Tech Talks Daily

What happens when AI systems stop acting like assistants and start acting like autonomous decision-makers inside your business? And if those systems are pulling information from fragmented, inconsistent, and poorly governed data environments, how much trust can organizations really place in the outcomes?
In today's episode, I'm joined by Terry Dorsey for a fascinating conversation about the growing gap between AI ambition and enterprise reality. Terry brings decades of experience spanning enterprise architecture, business intelligence, operations, healthcare, utilities, manufacturing, and defense. Long before AI became the headline topic dominating every boardroom conversation, he was already working deeply in semantic modeling, natural language systems, and the architectural foundations that modern AI now depends on.
At the center of our discussion is the new AI Trust Gap report from Denodo, which reveals why so many organizations are struggling to move AI projects from experimentation into reliable production environments. We explore why live data matters so much in an agentic AI world, why "more data" often creates more confusion instead of clarity, and how inconsistent business meaning across systems quietly undermines AI trust inside large organizations.
Terry explains why many enterprises are still operating on architectures originally designed for historical reporting and analytics, while now expecting those same environments to support autonomous AI systems making real-time operational decisions. From semantic sprawl and duplicated business logic to governance failures and fragmented security models, we unpack the hidden technical debt that AI is now exposing at scale.
The conversation also takes a deeper philosophical turn as we discuss why enterprise meaning itself may become the future control plane for AI. Terry shares why provenance, explainability, and semantic consistency are no longer optional concerns reserved for compliance teams, they are becoming foundational requirements for trustworthy AI systems capable of operating autonomously.
We also discuss why governance cannot be bolted on after deployment, how logical data management helps organizations reduce duplication and maintain operational trust, and why the companies that succeed with agentic AI will not necessarily be the fastest movers, but the ones building stable and reusable architectural foundations beneath the surface.
If your organization is rushing toward AI adoption while wrestling with siloed systems, disconnected data, and growing governance concerns, this episode offers a much-needed reality check. Because, as Terry explains, the future competitive advantage may have less to do with the AI model itself and far more to do with the architecture, meaning, and trust frameworks supporting it.
Useful Links
Terry Dorsey LinkedIn
Denodo LinkedIn
Denodo Website
The AI Trust Gap Report — global survey of 850 executives that explores why organizations are investing heavily in AI, but many still can't fully trust the data behind it.
O'Reilly's The Rise of Logical Data Management, by Christopher Gardner — explains what's necessary to enable true self-service data access and 24/7 AI-ready data.
The Enterprise AI and Data Management Glossary  — glossary that helps ensure both technical and non-technical professionals can make informed decisions, optimize strategies, and align on best practices for digital transformation.
The ROI of Using the Denodo Platform alongside the Modern Data Lakehouse — Drawing on interviews with numerous global enterprises and applying a comprehensive ROI methodology, this study, conducted by independent analyst Veqtor8, found that by using Denodo alongside their data lakehouse, they realized considerable benefits.
Agentic AI Manifesto — a blueprint for credible autonomy at enterprise scale. Denodo's standard for the next era of trusted, autonomous enterprise AI.

What does AI transformation actually look like inside one of the world's largest engineering organizations?
At Team '26 in Anaheim, I recently sat down with Jason Andrews to unpack how Cisco transformed decades of fragmented tooling, disconnected workflows, and spreadsheet-driven operations into a unified system of work built around Jira, Confluence, Jira Service Management, automation, and AI-ready workflows.
And honestly, this conversation felt refreshingly practical.
Jason oversees engineering operations across Cisco Networking, a business unit with around 22,000 engineers and product managers representing roughly $40 billion in annual revenue. So when he talks about transformation, this isn't theory. This is operational change happening at enterprise scale.
We discuss how Cisco consolidated more than 85 Jira instances, reduced tooling spend by 54%, and accelerated reporting by 40x while creating a far more scalable engineering organization. But as Jason explains throughout the conversation, the real challenge was never the technology itself. It was getting teams to rethink how they wanted to work moving forward rather than simply migrating years of technical debt into modern systems.
One of the strongest themes in this episode is the difference between transformation and migration. Jason explains why organizations often fail when they focus only on moving systems rather than changing workflows, behaviors, and operational culture at the same time.
We also dive deep into AI adoption inside engineering organizations. Jason shares how Cisco is already seeing significant productivity gains from AI-assisted development, why organizational context matters so much for enterprise AI success, and why he believes the industry is still massively underestimating how much structured data and workflow consistency AI systems actually require.
Along the way, we unpack scenario planning in the AI era, why annual planning cycles are becoming increasingly fragile, and how leaders can move from rigid long-term roadmaps toward more agile operational playbooks capable of adapting to constant disruption.
There's also a fascinating discussion around the so-called "SaaS apocalypse," the limits of AI-generated software, and why Jason believes humans will remain central to enterprise operations for years to come, especially in organizations managing millions of lines of legacy code and decades of accumulated institutional knowledge.
If your organization is currently navigating modernization, operational complexity, AI adoption, or large-scale systems transformation, this episode is packed with lessons learned from the front lines of enterprise change.
And perhaps most importantly, Jason offers a reminder that AI alone is not the strategy. The real opportunity comes from reducing friction, improving context, and helping teams spend more time solving meaningful problems instead of manually stitching systems together.

