The demo was impressive. Documents scanned, classified, and routed in seconds. The vendor’s sales engineer made it look effortless.

Three months post-implementation, the same system was achieving 61% accuracy on document classification. The client was hiring temps to manually fix the errors.

This gap between demo and reality isn’t unique to AI. But AI products have a particular vulnerability: the demo dataset is carefully curated, while your production data is messy, inconsistent, and full of edge cases nobody anticipated.

The Demo vs Reality Gap

Here’s what vendors typically show versus what you’ll actually encounter:

Demo Environment	Production Reality
Clean, structured data	Inconsistent formatting, missing fields
Common document types	Industry-specific variations
Standard workflows	Exception handling for 40% of cases
English-only content	Mixed languages, abbreviations, jargon
Ideal conditions	Network latency, system integrations

Red Flags in the Sales Process

After seeing dozens of enterprise AI implementations, certain patterns predict trouble:

“Our AI learns from your data”

This is technically true but practically misleading. Learning requires:

• Months of labelled training data
• Ongoing feedback loops
• Regular model retraining
• Someone to manage all of this

Ask: “How long until the system performs as well as your demo?” Honest answers are measured in quarters, not weeks.

Accuracy claims without context

“95% accuracy” means nothing without knowing:

• What’s the baseline? (Human accuracy on the same task)
• What’s the test set? (Representative of your actual data?)
• What’s the confidence threshold? (Are uncertain cases included?)

I’ve seen vendors claim 95% accuracy on the 60% of documents the system was confident about—while quietly routing the other 40% to manual review.

Integration “partnerships”

Check whether integrations are:

• Native and maintained by the vendor
• Third-party connectors with separate support
• “Available” but requiring custom development

The difference between these categories is roughly 3x in implementation time and cost.

Reference customers in different industries

If the vendor’s reference customers aren’t in your industry, ask why. Sometimes the honest answer is “we haven’t done this before.” That’s not necessarily disqualifying—but you should price the risk accordingly.

Questions That Reveal Reality

Before signing, get specific answers to:

1. What’s your longest-running production deployment in Australia?

Length of deployment matters more than number of customers. AI systems that work in month one often degrade without proper maintenance.

2. What percentage of your customers achieve the accuracy shown in demos?

Any honest vendor will admit it’s not 100%. The interesting question is what distinguishes successful deployments from struggling ones.

3. What does ongoing maintenance cost?

Many AI systems require regular retraining as data patterns shift. If the vendor’s pricing doesn’t include this, you’ll face surprise costs or degrading performance.

4. Can we talk to a customer who struggled?

Every vendor has implementations that went sideways. How they handled those situations tells you more than their success stories.

The Proof-of-Concept Trap

Many vendors offer free or discounted POCs. This sounds low-risk but creates several problems:

• POCs use vendor resources and attention you won’t get post-sale
• Success in a controlled environment doesn’t predict production success
• Sunk cost fallacy pushes you toward purchase despite warning signs

A better approach: negotiate a paid pilot with clear success criteria and exit clauses. If the vendor won’t agree, ask yourself why.

What Good Vendors Do Differently

The vendors worth working with typically:

• Lead with limitations. They explain what won’t work before what will.
• Provide realistic timelines. Implementation estimates include buffer for surprises.
• Share failure stories. They’ve learned from projects that struggled.
• Offer meaningful guarantees. Performance commitments with actual consequences.

Working with AI consultants Sydney firms independently can help evaluate vendor claims before you’re committed. Third-party assessment isn’t free, but it’s cheaper than a failed implementation.

When Overselling Becomes Lying

There’s a line between optimistic marketing and outright misrepresentation. You’ve crossed it when:

• Demo data is manipulated to perform better than real data would
• Customer testimonials are fabricated or misrepresented
• Capabilities are promised knowing they don’t exist
• Pricing excludes known required components

Document everything during sales. Email confirmations of verbal claims. Screenshots of demo performance. Written commitments on timelines.

If things go wrong later, this documentation matters—both for renegotiating contracts and for legal remedies.

A Healthier Approach

The enterprise AI market is maturing, but overselling remains common. Protect yourself by:

1. Separating evaluation from purchase decisions (different people, if possible)
2. Requiring production-like data in demos
3. Building pilot phases with genuine exit options
4. Involving technical staff in vendor conversations, not just executives

The vendors frustrated by this rigour aren’t the ones you want anyway. Good partners appreciate informed buyers.