Tech Transformed

Ecommerce no longer rewards scale alone. As customer expectations rise and margins tighten, revenue growth and conversion optimisation depend on how well organisations use their data, align their teams, and simplify their technology stack. Brands that fail to adapt are discovering that being data-rich but insight-poor is no longer a survivable position.In this episode of Tech Transformed, host Christina Stathopoulos, Founder of Dare to Data, speaks with Kailin Noivo, President and Co-Founder of Noibu, and Rohit Nathany, Chief Product and Technology Officer at Mejuri. Together, they unpack what is holding ecommerce teams back from sustained revenue and conversion growth and what actually works in practice.Ecommerce Revenue Growth in a High-Cost, High-Expectation MarketToday’s ecommerce environment is shaped by rising acquisition costs, operational sprawl, and customers who expect speed, relevance, and reliability by default. Rohit points to macroeconomic pressure, tariffs, and shifting buying behaviour as forces that are squeezing margins while raising the bar for customer experience.At the same time, brands are struggling to connect the dots between marketing spend, on-site behaviour, and conversion outcomes. Personalisation is widely discussed, but execution often breaks down when teams cannot see how customer interactions move from ad click to checkout. Kailin describes this as a “perfect storm”, explaining that: “infrastructure scaled rapidly during the pandemic, and now needs consolidation, optimisation, and clearer ownership.”Customer Experience, Team Alignment, and the Practical Use of AIImproving customer experience at scale requires more than simply adopting new technology. Organisations also need the right data, processes, and operational alignment to turn those tools into meaningful customer outcomes. It requires teams to work from the same signals and trust the same data. Both Kailin and Rohit stress that AI and automation only deliver value when they remove friction from day-to-day operations rather than adding another layer of complexity.Used well, AI can support data analytics by automating routine monitoring, surfacing patterns that matter, and freeing teams to focus on higher-value work. Used poorly, it becomes just another disconnected tool. The difference comes down to team alignment and culture, like clear ownership, shared goals, and a willingness to continuously refine how decisions are made.For ecommerce leaders, this is less about digital transformation as a slogan and more about operational discipline. Simplifying the stack, aligning teams around outcomes, and treating customer experience as a measurable business driver are what sustain revenue growth when conditions are uncertain.If you would like to find out more, visit: https://www.noibu.com/TakeawaysBuilding resilience in revenue and conversion growth is crucial.Ecommerce leaders face a perfect storm of challenges.AI is central to enhancing customer experience in ecommerce.Data-rich environments often lead to insight-poor outcomes.Connecting the dots between data and decisions is essential.A strong culture of experimentation fosters innovation.Tool consolidation can streamline operations and reduce costs.Visibility in data access is critical for effective decision-making.Speed of action is influenced by organisational culture.Establishing a KPI tree helps unify team efforts.Chapters00:00 Introduction to Ecommerce Challenges06:04 Real-World Applications of Ecommerce Analytics & Monitoring11:50 The Role of AI in Ecommerce17:57 Data Utilisation and Decision Making24:13 Culture and Team Alignment in Ecommerce29:56 Practical Strategies for Ecommerce Leaders

SaaS companies moving toward usage-based and hybrid pricing models are discovering that revenue is no longer secured when the contract is signed.Instead, revenue is earned continuously through product usage, introducing new challenges for finance teams around billing accuracy, revenue visibility, forecasting, and managing increasingly complex cost structures driven by AI-powered products.In the latest episode of Tech Transformed, host Dana Gardner speaks with Lee Greene, Vice President of Sales at Vayu, about how AI and usage-based pricing are reshaping the economics of SaaS and why many companies are discovering that their pricing strategy is only as strong as the infrastructure behind it.One idea from the conversation“Pricing strategy is only as strong as the infrastructure behind it.”What you will learn in this episodeWhy usage-based pricing exposes hidden revenue leakage in many SaaS companies• How AI-driven products introduce unpredictable cost structures and margin pressure• Why disconnected CRM, product, and ERP systems break revenue visibility• What finance and revenue teams need to support scalable usage-based billing and forecastingWhy SaaS Economics Are Breaking Away From Fixed SubscriptionsGreene argues that usage-based pricing isn’t simply an emerging trend. It is a response to assumptions that no longer hold true.Traditional SaaS subscription models were built around predictable costs and relatively stable product usage. AI-driven products have fundamentally changed that equation. Each interaction with an AI-powered system can create variable cost, making static pricing models increasingly difficult to sustain.This shift is also changing buyer expectations. Customers increasingly resist flat pricing structures and instead prefer models that reflect the value they actually receive. Usage-based pricing aligns economic benefit with real consumption, allowing buyers to justify spend internally while pushing vendors to be accountable for measurable outcomes rather than bundled feature sets.AI’s Double RoleThe conversation also highlights how AI is introducing a structural challenge for SaaS finance and revenue teams.Usage-based pricing generates enormous volumes of data across product usage, customer behaviour, and cost inputs. Traditional billing systems were not designed to process this level of complexity.At the same time, AI is also becoming the only scalable way to manage it. Automated usage tracking, dynamic pricing logic, and real-time billing reconciliation are increasingly necessary to maintain operational accuracy and financial control.Treating AI solely as a product capability, rather than embedding it into revenue operations, can leave organizations exposed to billing errors, misaligned pricing models, and revenue leakage.Revenue Management Shifts From Contracts to OperationsOne of Greene’s key observations is that usage-based pricing does not necessarily create revenue leakage. Instead, it reveals problems that already existed.The difference is visibility.In traditional SaaS models, revenue was largely secured at the moment of contract signature. In usage-based models, revenue must be earned continuously through product consumption. This means billing accuracy, system integration, and data flow directly influence financial performance.Disconnected CRM, product, and ERP systems can create gaps that lead to misbilling, delayed revenue recognition, and customer disputes. As a result, the infrastructure supporting revenue operations becomes inseparable from pricing strategy itself.What SaaS Leaders Must Build to Stay Economically ViableThe discussion concludes with a broader perspective on how SaaS companies must evolve to support this new economic model.The future belongs to organizations that design their pricing and revenue systems for variability. Pricing models must adapt to changing demand, and the systems behind them must support that flexibility without relying on heavy manual processes.Automation and no-code AI tools are increasingly enabling finance and revenue teams to adjust pricing models as usage patterns evolve. This agility is not simply about speed. It is about maintaining control in an environment where AI-driven cost structures and product usage can shift rapidly.Usage-based pricing is doing more than changing how SaaS products are sold. It is reshaping how companies think about value, risk, and revenue itself, making flexibility, intelligent automation, and data-driven decision making central to long-term success.About VayuVayu helps SaaS companies manage complex usage-based and hybrid revenue models by connecting product usage data, billing systems, and finance infrastructure.Learn more at:https://www.withvayu.com/TakeawaysThe shift from fixed subscription models to usage-based pricing driven by AI How AI is both creating and solving new pricing and billing challengesWhy revenue infrastructure plays a critical role in preventing revenue leakageThe importance of flexible pricing models that adapt to demand and usage patternsThe growing role of automation and AI in modern revenue operationsChapters00:00 – Introduction02:30 – The economic shift in SaaS: Moving toward usage-based models05:00 – The role of AI in transforming SaaS pricing and revenue streams06:47 – Buyer preferences and evolving value quantification08:38 – Infrastructure's role in supporting flexible billing models11:49 – How finance teams can shape technology to control revenue14:24 – Process reengineering and AI-driven automation17:15 – Adaptable SaaS infrastructure and market signals20:30 – Preparing for the unknown: sandboxing and scenario modeling24:49 – Opportunities in connecting SaaS apps and managing data flow28:54 – Building automated, scalable billing and integration flow

