In my recent piece on the uncanny valley of enterprise AI, I described a familiar trap: AI outputs that look polished and authoritative until you peel back the top layer and realize there isn’t much substance.

The uncanny valley is more than a quality problem. It’s a cost problem…and a massive one at that. Companies are exceeding their AI budgets by 300 to 400%! Skyrocketing token usage is just leading to skyrocketing token costs without gains in value. And on top of that, many enterprises are blindly operating without budgets for AI token usage, leading to alarmingly high AI-related bills.

The companies suffering didn’t choose bad tools. But they did underestimate what it actually takes to cross the uncanny valley into scalable AI.

Iteration is the real AI cost driver 

Here’s what most people get wrong about AI costs: the biggest expense isn’t human review of outputs. It’s what happens when AI is left to constantly re-review and rework its own outputs without adequate oversight or control. 

That’s the uncanny valley: it sounds like automation. It isn’t. It’s an expensive loop without any economies of scale.  

Some estimates suggest that 80% of tokens spent on AI coding tasks go toward AI assessing and fixing its own bugs, before a human ever sees the output. The AI is literally burning money on itself.

We can think of AI iteration like giving a cab driver vague directions instead of the exact location you’d like to. If you say, “take me somewhere in Southwest Tampa,” you’re going to spend at least twice as much money for a trip that takes exorbitantly longer, and you still may not even arrive where you want to be. 

But, if you say, “ take me to 483 7th Street,” you’re on a direct route with predictable costs. AI is no different. The more precision and guidance you bring upfront, the more efficiently it runs. 

Putting in the work upfront pays off

Traditionally, enterprise software touts real economies of scale. You spend a million dollars implementing it and after that, the incremental cost of each additional transaction is close to zero. 

But a true paradigm shift has happened with the adoption of AI. The cost of your five millionth token is going to be the same as your cost for the first one. In short, there is no cost advantage at scale, leaving little room for error.

Luckily, that can be short-circuited with alignment and tuning on the front end. But, most enterprises skip that step because it’s hard, it takes time, and it requires real domain expertise; so they opt for a cheap and fast solution and say ‘AI will figure it out’. But, they pay for it later, at scale, in token costs and rework cycles that never end.

Choose your adventure: cheap and fast or slow custom builds

When enterprises decide to operationalize AI for contract intelligence, they typically pick one of two paths. Which both have their own shortcomings:

The cheap and fast route: 

Plug in a general-purpose LLM with RAG architecture, get fast outputs, and ship it. The cost per query and ‘answer’ looks manageable until you realize the accuracy is stuck in the uncanny valley. 

It’s good enough for a demo, but not good enough for production. Teams end up manually auditing outputs, reworking compounds, rerunning queries and as a result, costs explode.

The slow-going custom build: 

Develop proprietary agents, fine-tune for your use case, build QA frameworks, and iterate over months or years. This can eventually reach enterprise-grade accuracy, but the cost in engineering time, token spend, and operational overhead is staggering. And for most enterprises, they don’t have 18 months (or longer) to get the right level of accuracy.

This isn’t a case of the tortoise and the hare, because neither truly works at the scale or speed modern enterprises demand. 

The third path no one talks about

My team and I have been on this journey. We used generic LLM outputs that didn’t hold up under scrutiny. Then we did the hard work: creating and fine-tuning alignment frameworks, human-generated answer keys, QA methodologies, and scoring systems until we reached 99% accuracy over the course of a year. 

Our AI spend has now plateaued and stabilized while everyone else’s is exploding.

We built AI TrueCheck to spare others the same journey.

TrueCheck is a three-pronged validation process: a human-curated answer key, AI-extracted output, and a second AI layer that validates accuracy. When all three align, you get a high-confidence score. When they don’t, the specific data point is automatically flagged for expert review. No open-ended rework loops and no AI burning tokens fixing its own mistakes in the dark.

The result: our approach reduces token spend and rework by 70 to 80% compared to the build-your-own path. Path A is cheap but unreliable. Path B is reliable but expensive. Pramata’s AI TrueCheck delivers Path B performance at Path A cost.

The uncanny valley has a price tag

Following AI outputs that are 85% accurate is like following GPS directions that are only 85% correct. Most of the time you’re fine, but that one wrong turn on a 10 million contract won’t just waste your time. It will cost you trust, revenue, and credibility.

The enterprises winning right now aren’t the ones moving fastest. They’re the ones who figured out how to move fast without the rework spiral.

At Pramata, we’ve already paid the uncanny valley tax. So our customers don’t have to.

Praful Saklani is CEO of Pramata, a contract intelligence platform that transforms commercial agreements into structured intelligence for enterprise AI applications. Learn more about Pramata here.