssembly's Data Collection in 2025 research, 98% of respondents whose teams still used manual data collection processes reported inefficiencies, while 77% identified limitations in their systems or technical expertise as barriers to further automation.

So, the question we started focusing on was how to help companies that recognise AI's potential to improve data management introduce it into their existing systems without putting additional strain on their teams or disrupting processes they already depend on. 

After all, adding new capabilities should make their work easier, rather than leave them managing more dependencies between tools, fixing problems that arise along the way and dealing with inconsistencies that compromise the quality of their data.

Introducing AI one task at a time without rebuilding existing systems

The main idea behind this approach is to introduce AI without putting unnecessary pressure on the company's existing systems or creating more work for its IT team.

As data volumes grow and the needs of employees or customers change, companies need ways to improve their operations without going into the expensive adventure of rebuilding the processes and systems around them. 

Adding an AI layer above their system gives them the option to introduce new capabilities where they need them, while allowing the rest of the system to continue operating as before.

So, rather than changing the entire process, the company could introduce an AI feature to help with that particular step and evaluate how reliably it works with the information employees handle.

For example, the first step could be introducing an AI tool customised to the organisation's daily routine tasks, that extracts information from PDFs or Excel spreadsheets and uses it to prepare reports employees can simply review before sending them to other departments or upper management.

This would give the company room to start on a smaller scale and understand what the integration requires before extending AI to other parts of its workflow. 

Of course, this doesn't remove the technical work involved. Developers still need to determine which information AI can access and what actions it can perform. 

But with those boundaries in place, companies can expand their systems' capabilities without having to redesign everything around AI.

The Second Challenge: How Can AI Realistically Improve Everyday Data Management?

Although newer tools allow company teams to manage large amounts of data, there are still limitations, particularly when it comes to the time employees spend entering information and checking its accuracy (since existing automation doesn't always eliminate the need for manual work).

And then this leads us back to the beginning, since manual work, as research shows, doesn't just cost time and resources.

It also introduces the risk of human error, which can leave organisations with inaccurate or inconsistent records and affect the reliability of the information they use in their everyday operations.

So, what can AI bring to data management?

• Reduce manual data processing: Document AI models can extract text, tables and relevant fields from documents, then organise that information into structured formats for employees to review and import.
  
  

• Improve data quality: AI-based record matching can identify potential duplicates despite differences in names or formatting, while machine learning can flag values that appear inconsistent with existing records.
  
  

• Simplify access to information: Language models can interpret questions written in everyday language and generate database queries, helping employees retrieve information without writing those queries themselves.
  
  

• Improve data analysis: Predictive models can use historical data to forecast future trends, while machine learning can help identify patterns and changes across large datasets.

To show you what that means in practice, here is one simple example from leveraging a large language model in managing information from clinical databases:

And now, let's take a look at the AI-powered data management features we built for a B2B company and how they help its customers spend less time on routine tasks like data entry, record updates and organising information.

Streamlining data management workflows with AI

About the company

Vinify is a platform for people and businesses that work with wine, from restaurants and wine bars to hotels, wine shops and private collectors.

It gives them one place to keep track of their wine collections, check what's in stock and where it's stored, manage prices and keep wine lists up to date.

For businesses, it can also connect with their point-of-sale systems, so sales are reflected in their stock.

The challenge: simplifying inventory data management for businesses with AI

With Vinify bringing different parts of inventory management together in one platform, its team started looking at how AI could help customers with some of the manual work that comes with managing their data.

As new products arrive and existing ones sell, there's a lot of information to deal with, and even relatively simple tasks like adding new entries or making changes to existing records can involve several steps.

But there was another question to consider: how could we introduce AI without changing the platform customers had grown accustomed to using?

Having worked with Vinify since their first release in 2021, which made us familiar with the product, it was easier for our forward-deployed engineers to develop the first three AI-powered features for the platform.

Solution 1 - adding large amounts of inventory data with less manual work

The first (and fairly obvious) problem we wanted to tackle was the manual work involved in keeping inventory records up to date as new wine products arrived.

Here is just one example of the problem - suppliers don't always present product information in the same way. 

If you happen to order wine from different suppliers, details like product names, quantities and prices can appear in different places across spreadsheets, invoices and other documents.

With the AI batch import tool, we wanted to give businesses (and their employees) a way to spend less time going through each document and manually transferring the data into their inventory.

Going back to our earlier example, if suppliers send information in different formats, such as PDFs, Excel spreadsheets or photos, businesses can simply upload the files one at a time.

