Every AI vendor claims 95%+ accuracy. In production, you'll be lucky to get 70%. Here's how to see through the marketing and evaluate what you're actually buying.
Test AI claims on your actual data. Vendor benchmarks mean nothing—your domain, your noise, your edge cases determine real performance. Pilot before committing.
Updated January 2026: Added AI Vendor Evaluation Scorecard for systematic assessment.
The AI sales cycle follows a predictable pattern: impressive demo, bold accuracy claims, enthusiastic pilot, disappointing deployment. This is why so many AI pilots fail to reach production. The gap between demo and reality isn't always intentional deception. Sometimes vendors genuinely believe their own benchmarks. The result is the same: enterprises buy capabilities that don't exist.
The Demo Is Not the Product
AI demos are carefully curated performances. The vendor controls inputs, environment, and success criteria. What you're watching isn't representative of production use.
Cherry-picked examples. Every demo uses inputs the system handles well. The speech recognition demo features a clear speaker with a standard accent in a quiet room. The computer vision demo shows well-lit, centered images. The chatbot demo asks questions it was trained to answer.
Preprocessed data. Demo data has been cleaned, normalized, and formatted in ways your production data won't be. The model works great on pristine inputs. It falls apart on real-world messiness.
Human in the loop. Many demos have humans quietly correcting outputs or routing queries. The "AI" you're watching is a hybrid system that won't scale.
The "Wizard of Oz" problem. In some cases, the demo is barely automated at all. The impressive responses come from humans behind the curtain. This is more common than you'd expect, especially with startups racing to close deals.
How Accuracy Numbers Lie
When a vendor claims "97% accuracy," that number almost certainly doesn't mean what you think it means. As Skywork AI's 2025 evaluation guide notes, a model boasting 99% accuracy on a contaminated benchmark may struggle with your proprietary workflows and domain-specific terminology.
Benchmark vs. Reality
AI accuracy is measured against benchmark datasets. These datasets are:
- Clean: Professionally recorded audio, studio-quality images, well-formatted text
- Balanced: Carefully curated to represent the problem evenly
- Static: The same test set used for years, allowing models to implicitly overfit
- Generic: General domains, not your specific industry vocabulary or use cases
Your production data is none of these things. It's noisy, imbalanced, constantly changing, and domain-specific. A model scoring 97% on the benchmark might score 70% on your data. That's not a bug. It's expected.
The Metrics Game
Vendors choose metrics that make their numbers look best:
Accuracy on easy cases. "97% accuracy" might mean 97% on the 80% of cases that are easy. It might completely fail on the 20% that matter.
Top-5 vs Top-1. "95% accuracy" might mean the correct answer is in the top 5 suggestions. Top-1 accuracy (actually getting it right) could be 60%.
Ignoring edge cases. Some benchmarks exclude "difficult" examples. The model's performance on excluded cases could be dramatically worse.
Precision vs Recall trade-offs. A model saying "I don't know" on hard cases can have high precision. When it answers, it's usually right. But it might have terrible recall, refusing to answer most of the time.
The Distribution Shift Problem
AI models learn patterns from training data. When production data differs from training data, accuracy drops. Different vocabulary, different accents, different image quality, different user behavior. This is called distribution shift. It affects every deployed model.
The vendor's benchmark was run on data similar to their training data. Your data is different. The accuracy gap is predictable and significant. This is the core of the demo to production gap that kills enterprise AI projects.
Specific Lies by AI Category
Speech Recognition / ASR
The claim: "98% word error rate on industry benchmarks."
The reality: That benchmark used clean, scripted audio with professional speakers. Your call center has background noise, accents, crosstalk, poor phone connections, and industry jargon the model never saw.
What to ask: "What's your accuracy on noisy audio with domain-specific vocabulary?" Get them to test on YOUR data before you sign.
Natural Language Processing / Chatbots
The claim: "Handles 90% of customer inquiries automatically."
The reality: It handles 90% of a curated list of expected questions. Real customers ask questions in unexpected ways. They combine multiple intents, make typos, and reference context the bot lacks. LLMs don't actually understand. They pattern match. Real inquiries don't match training patterns.
What to ask: "What happens when the bot doesn't understand?" Often the answer is escalation to a human. You're paying for AI that handles easy cases while humans still handle hard ones.
Computer Vision
The claim: "99% accuracy on object detection."
The reality: On benchmark images with good lighting, centered subjects, and standard orientations. Your security cameras have bad angles, variable lighting, motion blur, and weather effects.
What to ask: "Can we test on our actual camera feeds?" Models that work perfectly on stock photos often fail on real-world imagery.
Document Processing
The claim: "Extracts data from documents with 95% accuracy."
The reality: On documents formatted exactly like the training data. Different fonts, layouts, scan quality, or handwritten fields cause accuracy to plummet.
What to ask: "What happens with non-standard layouts?" and "How do you handle low-quality scans?" The answers reveal whether they've solved your actual problem.
The Pilot Trap
Vendors love pilots because pilots are designed to succeed. According to TechRepublic's vendor evaluation framework, the most successful AI adoptions occur when organizations challenge vendor claims against real-world benchmarks early in the process. Controlled conditions, high-touch support, and hand-picked use cases mean pilots often succeed. When the pilot succeeds, you sign a contract. Then reality hits.
Pilot success != Production success. The gap between pilot and production is where most AI initiatives die. Conditions that made the pilot work don't exist at scale: clean data, limited scope, vendor engineers on call.
Watch out for "we'll improve with more data." This is often true but overestimated. Yes, models improve with data. No, improvement isn't linear or guaranteed. Getting from 70% to 80% is usually feasible. Getting from 80% to 95% might be impossible with your data.
