Podcast
Tech Transformed
Hosted by EM360Tech · EN
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.
26episodes
Episodes
Newest firstAll episodes
How Do You Get Your Board Ready for Agentic AI?
Yesterday00:25:49Tap to summarizeFor years, enterprise AI conversations have centred on chatbots, search assistants, and tools that respond when asked, but that era is ending. A new class of AI system, one that reasons, plans, and takes autonomous action, is moving from the research lab into live production environments. For C-suite leaders, the question is no longer if AI will arrive in their organisations, but whether those organisations are ready for it.In a recent episode of Tech Transformed, host Christina Stathopoulos, founder of Dare to Data, sat down with Cathal McCarthy, Chief Executive Officer of Kore.ai, and Dan Leiva, founder of CXamplify and author of Amplified, to lay out what this shift actually means in practice and why most enterprises are less prepared than they think.Have a look at Artemis, the agent platform from Kore.ai, or you can book a demo.From AI Pilot Projects to ProductionMost large organisations have run AI pilots. Far fewer have moved those pilots into meaningful production at scale. McCarthy and Leiva argue that this gap is not primarily a technology problem. It is a governance and accountability problem.Conversational AI systems, which are the kind that answer questions or generate text, operate within a relatively contained risk envelope. A poorly worded response can be corrected, and a hallucinated answer can be flagged. The stakes, whilst real, are manageable.Agentic AI operates differently. These systems do not simply respond to prompts. They assess situations, make decisions, trigger actions, and in some cases instruct other AI agents or software systems to carry out tasks on their behalf. When something goes wrong in an agentic workflow, the consequences can cascade quickly, across processes, data, customer interactions, and operational outputs.This is why the move from pilot to production represents a fundamentally different risk conversation. As McCarthy puts it, "technology is now a decision-making actor." That framing has significant implications for how enterprises structure ownership, oversight, and accountability around their AI deployments.What Agentic AI Actually Means for Your OrganisationThe term “agentic AI” is often used loosely, so it is important to clarify what it actually means. An agentic system can:Break a complex goal down into sub-tasks without human prompting at each step.Use tools, APIs, databases, and other software to execute those tasks.Adapt its approach based on intermediate results.Operate across extended time horizons without continuous human input.This is meaningfully different from a large language model that generates a report when asked, or a copilot that suggests the next line of code. Agentic systems take initiative, which means it's both their value and their risk.Leiva's book, Amplified, explores how organisations can harness this capability without losing control of it. The central argument is that autonomy is not a binary switch; it is a dial. Organisations need to be deliberate about where they set that dial across use cases, risk profiles, and stages of deployment maturity.A Framework for Smarter AI DecisionsOne of the most practical tools discussed in the episode is the three-class decision model. Rather than treating all AI decisions as equivalent, it asks leaders to classify decisions by consequence and reversibility.The first class covers routine, low-stakes decisions where agentic systems can operate with high autonomy, like scheduling, data routing, and standard customer queries. The second class covers decisions with moderate consequences, where human review should be triggered before action is taken. The third class covers high-stakes decisions where human authority must remain the final step.Mapping AI deployments to this framework is the foundation of a defensible governance structure, one that can satisfy board scrutiny and regulatory requirements simultaneously. It also forces a critical question: who owns the decision about which class a given AI action falls into? That ownership question, the guests argue, is where most enterprise AI programmes currently have a blind spot.The Leadership ImperativeWith that said, the organisations that will benefit most from the agentic era are not necessarily those with the most sophisticated technology. As Leiva writes in Amplified, they are the ones who have thought most carefully about how to deploy that technology in a way