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Tech Transformed
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  • How Is Generative AI Transforming Customer Experience Today?
    With the rapid evolution of Generative AI, customer experience (CX) is evolving rapidly, too. In a recent episode of the Tech Transformed podcast, Mike Gozzo, Chief Product and Technology Officer at Ada, sat down with host Christina Stathopoulos, Founder of Dare to Data. They talked about how generative AI is changing business-to-customer interactions.“I view it not just as a business opportunity, but we are here to solve a problem that has existed as long as commerce has,” Gozzo said. He emphasised that AI's goal isn’t just efficiency. It is about building trust and clearly understanding customer needs to allow productive interactions.Artificial intelligence, he noted, “has really enabled what used to be much more costly to happen at scale.” The Ada Chief Product and Technology Officer pointed out that the best customer experiences are highly personalised. Comparing it to arriving at a luxury hotel where the staff already knows your name, even on your first visit. He noted that modern AI aims to make such experiences, which were once only for a select few, common for everyone.Looking to the future, Gozzo tells Stathopoulos he believes generative AI will foster more engagement between customers and brands. “If I consider the trend, I think we will have much more natural, personalised, and effortless interactions than ever before because of this technology.”Gen AI’s impact on Customer Data When discussing operational challenges, especially regarding customer data management, the guest speaker stressed quality over quantity. Gozzo explained that in most AI set-ups, “the real value lies not in the data you’ve collected, but in the understanding of how your business runs, operates, and the people doing the tasks you want to automate.”Governance, Human Orchestration & the Future of AIBeyond personalisation, AI should be implemented responsibly and monitored closely. “The first thing with any AI deployment is to avoid thinking of it as software you buy, deploy, and forget. They need ongoing monitoring, engagement, and maintenance,” Gozzo tells Stathopoulos. He suggested thorough testing processes and collaboration with specialised companies like AIUC, which verify AI systems against common risks. “These tests need to happen quarterly or yearly because the underlying models change so rapidly,” he added.In addition to regularly conducting AI checks, the human element is also critical. AI might automate up to 80% of routine tasks, but humans will still play a vital role. Gozzo described the human role as that of an orchestrator, managing teams that include both humans and AI systems and effectively delegating tasks between them.Finally, Gozzo talked about AI's immediate impact on customer experience. “Our leading customers’ AI agents are outperforming humans. They deliver higher-quality customer service experiences, and customers prefer interacting with their AI.” The key measure, he said, is the positive effect on business growth and customer lifetime value.The chief technology officer’s parting advice to IT decision makers is: “The people on your team know how to make AI work. Capture their insights. Don’t treat this as a technology project. The technologist will not dominate the next decade. This is about business leaders and experts doing the heavy lifting.”At the core of generative and agentic AI, Gozzo...
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  • The 3G Sunset Worldwide: How Enterprises Can Avoid Device Disruption
    The era of 3G is ending. For many industrial businesses, smart infrastructure systems, remote device management, and IoT connectivity rely on networks that are now being phased out globally. The question isn’t if—but when your operations could be disrupted.In this episode of Tech Transformed, Trisha Pillay speaks with Jana Vidis, Business Development Manager at IFB, about the worldwide 3G sunset, what it means for enterprises, and how proactive planning can prevent costly disruptions. They explore the reasons behind the transition to 4G and 5G, the impact on various industries, and the strategies organisations can implement to assess their reliance on legacy devices. Why the 3G Sunset Matters3G networks have powered connectivity for decades, offering wide coverage and reliability. But as global operators move to 4G and 5G, maintaining 3G is no longer sustainable. Carriers are discontinuing services, and support is dwindling, leaving legacy devices vulnerable to:Operational downtimeInconsistent performanceIncreased security risksJana emphasises:“Have a good understanding of what devices you have. Work with IT partners to prepare for future changes. Plan your transition and act before disruption hits.”Jana also stressed the importance of understanding current technology deployments, planning for transitions, and future-proofing investments to avoid disruptions. The conversation highlights the need for proactive measures in adapting to technological advancements and ensuring operational continuity.A Global TimelineThe transition is already well underway across multiple regions:North America: AT&T, Verizon, and T-Mobile 3G networks discontinued in February 2022; Canada’s shutdown begins in early 2025.Europe: Most countries, including the UK, Germany, Hungary, and Greece, will complete shutdowns by the end of 2025.Asia: Japan phased out 3G in 2022, Singapore in July 2024, and India plans completion by the end of 2025.Africa: South Africa started in July 2025; other countries are slowing the transition.South America: Providers like Telefonica, Entel, and Claro completed shutdowns in 2022–2023.Middle East: Oman started shutting down in July 2024; Zain Bahrain in Q4 2022; Kuwait, Iran, and Jordan are following.Industrial devices still using 3G must transition now to avoid operational disruption. From smart infrastructure to remote IoT systems, legacy devices left unaddressed can cause downtime, inconsistent performance, and increased security risks.Takeaways3G networks are being phased out to enable 4G and 5G development.Businesses must assess their reliance on 3G devices before shutdowns.Legacy devices can
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  • Why Do Most ‘Full-Stack Observability’ Tools Miss the Network?
