Testing Enterprise AI Before You Sign the Contract
The Problem: AI Fails on Messy Real-World Data
Most enterprise AI projects fail to deliver the savings promised by sales reps. This happens because companies buy AI the same way they buy standard software.
Traditional software does exactly what it is told, every time. AI does not. AI works on probability, which creates two common and expensive problems:
- Buying Toys Instead of Results: Without a strict rule for what matters, companies waste budget on low-value novelties—like meeting note-takers or draft writers—while core operations (underwriting, invoice processing, compliance) remain manual.
- The "95% Accuracy" Illusion: Software vendors run sales demos using clean, perfect sample files. But your real-world business records are messy: dark scans, weird layouts, odd handwriting, and foreign languages. When the software meets messy data, performance drops sharply.
- The Hidden Cost of Fixing Errors: A 5% error rate does not mean you keep 95% of your savings. When an AI makes a mistake, your staff must manually hunt down the error, untangle what went wrong, and fix it. Fixing an AI’s mistake often takes longer than doing the original task by hand. During busy periods, this work piles up and freezes your operations.
Example 1: How a 5% Error Rate Breaks Operations
A logistics company processes 50,000 invoices a month. A software vendor promises 95% accuracy, claiming the company can cut half its processing staff.
In reality, 5% of the invoices (2,500 per month) have blurry stamps, handwritten notes, or non-standard tables. The software misreads them.
A human processor normally takes 3 minutes to handle an invoice from scratch. But auditing, tracking down, and correcting an AI mistake takes 15 minutes.
Clearing those 2,500 errors requires 625 hours of emergency manual labor every month. The team falls weeks behind, suppliers threaten to hold shipments, and the company has to hire expensive temp workers to clear the backlog. The expected savings turn into an operational crisis.
The Solution: Stress-Test the Software Before Signing
Before committing to a multi-year software contract, treat the purchase like an insurance test: find out where it breaks before you release the money.
We use a simple, three-step test:
- Test on Your Messiest Files: We take 500 to 1,000 of your worst historical records (blurry scans, difficult formats, odd edge cases) and run them through the competing software offline.
- Calculate the True Staffing Cost: We measure exactly where the software fails, and calculate how many human workers you will actually need on shift to review and fix those errors.
- Rewrite the Contract in Your Favor: We use the real test results to change the vendor contract:
- If the software’s accuracy drops below the agreed level in production, the licensing fee automatically drops.
- You staff the review team correctly from day one, so your business never grinds to a halt.
Example 2: How the Pre-Contract Test Protects the Deal
A commercial real estate firm was prepared to sign a $1.2 million, three-year contract for an AI tool to extract payment terms and expiration dates from 8,000 complex leases.
Before signing, the CFO had Hardy & Co. run an offline test on 500 of their oldest, most complicated historical lease agreements.
The Test Results: Accuracy on simple standard leases was 94%, but on older commercial leases with handwritten amendments, accuracy plummeted to 58%. To keep up with daily intake, the firm would need to hire three full-time analysts just to review the software's work—erasing all projected ROI.
The Outcome: Armed with our test data, the CFO went back to the vendor. They renegotiated the contract price down by 35%, added a clause with automatic fee discounts if accuracy fell below 85%, and set up a properly sized review team before launch.
How an Engagement Works
Hardy & Co. does not sell software, does not take vendor commissions, and does not install anything on your internal IT network. We are completely independent.