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WisdomAI launches Live Apps: prompt-built, governed AI applications wired to live enterprise data, replacing the dashboard backlog. One customer already cut 15% of its dashboard estate in five weeks.

Computer Weekly's Developer Network covers the general availability of WisdomAI Live Apps, which turn a single prompt into a governed, interactive application connected to live enterprise data. CEO and co-founder Soham Mazumdar explains why static dashboards and ungoverned AI-built tools hold data teams back, and how Live Apps keep data, context, and controls in one governed environment.

BigDataWire runs WisdomAI's announcement of Live Apps, now generally available. Built from a prompt and connected to live enterprise data, Live Apps turn business questions and workflows into interactive applications in minutes, giving data teams a faster alternative to traditional dashboards without giving up accuracy, governance, or control.

TipRanks reports on the launch of WisdomAI Live Apps, which let data teams turn natural-language requests into governed, interactive AI applications connected to enterprise data. Live Apps inherit WisdomAI's certified metrics, security, and access controls, and query across federated data sources without moving the underlying data.

BigDataWire covers Teradata's expansion of Tera into an agentic coworker for enterprise data work. Through natural language and guided execution, Tera lets everyone from business analysts to platform engineers analyze data, build AI applications, and automate workflows from a single governed environment.

WisdomAI's agentic analytics platform — natively embedded in Tera — delivers natural-language analytics, single-prompt Live Apps, and proactive AI agents across all enterprise data, achieving 95%+ insight accuracy.

Enterprise AI applications are only as reliable as the business context guiding them. While companies have robust infrastructure for managing data and software code, business knowledge—such as policy updates, financial definitions, and operational rules—often lives scattered across prompts, documents, and code repositories. To prevent AI models from operating on outdated or conflicting rules as business logic evolves, organizations need a structured Context Development Lifecycle to version, test, approve, and distribute context across all enterprise AI systems.

In this episode Soham Azumdar, co-founder and CEO of Wisdom.ai, talks about what “context” really means in data engineering and AI systems. He explores why context has become such an overloaded term, spanning everything from semantic layers and data catalogs to tribal knowledge, query logs, dashboards, and even agent memory. Soham explained that the big shift is that context is no longer being prepared primarily for human analysts, but for LLMs and agents that can’t reliably fill in missing gaps on their own. That change raises the bar for how context is represented, validated, benchmarked, and maintained so that AI systems can produce trustworthy outcomes.

TheABM.info reports on WisdomAI’s survey of more than 200 VP and C-level AI, analytics, and data leaders at $1B+ enterprises. While AI experimentation is nearly universal, 81% still rely on dashboards and one-off requests, and organizations are now hiring dedicated roles to manage enterprise context.

MarTech Cube covers new WisdomAI research showing that 93% of enterprise data leaders are using or experimenting with AI in analytics, yet only 19% are very confident in AI-generated answers. To close the gap, 94% plan to change how they store and manage enterprise context within the next 12 to 18 months.

New WisdomAI Research Shows Enterprise AI Adoption Has Outpaced Trust, Leaving Most Deployments Stuck at the Pilot Stage

This Computer Weekly article highlights research from WisdomAI showing that enterprise data teams are restructuring to support production AI:

This article highlights how employees shifting from static dashboards to conversational AI tools forces enterprise analytics teams to move from building data infrastructure to reviewing AI reasoning. Because AI agents can access accurate data yet still misinterpret logic or context, governance is expanding beyond access controls, making analytics teams responsible for quality assurance tasks like validating outputs, benchmarking reliability, and investigating failure patterns.