that is accountable, adaptable, and aligned with how their people actually work.Boards are already asking harder questions about AI risk. Leaders who can answer them confidently because they have built the governance frameworks and defined the accountability structures will hold a material advantage. For leaders ready to move beyond the pilot stage, McCarthy and Leiva offer grounded guidance. Listen for more insights, and if you have any questions, feel free to get in touch with them directly.Connect with the guests:Cathal McCarthy — LinkedIn | Kore.aiDan Leiva — LinkedIn | CXamplifyFurther reading: Amplified by Dan Leiva — available on AmazonHave a look at Artemis, the agent platform from Kore.ai, or you can book a demoTakeawaysThe shift from conversational to agentic AIEnterprise AI governance and accountabilityOperationalising AI at scale and risk managementBuilding trust and transparency in autonomous AI systemsTurning AI experimentation into measurable business outcomesChapters00:00 – Welcome to the Agentic Era02:33 – The Shift in AI Utilisation06:47 – From Pilots to Production: Understanding Risks10:10 – Gaps in AI Readiness13:11 – Rethinking Governance and Accountability16:50 – Operationalising Agentic Systems20:09 – Applying Agentic Workflows in Practice22:43 – Actionable Advice for Leaders
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The Future of Customer Data: AI Agents, CDPs and AdTech Explained
1w ago00:28:21Tap to summarizePodcast: Tech TransformedGuest: Mihir Nanavati, GM and Product Executive in MarTech and AdTechHost: Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data JuiceAI might have overtaken the industry with processing data, automating workflows, and creating content. The next big thing could be a major one, says Mihir Nanavati, GM and Product Executive in MarTech and AdTech, “AI is moving from managing data to making decisions with it.”In the recent episode of the Tech Transformed podcast, host Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data Juice, sat down with Nanavati to talk about a larger transformation in data and decision-making systems driven by AI.They particularly focus on the integration of agentic AI in marketing and customer data platforms. They explore the challenges of fragmentation in ad tech, the importance of connecting customer data to revenue outcomes, and the transformative role of AI in decision-making processes. Mihir shares insights on how companies can leverage AI to enhance their marketing strategies and the future of first-party data."This is not a cost exercise, it’s about how much more you can get done and how many more ideas you can execute," said Nanavati.For years, enterprises went through waves of technological change, including cloud infrastructure, mobile platforms, and customer data platforms (CDPs). Each development helped enterprises collect, store, and manage larger amounts of data. However, Nanavati asserts that humans making most decisions will never change. Now, AI agents are introducing a new model.How AI has Moved from Data Navigation to Making DecisionsIn the past, customer data initiatives aimed to create a unified view of customers. Enterprises built warehouses, ETL pipelines, and data platforms that were designed to be reliable. However, Nanavati suggests that AI agents are changing these expectations. "Machines can reason, and that is fundamentally different."Rather than simply serving as another analytical feature in existing systems, AI agents are increasingly acting as decision-makers. They weigh trade-offs, learn from results, and execute plans based on specific goals.This change has significant implications for customer data platforms. CDPs are not just repositories for customer information now. Instead, they are becoming layers that enable intelligent actions."The role of customer data platforms is evolving into ‘how do you make meaning of this?’" While, decisions about which customer segment to target, which message to send, or which offer to present may increasingly be guided by AI-driven systems.What’s the Fragmentation Problem in Modern AdTechWhile AI agents create new opportunities, Nanavati pointed out a persistent issue in the AdTech and MarTech ecosystem – fragmentation. Brands today tend to lean towards deploying multiple advertising and customer engagement platforms. These include social platforms, retail media networks, email tools, and specialised ad technologies. Each system may optimise effectively within its own space, but often fails to connect at the customer level.Nanavati calls it a "paradox of choice." "Each system is optimising