Tech Talks Daily

• 3450: Why Predictive AI Delivers Real ROI While Generative AI Struggles

  What if the next big leap in business AI isn’t generative at all, but predictive? That’s the question at the heart of my conversation with Zohar Bronfman, CEO and co-founder of Pecan AI, a company helping business teams forecast outcomes with precision and turn historical data into future insights. Zohar explains why he believes predictive AI will deliver far greater enterprise value than the generative models dominating headlines. He points to research showing that most generative AI projects fail to produce ROI, while predictive systems built on a company’s own data can directly improve revenue, reduce churn, and guide smarter decisions. With Pecan’s no-code platform, marketing and operations teams can now create predictive models without needing data scientists—bridging the gap between technical expertise and business execution. Through stories like Little Spoon’s, a direct-to-consumer baby food brand that used Pecan AI to identify and retain at-risk customers, Zohar illustrates how predictive analytics turns data into real business impact. He also shares common mistakes companies make when implementing AI—starting with unclear objectives and misaligned resources—and why success depends on defining the problem before choosing the tool. Looking ahead, Zohar envisions predictive AI as the backbone of every organization, shifting business intelligence from reactive analysis to proactive action. As companies move beyond dashboards and toward dynamic decision-making, predictive insights may soon become as fundamental as spreadsheets. So, if your company could anticipate every challenge before it happened, how different would your strategy look? And are business leaders finally ready to treat predictive AI as core infrastructure rather than a passing trend? Share your thoughts after the episode.

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• 3449: How Urban SDK Is Using AI to Help Local Governments Save Lives

  What happens when artificial intelligence meets the everyday heroes of local government? That’s the question driving my conversation with Justin Dennis, co-founder and COO of Urban SDK, a geospatial AI company helping more than 250 North American cities make faster, safer, and smarter decisions. Justin shares how a Smart Cities Challenge from the U.S. Department of Transportation inspired him to co-found Urban SDK in 2018, and why he believes the future of public safety depends on replacing manual data collection with real-time intelligence. From traffic fatalities to hurricane recovery, he explains how the company’s HALO platform gives local leaders and emergency responders the insights they need to act before crises escalate. In a single platform, they can identify dangerous road zones, predict high-risk intersections, coordinate clean-up operations, and rebuild infrastructure based on data rather than guesswork. We also explore how AI is quietly reshaping government operations, from disaster management to traffic enforcement. Justin discusses the challenges of introducing cutting-edge technology into systems that still rely on spreadsheets and siloed workflows. Yet his optimism is clear. He believes governments are beginning to embrace AI not as a buzzword but as a practical tool to save time, resources, and lives. As one Florida community recently reported a 40 percent drop in traffic fatalities, the impact is already measurable. Urban SDK’s story is about technology meeting public service with purpose. So as we enter another year of rapid AI progress, how can data-driven insights continue to empower local leaders to protect citizens and improve quality of life? And what could your city achieve if every decision were powered by real-time intelligence? Share your thoughts after the episode.

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• 3448: Inside Kore.ai’s Mission to Make Agentic AI Accessible for Everyone

