3520: How Ecolab Is Rethinking Water Risk In An AI Driven World

Are we finally treating water risk like a board-level issue, rather than a line item that only shows up when something breaks?   In this episode, I'm joined by Emilio Tenuta, SVP and Chief Sustainability Officer at Ecolab, to unpack why water has become a strategic variable for business, right alongside energy and carbon. Ecolab works with customers across more than 40 industries in more than 170 countries, so Emilio has a front row seat to how quickly the conversation is changing.   Why water risk feels different in 2025 One of the most useful parts of this conversation is how Emilio frames water as "hyperlocal." A company can publish a global target, but the real pressure shows up basin by basin, site by site, community by community.   We also discuss the misconception that water is primarily an operational concern. The knock-on effects show up in uptime, expansion plans, permitting, reputation, and the social license to operate.   Emilio points to disclosure data that puts real money behind the issue. CDP has estimated water-related supply chain risks at $77 billion across responding companies, which helps explain why boards are paying closer attention.   Where AI meets water and energy   AI is a catalyst in two directions at once. It can help organizations measure, predict, and reduce waste, but it also drives demand for more data centers, more power, and more cooling.   We examine the tension many people are whispering about: building digital capacity in places already facing water stress. Emilio's view is pragmatic: the answer is responsible innovation, coupled with transparency on how water is used and how impacts are managed.   That takes us into Ecolab's push toward digital visibility and real-time control, because you cannot improve what you cannot see.   From "site to chip" cooling and smarter stewardship   Emilio shares that Ecolab's 3D TRASAR Technology for direct-to-chip liquid cooling is designed to protect high-performance servers by monitoring coolant health indicators in real time and translating that data into actionable steps for operators.   We also discuss what happens when AI is applied to the water side of the data center equation. Ecolab and Digital Realty have described a pilot across 35 US data centers to reduce water use by up to 15% and avoid up to 126 million gallons of potable water withdrawn annually.   To round things out, we discuss circularity as a business strategy, the role of collaboration through efforts like the Water Resilience Coalition, and why Ecolab's Watermark Study is worth reading if you want a pulse check on water stewardship and public sentiment.   So after listening, where do you land on the big question: is AI going to become a stress test for local water systems, or a tool that finally helps us run them better, and why? Useful Links:  Connect with Emilio Tenuta Learn more Ecolab Follow Ecolab on Linkedin Tech Talks Daily is sponsored by Denodo

How do you move faster with AI and cloud innovation without losing control of security along the way?   Recorded live from the show floor at AWS re:Invent in Las Vegas, this episode of Tech Talks Daily features a timely conversation with Kimberly Dickson, Worldwide Go-To-Market Lead for AWS Detection and Response Services. As organizations race to adopt agentic AI, modernize applications, and manage sprawling cloud environments, Kimberly offers a grounded look at why security must still sit at the center of every decision.   Kimberly explains how her role bridges two worlds at AWS. On one side are customers dealing with prioritization fatigue, fragmented security signals, and growing pressure to do more with fewer resources. On the other hand, there are the internal service teams building products like Amazon GuardDuty, Amazon Inspector, and AWS Security Hub. Her job is to connect those realities, shaping services based on what customers actually struggle with day to day. That perspective sets the tone for a conversation focused less on hype and more on practical outcomes.   We unpack how AWS thinks about security culture at scale, from infrastructure and encryption through to threat intelligence gathered across Amazon's global footprint. Kimberly shares how AWS uses large-scale honeypots to observe attacker behavior in real time, feeding that intelligence back into detection services while also working with governments and industry partners to take down active threats. It is a reminder that cloud security is no longer just about protecting individual workloads, but about contributing to a safer internet overall.   The conversation also dives into new announcements from re:Invent, including the launch of AWS Security Hub, extended threat detection for EC2 and EKS, and the emergence of security-focused AI agents. Kimberly explains how these tools shift security teams away from manual investigation and toward faster, higher-confidence decisions by correlating risks across vulnerabilities, identity, network exposure, and sensitive data. The goal is clear visibility, clearer priorities, and remediation that fits naturally into existing workflows.   We also explore how AWS approaches security in multi-cloud and hybrid environments, why foundational design principles still matter in an AI-driven world, and how open standards are helping normalize security data across vendors. Kimberly's reflections on re:Invent itself bring a human close to the episode, highlighting the pride and responsibility felt by teams building systems that millions of organizations depend on.   As AI adoption accelerates and security teams are asked to keep pace without slowing innovation, what would it take for your organization to move faster while still trusting the foundations you are building on?

