The institutional investment sector is facing a widening performance gap driven by the quality of underlying data infrastructure rather than the sophistication of AI models themselves. Clearwater Analytics announced at its Clearwater Connect 2026 event in Boise, Idaho, that nearly 900 of its clients are now running agentic workflows in production. This milestone suggests a transition from experimental generative AI use cases toward sustained, everyday integration across investment operations, risk management, and private markets. While 95% of institutional investors view AI as important or very important to their strategic goals, the company’s research indicates that a significant portion of the industry remains stuck in the implementation phase due to fundamental data integrity issues.
Scaling Agentic Workflows and Customization Metrics
Clearwater Analytics is reporting a significant surge in how its client base interacts with its platform's automated capabilities. Since January, the number of clients building custom workflows has risen by 197%, while the number of clients running active workflows has grown by 69%. This shift is characterized by a move away from simple conversational queries toward more complex, automated task execution. The company noted that automated workflow runs have increased by 105% since January, reaching a peak of 3,591 runs completed in a single recent week with a 96% success rate.
The company is positioning these agentic workflows as being powered by a single, continuously reconciled investment record. This architecture is intended to ensure that AI outputs—whether in conversational answers or automated tasks—trace back to source data with specific citations. By embedding these agents directly into the platform, Clearwater aims to address the "Data Divide" identified in its new global research report. The report highlights that while 79% of executives rate their data as complete, only 56% rate it as accurate. This discrepancy appears to be the primary bottleneck preventing firms from moving AI from the testing phase into core operational decision-making.
The Data Divide in Institutional AI Adoption
The "GenAI and the Data Divide" research report unveiled by Clearwater Analytics illustrates a disconnect between investor expectations and operational reality. Although 79% of institutional investors expect AI-enabled firms to outperform their non-AI peers, only 40% report that AI currently informs more than a quarter of their operational decisions. The research suggests that this hesitation is rooted in a lack of trust; 99% of surveyed institutional investors identified unreliable and opaque data as the most significant barrier to trusting AI-generated outputs.
Clearwater’s findings suggest that the firms successfully navigating this transition share four specific strategic pillars: an outcome-focused AI strategy, an embedded AI operating model, infrastructure specifically built for AI workloads, and rigorous data governance. The company is leveraging its platform to bridge this gap by providing what it calls an "immutable record" of facts across all assets and markets. For clients like Securian Asset Management and Blue Cross Blue Shield of Michigan, this foundation is being used to automate manual reviews and shift staff focus from "search and scan" tasks to exception-based investigation. By providing a single source of truth, Clearwater is attempting to turn shared data facts into actionable investment decisions.
Key Takeaways
- Nearly 900 Clearwater Analytics clients are currently running agentic AI workflows in production.
- The number of clients building custom workflows on the platform has increased by 197% since January.
- Research shows 99% of institutional investors cite unreliable and opaque data as the primary barrier to AI trust.
FinanceInsyte's Take
In our view, Clearwater Analytics is making a calculated bet that the next phase of fintech competition will not be won by the most advanced LLM, but by the most reliable data plumbing. The "Data Divide" identified in their research is a critical insight for the broader capital markets: the industry is currently attempting to build high-speed AI engines on top of fractured, unverified data foundations. The fact that 99% of investors distrust AI outputs due to data opacity suggests that the "AI hype" cycle is hitting a structural wall. Clearwater’s strategy to link agentic workflows directly to a single, reconciled investment record is a direct attempt to commoditize trust. If they can successfully prove that their 96% workflow success rate is a direct result of their data architecture, they will move from being a mere reporting tool to becoming the essential operating system for AI-driven institutional finance.
Questions & Answers
How does the "Data Divide" impact the ROI of institutional AI investments?
The divide suggests that AI investment may stall if firms lack high-quality data. While 95% of investors see AI as vital, only 40% use it for significant operational decisions because 99% of investors struggle to trust AI outputs due to unreliable or opaque data.
What specific growth metrics indicate increased AI adoption among Clearwater clients?
Since January, the company has seen a 197% increase in clients building custom workflows and a 69% increase in clients running workflows. Additionally, automated workflow runs have grown by 105% since the start of the year.
How does Clearwater Analytics address the issue of data opacity in AI outputs?
The platform runs AI capabilities against a single, continuously reconciled investment record. This allows outputs to include citations that trace back to source data, providing the step-level detail necessary to build trust in automated workflows.
What are the characteristics of firms successfully implementing AI according to the research?
According to the "GenAI and the Data Divide" report, successful firms utilize an outcome-focused AI strategy, an embedded AI operating model, infrastructure designed for AI workloads, and data governance that ensures accuracy and reconciliation.
Source: Businesswire
