From Hype to Hard Numbers
Our first GenAI build? Honestly, it was FOMO. Everyone around us was launching something, so we did too.
It was spring ’23. Quick prototype — took user stories and used GPT-3.5 to spit out test cases. Sounded smart. Demo looked slick. But once it hit our dev flow? It just cluttered things. QA didn’t trust the output. Engineers quietly skipped it. We never shipped it to prod.
We stopped talking about it after two sprints.
I’m not ashamed of that — we learned fast. But I’ve become allergic to the phrase “AI-powered” since. Most GenAI projects die not from tech failure, but from zero real use. Or worse: from pretending something works, just to justify the effort.
ROI isn’t a spreadsheet number. It’s when the team says: “Don’t roll this back.” It’s when someone fights for the feature because it saves their time every single day. That’s it.
We’ve shut down more than one GenAI initiative at Pynest. And kept two that stuck. This piece is about what made the difference.
Not All GenAI Is Built for Value
The truth? A lot of GenAI projects launched in 2023 were shiny tech demos wearing a product hat.
We’ve seen teams proudly announce “GPT integration” — only to quietly shelve the feature six weeks later. Why? No metrics. No adoption. Just a half-working UI glued to an LLM and a hunch that “this might be useful.”
There’s a big gap between building something impressive and building something that shifts a metric. A useful GenAI project starts with three things:
- A measurable KPI (and no, “user happiness” doesn’t count if you can’t quantify it),
- A clear user — someone whose day changes if the tool works,
- A connection to a business process that already matters.
Otherwise, it’s just a toy with fancy autocomplete.
Where ROI Is Real: Use Cases That Work
Automating Customer Support
Fintechs and service companies are seeing tangible returns by using GenAI to handle routine queries. It reduces First Response Time (FRT) and lowers the workload on human agents. We’ve worked with a SaaS support platform where a GenAI assistant deflected up to 60% of incoming tickets—without sacrificing resolution quality.
Generating SQL for BI Analysts
Writing SQL by hand often became a bottleneck in our analytics workflow. Once we introduced a GenAI tool that turned plain-language questions into working SQL queries, everything sped up. Dashboards that used to take a few days were ready in hours. Analysts didn’t have to wait on engineers — they could explore data on their own and walk into meetings with answers, not just questions.
Predictive Ticket Classification and Prioritization
AI-driven classification helps triage incoming support, ops, or product requests in real time. In one B2B use case, auto-tagging and routing based on GenAI predictions helped a product team cut backlog review time by 40%. Even before a human touched the ticket, the system predicted urgency, assigned ownership, and suggested next steps.
Expert Insight
Vijay Kotu, Chief Analytics Officer at ServiceNow, noted:
“Enterprises are focusing on clear AI ROI metrics like productivity, growth, and customer satisfaction. Aligning AI initiatives with business goals… before scaling.”
That approach mirrors what we see in successful implementations: GenAI projects with a narrow, measurable objective tend to succeed where broader, exploratory efforts stall. If there’s no clear KPI tied to adoption, it’s just another demo.
GenAI ≠ Scale (Yet)
A good GenAI demo is easy to build. A scalable system? That’s a different story.
We’ve seen MVPs that work great during a demo — the prompt is tight, the answer looks smart, and everyone’s impressed. But when you move past staging, you start hitting limits: API calls get expensive fast, context windows clip your inputs, and the same prompt stops working once users behave unpredictably.
Most LLM-based features aren’t plug-and-play at scale. You need fallback logic for low-confidence outputs. You need prompt versioning, prompt monitoring, and observability. And most of all — you need a reason to keep paying for inference once your monthly bill starts to resemble a senior engineer’s salary.
Hidden costs show up in strange places:
– Time spent reviewing answers for compliance
– Retraining users to “prompt better”
– Frustration when latency crosses 2–3 seconds
– Security reviews that block launch altogether
Just because GenAI works technically doesn’t mean it works operationally. If the value is fragile or the cost is unpredictable, it’s not ready for prime time.
