Before You Automate a Process, Make Sure It Makes Sense

Automation is supposed to make businesses more efficient. Less manual work, fewer errors, and more time for employees to focus on things that actually need their attention. What's not to like?

But there's a difference between making a process faster and making it better.

A recent Financial Express article about IBM highlights that distinction. IBM has deployed approximately 6,000 AI agents across more than 150 internal workflows, but the company isn't simply focused on automating existing tasks. It's also looking at how those processes work and whether they should be redesigned.

One of the more interesting points in the article is that IBM has actually seen instances where introducing AI resulted in lower productivity.

That's worth thinking about, especially as businesses rush to find new ways to incorporate AI and automation into their operations.

Are You Automating a Problem?

Consider a business where an employee spends several hours each week comparing customer records between a CRM and an accounting system.

The information doesn't always match. Customer names are formatted differently, some accounts have duplicate records, and contact information is outdated. Before the employee can prepare an accurate report, they have to sort through the discrepancies.

Automating that process sounds like a great idea. Instead of spending hours comparing spreadsheets, the business could use software to identify differences and generate reports automatically.

But why are those discrepancies happening in the first place?

If the underlying data is inconsistent, automation doesn't necessarily solve the problem. It may just create a faster way to work around it.

What if the business standardized its customer records, removed duplicates, and established consistent procedures for entering information?

Suddenly, there might not be nearly as much to reconcile.

The better solution may not be a new automation at all.

Start With the Process, Not the Technology

It's easy to understand why businesses are excited about automation. Technology can eliminate repetitive tasks, reduce manual errors, and free up valuable time.

But before deciding what to automate, businesses should understand how the process currently works.

Where is the information coming from? How many people or systems are involved? Why are employees making manual corrections? Are those corrections necessary, or are they compensating for problems elsewhere?

Sometimes the answers reveal opportunities to improve a process before introducing new technology.

For example, if a sales team regularly corrects lead assignments because customer territories are inaccurate, automating the assignment process won't fix the territory information.

If marketing campaigns repeatedly encounter bounced emails because contact records haven't been maintained, automating more campaigns won't improve the quality of the contact list.

And if management doesn't trust its monthly reports because different systems produce different numbers, generating those reports automatically won't make the information any more reliable.

These aren't necessarily technology problems. They're often data quality and process problems.

Good Data Makes Better Automation Possible

None of this means businesses should avoid automation. Quite the opposite.

When the underlying information is accurate, consistent, and organized, automation can be incredibly valuable.

A clean CRM can support more reliable lead routing. Accurate customer information can improve marketing campaigns. Consistent records across systems can make reporting faster and more useful.

The key is making sure the information supporting those processes is reliable before depending on automation to manage it.

That doesn't mean every record has to be perfect. It means businesses should understand where their data problems exist, which ones could affect the process, and what needs to be corrected.

Before You Automate It, Clean It Up

IBM's approach offers a useful reminder that successful automation isn't measured by how many tools a company implements. It's measured by whether those tools actually improve the way the business operates.

For smaller businesses, that improvement might begin with something much simpler than an AI agent.

It might mean cleaning up duplicate customer accounts, standardizing information across spreadsheets, or reviewing how data moves between systems.

At Zippered, we help businesses turn messy business data into information they can trust. Whether you're preparing for a CRM migration, considering automation, or simply trying to get more reliable reports, understanding the condition of your existing data is a good place to start.

Because sometimes the best way to improve a process isn't to automate it.

It's to fix what's making it complicated in the first place.

Source: Urvi Malvania, "IBM deploys 6,000 AI agents across consulting workflows," Financial Express, October 18, 2026 (updated October 19).