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Tech Transformed

Tech Transformed

De: 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.Copyright 2026 EM360Tech Economía Gestión Gestión y Liderazgo Política y Gobierno
Episodios
  • Building Resilience in Ecommerce Revenue and Conversion Growth

    Building Resilience in Ecommerce Revenue and Conversion Growth
    Mar 23 2026

    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 Market

    Today’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 AI

    Improving 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/

    Takeaways
    • Building 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.

    Chapters

    00:00 Introduction to Ecommerce Challenges

    06:04 Real-World Applications of Ecommerce Analytics & Monitoring

    11:50 The Role of AI in Ecommerce

    17:57 Data Utilisation and Decision Making

    24:13 Culture and Team Alignment in Ecommerce

    29:56 Practical Strategies for Ecommerce Leaders
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    32 m
  • The New Economics of SaaS: Why Usage-Based Models Are Reshaping Software Pricing

    The New Economics of SaaS: Why Usage-Based Models Are Reshaping Software Pricing
    Mar 11 2026
    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 ...
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    31 m
  • Mastering Manufacturing Complexity: Digital Thread Strategies for AI and Customisation

    Mastering Manufacturing Complexity: Digital Thread Strategies for AI and Customisation
    Feb 23 2026
    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
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    23 m
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