Managing product complexity has become increasingly critical as customers demand greater customisation. Manufacturers face the challenge of connecting disparate data systems effectively. In this episode of Tech Transformed, host Christina Stathopoulos and Laura Beckwith, Director of Product Management at Configit, discuss the complexities of managing product data in manufacturing, focusing on the concept of the digital thread. They explore the challenges manufacturers face in connecting disparate data systems, the importance of customisation, and how a Configuration Lifecycle Management (CLM) approach can provide a reliable foundation for digital threads. Understanding the Digital ThreadThe digital thread represents the traceability of all decisions and information regarding a product from its inception and throughout its lifecycle. According to Laura Beckwith, the digital thread allows manufacturers to trace decisions made during the requirements stage through to engineering and ultimately to manufacturing and service. This traceability is not just about having data; it’s also about ensuring that various teams and systems can access the right information to facilitate informed decision-making.Challenges in Implementing the Digital ThreadDespite the promise that digital threads hold, manufacturers face significant challenges in connecting data from multiple systems. Beckwith highlights the example of a smartphone, which undergoes various phases from design to manufacturing. Each phase involves distinct software systems—like CAD for design and ERP for manufacturing—many of which do not communicate well with one another. This lack of integration often leads to inefficiencies, such as manual data entry and miscommunication between teams.The Impact of Customisation on ComplexityAs customisation becomes the norm, the complexity of managing product data increases exponentially. Beckwith notes that while smartphones may have limited customisations, products like cars offer vast configurability. For instance, when configuring a car, consumers can choose from an extensive array of options. Behind the scenes, however, manufacturers must manage numerous engineering constraints and compliance regulations. This is where the digital thread becomes essential, enabling manufacturers to track and manage these complex configurations effectively.The Role of Configuration Lifecycle Management (CLM)The upcoming CLM Summit 2026 will focus on mastering customisation complexity and building a reliable data foundation for configurable products. Beckwith explains that a scalable CLM approach is crucial for establishing a reliable digital thread. It ensures that all product configurations, such as the combination of seat heating and memory seats in a car, are tracked accurately. This not only aids in the manufacturing process but also enhances customer service by allowing manufacturers to address issues based on specific configurations.More broadly, the digital thread provides manufacturers with a framework for managing the growing complexity of modern product development. By enabling seamless communication between data systems and implementing effective CLM practices, organisations can better align engineering, manufacturing, and service functions. For more information visit: https://configit.com/TakeawaysThe digital thread provides traceability of product decisions.Manufacturers face challenges due to siloed data systems.Customisation complexity is increasing in manufacturing.Digital threads are crucial for configurable products like cars.CLM helps bridge the gap between engineering and marketing.Starting small can lead to the successful implementation of digital threads.Data alignment is essential for effective communication.Real-world examples illustrate the benefits of digital threads.A strong digital thread enhances customer experience.AI can leverage data from digital threads for predictive maintenance.Chapters00:00 Introduction to Digital Threads in Manufacturing02:14 Understanding the Digital Thread06:47 Challenges in Connecting Data Systems11:12 Customisation, Complexity, and Digital Threads15:43 The Role of Configuration Lifecycle Management (CLM)20:23 Real-World Use Case: Implementing Digital Threads23:42 Guidance for Early Adopters of Digital Threads

As AI systems move rapidly from experimentation into production, organizations are discovering that adoption alone is not the hard part, understanding, governing, and trusting AI in live environments is. In this episode of the Tech Transformed, Shubhangi Dua speaks with Camden Swita, Head of AI, New Relic, about why AI observability has become a critical requirement for modern enterprises, particularly as agentic AI and AI-driven operations take on increasingly autonomous roles.The discussion explores how traditional observability models fall short when applied to probabilistic systems, why many AI ops initiatives stall at proof-of-concept, and what security and IT leaders must prioritize to safely scale AI in production.Be the first to see how intelligent observability takes you beyond dashboards to agentic AI with business impact at New Relic Advance, February 24, 2026.Why AI Adoption Is Outpacing Operational ReadinessWhile AI adoption is accelerating rapidly, most organizations still lack visibility into what their AI systems are actually doing once deployed. Generative AI is already widely used for natural language querying, coding assistants, customer support bots, and increasingly within IT operations and SRE workflows. As these systems move into production, new challenges emerge around cost control, governance, performance quality, and trust. Leaders recognize AI’s potential value, but without deep observability, they struggle to determine whether AI-enabled systems are delivering consistent outcomes or introducing hidden operational and security risks.How Observability Must Evolve for Agentic AI and AI OpsThe episode then examines how observability itself must evolve to support agentic and autonomous AI systems. While core observability principles still apply, AI introduces a new layer of complexity that requires visibility into model behavior, agent decision-making, and multi-step workflows. Modern AI observability extends traditional application performance monitoring by capturing telemetry from LLM interactions, agent orchestration layers, and automated evaluations of output quality against intended use cases. Without this visibility, teams are effectively operating blind, unable to diagnose failures, validate compliance, or confidently deploy AI at scale. At the same time, AI is increasingly being embedded into observability platforms to reduce noise, accelerate root cause analysis, and improve incident response.Making Agentic AI Work in PracticeSuccessful adoption starts with low-risk, high-friction tasks such as incident triage, dashboard interpretation, and runbook summarization, rather than fully autonomous remediation. These use cases deliver immediate productivity gains while preserving human oversight. Over time, stronger feedback loops, better context management, and human-in-the-loop learning allow agents to become more reliable and useful. Looking ahead, Camden predicts that 2026 will be a turning point for agentic AI in production, driven by maturing AI observability platforms, richer semantic data, and knowledge graphs that connect technical telemetry to real business outcomes.Listen to Are “Vibe-Coded” Systems the Next Big Risk to Enterprise Stability?When Vibe Code Breaks OpsAI-generated code is pushing prototypes into production faster than ops can cope. How observability becomes the gatekeeper for enterprise resilience.Key TakeawaysAI adoption is accelerating in enterprise environments.Organizations face complexities in productionizing AI features.Natural language querying is a common AI application.AI agents are increasingly used in IT operations.Observability is crucial for understanding AI systems.Traditional observability solutions are evolving to include AI monitoring.Incident response teams struggle with alert noise and context gathering.AI can assist in incident management and root cause analysis.Future trends include more reliable AI agents and monitoring solutions.Organizations need to invest in AI observability to succeed.Chapters01:20 The Current State of AI Adoption02:28 Purposeful AI Usage in Organizations04:40 Observability in the Age of AI08:05 Evolving Observability Solutions11:36 Challenges in Incident Response16:04 Integrating AI in Operations23:33 Future Trends in AI Monitoring30:29 Investment Strategies for AI Solutions#ArtificialIntelligence #EnterpriseAI #GenerativeAI #AgenticAI #AIAgents #AIObservability #AIInProduction #AIOps #AISecurity #AIGovernance #ModelMonitoring #LLMOps #ITOperations #SRE #DevOps #IncidentResponse #RootCauseAnalysis #DigitalTransformation #Automation #FutureOfAI

