Harsh Verma is the Principal Software Engineer – AI/ML at Palo Alto Networks, an IEEE Senior Member & BCS Fellow.
For much of the AI industry’s tremendous growth over the last few years, companies believed their advantage came from owning better models, with the goal of investing heavily to own superior models.
The AI landscape looks much different today. According to the Stanford AI Index Report 2025, performance gaps between leading frontier models are narrowing across many benchmarks. Open-source alternatives can also now perform tasks that only specialized systems could perform earlier.
Since companies have less ability to differentiate themselves by the models they choose, building better data orchestration becomes the moat for the next stage of the AI era. Data orchestration is the process of knowing which data to use to keep AI infrastructure running smoothly. If AI is the engine, data orchestration is the fuel system.
The Commoditization Of Intelligence
According to recent Fivetran research, enterprises spend about $29.3 million annually on data, yet 73% of enterprise data initiatives fail to see the expected returns.
These initiatives are crucial for AI success, which depends increasingly on reliable, real-time, well-governed data pipelines.
When Bloomberg developed BloombergGPT, for instance, what differentiated them was their access to decades of proprietary financial data, market information, news content and domain-specific knowledge.
The project highlighted an increasingly important reality: Unique data creates more defensibility than unique algorithms. Bloomberg’s moat is not its AI capabilities but the ability to feed high-quality financial intelligence into AI systems continuously.
As models are becoming more interchangeable, data orchestration becomes the strategic capability. Organizations should now ask, “Which model integrates better with our data, workflows and compliance?”
The Battle Below The Model Layer
In real-world deployments in scalable secure systems, I’ve seen teams invest heavily in selecting or fine-tuning the best model, only to see limited impact in production due to delayed pipelines, fragmented data sources and a lack of real-time context.
Consider a digital payment platform. The competitive advantage isn’t detecting fraud, since nearly every payment platform presumably has fraud-detecting capabilities. While a model alone might flag a transaction as risky, the decisions may contain high levels of inaccuracy without orchestrating the signals.
Crucial structures like real-time data pipelines, knowledge management, workflow orchestration and feedback loops are key aspects of data orchestration because they tell whether an AI system receives accurate, relevant and timely information.
From what I’ve seen, strong models fail in weak systems, while moderately good models perform well when supported by high-quality, well-orchestrated data.
The next generation of enterprise AI initiatives will need stronger data pipelines, cleaner data environments, better retrieval systems with continuous monitoring.
These components ensure the right data reaches the model at the right time with the right context, eliminating delays, fragmentation and blind spots that make even strong models ineffective. They turn isolated predictions into real-time, actionable decisions by continuously integrating, validating and feeding back data across the system.
The True Moat
The first phase of the AI race was about getting access to powerful models. Now that everyone has access to powerful models, the advantage comes from having the best, most up-to-date, and most accessible information feeding into their AI.
From my experience, the biggest gap between successful and struggling AI systems is context rather than intelligence. What competitors cannot copy easily are the years of enterprise knowledge, high-quality data pipelines, governance frameworks and operational workflows that deliver the right information at exactly the right moment.
Anyone can access a good model, but not everyone has high-quality, well-organized data.
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