How to Maximize ROI on AI in 2026

Nov 20, 2025

Sumeet Arora, Chief Product Officer at Teradata, dives into transforming AI promises into actionable results. He discusses the challenges of scaling AI effectively and the critical role of data quality in achieving measurable ROI. Sumeet emphasizes shifting from flashy demos to outcome-driven implementations and explains how enterprises can enhance workflows through AI. He shares compelling use cases, like improving customer satisfaction in banking, while advocating for AI literacy among employees to foster innovation and autonomy.

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ANECDOTE

Customer 360 Knowledge Layer Example

Teradata built a customer 360 knowledge layer combining structured records with chat transcripts and call data.
This unified view lets support anticipate intent and boost customer satisfaction metrics like NPS.

INSIGHT

Push Signals Replace Pull Queries

Modern systems shift from pull queries to push signals that proactively alert customers and teams.
Sumeet Arora highlights push systems as a new promise of knowledge platforms.

ADVICE

Give Agents Outcome-Oriented Goals

Design AI agents to optimize specific outcomes (e.g., increase NPS from 52 to 57) and give them constrained worldviews.
Let agents prioritize tasks using holistic data sources instead of manual backlog triage.

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The promise of agentic AI has been massive, autonomous systems that act, reason, and make business decisions, but most enterprises are still struggling to see results.

In this episode, host Chris Brandt sits down with Sumeet Arora, Chief Product Officer at Teradata, to unpack why the gap exists between AI hype and actual impact, and what it takes to make AI scale, explainable, and ROI-driven.

From the shift toward “AI with ROI” to the new era of human + AI systems and data quality challenges, Sumeet shares how leading enterprises are moving from flashy demos to measurable value and trust in the next phase of AI.

CHAPTER MARKERS

00:00 The AI Hackathon Era

03:10 Hype vs Reality in Agentic AI

06:05 Redesigning the Human AI Interface

09:15 From Demos to Real Economic Outcomes

12:20 Why Scaling AI Still Fails

15:05 The Importance of AI Ready Knowledge

18:10 Data Quality and the Biggest Bottleneck

20:46 Building the Customer 360 Knowledge Layer

23:35 Push vs Pull Systems in Modern AI

26:15 Rethinking Enterprise Workflows

29:20 AI Agents and Outcome Driven Design

32:45 Where Agentic AI Works Today

36:10 What Enterprises Still Get Wrong

39:30 How AI Changes Engineering Priorities

55:49 The Future of GPUs and Efficiency Challenges

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