Did you know that on average, 35 per cent of calls to automotive dealerships go unanswered? In today’s competitive market, missed calls mean missed sales and dealerships are turning to AI and analytics to fix this. In this episode of Tech Transformed, host Jon Arnold and Ben Chodor, Chief Executive Officer of CallRevu, about how AI is reshaping the way dealerships handle calls, manage repair orders, and engage with customers throughout their journey. They explore the role of real-time analytics in improving interactions, the importance of answering every incoming call, and why AI has become essential in modern dealership operations.Customer Experience Has ChangedThe customer journey is no longer a simple transaction. Today, it spans pre-purchase research, purchasing, and post-purchase support. Chodor highlights that every interaction matters; customers now expect engagement and guidance at every stage, not just information. Competition in automotive sales is fierce, and customers expect fast responses. Chodor notes that dealerships leveraging AI can provide updates on service times, answer inquiries promptly, and ensure no customer engagement is lost. Real-time insights also empower managers to make better operational decisions and improve the overall customer experience.AI in Automotive DealershipsAI technology is changing the way dealerships operate. Chodor discusses how CallRevu’s technology listens to every sales and service call, providing real-time analytics to dealerships. This capability allows managers to intervene in calls, ensuring that customer concerns are addressed promptly. For instance, if a call goes unanswered, the system can alert management, enabling them to engage with the customer immediately, thus reducing missed opportunities.The integration of AI and analytics in automotive dealerships is not just about improving sales; it's about transforming the entire customer experience. From ensuring every call is answered to providing real-time insights for better decision-making, technology is reshaping how dealerships engage with customers. As the automotive industry continues to evolve, those who prioritise customer experience through innovative solutions will undoubtedly lead the way.If you would like to find out more information, go to https://www.callrevu.com/TakeawaysAI enhances customer engagement in automotive dealerships.Real-time analytics can significantly improve communication.Every call to a dealership is crucial for sales.AI helps reduce the number of calls going to voicemail.Dealerships must adapt to a more competitive landscape.Customer experience is more than just selling cars.AI can provide instant responses to customer inquiries.Training tools powered by AI can improve sales techniques.The automotive industry is shifting towards data-driven decisions.AI is essential for modern dealership operations.Chapters00:00 Introduction to Customer Experience in Automotive Dealerships05:00 The Role of AI in Enhancing Communication10:08 Transforming Customer Engagement with Real-Time Analytics15:03 The Importance of Incoming Calls and Tracking19:55 AI's Impact on the Automotive Industry24:49 Future Trends in Automotive Technology

Podcast: Tech Transformed PodcastGuest: Manesh Tailor, EMEA Field CTO, New Relic Host: Shubhangi Dua, B2B Tech Journalist, EM360TechAI-driven development has become obsessive recently, with vibe-coding becoming more common and accelerating innovation at an unprecedented rate. This, however, is also leading to a substantial increase in costly outages. Many organisations do not fully grasp the repercussions until their customers are affected.In this episode of the Tech Transformed Podcast, EM360Tech’s Podcast Producer and B2B Tech Journalist, Shubhangi Dua, spoke with Manesh Tailor, EMEA Field CTO at New Relic, about why AI-generated code, also called vibe-coding, rapid prototyping, and a focus on speed create dangerous gaps. They also talked about why full-stack observability is now crucial for operational resilience in 2026 and beyond.AI Vibe Code Prioritising Speed over StabilityAI has changed how software is built. Problems are solved faster, prototypes are created in hours, and proofs-of-concept (POC) swiftly reach production. But this speed comes with drawbacks.“These prototypes, these POCs, make it to production very readily,” Tailor explained. “Because they work—and they work very quickly.”In the past, the time needed to design and implement a solution served as a natural filter. However, the barrier has now disappeared.Tailor tells Dua: “The problem occurs, the solution is quick, and these things get out into production super, super fast. Now you’ve got something that wasn’t necessarily designed well.”The outcome is that the new systems work but do not scale. They lack operational resilience and greatly increase the cognitive load on engineering teams.New Relic's research indicates that in EMEA alone:The annual median cost of high-impact IT outages for EMEA businesses is $102 million per yearDowntime costs EMEA businesses an average of $2 million per hourMore than a third (37%) of EMEA businesses experience high-impact outages weekly or more often.Essentially, AI-driven development heightens risks and increases blind spots. “There are unrealised problems that take longer to solve—and they occur more often,” Tailor noted. This is because many AI-generated solutions overlook operability, scaling, or long-term maintenance.Modern architectures were already complex before AI came along. Microservices, SaaS dependencies, and distributed systems scatter visibility across the stack.“We’ve got more solutions, more technology, more unknowns, all moving faster,” he tells Dua. “That’s generated more data, more noise—and more blind spots.”Traditional monitoring tools were built for known issues—predefined components, predictable dependencies, and static systems. “Monitoring was about what you already understood,” Tailor explained. “Observability is about the unknown unknowns.”AI-generated code complicates the situation because teams often lack detailed knowledge of how that code was created, how components interact, or how dependencies change over time.This is where full-stack observability becomes essential—not as a standalone tool, but as a coordinated capability that connects signals across applications, infrastructure, data, and AI systems in real time.Also Watch: How Do AI and Observability Redefine Application Performance?Reactive to Proactive: The Role of AI in ObservabilityIronically, the same AI that increases complexity is also necessary to manage it. According to New Relic data, 96 per cent of organisations plan to adopt AI monitoring and 84 per cent plan to implement AIOps by 2028.However, Tailor stresses that success relies on using AI to enhance—rather than replace—human expertise. “We have to leverage AI to establish baselines much faster,” he said. “But humans still bring experience and judgment that machines don’t have.”AI allows teams to shift from responding to known patterns to proactively spotting anomalies before they turn into customer-facing incidents.Beyond uptime and performance, observability is becoming a regulatory requirement. “If it’s not observed, then it’s rogue,” Tailor warned.New regulations like the EU AI Act and ISO 42001 will require organisations to show visibility into AI systems, decision-making processes, and operational behaviour. “You won’t be allowed to operate AI solutions without the right level of observability,” he added.The 2026 Takeaway: Observability is Essential for AIAs AI-driven development becomes the norm, Tailor’s message to CIOs, CTOs, and CDOs is: “Observability isn’t an option. Without it, your AI strategy simply won’t work.”Organisations that neglect to invest in centralised, full-stack observability risk more than outages—they risk compliance failures, security issues, and rising operational costs.“Otherwise,” Tailor stated, “you will limit the ability to benefit from your AI strategy.”To learn more, visit NewRelic.com or listen to the full episode of the Tech Transformed podcast at EM360Tech.com.Also Watch: How Can AI Bridge the Gap from Observability to Understandability?TakeawaysIf you don't get your observability house in order, all the grand plans with AI may be at risk.Speed has been favoured over good governance and engineering standards.Observability is about understanding the relationship between components, not just monitoring known issues.AI can help establish baselines faster in a rapidly changing environment.Without observability, you can't make your AI strategy work.Chapters00:00 Introduction to AI and Observability01:11 The Risks of Rapid Software Development04:21 Understanding the Cost of Outages06:30 Blind Spots in AI-Driven Systems11:29 Transitioning to Full-Stack Observability13:58 Moving from Reactive to Proactive Monitoring18:54 Real-World Applications of AI Monitoring19:51 The Future of AI and Observability#Observability #AIOps #AIDrivenDevelopment #FullStackObservability #ITOutages #VibeCoding #AIinProduction #DevOps #NewRelic #TechPodcast

