Show Notes
AI has officially moved beyond experiments and into real-world production — making decisions, taking actions, and reshaping how businesses operate.
But there’s a problem.
Even as adoption accelerates, most organizations still haven’t fixed the one thing AI depends on: their data.
In this episode of Today in Tech, Keith Shaw speaks with Gaurav Pathak, Senior Vice President of Product Management for Metadata and AI at Salesforce, about the growing “trust paradox” — where employees increasingly trust AI outputs, while leaders know their data foundations aren’t ready.
They break down:
Why AI adoption jumped from 48% to 69% in just one year
How agentic AI is spreading faster than any enterprise tech before it
Why data quality is still the #1 blocker to real AI deployment
The risks of “shadow AI” inside organizations
What leaders must fix now before AI mistakes hit customers and revenue
As AI systems become more persuasive — and more autonomous — the cost of bad data is no longer theoretical.
More from Today in Tech
Companies have spent billions racing to adopt artificial intelligence. But as AI moves from experimentation into production, are businesses discovering that the real costs are much higher than expected?
AI agents are moving beyond simple chatbots and isolated tasks. The next step is agent-to-agent communication, where AI systems operated by different companies could negotiate, exchange information, make decisions and even conduct business with each other.
AI was supposed to save us time. Instead, it may be creating a new problem: AI slop (polished-looking emails, reports, presentations, research and code that someone else has to review, fact-check and fix).
AI tools such as ChatGPT, Claude and Copilot are spreading rapidly through the workplace, but simply giving employees access to AI isn't the same as teaching them how to use it effectively.
Hackers are using AI, social engineering, and legitimate business tools to make cyberattacks look like normal work. Instead of breaking through security systems, they study how employees communicate, impersonate trusted vendors, manipulate email conversations, and exploit everyday business processes.