AI is transforming enterprise operations — but only as fast as their data infrastructure allows. In a recent discussion moderated by Nebula.io’s Jon Krohn, Nexus Cognitive Cofounder and CEO Anu Jain and Acceldata CMO Mahesh Kumar explored how modern enterprises can lay the groundwork for scalable, AI-powered outcomes by simplifying data complexity, embracing observability, and moving toward a modular, cloud-agnostic future.
Key takeaways
- Composable architecture accelerates time to value. Nexus Cognitive’s modular, services-automated platform allows enterprises to move from legacy systems to AI-ready infrastructures in days or weeks.
- Data observability is critical to trustworthy AI. Acceldata enables enterprises to proactively detect data issues — preventing drift, bias, and business-impacting model failures.
- Infrastructure agnosticism reduces lock-in and cost. By decoupling compute from storage, organizations can scale flexibly across cloud providers.
- AI is transforming governance. Companies are beginning to shift from “governance for AI” to “AI-powered governance,” using AI to automate compliance and policy enforcement.
- Start with business outcomes, not infrastructure. AI-first infrastructure strategies should be rooted in specific, revenue-linked use cases — not abstract IT modernization.
These insights came from our ScaleUp:AI event in November 2024, an industry-leading global conference that features topics across technologies and industries. Watch the full session below:
From services automation to AI outcomes in weeks
Nexus Cognitive delivers composable, outcome-focused data architectures. Jain describes their platform as turning the “ball of yarn” of data integrations into a unified engine, abstracting complexity and enabling enterprises to generate results in days or weeks.
At the heart of Nexus Cognitive is its NexusOne universal control plane, which integrates with managed services to automate workflows. The company is helping clients move from old, siloed infrastructures to AI-ready ecosystems — either modernizing existing parts or standing up net-new, fully integrated systems in a matter of days. “Think of it less like assembling car parts and more like buying the car ready to drive.”
Observability is the foundation of AI trust
While Nexus Cognitive streamlines infrastructure, Acceldata ensures its integrity. Kumar emphasized that “successful AI models depend on trustworthy data, both structured and unstructured.” He cited examples where customers reduced root cause analysis from weeks to hours, simply by implementing observability across their data pipelines.
One financial institution improved the accuracy of credit offers by identifying a credit score pipeline failure, previously undetectable without observability. “Even small data errors can create massive downstream impact,” Kumar noted. “In the AI era, with more AI agents being built for a lot of different tasks, it becomes that much more critical for you to have a handle on data and be able to provide very trusted data to build your models and also trusted data to make the predictions.”
“Governance needs to move with the data”
Governance has long been seen as a centralized, process-heavy burden. But both leaders agreed it’s due for reinvention. “Governance needs to move with the data,” said Kumar. “You have to govern the data wherever it is, rather than in a very centralized manner.” Acceldata’s observability platform enables policies to be applied directly at the data source, wherever it lives.
Jain added that using AI to automate metadata capture and compliance checks is rapidly reducing the burden. “Companies have had so much technical debt [using] so many different tools that it’s literally impossible for them to think about how to follow the data from source to digital twin to warehouse applications,” he explained. “What we’re finding is that as we’ve adopted a composable architecture with open standards using observability, we’re able to start to automate a lot of those governance features.”
Scaling with containerization
Kubernetes and containerization are doing more than just improving DevOps. For both Nexus Cognitive and Acceldata, they’re critical to reducing cost and scaling intelligently. Jain shared how one client reduced cloud costs from $800,000 to $40,000 by decoupling compute from storage through containerization.
Kumar emphasized the importance of dynamic scaling, particularly in data operations that spike seasonally or weekly. “The ability for the infrastructure to actually scale dynamically with these spikes of data becomes very, very critical,” he said.
Practical steps to build AI-ready infrastructure
What should enterprises do first? Jain is clear: “Don’t build infrastructure for infrastructure’s sake.” Start by identifying the AI use cases that drive real business outcomes, then build only what supports those goals. “We’re going to help you find those data catalogs, orchestrate that information, and get to those AI outcomes,” he said.
Kumar added that prioritizing your most critical data assets is essential. “Everything has a cost, even quality monitoring. Focus first on data that directly impacts revenue — then expand outward.”
“Being data-driven helps me sleep at night”
Both leaders highlighted how their own organizations use AI internally to great effect — from marketing and sales enablement to operations and culture. “Being data-driven helps me sleep at night,” said Jain. “It gives us confidence.”
Kumar echoed the sentiment: “We use AI across functions to improve business outcomes. The impact is real, even in a 300-person company.”
Note: Insight has invested in Nexus Cognitive and Acceldata.
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