In a world where climate change is reshaping the way we grow, transport, and consume the things we rely on, understanding the first mile of supply chains has never been more critical. That’s the stage where over 60 per cent of risks arise, yet it remains the hardest to measure and manage. In a recent episode of Tech Transform, Trisha Pillay sits down with Jonathan Horn, co-founder and CEO of Treefera, to explore how artificial intelligence is providing clarity, actionable insights, and sustainable solutions for this complex ecosystem.The First Mile and Climate PressuresHorn’s perspective comes from a mix of experience: growing up on a farm, studying physics, and working in investment banking. That combination gives him a lens on both the natural systems that underpin agriculture and the data-driven tools that help manage risk.Extreme weather patterns like droughts, heavy rainfall, and hurricanes are putting pressure on crops such as cocoa, coffee, wheat, and soy. The consequences ripple outward: production costs rise, commodity prices fluctuate, and supply chains become less predictable. A simple example illustrates this clearly: certain chocolate biscuits in the UK have moved from being chocolate-filled to chocolate-flavoured, reflecting disruptions in cocoa production in West Africa caused by extreme weather and disease. These changes are not isolated; they affect global markets and everyday products.Turning Data into Actionable InsightsAI can help make sense of the complexity. Treefera, for instance, combines satellite imagery, sensor data, and other datasets to provide insights on crop yields, supply risks, and climate impacts. Horn describes it like a car dashboard: “You don’t need to know every technical detail to understand what’s happening and act accordingly.”The value of AI lies not in flashy algorithms but in its ability to translate raw data into practical decision-making tools. By analysing multiple signals from weather events to agricultural output, AI can highlight trends, flag potential disruptions, and support planning for traders, insurers, or supply chain managers. The goal is clarity and action, not simply more information.Data, Regulation, and Responsible UseAlongside operational complexity, organisations face questions about data governance. Emerging regulations such as the EU AI Act aim to ensure AI is used responsibly, and companies need to maintain control over proprietary information while leveraging technology effectively. Horn stresses the importance of frugal, transparent AI applications that produce meaningful insights without unnecessary complexity.In practice, this means balancing innovation with compliance: using AI to understand risks, improve planning, and support sustainability without overstating its capabilities or creating new vulnerabilities. The conversation underlines a key point: the impact of AI is most tangible when it’s applied thoughtfully, in service of real-world decisions.In short, AI is helping organisations navigate the increasingly unpredictable intersection of climate, risk, and supply chain complexity. The first mile, long a blind spot, is becoming visible not through hype or marketing claims, but through practical, data-driven insight that helps people respond to the world as it is, not as we wish it to be.TakeawaysAI can significantly improve the management of supply chains.Climate change is causing more extreme weather patterns, affecting agriculture.Data sovereignty is crucial for companies to maintain control over their proprietary data.AI native businesses leverage AI as a core component of their operations.The EU AI Act aims to create a framework for responsible AI use.AI can help simplify complex information into actionable insights.Frugal AI usage can lead to more efficient operations.The evolution of AI technologies includes advancements in large language models.Understanding the risks associated with climate change is essential for supply chain management.Companies must balance compliance with innovation in AI applications.Chapters00:00 Introduction to AI in Supply Chains04:40 Navigating Climate Challenges with AI09:40 AI-Native Business Models13:57 The Evolution of AI Technologies18:16 Understanding Data Sovereignty21:54 Balancing AI Regulation and Innovation25:48 Future of AI in SustainabilityAbout John HornJonathan Horn is the founder of Treefera, an AI platform delivering accurate, auditable data to support carbon offsetting and nature-positive initiatives for landowners, investors, governments, NGOs, scientists, and marketplace participants. An innovative thinker and problem solver, he holds a PhD in Theoretical Fluid Dynamics and has extensive experience designing and implementing AI and data analytics solutions at scale. Jonathan has also applied data mesh principles to enable distributed, data-driven organisations, with a background in optimising banking operations through advanced data and AI systems.

Mass customisation has long been the holy grail for industrial manufacturers, offering the ability to provide highly tailored products while maintaining efficiency, scalability, and profitability. However, as products become increasingly complex, traditional methods of managing configurations are starting to reveal their limitations.In a recent episode of Tech Transformed, host Christina Stathopoulos, Founder of Dare to Data, spoke with Stella d’Ambrumenil, Product Manager at Configit, about the operational realities and future potential of generative AI technology in manufacturing.The Challenge of ComplexityModern manufacturers often operate somewhere between make-to-order and assemble-to-order models. While these approaches allow flexibility, they also expose companies to a major problem, such as fragmented configuration processes. Sales teams, engineers, and manufacturing units may all handle different aspects of customisation separately, relying on spreadsheets or outdated product documentation. The result is inefficiency, errors, and an inability to scale effectively.“The problem isn’t just that you have lots of options,” Stella explains. “It’s that the knowledge about those options is scattered. If configuration is handled differently across departments, you inevitably get mistakes and lost time.”Configit Ace® Prompt: Bridging the GapEnter Configit Ace® Prompt, the latest tool designed to tackle this very problem. At its core, Configit Ace® Prompt converts unstructured data into structured configuration logic that can be used across all departments. Formalising configuration knowledge ensures that customisation is accurate, repeatable, and manageable.This approach not only reduces errors but also democratizes access to critical product information. Engineers, product managers, and sales teams no longer need to interpret fragmented data manually — they can work from a single source of truth. Early adopters report significant time savings, fewer mistakes, and smoother collaboration.Why Configuration Lifecycle Management MattersConfigit Ace® Prompt is a key enabler of Configuration Lifecycle Management (CLM). CLM is an approach to maintaining consistent data and processes across the entire product lifecycle — from design and engineering to manufacturing and service. This is crucial for companies seeking to scale customisation without creating chaos in operations.By adding generative AI technology, manufacturers can implement a CLM approach faster to automate logic creation, catch configuration errors early, and ensure that complex products are delivered efficiently.Looking Ahead: CLM Summit 2026For professionals interested in deepening their understanding of configuration management, Configit’s CLM Summit 2026 — an online event scheduled for May 6 & 7 - will provide insights into best practices, advanced strategies, and tools like Configit Ace® Prompt. It’s an opportunity to see how companies can leverage configuration management to stay competitive in a world of growing product complexity.For more insights, visit: configit.comTakeawaysManufacturers face increasing challenges with product complexity and customisation demands.Configit Ace® Prompt helps convert unstructured product knowledge into usable configuration logic.Configuration Lifecycle Management (CLM) is crucial for establishing and maintaining a shared source of truth.Product data fragmentation leads to inefficiencies in manufacturing processes.AI can assist in catching errors in configuration data.The tool aims to lower the barrier to entry for data consolidation.Excel remains a popular tool, but Configit Ace® Prompt offers a familiar interface.Early beta testers have reported significant time savings with Configit Ace® Prompt.Generative AI has potential applications in guided configuration and data analysis.The upcoming CLM Summit will provide insights into product configuration management.Chapters00:00 Introduction to Tech Transformed and Configit02:48 Understanding Product Complexity in Manufacturing05:54 The Role of Configit Ace® Prompt in Configuration Management08:53 Configuration Lifecycle Management Explained11:52 The Importance of Data Consistency and Cleanup15:14 User Experience and Adoption of Configit Ace® Prompt17:54 Generative AI and Its Future Applications21:07 Conclusion and Future EventsAbout ConfigitAt Configit, we help our customers globally to master the challenges of getting configurable products to market faster, with higher quality and engineered at lower costs. As a pioneer of Configuration Lifecycle Management (CLM), we have been instrumental in driving the adoption of CLM solutions globally. Trusted by the world’s largest manufacturing companies for their mission-critical functions, our advanced configuration platform built on patented Virtual Tabulation® technology handles the most complex products on the market. Our customers include ABB, Jaguar Land Rover, John Deere, Grundfos, Vestas, Siemens, Danfoss amongst others.