In this episode of Tech Talks Daily, I sit down with Yuyu Zhang to unpack a shift that many developers can feel but struggle to articulate. Yuyu's journey spans academic research at Georgia Tech, building recommendation systems that power TikTok and Douyin at global scale, and leading the Seed-Coder project at ByteDance, which reached state-of-the-art performance among open source code models earlier this year. Today, he is part of Codeck, where the focus has moved beyond AI assistance toward autonomous coding agents that can plan, execute, and verify real engineering work. Our conversation begins with a simple but revealing observation. Most AI coding tools still behave like smarter autocomplete. They help you type faster, but they do not own the work. Yuyu explains why that distinction matters, especially for teams dealing with complex systems, tight deadlines, and constant interruptions. Autonomy, in his view, is not about replacing engineers. It is about giving them back their flow. We explore Verdent, Codeck's autonomous coding agent, and Verdent Deck, the desktop environment designed to coordinate multiple agents in parallel. Instead of one AI reacting line by line inside an editor, these agents operate at the task level. They plan work with the developer upfront, execute independently in safe environments, and validate their output before handing anything back. The result feels less like using a tool and more like managing a small engineering team. Yuyu shares how parallel agents change both speed and predictability. One agent can implement a feature, another can write tests, and another can investigate logs, all without stepping on each other. Just as important, he walks through the safeguards that keep humans in control. Explicit planning, permission boundaries, sandboxed execution, and clear, reviewable diffs are all designed to address the very real concerns engineering leaders have about letting autonomous systems near production code. The discussion also turns personal. Having worked on some of the highest-scale systems in the world, Yuyu reflects on why developers lose momentum. It is rarely about raw ability. It is about constant context switching. His goal with Verdent is to preserve mental focus by offloading interruptions and letting engineers return to work with clarity rather than cognitive fatigue. We close by looking ahead. The definition of a "good developer" is changing, just as it has many times before. AI is not ending programming. It is reshaping it, pushing human creativity, judgment, and design thinking to the foreground while machines handle the repetitive churn. If autonomous coding agents are becoming colleagues rather than helpers, how comfortable are you with that future, and what would you want to stay firmly in human hands?

How do you capture every moment of a golf tournament spread across hundreds of acres, tens of thousands of shots, and dozens of players competing at the same time? That question sits at the heart of this conversation recorded at AWS re:Invent, where I sat down with Eric Hansen, VP of Product at the PGA Tour, and Elaine Chiasson, who leads the global golf team at AWS, to unpack how data and AI are reshaping the way fans experience the game. Eric explains why modern professional golf has more in common with Formula 1 than most people realize. Every ball struck, every position on the leaderboard, and every shift in momentum generates data that needs to be processed instantly. With more than thirty thousand shots across a single tournament and only a fraction of them shown on traditional broadcasts, the PGA Tour faces a constant challenge. How do you give fans context, insight, and a sense of presence when most of the action is never seen on screen? Elaine shares how AWS has helped the Tour build the foundation to answer that question. From migrating decades of video and shot data into the cloud to applying generative AI for automated commentary, language translation, and real time insights, this partnership goes far beyond infrastructure. Together, they are experimenting with automated camera switching, AI driven production workflows, and personalized fan experiences that surface the right information at the right moment, whether you are following the leaderboard or a single favorite player. The conversation also digs into trust and accuracy. Eric walks through how the PGA Tour validates AI generated commentary to ensure it stays aligned with the sport's standards, while Elaine highlights why operational discipline and governance matter just as much as innovation. They explore what hyper personalization looks like inside the PGA Tour app, how global broadcasts could evolve, and why the long term opportunity lies in making every shot matter for every fan. As live sports move toward a future shaped by data, automation, and AI agents working behind the scenes, this episode offers a clear look at what that transformation really involves. So as golf continues to blend tradition with technology, what kind of fan experience do you want to see next, and how comfortable are you with AI calling the shots? Useful Links Connect with Eric Hansen, VP of Product at the PGA Tour. Connect with Elaine Chiasson Learn more about AWS and PGA Tour Tech Talks Daily is sponsored by Denodo

