The Pulse of mcube™ May 2026
Discover What’s New in mcube™
Welcome to the mcube™ Monthly Newsletter.
Stay ahead with the latest innovations, events, and insights shaping enterprise AI with mcube™.
Editorial:
As enterprises move from AI experimentation to execution at scale, the focus is increasingly shifting toward measurable business impact. This month’s edition highlights how domain intelligence, agentic AI, and accelerated innovation frameworks are helping organizations operationalize AI faster and more effectively.
From Debdas Sen’s conversation on Eye on AI with Craig Smith to TCG Digital’s showcase of the Bot-Assisted mcube™ Framework (BAmF) at UCSF, we explore how enterprises are building scalable, context-aware AI ecosystems designed for real-world outcomes.
Andreas Diggelmann
Chief Product Officer, TCG Digital
Spotlight: Why AI Without ROI Will Die (Again) — Debdas Sen on Eye on AI
In a recent episode of Eye on AI hosted by former New York Times correspondent Craig Smith, and featuring Debdas Sen, CEO, TCG Digital, the conversation explored what it truly takes for enterprise AI to create measurable business impacts in complex, high-stakes industries.
A few standout insights from the discussion:
- “Real differentiation in AI comes from deep domain expertise.”
- Enterprise AI success depends on combining domain intelligence, enterprise data, and AI ecosystems.
- Organizations must move beyond experimentation toward ROI-driven AI execution.
- “The ability to connect legacy systems with agentic infrastructure is critical.”
- In catalyst R&D, AI-driven experimentation reduced development timelines from 12 months to 1 month.
- The future of enterprise AI will be defined by contextual, execution-ready intelligence, not generic AI adoption.
TCG Digital at UCSF: Build an MVP in Days, Not Months with BAmF
At UCSF, TCG Digital showcased the Bot-Assisted mcube™ Framework (BAmF), an agentic AI framework designed to help researchers, scientists, and innovation teams rapidly transform ideas into functional MVPs.
Built on the principles of augmentation over automation, BAmF combines generative AI, domain context, structured workflows, and enterprise guardrails to streamline the entire product development lifecycle, from requirements and architecture design to code generation, testing, and deployment.
The session with Souvik Banerjee and Amitabha Sinha demonstrated how BAmF enables teams to move from concept to functional MVPs in a fraction of traditional timelines, helping startups, enterprises, and research organizations innovate with greater speed, scalability, and operational confidence.
Pro Tip: Use AI to draft the chart, then lock in the best version.
With AI-assisted chart generation in the Custom Chart widget, you can describe the visualization you need in plain language instead of manually configuring chart types, axes, labels, and data bindings. ezeXtend analyzes the uploaded or connected dataset, suggests a suitable chart, and generates the equivalent JSON configuration for you to review and refine.
For example, instead of hand-crafting the JSON from scratch, try prompts like:
- “Use the uploaded sales dataset to show total revenue by region as a stacked bar chart.”
- “Create a trend chart showing monthly order volume and highlight the highest month.”
- “Compare product sales across cities and use the most suitable visualization.”
The real developer advantage is Apply Code. As you iterate, the latest AI response may not always be the one you want. With Apply Code, you can pick any useful response from the conversation, even an intermediate version, apply it to the chart, and save that configuration. This lets you experiment freely, compare options, and retain the version that best fits the dashboard without losing a strong earlier result.
Use AI for speed, use Apply Code for control.
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