As organisations navigate the rapid rise of AI, the challenge is no longer simply acquiring technology; it’s preparing people to use it effectively. Many companies are realising that access to AI tools alone doesn’t translate into business impact. Employees need meaningful opportunities to develop skills that can be applied immediately, helping teams work smarter and make better decisions.In this episode of Tech Transformed, Christina Stathopoulos, Founder of Dare to Data, speaks with Gary Eimerman, Chief Learning Officer at Multiverse, about the pressing challenge of closing the AI and data skills gap in the workforce. They explore how organisations can build an AI-ready workforce, focusing on non-technical employees and the importance of a skills-first approach to learning.The Skills-First ApproachMultiverse champions a skills-first approach to upskilling employees in AI and data, asserting that this targeted training drives measurable business impact, including increased productivity, revenue growth, and time savings. This strategy moves beyond general AI literacy to focus on practical, applied learning. By diagnosing both organisational needs and individual skill levels, the approach identifies gaps and prescribes tailored, project-based learning experiences. Employees don’t just complete modules in isolation; they work on real-world projects that apply the skills they are learning from day one, reinforcing retention and ensuring that training contributes to tangible outcomes.Learning in the AI EraGary explains that learning in the AI era is not simply about providing tools or access to content; it’s about driving behaviour change, aligning learning with business outcomes, and embedding a culture of continuous skill development. As AI reshapes both the work we do and the way we learn, organisations that invest in people-first strategies position themselves to thrive rather than merely adapt. This conversation demonstrates that the future of work is always on learning, and that meaningful investment in AI and data skills is no longer optional; it’s a critical driver of business success.Unlocking Workforce PotentialBy combining practical, applied training with ongoing support and measurable outcomes, companies can not only close the AI skills gap but also unlock the full potential of their workforce in an era defined by rapid technological change.TakeawaysTechnology alone is never enough; people must be invested in.Reskilling is a necessity due to technological disruption.Organisations must focus on human behaviour change, not just software deployment.A skills-first approach is critical for effective learning.Learning should be project-based and applied immediately.Non-technical roles are increasingly adopting AI tools.Creating time and space for learning is essential.Highlighting success stories builds confidence in using AI.Measuring impact through metrics like revenue per employee is vital.The future of work requires a cultural shift towards continuous learning.Chapters00:00 Closing the AI and Data Skills Gap02:02 Challenges in Building an AI-Ready Workforce06:06 The Skills First Approach to Learning10:04 Supporting Non-Technical Employees in AI13:46 Measuring the Impact of AI Skills Investment18:13 The Evolution of Learning in the AI Era22:59 Preparing for the Future of WorkAbout MultiverseMultiverse is the upskilling platform for AI and tech adoption. Multiverse has partnered with over 1,500 companies to deliver a new kind of learning that’s transforming the workforce through tech skills.Multiverse apprenticeships are for people of any age or career stage and focus on critical AI, data and tech skills. Multiverse learners have driven $2bn + ROI for their employers, using the skills they’ve learned to improve productivity and measurable performance.For more information, visit www.multiverse.io

In the automotive industry, trust and transparency are no longer optional; they have become key components. Dealerships that communicate clearly and responsibly with their customers strengthen relationships and improve overall experiences. In this episode of Tech Transformed, host Trisha Pillay speaks with Sean Barrett, Chief Information Officer at CallRevu, about how dealerships can navigate the changing landscape of communication while maintaining accountability, compliance and operational resilience.The Evolution of Dealership CommunicationCommunication has always been at the heart of dealership operations. The phone system was once the primary lifeline between customers and dealerships, giving managers the visibility needed to ensure interactions were handled correctly. Today, communication extends far beyond the phone. SMS, MMS, instant messaging, and other channels allow customers to engage in multiple ways.Sean explains how integrating these channels into a single technology platform provides managers with a clear view of all interactions, ensuring employees follow policies and customers receive the attention they deserve. This approach strengthens trust and improves the overall customer experience.Compliance and Data Privacy in Automotive CommunicationAlongside multi-channel communication, compliance and data privacy are critical. Regulations like GDPR and UN R155 require dealerships to protect customer data while maintaining seamless communication. Transparent practices, combined with adherence to regional rules, help build trust and protect both customers and the dealership’s reputation. Observing patterns in customer interactions also allows dealerships to make informed decisions, improve processes, and enhance service quality. Using these data insights, dealerships can make communication more effective and meaningful for every customer.Infrastructure That Keeps Dealerships OperationalReliable infrastructure underpins all communication efforts. Sean shares how dealerships can prepare for unexpected disruptions with geo-redundant systems, cloud-based platforms, and layered internet backups, including options like Starlink or fibre connections. These measures ensure dealerships stay operational, customers can reach them without interruption, and business continuity is maintained.Preparing for Emerging Communication ChannelsAs new channels emerge, proactive preparation is key. Dealerships that view communication as an investment, rather than a cost, position themselves for long-term success. Monitoring trends, adapting quickly, and fostering transparency help maintain strong customer relationships even as expectations evolve.Training and Staff DevelopmentStaff development is a critical component of a communication strategy. By using insights from technology platforms, dealerships can guide employee training, build accountability, and create a culture of learning. Confident, well-trained teams contribute to consistent, high-quality interactions that enhance customer trust.Success in automotive communication isn’t just about adopting the latest tools—it’s about building systems and practices that protect customers, support employees, and foster trust at every touchpoint. Sean Barrett’s insights provide a roadmap for dealerships aiming to elevate communication strategies, improve customer satisfaction, and maintain resilience in an increasingly connected world.Learn more: callrevu.comTakeawaysTrust and transparency are essential for customer relationships.The evolution of communication systems has introduced multi-channel interactions.Compliance challenges vary across regions and regulations.AI can provide deeper insights into customer interactions.Training with AI can enhance employee performance and accountability.Dealerships should view communication as an investment, not an expense.Data insights are crucial for improving customer experience.Emerging communication channels require proactive preparation.A risk-free training environment can foster employee growth.Customer lifetime value is realised through effective data utilisation.Chapters00:00 Introduction to Trust and Transparency in Automotive Communication03:06 Evolution of Dealership Communication Systems05:02 Building Reliable Communication Infrastructure07:13 Navigating Compliance Challenges in Automotive09:12 Preparing for Emerging Communication Channels11:15 The Role of AI in Automotive Communication14:10 Bridging the Gap Between AI and Human Interaction16:35 Success Stories and Training Innovations18:22 Practical Actions for Dealerships22:14 Conclusion and Key TakeawaysAbout CallRevuCallRevu is the leading communication intelligence platform built for automotive retail, empowering dealerships to take control of every conversation, from the first ring to the final result. Its unified solution combines a hosted phone system, call monitoring, performance training, and reputation management fueled by AI-powered analytics that turn every customer interaction into actionable intelligence.Founded in a dealership in 2008, CallRevu was created by the industry, for the industry. We deliver the tools dealerships need to drive revenue, improve operations, and deliver exceptional customer experiences.