Organizational Fit > Model Quality
Everyone loves comparing models. GPT-4 vs Claude, Mistral vs LLaMA — the usual leaderboard debates. But in practice, the model is rarely the reason GenAI projects succeed or fail.
What matters more is whether the feature fits your workflow, and whether the right people are involved to own it. We’ve seen technically solid models fail in prod because they were built in isolation. A team of ML engineers shipped a great classifier — but no product owner ever defined the success metrics. Users didn’t understand what the output meant, so they ignored it. Six months later, the project was retired quietly.
GenAI needs cross-functional ownership.
ML engineers know how to build it.
Product managers define who it’s for.
Business teams decide what value looks like.
If you’re missing any part of that triangle, no model — no matter how good — can fix the gap.
Metrics That Matter: Proving ROI Internally
You can’t prove ROI if you never defined what you’re improving.
Too many GenAI pilots end with slides full of vanity metrics: tokens processed, prompts generated, model latency. None of that matters to the CFO. If your AI doesn’t move a business needle — it’s an experiment, not a product.
What does matter? Metrics with dollars attached.
→ CAC: Did your AI chatbot lower acquisition costs?
→ Churn: Did your personalized onboarding content retain users longer?
→ Time-to-resolution: Did ticket classification reduce average support cycles?
→ Uplift: Did your AI-written subject lines beat the control in A/B?
But here’s the catch: you can’t measure lift without a baseline.
That’s why every GenAI initiative should start with a simple question: “What would success look like, in hard numbers?” Then — benchmark it. Even a rough estimate of pre-AI performance gives you the clarity to validate impact post-launch.
Without it, you’ll end up with a cool demo and no buy-in.
Lessons from the Trenches
After launching — and killing — a few GenAI experiments, I’ve started to recognize the early signs of success (and failure).
You should move forward with a GenAI project when there’s a real pain point that people already complain about. If someone’s spending hours each week tagging tickets or writing similar replies, automation is more than justified. Second, you need to be able to measure the upside. If you can’t say “this saves time,” “this improves accuracy,” or “this drives revenue,” then it’s not worth doing. And finally, you need a clear owner. Too many AI pilots float between data science and product, with no one driving them to real outcomes.
On the other hand, it’s better to pause when the only reason is “we want to explore AI.” That’s great for a weekend project, but a terrible way to build. If your input data is scattered, messy, or locked in PDFs, even the best models will produce garbage. And if you don’t have real users validating the results, you’ll lose trust before the system has a chance to improve.
Before starting, I always ask five things: Is this problem painful enough? Do we trust the data? Can we prove ROI with a real metric? Who owns this project? And are we set up to learn once it’s live?
Because GenAI is not a silver bullet — it’s just leverage. If you don’t know where to apply it, it won’t move anything.
Final Thoughts: GenAI’s Real Value Is Operational
It’s easy to frame GenAI as an automation tool — something that replaces repetitive work, cuts costs, or reduces headcount. But that’s not where I see the biggest impact.
In practice, the real value shows up when GenAI supports better decisions. When it gives your sales team context before a call. When it helps analysts test more hypotheses in less time. When it shortens feedback loops between data and action. It’s not replacing people — it’s amplifying them.
This mindset should shape your roadmap. Don’t chase the flashiest features. Build tools that fit into how your team already works — and help them do that work faster, better, or more confidently.
And always stay grounded. Not every project will work. Some will flop. But if you treat GenAI as a system of leverage — not a magic wand — you’ll start spotting opportunities where others still see hype.
“In five years, I don’t think GenAI will be the shiny object anymore. It’ll just be part of how we work — quietly powering decisions, content, and support. The winners will be the ones who learned how to use it without chasing headlines.”
— Andrew Romanyuk, Co-Founder & SVP of Growth at Pynest