  Agentic AI is only interesting when it leaves the lab and takes responsibility for real outcomes. In this episode, I reconnect with Raj Koneru, Founder and CEO of Kore.ai, to talk about what that shift looks like inside large enterprises. Raj has been building conversational systems long before chatbots became dinner table conversation, and he is clear about where the action is now. Understanding intent is table stakes. The next frontier is planning, reasoning, and executing tasks through agents that can work across departments, respect policies, and prove their value in minutes, not months. We walk through how Kore.ai frames the stack. There is a layer below the line, where chips, data centers, clouds, and model providers keep advancing at speed. Above the line is where business value shows up. That is where companies design, deploy, and manage agents for customer service, employee productivity, and process automation. Raj describes Kore.ai as the operating system for that upper layer. The goal is simple to say and hard to deliver. Let teams build agents without writing code, plug into whichever models and clouds they prefer, and keep control through governance, security, and measurement. Interoperability runs through this conversation. Kore.ai partners with Microsoft, AWS, and G42’s Inception so customers can pick the environment that suits them. Vendor lock-in is a real anxiety for leaders. Raj’s answer is to be model agnostic, data agnostic, and cloud agnostic, while conforming to open standards. That way, enterprises spend their energy on the agents themselves rather than on maintaining a platform. It is a pragmatic view that reflects what I keep hearing on conference floors. Scale brings its own lessons. At Kore.ai’s volume, latency, accuracy, and security are non-negotiable, and governance is a daily practice rather than a slide in a deck. Raj talks candidly about no-code democratization, where a business user can assemble an agent for a focused task, then graduate to more complex workflows when ready. We also touch on the rise of on-device models in smartphones and where the boundary sits between quick local tasks and heavier actions that still rely on hosted models. If you care about agentic AI that does real work, this one is worth your time. Raj shares a thoughtful take on collaboration, standards, and why leaders should read widely, question everything, and start building with what is available today. Links are in the show notes.

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• 3447: How DXC Is Using AI to Outsmart Cybercriminals

  In this episode, Mike Baker, Vice President and Global CISO at DXC Technology, says the cyber industry has been focusing on the wrong side of AI. He believes too many companies use it only to block threats instead of studying how criminals use it to scale phishing, bypass defenses, and deploy adaptive malware. Attackers are learning faster than ever, and security teams must catch up. Mike argues that defenders need to think differently and use AI as both protection and opportunity. He shares how DXC is already doing this. The company has brought autonomous AI agents into its security operations through a partnership with 7AI. These agents process alerts that used to require hours of human effort. The result is faster detection, less burnout, and more time for analysts to investigate real threats. By cutting manual work by more than eighty percent, DXC has shown how AI can make cybersecurity teams stronger, not smaller. Zero Trust remains a core part of DXC’s strategy. Mike calls it a journey that never ends. It needs cultural change, constant learning, and leadership that keeps security invisible to end users. AI now plays a role here too, improving identity checks and spotting access issues in real time. Yet, he reminds us, AI still needs people in the loop for oversight and judgment. We also talk about supply chain risks. Too many companies still treat risk assessments as one-time tasks. Mike pushes for continuous monitoring and close collaboration with suppliers. He closes the conversation on a hopeful note. AI will not replace people in cybersecurity, he says. It will make their work more meaningful and more effective if used with care and common sense.

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• 3446: How Atlassian Is Redefining Teamwork with AI at Team 25

  What happens when the future of teamwork collides with the power of AI? That’s the question at the heart of this episode as Tiffany from Atlassian joins me from Barcelona during Team 25, where Atlassian is showcasing how AI-powered collaboration is redefining how work gets done. We talk about how Atlassian’s mission to unleash the potential of every team is coming to life through its bold decisions, from sunsetting data center products to expanding its multi-cloud partnerships with Google. Tiffany offers a front-row view of how Atlassian’s evolving cloud platform is designed to help customers work smarter while enabling secure, scalable innovation across some of the world’s most complex enterprises. The conversation also uncovers the thinking behind the teamwork graph, Atlassian’s powerful data intelligence layer that connects billions of work objects to create truly personalized AI experiences. Tiffany shares how companies like Royal Caribbean and Mercedes-Benz are already seeing measurable performance gains and how AI is becoming a real teammate that unifies knowledge, connects tools, and drives better outcomes. We discuss what it means to build a “system of work,” why flexibility and context matter, and how Atlassian’s open approach allows teams to build custom systems tailored to their own culture and workflows. Beyond the technology, this is a story about continuous learning, adaptability, and human-centered progress. Tiffany’s reflections on learning from the toughest customers, embracing change, and reimagining the browser as an active workspace reveal how Atlassian is blending AI with empathy and purpose. As AI becomes inseparable from teamwork, what steps will you take to unleash what your team can do next? I’d love to hear your thoughts.

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