In a world where customer expectations evolve faster than ever, organisations are rethinking how they manage and leverage data. Legacy, monolithic Customer Data Platforms (CDPs) are increasingly challenged by rigidity, slow adaptability, and regulatory pressures. In this episode of Tech Transformed, Christina Stathopoulos, Founder of Dare to Data, speaks with Joe Pulickal, Director of Product Management at Uniphore, about the shift to composable CDPs and what it means for modern marketing technology.Moving Away from Monolithic CDPsOrganisations are moving away from rigid, all-in-one CDPs as regulations around data privacy, consent, and cross-border data flows intensify. Joe explains that companies can no longer rely on systems that lock them into a single architecture or make compliance retrofitting difficult. Data governance, consent management, and data sovereignty have become critical considerations in every technology decision, forcing leaders to rethink the underlying structure of their CDPs.Challenges in Composable SystemsWhile composable CDPs offer flexibility, they introduce new challenges. Organisations must define ownership and accountability within modular systems to prevent fragmentation and ensure consistent data quality. Leadership must consider how compute, storage, and access are distributed across modules while maintaining compliance and security standards. Joe notes that without clarity on ownership, organisations risk operational inefficiency and weakened governance.Flexibility and Modularity in Data ManagementThe core advantage of composable architectures lies in modularity. By decoupling components from data ingestion to activation, organisations gain the freedom to innovate without being constrained by a monolithic platform. Joe emphasises: “You need flexibility in where data lives, how compute happens, ultimately doubling down on sovereignty, security, and that composable idea that initially started with data.” This approach allows teams to adopt new tools, scale selectively, and respond to changing business or regulatory requirements with agility.Embracing First-Party Data StrategiesThe shift to first-party data strategies is essential in today’s marketing landscape. With third-party cookies being phased out and privacy regulations tightening, companies must rely on direct, trusted data from their customers. Composable CDPs provide the framework to centralise first-party data while giving teams the ability to personalise experiences, maintain compliance, and safeguard trust. Joe highlights that organisations need to view data not just as an asset, but as a responsibility, balancing customer value with ethical management.Here are what leaders can do:Rethink data architecture: Move from monolithic to composable systems to gain flexibility, scalability, and regulatory alignment.Prioritise governance: Define ownership, consent management, and security practices across modular components.Focus on first-party data: Build direct customer relationships and leverage trusted data responsibly.Embrace modularity: Enable innovation, adaptability, and resilience in data management through composable design.This episode offers practical insights for leaders navigating the transition from traditional CDPs to composable architectures. It highlights how thoughtful design, governance, and first-party data strategies empower organisations to act with agility, comply with regulations, and deliver better customer experiences.For more information, book a demo with Uniphore.TakeawaysOrganisations are moving away from rigid monolithic CDPs due to regulatory pressures.Composable architectures offer flexibility and modularity in data management.The shift to first-party data strategies is essential in the current landscape.Data governance and consent management are critical in modern marketing.Organisations face challenges in defining ownership within composable systems.Uniphore supports hybrid deployment options for data management.AI integration is crucial for enhancing CDP functionalities.Flexibility in architecture helps avoid vendor lock-in.People and processes are as important as technology in CDP implementation.Clear alignment on objectives is necessary for successful transitions.Chapters00:00 The Shift from Monolithic CDPs to Composable Architectures08:18 Understanding the Limitations of Monolithic CDPs10:59 Rethinking First-Party Data Strategies17:44 Challenges in Implementing Composable CDPs21:18 Uniphore Role in Composable Marketing Intelligence25:28 Future Considerations for CDP EcosystemsAbout UniphoreUniphore is a B2B artificial intelligence (AI) company that provides a full-stack Business AI Cloud platform for enterprises to manage customer interactions, sales, marketing, and internal operations. Founded in 2008 and incubated at IIT Madras, Uniphore has dual headquarters in Palo Alto, California, and Chennai, India, and reached unicorn status with a $2.5 billion valuation in 2022. The platform is designed to be sovereign, composable, and secure, allowing businesses to connect diverse data sources, leverage AI models, and deploy AI agents across the enterprise. Uniphore’s offerings include Customer Service AI for agent guidance and analytics, Sales AI for real-time insights, Marketing AI with CDP capabilities, and People AI for HR automation.

As companies rethink how they provide customer experiences (CX), a new form of AI capability, agentic AI, is quickly changing how work is accomplished in contact centres. In the recent episode of the Tech Transformed podcast, Dialpad Lead Product Manager Calvin Hohener sits down with host Jon Arnold, Principal at J Arnold & Associates. They discuss the transition from legacy chatbots to more autonomous agents capable of completing tasks and improving customer interactions.The conversation highlights the importance of understanding the technology's impact on enterprise architecture, the need for clean data, and the strategic implications for C-level executives. Hohener emphasises the importance of starting with clear use cases and working closely with vendors to maximise the potential of AI in business operations.From Legacy Chatbots to Agentic AIMost people have used chatbots and found them lacking. Hohener explains why: earlier conversational AI was based on retrieval-augmented generation (RAG). These systems could take user input, search a knowledge base or the internet, and provide an answer. This was helpful for customer service queries, but limited.“Previous AI models could retrieve and return information, but now we’re moving into a new phase with agentic AI.” Agentic AI can take action rather than just providing information. For AI agents to succeed, organisations must first organise their data. “How your internal knowledge is structured is crucial. Even if the data is unorganised, you need to know its location and ensure it’s clean,” stated Hohener.Agentic systems depend on internal knowledge, including knowledge base articles, CRM notes, and process documentation. If this foundation is disordered, the agent’s output will not be reliable. This isn’t about achieving ideal data cleanliness from the start; it’s about knowing what information exists, where it is, and whether it can be trusted. If an AI agent bases its decisions on outdated, conflicting, or incomplete content, it will struggle to perform tasks aptly, regardless of how sophisticated the model is. Enterprises need at least basic clarity about which systems hold which knowledge, who is responsible for them, and whether there is consistency across sources.Hohener noted that organisations often overlook how quickly conflicting information can undermine an otherwise well-designed agent. A single outdated procedure or mismatched policy in a knowledge repository can lead an AI to produce incorrect results or halt during workflow execution. Keeping internal content clean, deduplicated, and consistent gives the agent a reliable, valid source. This reliability becomes crucial when AI starts taking meaningful actions, not just providing answers.By focusing on data readiness early, enterprises not only reduce deployment obstacles but also set the stage for scaling agentic AI across more complex processes. In many ways, preparing data isn’t just a technical task; it’s an organisational one. How Human Agents Work with AI Agents?The Dialpad Lead Product Manager noted that human roles, too, will evolve with agentic AI entering the contact centre. For instance, human agents will take on more of an advisory role—reviewing conversation traces and helping adjust the models.”Instead of just resolving customer issues, they will help refine and oversee AI workflows. This includes reviewing conversation logs, noting where an AI agent may have misinterpreted intent, and providing feedback to improve the models.This mentor-mentee relationship between human and digital agents becomes crucial as organisations increase automation. Human agents bring domain knowledge, contextual judgment, and the ability to handle unique situations, all of which help the AI improve over time. In return, digital agents reduce the repetitive workload and allow humans to engage in higher-level thinking. TakeawaysThe time is now for adopting agentic AI solutions.Agentic AI represents a significant shift in customer experience technology.Legacy chatbots have limitations that agentic AI can overcome.Real-world applications of agentic AI include mundane tasks like scheduling and verification.Quantifying time savings is crucial for measuring ROI with AI.AI agents can improve customer satisfaction metrics when implemented correctly.Data organisation is essential for effective AI deployment.Human agents will focus on more complex cases as AI handles routine tasks.C-level executives should view AI as a strategic investment.Collaboration with reputable vendors is key to successful AI implementation.Chapters00:00 Introduction to Agentic AI05:10 Evolution Beyond Legacy Chatbots11:44 Real-World Applications of Agentic AI15:38 Impact on Enterprise Architecture21:41 Strategic Considerations for C-Level Executives