    Tech leaders are often led to believe that they have “full-stack observability.” The MELT framework—metrics, events, logs, and traces—became the industry standard for visibility. However, Robert Cowart, CEO and Co-Founder of ElastiFlow, believes that this MELT framework leaves a critical gap. In the latest episode of the Tech Transformed podcast, host Dana Gardner, President and Principal Analyst at Interabor Solutions, sits down with Cowart to discuss network observability and its vitality in achieving full-stack observability.The speakers discuss the limitations of legacy observability tools that focus on MELT and how this leaves a significant and dangerous blind spot. Cowart emphasises the need for teams to integrate network data enriched with application context to enhance troubleshooting and security measures. What’s Beyond MELT?Cowart explains that when it comes to the MELT framework, meaning “metrics, events, logs, and traces, think about the things that are being monitored or observed with that information. This is alluded to servers and applications.“Organisations need to understand their compute infrastructure and the applications they are running on. All of those servers are connected to networks, and those applications communicate over the networks, and users consume those services again over the network,” he added.“What we see among our growing customer base is that there's a real gap in the full-stack story that has been told in the market for the last 10 years, and that is the network.”The lack of insights results in a constant blind spot that delays problem-solving, hides user-experience issues, and leaves organizations vulnerable to security threats. Cowart notes that while performance monitoring tools can identify when an application call to a database is slow, they often don’t explain why.“Was the database slow, or was the network path between them rerouted and causing delays?” he questions. “If you don’t see the network, you can’t find the root cause.”The outcome is longer troubleshooting cycles, isolated operations teams, and an expensive “blame game” among DevOps, NetOps, and SecOps.Elastiflow’s approaches it differently. They focus on observability to network connectivity—understanding who is communicating with whom and how that communication behaves. This data not only speeds up performance insights but also acts as a “motion detector” within the organization. Monitoring east-west, north-south, and cloud VPC flow logs helps organizations spot unusual patterns that indicate internal threats or compromised systems used for launching external attacks.“Security teams are often good at defending the perimeter,” Cowart says. “But once something gets inside, visibility fades. Connectivity data fills that gap.”Isolated Monitoring to Unified Experience Cowart believes that observability can’t just be about green lights...
