My ChatGPT journey began in the dark ages (late 2022, if you can believe it). Back then, amid the flood of AI-generated poems and songs, I dismissed it as just another flash in the tech pan.
Then came my Excel moment.
After hours of wrestling with a particularly nasty formula—the kind that makes you question your life choices—I thought, “What the hell, let’s see what ChatGPT can do.” The formula it generated worked perfectly on the first try. Mind. Blown.
Emboldened, I threw a more complex formula at it. ChatGPT fired back with confidence, but the formula worked about as well as those ‘AI-generated’ profile pictures that give people three hands. Just as I was ready to dismiss it as another party trick, I had an idea. ChatGPT couldn’t “see” my Excel file, but what if…? I dropped the file into Google Drive and shared the link, expecting nothing.
“Ah, now I see what you’re looking for,” it replied, before generating a perfect solution. That moment changed everything. I’d not only found a way through OpenAI’s early guardrails (a hole they quickly patched) but discovered something crucial: success with AI wasn’t just about asking questions—it was about providing the right context.
Fast forward to late 2024, and I’m neck-deep in production deployments of RAG solutions and countless prototypes. I’ve become a firm believer that Generative AI is like a flawed superhero—incredibly powerful but far from perfect. And that’s okay.
Here’s the thing about Gen AI: it operates in a probabilistic world, while enterprise solutions demand deterministic certainty. This fundamental mismatch is where many organizations stumble. They see the magic—just as I did with that first Excel formula—and immediately want to transform their entire business. But that initial success can be deceiving. The path from prototype to production isn’t just about scaling up technology; it’s about bridging the gap between AI’s probabilistic nature and business’s need for certainty.
Let me show you how this plays out in the real world…
Every day brings a flood of AI innovations—new models, frameworks, and evaluations, each claiming to push the boundaries of what’s possible. “Model X beats current state-of-the-art by Y%” has become the tech equivalent of “This one weird trick…”
If you’re running a business, it’s getting harder to ignore. When everyone from your competitors to your coffee shop is implementing AI solutions, the pressure to jump in becomes intense.
Here’s a scene that’s becoming all too familiar. Picture this: You’re leading an engineering team, and your customer, an insurance company, wants to modernize their claims processing with AI. The pitch is compelling – reduce processing time, cut costs, improve consistency. The financial benefits seem impossible to ignore.
Here’s where the traditional engineering approach kicks in: The customer, being appropriately cautious, asks for a prototype. They provide a handful of insurance contracts and claims as test cases. Perfect! You dive in with the latest tools: a sophisticated RAG (Retrieval-Augmented Generation) pipeline, GPT-4 as your model of choice, the works. Sentence transformers, PyMuPDF, vector databases, cosine similarity – you deploy the full arsenal of modern AI engineering.
(In retrospect, this is where having a Consulting Engineer’s mindset would have made all the difference.)
The prototype works beautifully. Given a test claim, your system correctly determines it should be rejected. The customer is impressed. Green lights all around! Phase 1 will handle 10,000 contracts – a perfect start. It was that initial success all over again—just like my Excel epiphany. And just like my experience, that initial success would prove to be both enlightening and misleading.
You scale up the prototype, moving it to Azure with all the trimmings – AI Search, API Management, Document Intelligence. Your UI team delivers a gorgeous interface. Everything looks perfect.
Then testing begins, and the color drains from your face. Accuracy: 40%.
You roll up your sleeves and optimize everything: – Refined chunking strategies for better document processing – Enhanced context in the vector data – Improved query understanding with robust guardrails – Advanced retrieval and ranking methods
Just as I had learned to provide mind-numbingly detailed context to get my Excel formulas right, we were now discovering that enterprise-scale AI needed equally meticulous attention to context—but at a whole different level of complexity.
After several weeks of overrun, you hit 80% accuracy. The IT team is impressed with the technical achievement. But then comes the meeting with the business team, and they ask the million-dollar question:
“So, you’re saying we still need someone to review every AI decision? Where exactly are the cost savings?”
Cue the second face-draining moment.
Did this project fail? Yes and no. The technology worked – 80% accuracy is actually impressive for complex document analysis. But the business case collapsed because we were solving the wrong problem.
The solution, as with my Excel journey, wasn’t to abandon the technology when it showed its flaws, but to understand its sweet spot. This wasn’t about finding a perfect solution—it was about finding the right application for an imperfect but powerful tool.
Here’s where it gets interesting. The same technology, applied differently, could have delivered significant value. Let me show you two alternative approaches that could have worked:
Instead of trying to replace human decision-making entirely, imagine using AI as a first-pass triage system: – High-confidence cases (95%+ certainty) get fast-tracked – Moderate-confidence cases get human review with AI-generated summaries – Complex cases get full human attention
Even if only 20% of claims qualify for fast-tracking, that’s still significant time savings. Plus, the AI-generated summaries help speed up human review of the remaining cases.
Or consider using AI as a document pre-processing assistant: – Automatically extract key information (dates, policy numbers, claim amounts) – Flag potential issues or missing information – Cross-reference policy terms – Create structured summaries
This approach doesn’t remove the need for human judgment, but it can help claims processors handle 3x more claims per day by eliminating manual document parsing.
Align Technology with Business Goals – If ROI had been the north star from the beginning, the solution would have looked very different. Sometimes the best AI solution isn’t full automation – it’s smart augmentation of human capabilities.
Data is King, Context is Queen – Enterprise data is typically built for human consumption, assuming years of background knowledge and context. An AI system can’t magically acquire this context – it needs to be explicitly provided. Most enterprise data requires significant enrichment before it’s AI-ready.
Looking back at my journey from that first Excel formula to complex enterprise deployments, I’ve learned that success with Generative AI isn’t about perfect execution—it’s about embracing and working with imperfection. Just as I learned to work with ChatGPT’s quirks rather than against them, businesses need to find their own way to harness this powerful but flawed technology.
The key isn’t to avoid AI – it’s to embrace it wisely, with a clear understanding of both its current limitations and its extraordinary potential. Here’s your roadmap for getting started:
Remember, you don’t have to get everything right on the first try. With the right mindset and a willingness to learn from both successes and stumbles, you can turn this powerful but imperfect technology into your most valuable business asset.
The future belongs not to those who can build the most sophisticated AI systems, but to those who can best bridge the gap between human expertise and artificial intelligence.
Instead of trying to replace human decision-making entirely, imagine using AI as a first-pass triage system: – High-confidence cases (95%+ certainty) get fast-tracked – Moderate-confidence cases get human review with AI-generated summaries – Complex cases get full human attention
Even if only 20% of claims qualify for fast-tracking, that’s still significant time savings. Plus, the AI-generated summaries help speed up human review of the remaining cases.
But here’s the rub: Gen AI has one major pitfall that we need to talk about. It operates in a probabilistic paradigm, not the deterministic one that enterprise solutions are built on. While ML engineers and users understand this well, typical enterprise applications deal in ones and zeros—no room for gray areas. Trying to force-fit an inherently fallible technology into such systems without acknowledging this fundamental difference is a recipe for failure and frustration.