Client service teams are at a breaking point. Margins are shrinking, the demand keeps rising, and much of the day is consumed by work that doesn’t move the needle. As a result, skilled people often spend hours reconciling spreadsheets, re-entering the same data across multiple systems, and chasing updates, time that should be spent on the work clients actually pay for. Every hour lost to manual admin is an hour of revenue slipping away. In this day and age, that’s a hit no business can afford.AI isn’t just a buzzword here; it’s a practical lever. It can cut through the repetitive tasks that slow teams down, surface the information they need instantly, and free them to focus on high-value work. The companies winning aren’t replacing staff; they’re removing the obstacles that keep people from doing their best. In a world where speed and accuracy matter more than ever, ignoring that shift isn’t optional.In the latest episode of Tech Transformed, hosted by Christina Stathopolus, founder of Dare to Data, Daniel Mackey, CEO of Teamwork.com, discussed how AI is reshaping the daily operations of client service teams. From automating repetitive admin tasks to surfacing critical information faster, AI is giving teams the bandwidth to focus on the work that truly drives value for clients. AI and Business Transformation in PracticeDuring the conversation, Mackey highlighted how AI is reshaping business operations, emphasising efficiency and productivity rather than job displacement. “AI has transformed our company,” he noted, pointing to tangible improvements across workflow and project management. Teams are now able to focus on strategic initiatives, leaving repetitive tasks to intelligent systems.The Teamwork.com CEO also shared a recent example from a government agency that integrated AI into its processes. By automating routine administrative work, the agency experienced better resource allocation and improved project outcomes. “They’re more efficient, higher quality,” Mackey said. “AI allows them to focus on the bigger parts of the business.”Rethinking Productivity and Client DeliveryOne of the challenges in the industry is that most AI features are added onto existing tools that weren’t designed for client services. Mackey discussed how TeamworkAI addresses this gap. Built into a platform designed specifically for managing client services end-to-end, TeamworkAI connects projects, people, and profits in one system.By integrating AI directly into client delivery workflows, organisations can streamline project management, reduce manual reporting, and ensure that technology enhances rather than disrupts service delivery. This approach allows businesses to use technology strategically, rather than simply automating isolated tasks.Technology and the Future of WorkThe discussion also touched on the broader impact of AI on traditional business models. Organisations that adopt AI thoughtfully can improve their internal processes, freeing employees from repetitive tasks and enabling them to contribute to higher-value projects. Mackey emphasised that the goal isn’t just automation, it’s profitable client delivery. AI can unlock both time and insight, allowing businesses to prioritise the most impactful work.AI is redefining how businesses allocate resources, manage projects, and deliver value to clients. By eliminating repetitive work and connecting projects, people, and profits, technologies like TeamworkAI show how AI can drive efficiency, productivity, and business transformation without sacrificing the human element.To discover how your team can leverage TeamworkAI to deliver seamless projects, smarter resourcing, and stronger profits, visit teamwork.comTakeawaysProfitable client delivery now depends on AI – learn what actually works before investing in the wrong tools.Efficiency gains from AI lead directly to better performance, improving output and reducing wasted effort.AI and automation free up resources, allowing organisations to focus on strategic priorities.Technology adoption reshapes business models, ensuring organisations remain competitive in a rapidly changing landscape.AI integration is critical for competitiveness; without it, margins and business outcomes could be at risk.Chapters-00:00 Introduction to Client Services and AI- 3:00 The Role of AI in Streamlining Workflows- 9:00 Centralising Data for Efficiency- 15:00 Maintaining Human-Centred Client Relationships- 21:00 Future of Technology in Client Services- 27:00 Takeaways for CTOs and CISOsAbout Teamwork.comTeamwork.com is an AI-powered project and resource management platform designed to keep client projects running smoothly, simplify resource planning, and help businesses protect their margins.Headquartered in Cork, Ireland, Teamwork.com employs more than 200 people worldwide, with additional hubs in Denver, Gdańsk, and Belfast. The company supports teams across industries with tools that make collaboration clearer, workflows more efficient, and delivery more predictable.

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 reminded listeners, are humans—the operational leaders and domain experts who embed knowledge into AI systems and drive real change.TakeawaysAI can transform customer experience from reactive to proactive.Quality data is more important than large data sets.Human insight is crucial for effective AI implementation.Governance and continuous monitoring are essential for AI systems.Collaboration between humans and AI is the future of work.The role of human agents will shift from execution to management and delegation.The most mature companies are moving beyond metrics like CSAT to measure AI’s impact of outcomes like customer lifetime value and retention.Testing and validation processes are key to successful AI deployment.AI can and is delivering better-than-human customer service.The success of AI initiatives relies on operational leaders' insights.Chapters00:00 Transforming Customer Experience with AI02:51 The Role of Data in AI Solutions05:48 Governance and Security in AI Deployment09:03 Human-AI Collaboration in Customer Service12:01 The Future of AI in Customer ExperienceAbout AdaAda is the trusted AI-native customer service company, built to transform how enterprises engage with customers. Powered by the Ada ACX operating model—which unifies technology, methodology, and expertise—Ada deploys high-performing AI agents that deliver personalized, efficient interactions across every channel and language. Since 2016, Ada has powered over 5.5 billion interactions for leading brands like Square, Peloton, Canva, and monday.com, delivering extraordinary experiences at scale through higher-quality, more efficient customer service. With enterprise-grade trust, security, and compliance (SOC 2, GDPR, HIPAA, AIUC-1), Ada enables organizations to reduce cost-to-serve, elevate CSAT, and confidently scale AI-powered customer experience. Learn more at ada.cx.

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 cause operational disruptions if not addressed.Understanding current technology deployments is crucial for businesses.Proactive planning can mitigate risks associated with network changes.Investing in infrastructure now can save costs in the future.Collaboration with IT partners is essential for smooth transitions.Testing new devices before rollout is a best practice.The demand for faster connectivity is driving technological advancements.Future-proofing technology investments is key to long-term success.Chapters00:00 The Global Shift from 3G Networks02:48 Understanding the Impact on Businesses05:44 Assessing Reliance on 3G Devices08:57 Operational Changes and Disruptions12:02 Strategies for Transitioning to New Technologies14:55 Future-Proofing Connectivity Decisions17:44 Key Takeaways and Final ThoughtsAbout Jana VidisJana Vidis is a cybersecurity and technology expert passionate about helping businesses protect their operations and make technology work smarter. At IFB, she delivers tailored solutions, supports clients, and drives growth through strategic business development.A champion for women in tech, Jana leads Women in Tech Aberdeen, promoting diversity, inclusion, and empowerment in the North East of Scotland. Her expertise spans cybersecurity, disaster recovery, resilience planning, connectivity, hosted voice, and cloud solutions.

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 and red lights, or whether a switch is on or off. It has to stress the experience, not only the user’s but also how one application interacts with another.“The biggest shift organizations need to make,” the ElastiFlow CEO advises, “is to move from infrastructure health to usage experience. What really counts is whether the service being delivered is performing as it should.”A unified experience can be achieved if leaders disband conventional barriers between teams. Imagine a Venn diagram where DevOps, NetOps, and SecOps overlap—each relying on network data as a common source of truth. The network becomes the central point that connects performance, reliability, and security around shared goals as per Cowart. This philosophy supports ElastiFlow’s latest innovation – integrating network flow data directly into OpenTelemetry traces. Rather than treating network analytics as a separate console, ElastiFlow now includes connectivity information as part of the same trace that developers and SREs use to monitor applications.“It’s the first time anyone has done this,” Cowart points out. “An engineer can now see within their observability platform not just an HTTP call but also the related network session, latency, and context—all in one view.”By embedding the “fifth pillar” of observability—network flows— ElastiFlow aims to prepare organizations for future challenges. With Kubernetes, hybrid cloud, and microservices making the network more abstract yet increasingly essential, visibility at this level is no longer optional; it’s strategic.“As infrastructure becomes self-healing and redundant,” Cowart concludes, “leaders need to concentrate less on isolated device health and more on the experience being delivered. That’s where the business value lies, and that’s where the network finally becomes clear.”TakeawaysLegacy observability tools often overlook network connectivity.The MELT framework leaves a critical gap – network flow data.Network blind spots lead to extended troubleshooting cycles.XOps (NetOps, DevOps, SecOps) data siloshinder effective problem resolution.Security threats exploit the lack of network visibility, especially in east-west traffic.Network flow data is essential for comprehensive observability.Integrating application context enhances network data utility.Collaboration between IT teams is necessary for effective observability.Chapters00:00 Introduction to Network Observability06:04 The Blind Spot in Traditional Observability12:08 Security Implications of Network Observability18:03 Integrating Network Data with Application Context24:08 Future of Comprehensive Observability

