AI and SAP Data Management: Where AI Actually Adds Value

AI and SAP Data Management: Where AI Actually Helps

I have been working with SAP data and archiving for over 25 years. Now we are hearing AI everywhere. Almost every product has AI attached to it, and sometimes it is difficult to tell where AI is really useful and where it is just hype.

From what I have seen, AI has a place in SAP data management, but it is not going to replace the basic work that still needs to be done.

You still need to understand the SAP data.

AI Doesn't Fix Bad Data

This is probably the first thing I would say.

If your SAP data is not understood, classified correctly or managed properly, adding AI isn't going to magically fix it.

You still need to know your tables, business objects, archive objects, retention requirements and dependencies. You need to know what data should be kept and what can be removed.

AI can help with that work, but somebody still needs to understand what the results mean.

That SAP knowledge is still important.

Finding Data Is Where AI Can Help

One area where I see a lot of potential is finding information.

SAP has thousands of tables and a lot of relationships between them. Someone from the business shouldn't necessarily have to know whether the information they need is in BKPF, BSEG, EKKO, EKPO, ACDOCA or another table.

They know the business question.

AI can help translate that business question into something that can be used to locate the right information.

Instead of asking a user to understand the technical SAP structure, we should be making it easier for them to ask normal questions about their data.

AI Can Help Us Understand Large Data Volumes

Another area is data analysis.

When you are looking at a large SAP environment, there can be thousands of tables and huge amounts of historical data.

Before an S/4HANA migration or a decommissioning project, somebody has to figure out what all of that data is and what should happen to it.

AI can help with things like classification, identifying patterns and helping analyze large amounts of metadata.

It doesn't make the final decision, but it can reduce some of the manual work needed to get there.

Archiving Is Another Good Use Case

I also see AI helping with SAP data archiving.

Today a lot of archiving work still depends on technical knowledge of archive objects, tables, residence times and dependencies.

AI could help identify possible archiving candidates, explain why certain data is growing and help teams understand which archive objects may need attention.

But I would not let AI decide on its own that data should be deleted.

There are business, legal and compliance reasons why data needs to be retained. Those decisions still need people involved.

Historical Data Is Important Too

Companies spend a lot of time talking about new data and real-time data, but they also have years of historical SAP information.

That historical information can still have value.

It can help with audits, reporting, trend analysis and understanding what happened in the business years ago.

As companies move from ECC to S/4HANA and retire older systems, I think AI can make it easier to work with historical information without expecting every user to understand the old SAP environment.

Where I Think the Hype Starts

I become skeptical when AI is presented as if it understands everything automatically.

SAP data is complicated.

A field name by itself doesn't always tell you what the data means. The same information can depend on configuration, company code, business process or other related records.

If AI gives you an answer without understanding that context, it can give you a very confident answer that is still wrong.

That is why I think AI needs to work together with SAP knowledge instead of trying to replace it.

Where I See This Going

I don't think the future is AI replacing SAP consultants or SAP data-management people.

I think the better use of AI is taking some of the repetitive work away from them.

Let AI help search.

Let it help classify.

Let it analyze metadata.

Let it identify patterns.

Let it help explain complicated information.

Then let the people who understand SAP and the business decide what to do with that information.

After more than 25 years working with SAP data, I have seen a lot of technology changes. AI is another big change, and I think it will become an important part of SAP data management.

But the companies that get the most value from it probably won't be the ones that simply say, "We have AI."

They will be the ones that figure out where AI actually helps solve a real SAP data problem.