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  • How HashiCorp and Red Hat are preparing enterprises for AI at scale
    Enterprises are discovering that the first wave of cloud adoption didn’t simplify operations. It created flexibility, but it also introduced fragmentation, rising costs, and skills gaps that now make AI adoption harder to manage. In this episode of Tech Transformed, analyst and host Dana Gardner speaks with two leaders from across the IBM portfolio: Maria Bracho, CTO for the Americas at Red Hat, and Tyler Lynch, Field CTO for the HashiCorp product suite. They discuss how organisations can move from scattered cloud operations to a unified, automated model that supports AI securely and at scale. The conversation covers the pressures leaders face today, the role of automation, and the skills and operating model changes required as AI becomes core to enterprise strategy. What you’ll learn Why tool sprawl and shrinking teams are increasing operational risk How AI amplifies gaps in data, security, and processes What skills and operating model changes CIOs must prioritise Why hybrid cloud is essential for multi-model AI workloads The growing importance of automation in cloud and AI delivery How poor data hygiene can rapidly increase AI costs Practical steps for building secure, reliable AI operations Key insights from the discussion Cloud complexity is accelerating Most organisations now run “a sprawl of tool sets and environments,” Bracho notes, often without the people or standardized processes to manage them. While cloud created opportunities, the operational overhead has increased. AI raises the stakes Training, tuning, and inference often run in different environments, each with separate performance and security requirements. Bracho describes AI as “the killer workload,” reinforcing the need for robust hybrid architectures. Skills gaps slow progress Lynch highlights the disconnect between AI teams and production engineering teams. Without alignment, model deployment becomes slow and risky — echoing findings from the HashiCorp 2025 Cloud Complexity Report, where most organizations say platform and security teams are not working in sync. AI exposes underlying weaknesses “AI is not going to solve complexity; it will amplify what you already have,” Bracho says. But with structured processes and automation, AI can reduce operator workload and help teams adopt best practices faster. Automation is becoming essential The Cloud Complexity Report shows that more than half of enterprises see automation as key to unlocking cloud innovation. With the foundations already laid, AI can accelerate progress by improving consistency and reducing manual effort. Modernization is continuous Both guests emphasise that AI success depends on long-term investment in people, operating rhythms, and security. Consulting can help organizations start strong, but lasting results come from internal alignment and disciplined execution. Episode chapters 00:00 Navigating cloud complexity08:11 Skills and operating model challenges15:13 Automation for cloud and AI productivity21:48 How consulting accelerates AI readiness24:10 Final guidance for CIOs About...
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  • AI-Powered Chip Design: Real World Impact Across Silicon to Systems
    The semiconductor industry is at an inflection point. As systems become more intelligent, connected, and software-defined, chip design is growing too complex for humans alone. Advances in electronic design automation are reshaping how silicon is built and verified, enabling faster, smarter, and more reliable innovation from data centers to edge devices.How AI Is Changing EDA and Chip DesignIn the latest episode of Tech Transformed, host John Santaferraro speaks with Dr. Thomas Andersen, Vice President of AI and Silicon Innovation at Synopsys, about the real-world impact of AI in chip design. Together, they explore how AI and automation are redefining EDA, how generative AI is accelerating design efficiency, and what the Synopsys acquisition of Ansys means for the future of simulation and system-level integration.As Dr. Andersen explains, “AI is transforming EDA. Synopsys leads in silicon design, and the Ansys acquisition expands our capabilities across multiphysics simulation and system optimization.”From Silicon to SystemsThe integration of complex hardware and software has become one of the greatest challenges in semiconductor and OEM innovation. Traditional sequential development, where software waits for hardware, often causes delays and missed targets. Advances in EDA tools and virtual prototyping now enable engineers to initiate software design months before silicon is finalised, thereby accelerating bring-up and enhancing collaboration across the supply chain.“Generative AI enables more efficient design,” says Andersen. “AI reshapes engineering workflows, but human expertise remains essential.”The result is faster time-to-market, enhanced design verification, and greater overall system reliability.Listen to the full conversation on the Tech Transformed podcast to discover how Synopsys is advancing electronic design automation, improving engineering workflows and chip design from silicon to systems.For more insights follow Synopsys:X: @SynopsysInstagram: @synopsyslifeFacebook: https://www.facebook.com/Synopsys/LinkedIn: https://www.linkedin.com/company/synopsys/TakeawaysAI is transforming EDA and chip design by automating complex processes.Synopsys is a leader in silicon-to-systems design, providing critical software for chipmakers.The acquisition of Ansys expands Synopsys' capabilities beyond EDA.Generative AI is enabling more efficient and adaptable chip design.AI-powered observability is reshaping engineering workflows.The complexity of chip design has increased, requiring advanced tools and automation.Human expertise remains essential in chip design, despite advances in automation.EDA tools simulate chip...
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