Not a Demo: Making Agentic AI Actually Work
Agentic AI has quickly become one of the most talked-about shifts in enterprise AI. Instead of waiting for analysts to ask questions or dashboards to refresh, agentic AI acts: it monitors, reasons, and triggers workflows. But most of the conversation is stuck at the “what if” level—what if AI could manage your pipeline, your churn risk, or your customer health?
The reality is more pragmatic: agentic AI is already here, and it’s only as good as the data foundation you give it.
TL;DR:Unstructured data unlocks exploratory power; structured data unlocks operational power.
Structured vs. Unstructured: Where Agents Thrive
Agentic AI doesn’t require everything to be perfectly modeled before it delivers value. Many powerful use cases begin with unstructured data:
Reading support tickets and tagging urgency levels.
Parsing call transcripts for next-best actions.
Summarizing product feedback from surveys.
Here, the agent is creating structure on the fly—extracting entities, classifying text, surfacing trends. That’s real value, and often the fastest way to get started.
But there’s a tradeoff: scale, reliability, and governance.
Unstructured-first agents shine in exploration. They unlock insights from messy sources, but their outputs can be probabilistic and harder to govern.
Structured-first agents excel in operations. Once data is modeled and centralized in an enterprise data platform, agents can run reliably, be audited, and feed results into downstream workflows through the BI layer or other operational systems.
Example: Evaluating Customer Goals with Agentic AI
At one SaaS company, we faced a familiar challenge: thousands of customer goals were logged across the CSM organization, but quality varied widely. Some were precise, measurable, and time-bound. Others were vague aspirations that added little value.
Manually auditing thousands of goals wasn’t feasible. This is where agentic AI proved its value.
Here’s how it worked:
Centralize the data: Goals were brought into the cloud data platform, joined with customer metadata, and cleaned using transformation pipelines.
Define the task: Each goal needed to be scored against a standard like SMART (Specific, Measurable, Achievable, Relevant, Time-bound).
Deploy the agent: An embedded AI service evaluated thousands of goals in parallel, outputting structured results:
SMART score (0–5)
Improvement recommendation
Confidence level
Close the loop: The output was written back into the data platform and visualized in the BI layer, enabling CSM leaders to see at a glance which accounts had high-quality goals and which required intervention.
This wasn’t a proof-of-concept demo. It was an agent operating at enterprise scale, integrated into existing workflows. And the reason it worked wasn’t just the AI—it was the structured foundation underneath.
Why Structured Data Matters for Agentic AI
This use case highlights the point: AI can work directly on unstructured text, but structure makes it operational.
If customer metadata is missing, AI can’t contextualize goals.
If goals are stored as free text without ownership or dates, AI struggles to evaluate them meaningfully.
If results don’t flow back into the system of record, the analysis dies in a silo.
Structured data is the connective tissue. It turns AI from a one-off analysis into a repeatable, governed capability.
Other Practical Applications
Once this foundation is in place, you can apply agentic AI to dozens of structured-data problems:
Pipeline management: Continuously monitor opportunities and flag when a deal has stalled beyond statistical norms.
Churn prediction: Watch product usage signals and proactively open tasks for CSMs when engagement drops.
Expense optimization: Scan expense data and recommend outlier reviews without waiting for audits.
Goal alignment: Apply standardized frameworks to OKRs, KPIs, or performance metrics at scale, the same way SMART scoring was applied to customer goals.
Each example uses the same pattern: cloud data platform → task definition → embedded AI service → BI layer or operational action.
Closing Thoughts
Agentic AI isn’t about building a futuristic autonomous enterprise. It’s about embedding intelligence into the workflows you already run.
Unstructured data will always have a place—it’s the raw material that unlocks new insights. But structured data is what turns those insights into decisions, and decisions into action.
The companies that win with agentic AI won’t be those with the flashiest demos. They’ll be the ones that have invested in data modeling, governance, and integration—and then let AI agents do the repetitive reasoning at scale.