How do you make sense of an industry that is changing at a pace few predicted, especially with SIGNAL London still fresh in our minds and Twilio unveiling the next stage of its vision for customer engagement? That question sits at the heart of today's conversation with Peter Bell, VP of Marketing for EMEA at Twilio, who joined me to unpack what the past year has taught both companies and consumers about AI's role in shaping modern experiences. Peter begins by grounding everything in a single, striking shift. Only a year ago, AI-powered search barely registered in global traffic. Today it accounts for around a fifth of all searches. That leap signals a broader behavioral shift as consumers move instinctively toward conversational interfaces, which, in turn, leaves brands with a clear message. The clock has moved on. AI is no longer a nice-to-have. It is a direct response to how people now choose to discover, question, and buy. Our conversation turns to the gap between customer expectations and the experiences they receive. Peter discusses why brands often struggle to integrate channels, data, and AI coherently. He explains how first party data has become the anchor for any serious AI strategy, why generic public models cannot solve brand-specific tasks, and why the most successful teams start with simple, tightly scoped problems. A password reset may not sound glamorous, yet it is the kind of focused use case that teaches teams how to govern data, automate safely, and build confidence in the process. We also spend time on branded calling, RCS, and the evolution of voice. Peter breaks down what modern messaging now looks like and why trust sits at the center of every interaction. His explanation of Conversational Relay shows why natural voice exchanges finally feel within reach after years of frustration with rigid IVR systems. The thread running through all of this is clear. Consumers want speed and clarity, but they want reassurance too, and brands need to honor both sides of that equation. Later in the conversation, Peter makes one of the episode's most compelling points. Brand visibility has become harder, not easier, because much of the early research now occurs within AI tools. Buyers form opinions long before they speak with a sales rep. That shift explains why so many B2B companies are returning to high-impact brand channels, whether that is F1 sponsorships or other standout moments that keep them in the initial consideration set. We close with the topic that Peter believes will define the next stage of enterprise AI. Model Context Protocol. MCP has emerged as a quiet breakthrough, enabling LLMs to access data across CRM systems, files, and other software through a standard protocol. This removes one of the biggest blockers in AI projects: the practical challenge of connecting disparate data to a model built for a specific purpose. As Peter puts it, MCP gives companies a realistic way to make the special-purpose models that deliver reliable ROI. It is a wide-ranging conversation shaped by SIGNAL London's announcements, the evolving customer journey, and a year in which AI moved from curiosity to expectation. I would love to know what part stood out most to you. Are you seeing the same shifts Peter describes in your own business, and how are you preparing for the year ahead? Useful Links Interact with the Inside the Conversational AI Revolution report. Learn more about the Signal event Connect with Peter Bell, VP of Marketing for EMEA at Twilio. Tech Talks Daily is sponsored by Denodo

Why do smart people still click when every instinct tells them they should pause first? That question sits at the heart of this conversation with Denny LeCompte, CEO of Portnox and a rare cybersecurity leader who brings a background in cognitive psychology to identity, trust, and human error.   It is a discussion that pulls back the curtain on the habits, shortcuts, and blind spots that shape our decisions long before a breach becomes a headline.   Denny explains why people rely on benevolence cues, confirmation biases, and loss aversion, and then shows how attackers weaponize each. He explains why training alone cannot fix human fallibility and why a different design mindset is needed if we want security people can actually live with.   Through clear examples and thought-provoking analogies, he describes how teams can build environments that remove opportunities for mistakes rather than punishing people for being human.   We also explore what Zero Trust really means beyond marketing-speak. Denny cuts through the noise and frames it as a mindset shift rather than a product category. He draws on real conversations with CISOs to explain why passwordless adoption moves slowly and why the next wave of identity risk will come from AI agents operating within networks. It is a future in which the line between human and machine identity blurs, requiring access control to evolve just as quickly.   Later, Denny shares a personal story about a mentor who influenced his views, then explains Portnox's unified access control approach as organizations retire VPNs and passwords. His main point: security only works when systems reflect human nature, removing friction and helping people make safe choices. Every policy and workflow is a decision that impacts security outcomes.   What part of Denny's perspective made you reconsider your habits?   Useful Links Connect with Denny LeCompte, CEO of Portnox Learn more about Portnox Tech Talks Daily is sponsored by Denodo    