Pilot metrics vs business metrics. Pilots measure technical metrics: accuracy, latency. Business success requires different metrics: cost savings, error reduction, customer satisfaction. Make sure you evaluate what actually matters.
Questions to Ask Before Buying
About Accuracy Claims
- What benchmark are these numbers from? Can I see the benchmark dataset?
- What's your accuracy on noisy/messy/real-world inputs?
- How does accuracy vary by input quality/type/domain?
- Can you test on OUR data before we sign anything?
About Production Deployment
- What percentage of inputs will require human review/fallback?
- How do you handle cases the model can't process?
- What's the latency in production, not in demos?
- What infrastructure do we need to run this?
About Ongoing Performance
- How do you handle model drift over time?
- What's the retraining process and frequency?
- How do you handle new edge cases we discover?
- What's the total cost of ownership, not just license fees?
About References
- Can I talk to customers in my industry who've been in production for 6+ months?
- What accuracy did they achieve on real data?
- What problems did they encounter?
How to Trap a Vendor
Don't just ask questions. Set traps. Here are two that have saved my clients millions.
The Poison Pill Dataset
Don't just ask for a demo. Give them a "Poison Pill" dataset. Take 50 rows of your actual data. Intentionally corrupt 5 of them with realistic noise: misspellings, wrong formats, sarcastic customer comments, incomplete fields. Include one obviously wrong entry that any human would catch.
If their model reports 100% accuracy on that dataset, they didn't run it. They lied. Walk away.
If they claim they can't test on your data "for security reasons," offer to run the test yourself on their platform. If they still refuse, they're hiding something. The test takes an hour. Their reluctance tells you everything.
The Mechanical Turk Sting
Ask for the latency distribution graph. Not average latency—the full distribution. If there's a suspicious spike at 30-60 seconds, that's not a slow GPU. That's a human in a call center typing the answer.
You're not buying AI. You're buying Mechanical Turk at SaaS prices.
I've seen vendors charge enterprise rates for systems where "difficult" queries get routed to humans overseas. The AI handles 80% of easy cases. Humans handle the 20% that matter. You're paying for the illusion of automation.
How to verify: Submit 10 queries with deliberate ambiguity at 3 AM their time. Check response times. If the "AI" suddenly gets slower when humans are sleeping, you have your answer.
Red Flags in AI Sales
Walk away - or at least proceed with extreme caution - when you see:
No testing on your data. If they won't test on your actual data before signing, they're hiding something.
Accuracy claims without context. "95% accuracy" means nothing without knowing the benchmark, conditions, and metric.
Demo-only evaluation. If the entire sales process is demos and slides, you're not evaluating the product.
Vague answers about failures. Every AI system fails on some inputs. If they can't clearly explain failure modes, they haven't deployed at scale.
"It will improve with your data." Maybe. Or maybe not. Get commitments, not promises.
Resistance to defined success criteria. If they won't agree to specific, measurable goals before the pilot, they don't believe they can meet them.
AI Vendor Evaluation Scorecard
Score the vendor on these dimensions before signing anything:
How to Actually Evaluate AI
Define success criteria before you start. What accuracy on what metrics would make this worthwhile? Get agreement before any demos or pilots.
Test on your data. Not their demo data. Not benchmark data. Your actual, messy, production data. If they won't do this, walk away.
Include edge cases. Your hardest cases, your weirdest inputs, your most critical scenarios. This is where AI usually fails. This is where failure is most costly.
Measure total cost. Licensing is just the start. Add infrastructure, integration, training, maintenance, human review for failures, and monitoring. The real cost is often 3-5x the license fee.
Plan for failure. What happens when the AI is wrong? You need human fallbacks, error handling, and processes for cases AI can't handle. Build these into evaluation.
Get production references. Not pilots. Production deployments in your industry, running for at least six months. If they don't have these, you're their guinea pig.
AI Vendor Evaluation Scorecard
Score each vendor before signing anything. Each criterion is worth 0-3 points:
| Dimension | 0 Points | 1 Point | 2 Points | 3 Points |
|---|---|---|---|---|
| Testing Access | Demo only | Their test data | Your data, their env | Your data, your env |
| Accuracy Disclosure | "High accuracy" | Benchmark only | Benchmark + caveats | Per-domain breakdown |
| Failure Handling | Undefined | "Escalates" | Documented fallback | Fallback + SLA |
| Production References | None | Different industry | Same industry, <6mo | Same industry, 6mo+ |
| Cost Transparency | License only | + Infrastructure | + Integration | Full TCO model |
| Latency Evidence | None | Average only | Distribution provided | P95/P99 with volume |
The Bottom Line
AI capabilities are real and improving. But the gap between marketing and reality remains enormous. Vendors have strong incentives to oversell. They have weak incentives to set realistic expectations.
This doesn't mean AI is useless - it means AI requires rigorous evaluation. The enterprises that succeed with AI are the ones that:
- Define clear success metrics before evaluation
- Test on real production data
- Plan for AI failure modes
- Measure total cost of ownership
- Start narrow and expand based on results
The enterprises that fail are the ones that buy based on demos, skip testing on their own data, and assume production will look like the pilot.
AI vendors aren't necessarily lying. They might genuinely believe their benchmarks. But their incentives don't align with your outcomes. Your job is to verify, test, and plan for reality.
"AI vendors aren't necessarily lying. They might genuinely believe their benchmarks. But their incentives don't align with your outcomes."
Sources
- ISACA: The Reality of AI - Oversold and Underdelivered — Industry analysis on AI vendors under pressure to claim capabilities that aren't production-ready
- Closing the Eval-Deployment Gap in AI Systems — Technical analysis on how systems showing 95% in lab can drop to 60% in deployment
- Fortune: Stop Chasing AI Benchmarks — Why organizations need their own evaluation frameworks rather than trusting vendor metrics
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