Tech Transformed

EM360Tech
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Explore how tech is shaping the future of business and share best practices for implementing these innovations. With expert interviews, in-depth analysis, and practical advice, you'll stay ahead of the curve and make informed decisions for your enterprise. Join us to debunk myths, dive into the latest trends, and cut through the AI noise with “Tech Transformed.” Tune in and transform your understanding of technology and its potential.

  1. The New Economics of SaaS: Why Usage-Based Models Are Reshaping Software Pricing

    2D AGO

    The New Economics of SaaS: Why Usage-Based Models Are Reshaping Software Pricing

    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 forecasting Why 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 operations Chapters00:00 – Introduction 02:30 – The economic shift in SaaS: Moving toward usage-based models 05:00 – The role of AI in transforming SaaS pricing and revenue streams 06:47 – Buyer preferences and evolving value quantification 08:38 – Infrastructure's role in supporting flexible billing models 11:49 – How finance teams can shape technology to control revenue 14:24 – Process reengineering and AI-driven automation 17:15 – Adaptable SaaS infrastructure and market signals 20:30 – Preparing for the unknown: sandboxing and scenario modeling 24:49 – Opportunities in connecting SaaS apps and managing data flow 28:54 – Building automated, scalable billing and integration flow

    31 min
  2. Mastering Manufacturing Complexity: Digital Thread Strategies for AI and Customisation

    FEB 23

    Mastering Manufacturing Complexity: Digital Thread Strategies for AI and Customisation

    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 Manufacturing 02:14 Understanding the Digital Thread 06:47 Challenges in Connecting Data Systems 11:12 Customisation, Complexity, and Digital Threads 15:43 The Role of Configuration Lifecycle Management (CLM) 20:23 Real-World Use Case: Implementing Digital Threads 23:42 Guidance for Early Adopters of Digital Threads

    23 min
  3. How Do You Monitor AI Agents in Production Without Breaking Incident Response?

    FEB 18

    How Do You Monitor AI Agents in Production Without Breaking Incident Response?

    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

    22 min
  4. How AI and Analytics Are Transforming Automotive Call Tracking and Repair Orders

    FEB 12

    How AI and Analytics Are Transforming Automotive Call Tracking and Repair Orders

    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 Dealerships 05:00 The Role of AI in Enhancing Communication 10:08 Transforming Customer Engagement with Real-Time Analytics 15:03 The Importance of Incoming Calls and Tracking 19:55 AI's Impact on the Automotive Industry 24:49 Future Trends in Automotive Technology

    28 min
  5. Are “Vibe-Coded” Systems the Next Big Risk to Enterprise Stability?

    JAN 22

    Are “Vibe-Coded” Systems the Next Big Risk to Enterprise Stability?

    Podcast: Tech Transformed Podcast Guest: Manesh Tailor, EMEA Field CTO, New Relic Host: Shubhangi Dua, B2B Tech Journalist, EM360Tech AI-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

    22 min
  6. AI in Sustainability: Frugal, Transparent, and Impactful Supply Chain Solutions

    JAN 21

    AI in Sustainability: Frugal, Transparent, and Impactful Supply Chain Solutions

    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 Chains 04:40 Navigating Climate Challenges with AI 09:40 AI-Native Business Models 13:57 The Evolution of AI Technologies 18:16 Understanding Data Sovereignty 21:54 Balancing AI Regulation and Innovation 25:48 Future of AI in Sustainability About 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.

    27 min
  7. How Gen-AI Will Impact Mass Customisation Today and in the Future

    JAN 20

    How Gen-AI Will Impact Mass Customisation Today and in the Future

    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.com TakeawaysManufacturers 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 Configit 02:48 Understanding Product Complexity in Manufacturing 05:54 The Role of Configit Ace® Prompt in Configuration Management 08:53 Configuration Lifecycle Management Explained 11:52 The Importance of Data Consistency and Cleanup 15:14 User Experience and Adoption of Configit Ace® Prompt 17:54 Generative AI and Its Future Applications 21:07 Conclusion and Future Events About 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.

    29 min
  8. AI-Ready Employees: How Skills-First Training Drives Business Impact

    JAN 14

    AI-Ready Employees: How Skills-First Training Drives Business Impact

    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 Gap 02:02 Challenges in Building an AI-Ready Workforce 06:06 The Skills First Approach to Learning 10:04 Supporting Non-Technical Employees in AI 13:46 Measuring the Impact of AI Skills Investment 18:13 The Evolution of Learning in the AI Era 22:59 Preparing for the Future of Work About 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

    26 min
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Explore how tech is shaping the future of business and share best practices for implementing these innovations. With expert interviews, in-depth analysis, and practical advice, you'll stay ahead of the curve and make informed decisions for your enterprise. Join us to debunk myths, dive into the latest trends, and cut through the AI noise with “Tech Transformed.” Tune in and transform your understanding of technology and its potential.

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  • Creator
    EM360Tech
  • Years Active
    2019 - 2026
  • Episodes
    300
  • Rating
    Clean
  • Copyright
    © Copyright 2026 EM360Tech
  • Show Website
    Tech Transformed