Have you ever wondered what it takes to run technology for one of the largest commercial real estate companies in the world? That question shapes my conversation with Yao Morin, Global CTO at JLL, as we look at how AI is changing the places where we work, shop, and gather. Real estate may seem traditional from the outside, yet inside JLL the pace is intense. With more than 5 billion square feet under management and huge volumes of daily activity, the pressure on property teams is real and the limits of manual work are easy to see. Yao explains how this reality led to the creation of Property Assistant, JLL's new AI solution built on JLL Falcon. Falcon acts as the company's enterprise AI foundation, giving teams a secure and scalable way to use data across global operations. She describes how the platform hides complexity so developers and property teams can work with AI without thinking about which model sits behind it. We talk through everyday examples, like overcrowded meeting rooms and confusing layouts, that the assistant can flag and address through recommendations drawn from live sensor data. The assistant goes far beyond space planning. It helps teams understand rising tenant concerns, patterns in work orders, and hidden risks before they grow into larger operational issues. Yao sees AI as a partner that handles heavy data processing so people can focus on the messy, human context. That balance is central to how JLL builds its tools, and she explains why this approach gives property teams more confidence and clarity in fast-changing environments. We also explore how AI is influencing the future design of buildings. As hybrid work, flexible retail, and rising industrial needs continue to shift demand, AI can gather layouts, analyze usage, and offer guidance at a speed traditional methods cannot match.  This creates a continuous feedback loop that helps teams adjust space before frustrations grow. For Yao, it is a way to bring real-time understanding into a sector that once relied on long cycles and guesswork. Security surfaces often in our conversation. Yao details how Falcon enforces monitoring, privacy controls, and consistency across the company, which is vital when working with sensitive client data across many regions. A centralized platform allows JLL to invest deeply in safeguards rather than spreading risk across scattered tools. She highlights how trust sits at the center of the brand and why it shapes every AI decision they make. As we shift toward the future, Yao shares how JLL is expanding its pipeline to more than fifty AI assistants aimed at productivity, client insight, and sustainability. She gives examples of tools that adjust energy usage and support portfolio planning, offering a view into how AI will support both performance and environmental goals. It is clear that AI has moved from experimentation to daily use inside JLL, with real business impact already taking shape. The episode closes with a powerful reflection on leadership and representation. Yao talks openly about her own journey, the weight of visibility, and how she learned to turn moments of feeling out of place into motivation. She explains why active sponsorship matters, why belonging is a measurable business priority, and how diverse viewpoints reduce blind spots in product design. Her message is heartfelt, practical, and filled with hope for the next generation of leaders. As you listen, I would love to know which part of Yao's story stays with you. Do you see AI changing your own workplace or the spaces you pass through every day? And how do you think better representation shapes the products we build? Share your thoughts and join the conversation. Useful Links Connect With Yao Morin Learn more about JLL Tech Talks Daily is Sponsored By Denodo. To learn more, visit denodo.com

Did you ever stop and wonder how many hours you lose each week hunting for files, tabs, links, or half-written ideas scattered across your apps? It is a familiar frustration, and it sits at the center of today's conversation with Dropbox VP of Engineering, Josh Clemm. Josh has spent two decades building products shaped around scale, personalisation, and clarity, and he brings that mix of experience to Dropbox's push into AI and knowledge management. In this episode, Josh shares stories from his time at LinkedIn and Uber, including the surprising Krispy Kreme promotion that took down Uber Eats across the globe and triggered a major rethink of architecture and resiliency. That experience shaped his belief that chaos often teaches the most. It also sets the stage for why he sees AI fluency as a leadership requirement rather than a trend.  You will hear how Dropbox is approaching internal experimentation, why context rot and work slop are real problems inside companies, and why the empty chat box often creates more anxiety than opportunity. Josh walks through the thinking behind Dropbox Dash, a standalone AI powered knowledge layer that connects all of your cloud apps, understands their content, and turns search into something sharper and faster. He explains why context aware AI is the next leap, how Dash builds knowledge graphs across apps, and why the future of AI might look less like single player workflows and more like tools that sit inside the flow of teamwork. It is a wide ranging conversation that moves from engineering history to the practical steps behind building AI products that feel useful rather than overwhelming. So here is the question that sits underneath everything Josh shared. What would your day look like if your information finally made sense without you having to chase it? Tech Talks Daily is Sponsored By Denodo. To learn more, visit denodo.com