locally for its own clicks and conversions, but none of that is coordinated at the consumer level."The result is a customer experience that many consumers notice, alluding to repeated retargeting for products they have already bought, irrelevant recommendations, or disconnected interactions across channels.As enterprises adopt AI agents, fragmented data environments may become an even bigger problem. AI systems can process information quickly, but they still rely heavily on context. "AI doesn't need perfect data in many cases, but it needs context."What’s Next for Enterprise Tech?As AI adoption continues, Nanavati believes that successful enterprises will be recognised not by how many experiments they run, but by how fast they learn and use the results."Learn very rapidly. Then scale what you've learned." For leaders, this may require a stronger commitment than just isolated pilot programs or limited rollouts. It may also need organisational changes that place AI decision-making and customer context at the centre of growth strategies.For companies navigating the intersection of AI agents, CDPs, and customer data, the question may no longer be whether AI can automate processes. The ultimate question is about who is calling the shots.Key TakeawaysAI is fundamentally changing how decisions are made in marketing.The shift from third-party to first-party data is crucial for businesses.Fragmentation in ad tech leads to a paradox of choice for brands.Connecting customer data to revenue outcomes is essential for success.AI can help marketers make better decisions without needing perfect data.Customer data platforms are evolving to support real-time decision-making.Companies can run significantly more marketing experiments with AI.Leaders must personally drive change in their Enterprises.Successful AI implementation requires a focus on revenue outcomes.First-party data collection is becoming more sophisticated and essential.Chapters00:00 Navigating the Shift in Data and AI03:03 The Evolution of Decision-Making in Marketing05:55 Challenges of Fragmentation in Ad Tech09:00 Connecting Customer Data to Revenue Outcomes11:56 The Role of AI in Customer Data Platforms14:55 Real-World Applications of Agentic AI18:05 Blueconic's Approach to Customer Growth21:14 The Future of First-Party Data24:02 Building Habits for Successful AI ImplementationListen to the full episode of Tech Transformed for a deeper discussion on AI agents, customer data platforms (CDPs), first-party data strategies and the future of AdTech. Subscribe for upcoming episodes and join the conversation across our social channels.BlueConic LinkedIn: @BlueConicEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360TechFor more information, please visit em360tech.com and blueconic.com.
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Why Are Companies Struggling to Integrate AI Models into Business Workflows?
3w ago00:27:18Tap to summarizePodcast: Tech TransformedGuests: Maxim Fateev, Co-Founder and CTO, Temporal Technologies and Cornelia Davis, Developer Advocate, Temporal TechnologiesHost: Kevin Petrie, VP of Research at BARCArtificial Intelligence (AI) models have been breaking ground in the last three years. In the race to boost capabilities month by month among platforms like OpenAI, Anthropic, and Google’s Gemini models. However, for many enterprises, the main challenge is not creating AI prototypes; it's ensuring they can reliably support real business processes.In a recent episode of the Tech Transformed podcast, Kevin Petrie, VP of Research at BARC, hosted a discussion with Maxim Fateev, Co-Founder and CTO, Temporal Technologies and Cornelia Davis, Developer Advocate, Temporal Technologies. They talked about why enterprises find it hard to transition AI from experimentation to production and how infrastructure must change to support autonomous systems.Why AI Demos Break in the Real WorldAccording to Davis, many organisations make a common mistake: they focus on the "happy path" during experiments and overlook real-world operational challenges. “We have always ignored the non-functional requirements until we go to prod at our peril,” Davis said. “A lot of our experimentation is so focused on the models that we forget about the non-functional requirements.”This means developers often prioritise model performance but neglect reliability, scaling, and system resilience. Agent frameworks used in experiments—usually lightweight Python or TypeScript libraries—add to the issue.