What Happens When AI Starts Protecting the Power Grid Before Humans Even Spot the Problem?
In this episode of Tech Talks Daily, I speak with Kaitlyn Albertoli, co-founder and CEO of Buzz Solutions, about how AI, drones, and computer vision are changing the way utilities inspect and maintain power infrastructure. As weather events become more frequent and energy demand continues to rise from EV adoption, renewable energy growth, and AI-driven data centers, utilities are under growing pressure to modernize systems that were built decades ago.
Kaitlyn explains how utilities once relied on crews walking transmission lines with binoculars and handwritten notes before moving toward helicopter inspections and aerial imaging. Today, autonomous drones and aircraft can capture hundreds of thousands of inspection images every year. The real challenge now is turning that mountain of visual data into useful action before damaged equipment leads to outages, fires, or safety risks. We discuss how Buzz Solutions processes enormous image datasets in hours instead of weeks, helping utilities identify damaged insulators, corrosion, vegetation risks, and failing components before they become larger problems.
We also talk about the people behind the infrastructure. Kaitlyn shares why AI should support frontline workers rather than replace them, especially as utilities face an estimated shortage of thousands of skilled linemen over the next several years. The conversation covers balancing false positives with missed detections, reducing operational data silos, and why partnerships with companies like Skydio and Esri are helping utilities connect inspection workflows more effectively.
Kaitlyn also shares how Buzz Solutions is expanding into solar inspections, where AI can detect damaged or underperforming panels before warranties expire and energy production quietly drops over time. Alongside the technology discussion, she reflects on how competing in the 2012 U.S. Olympic Trials shaped the resilience and mindset she now brings to building a fast-growing AI company.
From wildfire prevention and storm recovery to renewable energy operations and autonomous inspections, this episode looks at how AI is quietly becoming part of the infrastructure keeping modern society running.
As utilities modernize aging systems under growing environmental and operational pressure, can AI help prevent the next major outage before it happens?

What happens when the cybersecurity industry stops debating whether agentic AI is a future problem and starts treating it as a present-day reality?
In this episode of Tech Talks Daily, I sit down with Tim Freestone to unpack the biggest shift coming out of this year's RSA Conference. After attending RSA for more than two decades, Tim describes 2026 as the year the energy returned to the cybersecurity world, driven by one unavoidable topic: agentic AI.
We explore why the conversation has rapidly evolved from curiosity to urgency, and why organizations are suddenly confronting an uncomfortable truth. AI agents are already operating inside businesses, often without visibility, governance, or control. Tim explains how shadow AI is spreading faster than many leadership teams realize, with employees experimenting with autonomous tools that connect directly to company data and external AI models.
Our conversation also looks at the growing gap between visibility and control. Security teams may be discovering agents across their networks, but stopping risky behavior is an entirely different challenge. Tim argues that companies focusing purely on infrastructure are already falling behind, and that the real battleground is now the data layer itself. We discuss why data governance, audit trails, and access controls are becoming central to the future of cybersecurity strategy.
Tim also shares his thoughts on state-sponsored AI threats, the rise of autonomous espionage operations, and why open-source AI models present a completely new level of risk for defenders. At the same time, he offers practical advice for IT and security leaders trying to figure out where to start amid the noise, complexity, and endless flood of new tools entering the market.
If your organization is trying to understand how AI changes cybersecurity, governance, compliance, and risk management, this conversation offers a clear look at what security leaders are actually worried about right now, and why the next 12 months may redefine how companies think about protecting data altogether.
Useful Links
Connect with Tim Freestone

Learn More About Kiteworks

Data Security and Risk Report

Kiteworks Substack

Kiteworks LinkedIn Newsletter

Please check the partners of the Tech Tech Talks Network
Learn more about the NordLayer Browser
Visit Denodo.com

What if some of the most important applications of AI today have nothing to do with productivity, marketing, or enterprise automation, and everything to do with helping people survive crisis, displacement, and uncertainty?
In this episode, recorded at, I sit down with André Heller Pérache to explore how technology originally designed for customer service has evolved into humanitarian infrastructure supporting refugees and displaced communities around the world.
André shares the story behind Signpost, a global digital initiative from the International Rescue Committee that now operates across roughly 30 countries and 25 languages, helping register more than 20 million users while supporting over 500,000 digital social work consultations.
But this conversation goes much deeper than technology.
We discuss what happens when trusted information becomes as important as food, shelter, or medical support during times of crisis. André explains how Signpost was born from the realization that vulnerable communities were already living digitally through smartphones, WhatsApp, Facebook, and social platforms while much of the humanitarian sector still relied on traditional offline systems.
We also explore the responsible use of AI in high-stakes environments where mistakes can have real-world consequences for refugees, families, and vulnerable populations. André shares why the IRC sees AI as one of the humanitarian sector's biggest bets at a time when armed conflict, climate disasters, and shrinking budgets are putting enormous pressure on aid organizations globally.
From misinformation and trust to reducing cognitive burden and scaling empathy through technology, this episode offers a powerful reminder that behind every AI conversation are ultimately human beings searching for dignity, safety, clarity, and hope.