What does learning look like when technology shifts faster than most university systems can adapt? That question shaped my conversation with Rob Telfer, who leads education strategy for D2L across Europe, the Middle East, and Africa. Rob returned to the show with a clear view of how AI is transforming higher education and why so many institutions are struggling to keep pace with expectations from students, employers, and society. Rob opened by laying out the reality universities face today. Financial strain, fluctuating enrolment, employer demands changing at speed, and a generation of learners preparing for roles that may not even exist yet. Against that backdrop, he described AI as the biggest catalyst the sector has seen in decades and explained how it has already reshaped academic policy, assessment models, and daily teaching practice. We explored practical examples of where AI is already creating meaningful change. Rob shared how D2L is helping institutions introduce adaptive learning, on demand student support, and content creation tools that reduce the pressure on educators. These are not speculative ideas. They are used by universities serving tens of thousands of learners, improving accessibility, easing workloads, and giving students faster, more personal support. The conversation moved to employability, a worry at the centre of almost every higher education debate. Rob explained how curriculum design needs to shift from theory first to skill first, and how deeper collaboration between academia and industry can help close widening gaps. He described why AI should be woven through the learning experience rather than bolted on at the end, and how that alignment can shape graduates who are confident with the tools they will soon use in the workplace. A striking theme came from the mismatch between student behaviour and institutional policy. Many students use AI daily, even where guidance is unclear or restrictive. Rob argued that ignoring the reality only pushes students into the shadows. Universities that teach responsible use, clear evaluation methods, and prompt literacy will better prepare their learners for the world they are about to enter. We ended by looking ahead to 2026. Rob believes the institutions that thrive will be the ones that act with intent, create clear AI policies, invest in meaningful technology, and keep human connection at the centre of learning. Those that resist or delay may find themselves struggling to compete in a sector where expectations rise quickly and alternatives for learners continue to grow. If you work in education or care about the future of learning, Rob's insights offer a candid, practical view of what must change. Which of his observations resonates most with your own experience, and how should universities evolve from here? I would love to hear your thoughts. Useful Links Connect with Rob Telfer on LinkedIn Learn more about D2L Follow on LinkedIn, Twitter, and Facebook Tech Talks Daily is Sponsored By Denodo. To learn more, visit denodo.com

*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" tabindex="-1" data-turn-id= "request-WEB:221c7553-c733-4456-a06c-c66c0626b35b-7" data-testid= "conversation-turn-16" data-scroll-anchor="true" data-turn= "assistant"> How do you guide a workforce through the fastest shift in technology most of us have seen in our careers? That question shaped my conversation with David Martin from BCG, who works at the intersection of talent, culture, and AI. He joined me from New York and quickly painted a clear picture of what is really happening inside global enterprises right now. We started with the widening split between AI fluent teams and those stuck in endless pilots. David explained why the organizations getting results are the ones doing fewer things with far greater ambition. Many others scatter energy across small use cases, save minutes instead of hours, and never reach a scale where value becomes visible. Training surfaced early as one of the biggest gaps. Not surface level workshops, but the deeper hands-on learning that helps people change how they work. David described why frontline teams lag behind, why engineers still miss major capabilities, and how leadership behaviour dramatically affects adoption. Curiosity and communication play a bigger role than most expect. We explored the move from isolated AI experiments to real workflow transformation. David shared examples from engineering, customer service, and operations where companies are finally seeing measurable results. He also explained why agents remain underused, with hesitation, data quality, and unfamiliarity still slowing progress. Shadow AI added another layer, with half of workers already using tools outside corporate systems. The conversation returned often to people. David outlined BCG's 10-20-70 rule, showing why technology is never the main bottleneck. Culture, roles, and process make or break outcomes. Leaders who provide clarity and a sense of direction see faster adoption. Those who remain hesitant create uncertainty that spreads across teams almost instantly. As we looked toward 2026, David shared cautious optimism. He sees huge potential in areas like healthcare and sustainability, along with a wave of workflow redesign that will reshape daily work. His own learning habits are simple, from podcasts to regular reading, and driven by a desire to set a strong example for his children as they grow into a world shaped by AI. If you want a grounded view of where AI is genuinely delivering change, this conversation offers rare clarity. What resonates with you most from David's perspective, and how will you approach your own learning in the year ahead? I would love to hear your thoughts.   Tech Talks Daily is Sponsored By Denodo. To learn more, visit denodo.com