“What you’re really building is a highly distributed system that’s calling Large Language Models (LLMs) that will be rate-limited… networks are going to go down,” Davis explained. “When we move into prod, we haven’t considered scale or instability.”As enterprises expand AI into their workflows, these overlooked details become imperative. A single outage, rate limit, or infrastructure failure can disrupt a complicated workflow that involves multiple AI steps.Also Watch: Developer Productivity 5X to 10X: Is Durable Execution the Answer to AI Orchestration Challenges?What Risks are Surfacing Since the Rise of Agentic Systems?The transition from simple AI workflows to autonomous agents adds a new layer of complexity. Traditional AI applications have predictable flows—such as summarising documents, tagging data, or creating recommendations. In contrast, agentic systems choose tools and decide on actions dynamically.“When we move from non-agentic to agentic, we introduce unpredictability,” Davis said. “The tools and the order they run in are unpredictable. Whether we go through the agentic loop once or a hundred times is unpredictable.”Such unpredictability creates new governance and compliance challenges, especially in regulated industries. “Enterprises are still responsible for predictable outcomes,” Davis noted. “We need stronger audit trails to understand why the agent made the decisions it did.”For enterprises, this means AI systems must ensure traceability, accountability, and compliance, even when decision paths differ from one interaction to another.Why is Durable Execution the New Foundation for Enterprise AIFateev argues that to manage such newly surfacing risks, enterprises need a new architectural layer focused on reliability. His concept, “Durable Execution,” aims to ensure that complex workflows keep running even when infrastructure fails.“You write code as if failures don’t exist,” Fateev explained. “If a process crashes, we recover all the state and continue executing.” In practical terms, Durable Execution allows long-running AI workflows to survive interruptions—from network outages to system crashes—without losing progress or data.This is essential as agents start interacting with real systems and taking real actions. “The moment agents start acting on the external world—changing files, submitting orders—you absolutely don’t want those things to get lost,” Fateev said.The Temporal co-founder further emphasised that enterprise AI will not completely replace traditional software systems.“You will always have deterministic code,” he said. “You can’t imagine banks dynamically deciding what a money transfer means.”Instead, the future architecture will combine deterministic software with agents that interact through controlled tools and reliable communication layers.Also Watch: How Do You Make AI Agents Reliable at Scale?Key TakeawaysAI projects fail in production when non-functional requirements are ignoredAgentic systems bring unpredictability, making governance, traceability, and auditability essential.Lightweight experimentation frameworks aren't suited for enterprise workloads.Durable execution enables reliable AI workflows, ensuring processes continue despite infrastructure failures.Enterprise AI will blend deterministic software with agents.Chapters00:00 Introduction to AI's Impact on Business03:53 Challenges in Integrating AI into Business Workflows13:00 Understanding Non-Functional Requirements in AI19:14 The Role of Orchestration in AI Systems24:26 Exploring Durable Execution in AI Workflows30:28 Future Architectures for Autonomous AI Systems36:05 Key Takeaways for Executives in AI ImplementationFor more information, please visit em360tech.com and temporal.io.To learn more about Temporal and Durable Execution, follow:Temporal LinkedIn: Temporal TechnologiesTemporal X: @TemporalioTemporal YouTube: @TemporalioEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360Tech#DurableExecution #EnterpriseAI #AIToProduction #AIOrchestration #TemporalTech #AutonomousAgents #SystemReliability #LLMs #TechTransformed #AIWorkflows
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Why Data Sovereignty Now Drives Enterprise Resilience and Autonomy
