Are LLMs about as good as they are going to get for now?

What That Means for Your Enterprise Productivity

Large Language Models (LLMs) have felt like a non-stop rocket ship since ChatGPT first launched, delivering exponential improvements every few months. For the last couple of years, the right strategy was often to wait and see—after all, a groundbreaking update was usually just around the corner.

But that rapid pace of progress may be starting to plateau.

Here at Nearly Human AI we specialize in high-accuracy, high-ROI enterprise AI solutions. And in the last three to six months, we've started seeing indicators that the frenetic pace of language model improvement is slowing down.

The Plateau is Here (For Now)

While we’ve seen major new releases, including the most recent versions, their underlying quality hasn't shown the same fundamental leap we saw between earlier generations. The core technology of LLMs appears to be reaching an optimal level of quality—one that still has a noticeable "error gap" we all recognize when using these tools.

This is a natural part of machine learning's evolution. What we're observing now is a shift: companies are focusing their efforts on building applications around the existing LLMs rather than overhauling the underlying models themselves.

For the enterprise, this marks a critical inflection point in your AI strategy.

 
The New Strategy: Stop Waiting, Start Integrating

For two years, the substantial, game-changing releases made waiting worthwhile. That benefit is largely gone. Over the next year, until we see another true technological breakthrough, the LLM capabilities we have right now are essentially the ones you'll be working with.

This means now is the time to act.

While the current technology has a quality gap, it also has immense, untapped utility. The key to extracting value is changing your approach from waiting for better models to mastering implementation and application design around the models you have.

Here's how to do it:

1. Shrink the Problem and Narrow the Scope

To achieve high ROI, you can't just throw a general-purpose LLM at a massive, complicated problem. Focus on:

Specific Use Cases: Identify high-volume, repetitive tasks that can be clearly defined.
Data Curation and Quality: Invest cycles in ensuring the data fed into the system for those specific use cases is clean, relevant, and high-quality. This is how you drive accurate outputs.

2. Bring Human Experts Seamlessly into the Loop

The reality is that LLMs will hallucinate or make errors. The most effective way to address this is by designing systems that integrate human expertise to manage risk:

Drive Hallucinations to Zero: We specialize in bringing human experts "into the loop" to review model outputs specifically when there's a risk of error.
Massive Workload Reduction: Our policy and procedure bots, for example, can see 80-85% automation on sophisticated problems. This doesn't replace the expert; it frees them up to focus on the crucial 15-20% of issues that truly require advanced knowledge and experience.

3. Shift Your Investment from "Improvement" to "Integration"

If you're already working on LLM projects, stop spending excessive cycles trying to wring out tiny improvements from the base model. Instead, focus on integrating the current level of quality into your enterprise systems in a way that generates real value.

And if you haven't started yet—the time is now. You won't get a "late start advantage" anymore by waiting for the next huge, labor-saving model to drop.

 
The Future is Bolt-On

By building your core systems now, you're not locking yourself in. When new breakthrough technologies eventually emerge, they will be able to bolt onto your existing, production-ready systems, expanding the set of use cases that have a positive ROI.

Don't wait any longer. Move these projects into production, use the current state of the technology, and start extracting massive value today.

Need help defining your highest ROI automation projects?  Schedule a consultation. We’d be happy to help you with your enterprise AI strategy.