Tech Transformed

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 Thread
The 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 Thread
Despite 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 Complexity
As 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/
Takeaways
The digital thread provides traceability of product...

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 Readiness
While 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 Ops
The 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 Practice
Successful 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 Ops
AI-generated code is pushing prototypes into production faster than ops can cope. How observability becomes the...

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 Changed
The 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 Dealerships
AI 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/
Takeaways
AI 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...

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 Stability
AI 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 year
Downtime costs EMEA businesses an average of $2 million per hour
More 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...

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 Pressures
Horn’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 Insights
AI 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 Use
Alongside 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.
Takeaways
AI 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...