May 500:22:26Tap to summarizeFor years, data sovereignty was treated as a compliance requirement, focused mainly on keeping data within specific geographic borders. Today, that definition is no longer sufficient. True data sovereignty now encompasses control, visibility, and accountability over data wherever it resides, moves, or is processed. In an era shaped by AI adoption and increasingly fragmented cloud environments, sovereignty has become a core driver of enterprise resilience and operational autonomy rather than a regulatory checkbox. In this episode of The Security Strategist, Tim Pfaelzer, Senior Vice President and General Manager, EMEA at Veeam, explains how the meaning of data sovereignty has fundamentally changed.From Compliance Concept to Strategic PriorityA decade ago, data lived in well-defined corporate environments managed by internal IT teams. Today, it is distributed across public cloud platforms, SaaS ecosystems, edge devices, and third-party suppliers. This distribution has expanded the attack surface while making ownership and control significantly harder to define.As a result, organisations are being forced to rethink sovereignty not as a legal constraint, but as a foundation for resilience, security, and trust.Why Data Sovereignty Requires Cultural ChangeOne of the key arguments Pfaelzer makes is that data sovereignty cannot be solved through technology alone. It requires organisational alignment and executive ownership.Data is now created and consumed across every business function, which means governance must extend beyond IT. Leadership teams must treat data as a critical business asset, with clear accountability structures across its lifecycle.This shift is reinforced by regulatory pressure. Frameworks such as GDPR, the EU Data Act, and emerging AI governance rules now require organisations to demonstrate not only where data is stored, but how it is accessed, processed, and protected.The Five Dimensions of Modern Data ControlPfaelzer outlines five core dimensions that define effective data sovereignty today:Visibility: Knowing where all data exists, including backups and third-party copiesOwnership: Clear accountability for data across its lifecycleAccess governance: Controlled and regularly reviewed permissionsPortability: The ability to move data without vendor lock-inCompliance readiness: Continuous compliance rather than audit-only validationTogether, these determine how much real control an organisation has over its data estate.Data Sovereignty as the Foundation of ResilienceModern resilience is no longer defined by backup alone. It is defined by recovery speed, completeness, and operational continuity. A prolonged outage or ransomware incident can cause significant damage, but the difference between minutes and days of downtime often comes down to recovery architecture and how rigorously it has been tested under real-world conditions. In this context, sovereignty and resilience are directly linked. Without control over data, there is no predictable recovery.AI Has Raised the StakesArtificial intelligence has introduced a new layer of data risk that many organisations are still underestimating. As AI systems increasingly automate decision-making and customer interactions, the quality and integrity of training and operational data become critical. If that data is corrupted, incomplete, or outdated, the impact can spread silently across business processes before detection.Unlike infrastructure failures, AI-driven data issues are not always immediately visible. This makes governance even more important. Pfaelzer argues that AI systems should operate under the same strict data controls as human users, including lineage tracking, access controls, and continuous validation of data integrity.Why Data Sovereignty Now Defines Enterprise AutonomyUltimately, data sovereignty has changed into a measure of enterprise independence. Organisations that understand, govern, and control their data are better positioned to manage risk, comply with regulation, and adopt new technologies such as AI safely. Those who do not risk becoming dependent on opaque systems where visibility and control are limited. In 2026 and beyond, sovereignty is no longer just about where data lives. It is about who controls it, how it is used, and how quickly an organisation can recover when things go wrong.TakeawaysData sovereignty beyond geographic boundariesRisks of data fragmentation across cloud and edge environmentsStrategies for rapid data recovery and resilienceEnsuring data integrity and trust in AI systemsControl and ownership of data in a distributed landscapeChapters00:00 Introduction to Data Sovereignty and Resilience02:49 The Evolution of Data Management06:03 Control, Risk Exposure, and Accountability in Data08:57 Data Sovereignty Beyond Geography12:04 Ensuring Data Integrity in AI Systems15:05 Human Error and Data Management18:02 Case Study: University of Manchester's Data Strategy21:01 Non-Negotiables for Building a Resilient Data Strategy
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How Can Enterprises Turn Fragmented Data into Strategic AI Advantage?
