There’s no shortage of AI pilots, prototypes, and flashy demos in the world today. But how much of it is useful? Not “useful” in a vague, visionary sense but useful in the day-to-day context of enterprise AI like KPI-moving, operations-improving, and specifically grounded in real workflows.
In his latest article, “Less FOMO, More Function: A Wake-Up Call for AI Adoption”, Syren’s Director of Engineering, Avinash V, breaks down what’s going on under the surface of enterprise AI adoption. And it's not pretty. While leadership teams race to tick the AI box, many of the solutions being built have no real connection to business outcomes. They look impressive in a demo but get stuck in production.
What We’re Getting Wrong
Today’s AI stack, especially LLMs, is powerful but still lacks key components like memory, reasoning, and planning, essential for real-world complexity. Think supply chain simulations, cross-system root cause analysis, and automated workflow resolution. These are the engineering challenges and limitations of the current enterprise AI foundation itself.
But beyond the tech, Avinash dives into the behavioral traps that teams fall into:
- Executive FOMO driving half-baked projects
- Overhiring data scientists while ignoring broken data pipelines
- Celebrating model accuracy when the business still can’t make decisions faster
- Assuming pattern = causality and deploying models that miss the nuance of real operations
For leaders who have invested in AI tools that ended up gathering dust, you’ll recognize the pattern.
What Useful AI Looks Like
Avinash shares his insight on what practical, value-driving AI looks like, with two sharp examples:
- A global manufacturer reduced unexpected downtime by 27% in six months by integrating logs, sensor data, and maintenance records to target a single KPI and avoiding building large AI for enterprise models.
- A fintech team embedded a thin AI copilot into their underwriting workflow. Because it was grounded in curated risk tables, it dropped review time from 30 minutes to 5, without triggering compliance flags.
What’s common across these cases? Size of the model? Tech stack? NO!
Just three things: a clearly defined business outcome, a solid data foundation, and a thin layer of AI that fits into how people already work.
From FOMO to Flow: The Shift We Need
In the article, Avinash offers a three-step playbook that flips the script on typical enterprise AI projects:
- Start with one KPI. Not an enterprise-level roadmap. Just one problem worth solving.
- Fix your data layer first. If the data is scattered, incomplete, or duplicated, the model will fail.
- Build something small and useful. Implement a "minimum lovable copilot" and iterate from real usage.
It is a practical approach to how Syren is building AI into real systems today. Avinash also shares where things are headed on the engineering front. With tools like Code Interpreter and Devin accelerating developer workflows, he predicts a shift toward smaller, AI-orchestrated teams. Think of five engineers doing what once took thirty, with systems that handle 80% of the heavy lifting.
Conclusion
Enterprises don’t fall short on AI because of a lack of ambition; they fall short because the focus drifts from outcomes to optics. Avinash’s article brings the conversation back to what matters: clear problem statements, reliable data foundations, and AI that’s embedded in how teams work.
For leaders trying to cut through the noise and build AI that moves the business forward, this piece offers sharp insight, hard-won lessons, and a practical way to get started.
If you're tired of AI that demos well but doesn’t deliver, this is worth a read.
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