Mia Urman, CEO of AuraPlayer and Oracle ACE Director, is a leading AI and modernization expert helping enterprises evolve their ERP systems.
Last month, my son used AI to organize a school project. He dictated his ideas, asked follow-up questions, reorganized his outline and finished a polished draft in about 15 minutes. At his age, the same assignment would have had me buried in Encyclopedia Britannica for days.
The next day, I sat with the finance team of a Fortune 500 company. As we reviewed their Oracle workflows, I watched an accounts payable (AP) clerk open an email, download an invoice, look up the supplier in Oracle E Business Suite, compare it against the purchase order, resolve exceptions by hand and route it for approval. Then she did it again. And again. Later, I learned she performs that same process hundreds of times every month.
What struck me was that entering invoices, validating suppliers and matching purchase orders all follow business rules that already exist in Oracle E Business Suite, spreadsheets or approval matrices. Yet in many organizations, employees are still acting as the integration layer between disconnected systems while AI capabilities wait on the sidelines.
I call this the “Enterprise Gap”: the distance between what AI can do and what enterprise organizations are actually using it to accomplish.
The Difference Between AI Hype And AI Value
The excitement around AI has created enormous pressure on executives to “do something with AI.” Boardrooms want to know where AI can be deployed as vendors continue to unveil impressive capabilities, and nearly every conference promises another wave of transformation.
The desire to do something, though, is mitigated by reports that organizations are struggling to generate value with AI.
MIT’s Project NANDA research, for example, found that despite the $30 billion to $40 billion invested in enterprise generative AI, only 5% of 300 public AI implementations produced measurable business value.
What’s more interesting than why just 5% succeeded is why so many others didn’t.
NANDA also found that organizations partnering with specialized AI providers reached production deployments twice as often as those building internally, 67% compared with 33%. As lead researcher Aditya Challapally told Fortune (subscription required): “It’s because they pick one pain point, execute well and partner smartly with companies who use their tools.”
That strategy closely reflects what I’ve seen working with my own clients on ERP automation. The organizations generating the greatest value start with one operational bottleneck, integrate AI into an existing workflow and measure success through business outcomes.
One manufacturer, for example, received purchase orders in dozens of customer formats, many containing missing or inconsistent information. Employees manually entered the data, validated pricing and products and then created orders in Oracle E Business Suite.
By combining intelligent document processing, AI and existing ERP business rules, the company automated nearly all of that work in about a month. Processes that once took hours were completed in minutes, productivity increased and errors dropped. Employees shifted their attention from repetitive data entry to resolving the remaining small number of true business exceptions.
Why AI Can’t Automate A Workflow Nobody Understands
Technology alone doesn’t fix operational complexity. Identifying the business challenge and understanding the workflow must come first.
When manual work is spread across disconnected systems, inconsistent data and unclear ownership, adding a generic AI tool without a clear discovery process rarely produces results. Organizations simply inherit another application to maintain, just with a flashier logo.
The solution is surprisingly simple: Before evaluating vendors or selecting models, you need to identify and fully understand the manual workflow itself. Organizations should begin by asking themselves: “Where are we losing multiple hours every day to repetitive work?”
Here’s the exercise I give every client, and it surprises people because it’s so unglamorous. Map out every step it takes to process one AP invoice from beginning to end. For example: Open the email. Download the attachment. Verify the customer. Validate data in the ERP system. Handle exceptions. Route the approval. Post the transaction.
By the time you’re finished, you’ll usually identify 20 or 30 separate steps. That exercise quickly reveals where AI can increase efficiency, where optical character recognition belongs, where existing business rules can help automate decisions and where experienced employees should remain involved.
How AI Should Strengthen Financial Controls
The most successful AI projects combine technologies that each perform a specific task while preserving human oversight where judgment truly matters.
Just as no CFO would approve financial statements without reviewing them first, AI shouldn’t get a free pass simply because it’s newer technology. Human oversight remains essential for maintaining accuracy and trust.
The objective of AI has never been to remove people from the finance loop. It’s to eliminate the repetitive work that prevents finance professionals from focusing on work that requires judgment and business insight.
The payoff of AI implemented in this way is tangible. AP teams process invoices faster, financial closes accelerate, and employees spend more time resolving meaningful exceptions instead of correcting routine data entry errors.
Closing The Gap
The organizations seeing meaningful results are taking a disciplined approach to AI. They are choosing one repetitive process, mapping it from beginning to end, introducing AI where it creates measurable value and keeping people involved where judgment remains essential.
Success is measured in hours returned to employees, faster financial closes, improved accuracy and stronger operational performance.
Bridging the “Enterprise Gap” means bringing today’s AI capabilities into the daily work of the people processing invoices. When organizations close that gap, they will reap huge rewards and be at least as productive with AI as my 13-year-old son.
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