Apr 2200:27:42Tap to summarizePodcast: Tech Transformed podcastGuest: John Newton, Chief Innovation Strategist at HylandHost: Dana Gardner, President and Principal Analyst at Interabor SolutionsEnterprise leaders rushing to integrate artificial intelligence (AI) into their operations often think the biggest challenge is the technology itself. In reality, the issue is much closer to home. It’s in the piles of unstructured enterprise data spread across documents, systems, and repositories.In the recent episode of the Tech Transformed podcast, John Newton, Chief Innovation Strategist at Hyland, sits down with host Dana Gardner, President and Principal Analyst at Interabor Solutions. They discussed how enterprises can unlock the full value of enterprise AI by addressing fragmented information and building stronger governance frameworks.Their conversation highlights that unstructured data is not an obstacle; it is the foundation for next-generation AI-driven productivity. As Newton stated, “The opportunity to truly use AI and use it effectively in your organisation really depends on that unstructured information.”For companies looking to adopt AI on a large scale, the real work is in organising and contextualising their internal knowledge.Is Unstructured Data the Hidden Fuel for Enterprise AI?Most enterprise data does not sit neatly in structured databases. Instead, it exists in contracts, reports, emails, videos, policies, and operational documents, creating a vast amount of unstructured content.The enormous amount of such unstructured data ends up creating a challenge for AI projects that rely solely on foundation models. Large language models (LLMs) may be trained on public data, but they cannot inherently access proprietary business intelligence.Newton argued that enterprise AI must therefore be built around internal knowledge systems. “Foundation models can’t train on your internal information,” he explained. “What you really want is that information to be part of the AI when you’re answering questions, doing research, or executing business processes.”This change requires organisations to rethink how information flows across the enterprise. Instead of isolated systems—CRM platforms, ERP databases, content repositories—companies need an interconnected information structure that connects multiple sources in real time.Such a structure enables AI systems and AI agents to find the right data at the right time. This also improves decision-making, automation, and operational intelligence.How to Reorganise Chaotic Unstructured Data?If unstructured data is the fuel, curation is the engine that drives effective AI. Newton emphasised that an enterprise data strategy must start with mapping, organising, and cleaning information assets. The aim is to reduce noise and increase clarity.“I like to look at things from a signal-to-noise perspective,” Newton says. “Curation is the key to removing uncertainty in the information.”The method could typically comprise a combination of several enterprise technologies such as content management platforms with business process management (BPM) and AI agents and LLMs.A pairing of the above strategies is aimed at helping enterprise data become more valuable. Enterprises can implement AI models to automate workflows, enhance knowledge discovery, and speed up processes across departments—from finance and manufacturing to customer operations.Importantly, Newton noted that this work also allows flexibility in the AI ecosystem. With a solid information foundation, companies can use open-source models, hyperscaler services, or internal AI deployments without tying themselves to a single vendor.In other words, an enterprise AI strategy should first focus on data readiness, not model selection.Key TakeawaysUnstructured data is the foundation for effective enterprise AI.Data curation improves AI accuracy and reduces information noise.Connecting enterprise systems enables AI to deliver real-time insights.AI guardrails help manage security, compliance, and data governance.AI automation boosts employee productivity by reducing repetitive work.Chapters00:00 Unlocking AI's Potential with Unstructured Data05:20 Signal to Noise: The Clarity Challenge11:21 Guardrails for AI: Balancing Control and Flexibility14:41 Harnessing the Enterprise Context Engine17:48 Real-World Applications: Case Studies in AI20:37 Curation: The Key to Effective Automation22:21 Future Business Value: Productivity and BeyondFor more information, please visit hyland.comTo stay updated on B2B Tech front and centre, follow EM360Tech:YouTube: @enterprisemanagement360LinkedIn: @EM360TechX: @EM360TechFollow Hyland on all its major platforms:YouTube: @HylandAILinkedIn: HylandX: @Hyland#UnstructuredData #EnterpriseAI #DataCuration #AIGuardrails #LLMs #AIAutomation #FragmentedData #InformationManagement #SignalToNoise #EnterpriseContext #TechTransformedPodcast #Hyland #B2BTech
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Building Resilience in Ecommerce Revenue and Conversion Growth
Mar 2300:31:53Tap to summarizeEcommerce 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
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The New Economics of SaaS: Why Usage-Based Models Are Reshaping Software Pricing
Mar 1100:31:28Tap to summarizeSaaS 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
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Mastering Manufacturing Complexity: Digital Thread Strategies for AI and Customisation
Feb 2300:22:56Tap to summarizeManaging 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
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How Do You Monitor AI Agents in Production Without Breaking Incident Response?
Feb 1800:21:43Tap to summarizeAs 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
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How AI and Analytics Are Transforming Automotive Call Tracking and Repair Orders
Feb 1200:28:16Tap to summarizeDid 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
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