The long-held notion of a ‘data moat’ as a defensible strategic asset is undergoing a profound transformation, driven by the integration of artificial intelligence. While accumulating vast amounts of data remains important, its true value is increasingly unlocked not by sheer volume, but by the ability to interpret and operationalize it through context, metadata, and domain-specific intelligence.
The Evolving Data Moat
Ahsan Shah, SVP of analytics and AI at Billtrust, explained to PYMNTS during a discussion for the April edition of the ‘What’s Next in Payments’ series, ‘The Data Game,’ that ‘Every company has a data moat.’ However, he emphasized that with AI’s ascendant role, the definition of this moat is shifting. ‘Data is the oil. And we’ve heard this for decades now. But now with AI, I think the game is changing,’ Shah stated. ‘What’s the point of having terabytes of data when you can’t actually tap into it?’
The differentiator for leading organizations is no longer just access to data, but the sophisticated interpretation and application of that data. This structural shift is compelling businesses to re-evaluate their entire operational frameworks, from underlying infrastructure to the customer experience.
The Rise of the Context Layer
Data in its raw form is inert; context is what renders it actionable. Shah described a ‘context layer’ as a structured overlay comprising metadata, governance protocols, and domain-specific intelligence. This layer enables AI systems to comprehend and act upon enterprise information effectively. ‘Can AI understand your data? What is the context? How do you govern it? How do you put guardrails? That’s the new ball game,’ he noted.
This evolution is already evident in how companies approach analytics. Traditional static dashboards and one-off insights are being supplanted by dynamic, AI-driven reasoning systems. Shah highlighted that AI can now elucidate not only ‘what happened, but why did it occur.’ This leads to enhanced efficiency and velocity, with feedback loops from customer input to product iteration becoming AI-native. He pointed to AI-assisted coding and automated product development cycles, stating, ‘Something that would’ve taken three months to build is taking three days to build.’
AI’s Impact on Customer Experience and Operations
Nowhere is this transformation more apparent than in customer experience, particularly within the payments sector. Historically governed by rigid rules and static processes, payments are becoming more fluid and adaptive. Companies can now tailor decisions in real time, offering targeted early payment discounts, dynamically adjusting risk thresholds, or automating collections strategies based on observed behavioral patterns, rather than applying broad, uniform policies.
While AI is increasingly handling analysis and recommendations, Shah clarified that ‘You still have human in the loop.’ The balance is shifting, with humans focusing on judgment and oversight while AI manages the analytical heavy lifting.
Expanding the Data Universe
Underpinning this paradigm shift is a significant expansion in what is considered usable data. Beyond structured transaction records, unstructured inputs such as emails, call transcripts, invoices, and CRM notes are now being integrated into a unified intelligence layer. ‘We used to say, ‘You’ve got to conform to these six things.’ Now, we’re getting into: Give us the ocean,’ Shah remarked. ‘The diversity of data alongside this AI layer of context is the most powerful asset for any company.’
AI’s capacity to parse and synthesize vast, heterogeneous datasets liberates companies from rigid schemas, allowing them to embrace complexity and extract value from it. ‘Data management strategies for enterprises are the foundation of everything,’ Shah stressed. ‘If you have bad data, you won’t get very far.’
The Future: Agent Harnesses and Autonomous Workflows
Looking ahead, Shah anticipates the emergence of ‘agent harnesses’ – systems designed to orchestrate multiple AI agents for autonomous execution of complex workflows. This represents a move beyond mere assistance to delegation, where AI takes ownership of entire processes from design to completion. ‘I’m going to give you everything you need, and I’m going to leave. By the time I come back, I want you to be done,’ he described of this forthcoming technological paradigm.
This capability offers an ‘amazing unlock,’ enabling teams to develop multiple features concurrently and dramatically boost output. However, it also introduces new challenges related to governance, cost, and reliability. Shah cautioned against the notion of complete human obsolescence: ‘What won’t work is to say, ‘We don’t need human beings at all — just hit a button.’’ Robust guardrails, enterprise security, and careful consideration of storage, token costs, and ROI are essential.
For enterprises navigating this transition, the message is unequivocal: AI is no longer optional. Shah concluded, ‘In the last six to 12 months, there’s no debate now. AI is about survival, it’s existential.’
