By Sagar Mahurkar
At a large manufacturing firm in India, planners recently faced a familiar but costly problem. Demand for one product line surged unexpectedly in the west, while inventory quietly piled up in the north. Forecasts looked “accurate” on paper, yet shelves were empty where customers needed stock and overfilled where they didn’t. The issue wasn’t lack of AI. It was that insights never translated cleanly into decisions and actions.
This gap, between intelligence and impact, s where the next generation of AI startups is quietly doing its most meaningful work.
Having worked closely on dozens of enterprise AI deployments across industries, one pattern stands out clearly. The real winners in AI aren’t building smarter models, they’re building systems that turn enterprise data into decisions and decisions into results. Models have become more capable as global technology giants invest hundreds of billions of dollars annually in infrastructure. Yet enterprise AI success still hinges on something far less glamorous: how well intelligence is grounded in business context and operational reality.
Manufacturing: From prediction to action
Manufacturing planning has become materially harder since the pandemic. Seasonality has broken down. Tariffs shift pricing overnight. Supply chains remain fragile. Add the explosion of data centers and infrastructure demand, and a single planning error can cascade into excess inventory in one region and lost sales in another.
Startups creating value here don’t stop at forecasting demand. They build closed loops.
First, they unify signals: internal data like sales, inventory, promotions, lead times, and returns combined with external drivers such as weather, construction activity, and market indicators.
Next, they support decisions: instead of a single “best” forecast, planners see multiple constraint-based scenarios, allowing SIOP teams to understand risk and make informed trade-offs.
Finally, they drive execution: outputs are translated into concrete actions, reorder points, allocation rules, production plans, and inventory positioning.
The results are tangible. In real deployments, enterprises have reduced inventory by roughly 15% while maintaining service levels, and improved revenue by around 3% simply by reducing lost sales through better availability.
Agriculture: Turning fragmented data into timely decisions
Agriculture is rich in data but poor in decision velocity. Satellite imagery, weather models, farm logs, and mill telemetry all exist, yet insights often arrive too late or without context.
High-impact AI startups are stitching together the entire farm-to-factory chain into a single decision fabric. They detect crop stress, water content, and growth variability through remote sensing and agronomy-aware analytics. These signals are converted into clear recommendations, when to irrigate, when to harvest, how to schedule logistics. Downstream, mill operations are optimized by adjusting setpoints to improve recovery and throughput.
Here, value shows up not in dashboards, but in the physical outcomes of the season. Large agricultural operations have seen around a 4 percent improvement in field density and yield, along with roughly a 6 percent gain in mill recovery when recommendations are consistently executed and tracked.
Legal: Speed with defensibility
Legal teams operate under intense time pressure, dealing with massive volumes of unstructured data, dockets, exhibits, contracts, correspondence, and filings. Speed matters, but so does trust. An answer without evidence is a liability.
The startups delivering impact here design for defensibility from day one. Outputs are grounded in source documents with citations. Systems support rapid triage, timeline building, and issue spotting while fitting seamlessly into existing case workflows, complete with permissions and audit trails.
General-purpose chat tools can assist with drafting, but they are rarely built for enterprise-scale repositories, granular access controls, or repeatable evidence chains. In practice, the biggest measurable win is time: teams cut first-pass review cycles by 30 to 50 percent while improving consistency because every insight is traceable back to the record.
What truly separates impact from hype
Across industries, the playbook is remarkably consistent.
Start with the data, the domain, and the decision, not the model.
Get information architecture right before applying artificial intelligence.
Ensure recommendations connect directly to actions and feedback loops.
Track adoption and ROI with the same rigor as any operational program.
The next wave of AI success will not be defined by who uses the most advanced model. It will belong to startups that configure intelligence around enterprise realities and deliver outcomes leaders can stand behind in the operating review.
(The above article is authored by Sagar Mahurkar, VP at Findability Sciences. Views are his personal.)
Last Updated on Tuesday, February 10, 2026 10:49 am by Startup Magazine Team
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