Why Enterprise Buyers Don't Understand Your Value, And How to Fix It

Every week, an AI founder shares a version of this story with us at ThoughtCred:

"The demo went perfectly. They said all the right things. Then we got to pricing and they looked confused. Three weeks later, we lost to a competitor with worse technology."

The problem isn't your product. The problem is that enterprise buyers don't understand your value in terms that matter to their organization.

The Language Gap That Costs Millions

Because 97% of enterprises still struggle to demonstrate business value from their early generative AI efforts. There's a fundamental disconnect between what AI companies say and what buyers need to hear.

At ThoughtCred, we've analyzed hundreds of AI company pitch decks. The pattern is consistent:

What AI Companies Say	What Enterprise Buyers Hear	Why Buyers Don't Understand the Value
Our LLM achieves 95% accuracy	Technical jargon I can't evaluate	No connection to business outcome
We leverage state-of-the-art transformer architecture	Risky, unproven technology	Sounds experimental, not production-ready
Our AI reduces operational costs	Vague promise with no timeline	No specificity means no credibility
We integrate with your existing stack	Implementation headache	Benefit sounds like work

When enterprise buyers don't understand your value, they default to the safest option: doing nothing, or choosing the vendor with the most recognizable logo.

Why Enterprise Buyers Don't Understand AI Value

Finding 1: GenAI Systems Don't Learn

Most GenAI systems don't retain feedback, adapt to context, or improve over time. Buyers have been burned by AI that demos well but performs poorly in production.

Finding 2: The Shadow AI Problem

The "shadow AI" phenomenon—where 90% of employees use personal AI tools like ChatGPT for work—means buyers already have a mental model for AI. And that model is "helpful assistant," not "enterprise transformation."

Finding 3: The Trust Paradox

Vendor-led AI projects succeed more than internal builds. But buyers often don't trust vendors to understand their specific context.

This creates a perfect storm: buyers are skeptical, overwhelmed with choices, and lack frameworks for evaluating AI value.

Companies like Databricks and Snowflake succeed because they've spent years educating the market on data platform value. They didn't assume buyers understood—they taught them. AI-native companies rarely invest in this market education, and they pay the price in stalled deals.

The Three Audiences Who Need Different Value Messages

At ThoughtCred, we help AI companies understand that enterprise buyers don't understand value because there's no single "enterprise buyer." There's a committee—and each member needs different value language.

Audience 1: The User/Champion

Example: Algolia wins users by letting them experience search improvement immediately—not by explaining ranking algorithms.

Audience 2: The Economic Buyer (Finance/Exec)

Example: BetterUp publishes ROI research showing leadership coaching impact on retention and performance metrics—quantified.

Audience 3: The Technical Evaluator (IT/Security)

When enterprise buyers don't understand your value, it's usually because you're speaking to one audience in the language of another.

The Aha Moment: You're Not Selling AI—You're Selling Words

Here's what we've learned at ThoughtCred from helping AI companies crack enterprise sales: Your value proposition should change based on who's in the room, but your differentiation story must stay consistent.

That's not a feature—it's a business capability. And it's the same story whether he's talking to his CFO, his sales team, or a conference audience.

The Gong 2025 study found that 7 in 10 enterprise revenue leaders now trust AI to regularly inform business decisions. Buyers aren't skeptical of AI anymore. They're skeptical of vendors who can't explain themselves clearly.

If enterprise buyers don't understand your value, the fix isn't better demos. It's better words.

The ThoughtCred Value Translation Framework

When AI companies come to us because enterprise buyers don't understand their value, we work through this framework:

Step 1: Identify the "Before State"

What is life like for your buyer before they have your solution? Be specific—not "they're inefficient" but "they spend 14 hours weekly reconciling data across three systems."

Step 2: Name the Enemy

What's causing the pain? Not your competitor—the underlying problem. "Manual processes" or "siloed data" or "inconsistent customer experience."

Step 3: Show the Transformation

What does the "After State" look like? Quantify it: "14 hours becomes 20 minutes" or "90% reduction in manual review."

Step 4: Prove It's Possible

Who has achieved this transformation? Name them, quantify their results, offer reference calls.

This framework works because it tells a story buyers can see themselves in. And stories travel through organizations far better than feature lists.

ThoughtCred Value Translation Framework – Quiz

1. What does Step 1 ("Before State") focus on?

Describing the buyer’s current pain in specific, measurable terms Listing product features Benchmarking against competitors Talking about future roadmap

2. What is the goal of Step 2 ("Name the Enemy")?

Identifying the underlying problem causing the pain Naming your top competitor Highlighting security certifications Explaining pricing tiers

3. Which option best demonstrates Step 3 ("Show the Transformation")?

“14 hours of manual reconciliation becomes 20 minutes.” “Our LLM uses advanced transformers.” “We are the leading AI platform.” “We’re adding new features soon.”

4. What does Step 4 ("Prove It’s Possible") require?

Real customer outcomes, quantified proof, and reference calls A product roadmap Architecture diagrams A long feature list

5. Why does this framework resonate inside enterprises?

Because transformation stories spread internally faster than feature lists Because it lists every technical detail Because AI accuracy is the only thing that matters Because procurement prefers generic messaging

Making Enterprise Buyers Understand Your Value

If enterprise buyers don't understand your value, they can't buy from you—no matter how good your technology is.

The companies winning enterprise AI deals in 2025—Databricks, Snowflake, Fivetran, Adobe, Cohere—share a common trait: they invested heavily in market education and value articulation. They didn't assume buyers understood. They made sure they did.

At ThoughtCred, we believe the next generation of AI category leaders will be built on narrative excellence, not just technical excellence.