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 the guests Maria Bracho CTO, Americas at Red Hat. Maria advises enterprise customers on hybrid cloud architectures, platform strategy, and AI-enabled operations across large-scale environments.Connect on LinkedIn Tyler Lynch Field CTO for the HashiCorp product suite within the IBM portfolio. Tyler works with organisations to modernise infrastructure, security, and automation practices that support cloud and AI workloads. Connect on LinkedIn About HashiCorp HashiCorp is the Infrastructure Cloud company, helping organisations automate hybrid and multicloud environments with Infrastructure Lifecycle Management and Security Lifecycle Management. Offerings include managed services on the HashiCorp Cloud Platform (HCP), self-hosted enterprise products, and source-available tools.Visit hashicorp.com About Red Hat Red Hat delivers enterprise open-source solutions, including Red Hat OpenShift and Red Hat Enterprise Linux, enabling organisations to build and manage hybrid cloud environments with speed, reliability, and consistency.Visit redhat.com About IBM IBM provides hybrid cloud, AI, and consulting expertise that brings together Red Hat, HashiCorp, and other portfolio capabilities. IBM helps organisations modernise applications, streamline operations, strengthen security, and prepare for AI at scale.Vist ibm.com 

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 designs, reducing the need for physical prototypes.Agent engineers are the future, automating tasks traditionally done by humans.The integration of AI in EDA is making the process faster, smarter, and more collaborative.Chapters00:00:00 Introduction to EDA and AI00:03:00 Synopsys and Its Role in Chip Design00:09:00 The Impact of AI on EDA00:15:00 Generative AI and Automation00:21:00 Ansys Acquisition and Future ProspectsAbout SynopsysSynopsys, Inc. (Nasdaq: SNPS) is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP, simulation and analysis solutions, and design services. We partner closely with our customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow. Learn more at www.synopsys.com.Follow EM360Tech for more insights:Website: www.em360tech.comX: @EM360TechLinkedIn: EM360Tech

As enterprises push to deliver software faster and more efficiently, continuous integration and continuous delivery (CI/CD) pipelines have become central to modern engineering. With increasing complexity in builds, tools, and environments, the challenge is no longer just speed, but it’s also about maintaining flow, consistency, and confidence in every release.In this episode of Tech Transformed, host Dana Gardner joins Arpad Kun, VP of Engineering and Infrastructure at Bitrise, to explore how solid CI/CD foundations can drive innovation and enable enterprises to harness AI in more practical, impactful ways. Drawing on findings from the Bitrise Mobile DevOps Insights Report, Kun shares how teams are optimising mobile delivery pipelines to accelerate development and support intelligent automation at scale.Complexity of Continuous Integration“Continuous integration pipelines are becoming more complex,” says Kun. “Build times are decreasing despite increasing complexity.” Faster compute and caching solutions are helping offset these pressures, but only when integrated into a cohesive CI/CD platform that can handle the rising demands of modern software delivery.A mature CI/CD environment creates stability and predictability. When developers trust their pipelines, they iterate faster and with less friction. As Kun notes, “A robust CI/CD platform reduces anxiety around releases.” Frequent, smaller iterations deliver faster feedback, shorten release cycles, and often improve app ratings—especially in the fast-paced world of mobile and cross-platform development.AI Ambitions with Engineering RealityIt’s easy to become swept up in the potential of AI without considering whether existing foundations can support it. Many development environments are not yet equipped to handle the iterative, data-intensive nature of AI-powered software engineering. Without scalable CI/CD pipelines, teams risk encountering bottlenecks that can cancel out the potential benefits of AI.To truly drive innovation, enterprises must align their AI ambitions with robust automation, strong observability, and disciplined engineering practices. A well-designed CI/CD platform allows teams to integrate AI responsibly, accelerating testing, improving deployment accuracy, and maintaining agility even as complexity grows.TakeawaysContinuous integration pipelines are becoming more complex.Build times are decreasing despite increasing complexity.Faster computing and caching are key to improving delivery speed.Flaky tests have increased significantly, causing inefficiencies.Monitoring and isolating flaky tests can improve build success rates.Maintaining flow for engineers is crucial for productivity.A robust CI/CD platform reduces anxiety around releases.Frequent iterations lead to faster feedback and improved app ratings.Cross-platform development is on the rise, especially with React Native.The future of software development will be influenced by AI.For more insights, follow Bitrise:X: @bitriseInstagram: @bitrise.ioFacebook: facebook.com/bitrise.ioLinkedIn: linkedin.com/company/bitriseChapters00:00 Introduction to Tech Transformed Podcast01:08 The Complexity of Continuous Integration04:24 Challenges of Increased Speed in Development08:09 The Importance of Continuous Integration and Deployment10:32 Building a Robust CI/CD Platform14:25 The Impact of Release Frequency on Business16:52 Types of Applications and Development Trends18:47 Aligning Development with Business Goals24:15 Preparing for the Future of Software DevelopmentAbout BitriseBitrise is a top mobile CI/CD platform, streamlining build, test, and deployment for mobile apps. It offers a user-friendly interface, robust integrations, and scalable infrastructure to simplify development and ensure efficient delivery of high-quality apps.Follow EM360Tech for more insights:Website: www.em360tech.comX: @EM360TechLinkedIn: EM360Tech

For years, observability sat quietly in the background of enterprise technology, an operational tool for engineers, something to keep the lights on and costs down. As systems became more intelligent and automated, observability has stepped into a far more strategic role. It now acts as the connective tissue between business intent and technical execution, helping organizations understand not only what is happening inside their systems, but why it’s happening and what it means.This shift forms the core of a recent Tech Transformed podcast episode between host Dana Gardner and Pejman Tabassomi, Field CTO for EMEA at Datadog. Together, they explore how observability has changed into what Tabassomi calls the “nervous system of AI”, a framework that allows enterprises to translate complexity into clarity and automation into measurable outcomes.Building AI LiteracyAI models make decisions that can affect everything from customer experiences to financial forecasting. It's important to understand that without observability, those decisions remain obscure.“Visibility into how models behave is crucial,” Tabassomi notes. True observability allows teams to see beyond outputs and into the reasoning of their systems, even if a model is drifting, automation is adapting effectively, and results align with strategic goals. This transparency builds trust. It also ensures accountability, giving organizations the confidence to scale AI responsibly without losing sight of the outcomes that matter most.Observability Observability is not merely about monitoring; it is about decision-making. It provides the insight required to manage complex systems, optimize outcomes, and act with agility. For organizations relying on AI and automation, observability becomes the differentiator between being merely efficient and achieving a sustainable competitive edge. In short, observability is no longer optional; it is central to translating technology into strategy and strategy into advantage.For more insights follow Datadog:X: @datadoghq Instagram: @datadoghq Facebook: facebook.com/datadoghq facebook.comLinkedIn: linkedin.com/company/datadogTakeawaysObservability has evolved from cost efficiency to a strategic role in AI.Integration with automation ensures explainable and predictable outcomes.Standards like OpenTelemetry unify observability efforts.Observability enhances security and governance in AI applications.Real-world examples show observability's impact on business processes.Strategic steps for leaders to leverage observability are provided.Observability provides visibility and governance for AI-driven businesses.Automation and observability integration are crucial for business success.Observability aids in enhancing security across AI applications.Chapters00:00:00 Introduction to Observability and AI00:03:00 The Evolution of Observability00:06:00 Observability as a Governance Tool00:09:00 Automation and Observability00:12:00 Standardization and Open Telemetry00:15:00 Security and Observability00:18:00 Practical Applications and Case Studies00:21:00 Strategic Steps for Leaders00:24:00 Conclusion and Future OutlookAbout DatadogDatadog is a SaaS platform that integrates and automates infrastructure monitoring, application performance monitoring, log management, real-user monitoring, and more—delivering unified, real-time observability and security across the entire technology stack. Used by organizations of all sizes and industries, Datadog enables digital transformation and cloud migration, fosters collaboration among development, operations, security, and business teams, accelerates time to market, reduces problem resolution time, and provides insight into user behavior and business performance.Follow EM360Tech for more insights:Website: www.em360tech.comX: @EM360TechLinkedIn: EM360Tech