The AI then reads each file, extracts the product details and organises the information into a format that's ready to import into the digital inventory, saving employees up to an hour of manual work that would otherwise go into preparing those records by hand.

Of course, employees still have the final say, as they can review the extracted information, make any necessary corrections and confirm everything before the records are added to their inventory.

Solution 2: updating wine inventory data through a conversation with AI

With the second solution, we wanted to eliminate more of those “small, tedious tasks”, and this time we focused on the manual steps involved in managing inventory data.

Let's say a restaurant employee wants to check how many bottles of a particular wine are left  in the inventory.

Usually, they'd have to go into the inventory system, search for that particular wine and pull up its stock details, repeating the process whenever they need to check another record or make a change.

And if some bottles had just been sold in the meantime, they'd also need to update the record to reflect the change.

With the AI cellar assistant, they can handle these tasks simply by talking to it, whether that's asking how many bottles of a particular wine are left and where to find them, or saying something like, “Remove a bottle of 2006 Château Margaux from the cellar.”

How can businesses find the right opportunities for AI in data management?

Looking at the examples we've covered, you might recognise similar tasks in your own business, whether that's entering information from documents, searching through records or making changes to existing data.

But before deciding which of those tasks could benefit from AI, it's worth looking at where employees are actually running into difficulties.

Identify where AI could actually help

For instance, if employees spend a considerable amount of time entering information into a database, what makes that work difficult? 

Is it the sheer number of documents they receive, the different ways information is presented or the fact that someone has to check and correct the details before they're added?

The answers can point towards quite different solutions. If the information follows a predictable format and a consistent set of rules, conventional automation might be enough. 

But if employees regularly have to interpret documents with different layouts or recognise information that refers to the same record despite differences in wording, AI may be worth exploring.

Test AI before committing to development

Assuming you found a possible use case for AI in your business, it is recommended that you test it on a small scale first.

And make sure you're using real information from your existing systems rather than carefully prepared examples, because that's where you'll find out whether the AI can handle the inconsistencies and complications your employees regularly deal with.

You'll also need to consider how much work employees have to do after using the AI feature.

For instance, if they still need to spend considerable time checking its answers or correcting changes it makes to your database, you might end up replacing one time-consuming task with another.

So, when you're evaluating the results, compare the time employees spend completing the entire process with and without AI.

Include any additional work needed to verify the information or correct mistakes, because that will tell you whether introducing AI would actually reduce the workload enough to justify further development.

FAQ

1. How much does it cost to develop an AI-powered data management tool or a custom solution?

That depends on what you want the feature to do and how much development is needed to make it work with your existing software.

For example, building a feature that extracts information from a particular type of document is quite different from developing an AI assistant that can search several databases and make changes to records.

There are also ongoing costs to consider, particularly if the feature uses an external AI model that charges based on usage. So, before estimating a budget, we'd want to understand the task, the amount of data involved and how frequently people would use the feature.

2. Can we use AI to manage sensitive business data without exposing it to unnecessary risks?

Yes, but we'd need to look carefully at where your data is stored, which AI services would process it and what information those services actually need to receive.

For instance, an AI feature designed to answer questions about customer records doesn't necessarily need unrestricted access to every field in your database.

We'd also consider who can use the feature, what permissions they have and whether the AI provider retains any of the information it processes. Those details should be established before connecting AI to sensitive business records, rather than treated as something to resolve after development.

3. Can AI work with our existing ERP, CRM or inventory management software?

In many cases, yes, although the possibilities depend on how your software is built and what integration options it provides.

If your system has an API, for example, we may be able to use it to let an AI feature retrieve information or perform specific actions. With older or heavily customised software, we'd first need to examine what access is available and whether additional development is required.

4. What happens if AI extracts incorrect information or updates the wrong record?

That's something we need to plan for when designing the feature, especially if it can make changes to business data.

For document processing, we can build in checks that identify missing information or results that need closer attention. For features that update records, we can restrict which actions the AI is allowed to perform and require confirmation before certain changes are saved.

It's also worth keeping a record of what was changed, when it happened and who approved it. That makes it easier to investigate a mistake and, where the system supports it, restore the previous information.

5. Can AI help if our business data is spread across several different systems?

Potentially, yes. Let's say your customer information is stored in a CRM, while orders and invoices are managed elsewhere.

An AI assistant could be designed to retrieve relevant information from both systems, provided the necessary connections and permissions are available.

However, there's an important distinction between accessing information from several places and bringing that information together correctly.

If the same customer appears under different identifiers, for instance, we'd need a reliable way to establish which records belong together.

Depending on the situation, that might involve existing database relationships, matching rules or AI-assisted record matching.