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 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.

Chapters

00:00 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

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 gatekeeper for enterprise resilience.

Key Takeaways

1. AI adoption is accelerating in enterprise environments.
2. Organizations face complexities in productionizing AI features.
3. Natural language querying is a common AI application.
4. AI agents are increasingly used in IT operations.
5. Observability is crucial for understanding AI systems.
6. Traditional observability solutions are evolving to include AI monitoring.
7. Incident response teams struggle with alert noise and context gathering.
8. AI can assist in incident management and root cause analysis.
9. Future trends include more reliable AI agents and monitoring solutions.
10. Organizations need to invest in AI observability to succeed.

Chapters

1. 01:20 The Current State of AI Adoption
2. 02:28 Purposeful AI Usage in Organizations
3. 04:40 Observability in the Age of AI
4. 08:05 Evolving Observability Solutions
5. 11:36 Challenges in Incident Response
6. 16:04 Integrating AI in Operations
7. 23:33 Future Trends in AI Monitoring
8. 30:29 Investment Strategies for AI Solutions

#ArtificialIntelligence #EnterpriseAI #GenerativeAI #AgenticAI #AIAgents #AIObservability #AIInProduction #AIOps #AISecurity #AIGovernance #ModelMonitoring #LLMOps #ITOperations #SRE #DevOps #IncidentResponse #RootCauseAnalysis #DigitalTransformation #Automation #FutureOfAI

Did you know that on average, 35 per cent of calls to automotive dealerships go unanswered? In today’s competitive market, missed calls mean missed sales and dealerships are turning to AI and analytics to fix this. 

In this episode of Tech Transformed, host Jon Arnold and Ben Chodor, Chief Executive Officer of CallRevu, about how AI is reshaping the way dealerships handle calls, manage repair orders, and engage with customers throughout their journey. They explore the role of real-time analytics in improving interactions, the importance of answering every incoming call, and why AI has become essential in modern dealership operations.

Customer Experience Has 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 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.

Chapters

00:00 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

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:

1. The annual median cost of high-impact IT outages for EMEA businesses is $102 million per year
2. Downtime costs EMEA businesses an average of $2 million per hour
3. 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 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 Observability

Ironically, 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 AI

As 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?

Takeaways

1. If you don't get your observability house in order, all the grand plans with AI may be at risk.
2. Speed has been favoured over good governance and engineering standards.
3. Observability is about understanding the relationship between components, not just monitoring known issues.
4. AI can help establish baselines faster in a rapidly changing environment.
5. Without observability, you can't make your AI strategy work.

Chapters

1. 00:00 Introduction to AI and Observability
2. 01:11 The Risks of Rapid Software Development
3. 04:21 Understanding the Cost of Outages
4. 06:30 Blind Spots in AI-Driven Systems
5. 11:29 Transitioning to Full-Stack Observability
6. 13:58 Moving from Reactive to Proactive Monitoring
7. 18:54 Real-World Applications of AI Monitoring
8. 19:51 The Future of AI and Observability

#Observability #AIOps #AIDrivenDevelopment #FullStackObservability #ITOutages #VibeCoding #AIinProduction #DevOps #NewRelic #TechPodcast

In a world where climate change is reshaping the way we grow, transport, and consume the things we rely on, understanding the first mile of supply chains has never been more critical. That’s the stage where over 60 per cent of risks arise, yet it remains the hardest to measure and manage. In a recent episode of Tech Transform, Trisha Pillay sits down with Jonathan Horn, co-founder and CEO of Treefera, to explore how artificial intelligence is providing clarity, actionable insights, and sustainable solutions for this complex ecosystem.

The First Mile and Climate 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 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.

Chapters

00:00 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 Horn

Jonathan Horn is the founder of Treefera, an AI platform delivering accurate, auditable data to support carbon offsetting and nature-positive initiatives for landowners, investors, governments, NGOs, scientists, and marketplace participants. An innovative thinker and problem solver, he holds a PhD in Theoretical Fluid Dynamics and has extensive experience designing and implementing AI and data analytics solutions at scale. Jonathan has also applied data mesh principles to enable distributed, data-driven organisations, with a background in optimising banking operations through advanced data and AI systems.

Mass customisation has long been the holy grail for industrial manufacturers, offering the ability to provide highly tailored products while maintaining efficiency, scalability, and profitability. However, as products become increasingly complex, traditional methods of managing configurations are starting to reveal their limitations.

In a recent episode of Tech Transformed, host Christina Stathopoulos, Founder of Dare to Data, spoke with Stella d’Ambrumenil, Product Manager at Configit, about the operational realities and future potential of generative AI technology in manufacturing.

The Challenge of Complexity

Modern 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 Gap

Enter 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 Matters

Configit 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 2026

For 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

Takeaways

• Manufacturers 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.

Chapters

00:00 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 Configit

At Configit, we help our customers globally to master the challenges of getting configurable products to market faster, with higher quality and engineered at lower costs. As a pioneer of Configuration Lifecycle Management (CLM), we have been instrumental in driving the adoption of CLM solutions globally. Trusted by the world’s largest manufacturing companies for their mission-critical functions, our advanced configuration platform built on patented Virtual Tabulation® technology handles the most complex products on the market. Our customers include ABB, Jaguar Land Rover, John Deere, Grundfos, Vestas, Siemens, Danfoss amongst others.

As organisations navigate the rapid rise of AI, the challenge is no longer simply acquiring technology; it’s preparing people to use it effectively. Many companies are realising that access to AI tools alone doesn’t translate into business impact. Employees need meaningful opportunities to develop skills that can be applied immediately, helping teams work smarter and make better decisions.

In this episode of Tech Transformed, Christina Stathopoulos, Founder of Dare to Data, speaks with Gary Eimerman, Chief Learning Officer at Multiverse, about the pressing challenge of closing the AI and data skills gap in the workforce. They explore how organisations can build an AI-ready workforce, focusing on non-technical employees and the importance of a skills-first approach to learning.

The Skills-First Approach

Multiverse 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 Era

Gary 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 Potential

By 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.

Takeaways

• Technology 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.

Chapters

00:00 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 Multiverse

Multiverse is the upskilling platform for AI and tech adoption. Multiverse has partnered with over 1,500 companies to deliver a new kind of learning that’s transforming the workforce through tech skills.

Multiverse apprenticeships are for people of any age or career stage and focus on critical AI, data and tech skills. Multiverse learners have driven $2bn + ROI for their employers, using the skills they’ve learned to improve productivity and measurable performance.

For more information, visit www.multiverse.io

In the automotive industry, trust and transparency are no longer optional; they have become key components. Dealerships that communicate clearly and responsibly with their customers strengthen relationships and improve overall experiences. In this episode of Tech Transformed, host Trisha Pillay speaks with Sean Barrett, Chief Information Officer at CallRevu, about how dealerships can navigate the changing landscape of communication while maintaining accountability, compliance and operational resilience.

The Evolution of Dealership Communication

Communication has always been at the heart of dealership operations. The phone system was once the primary lifeline between customers and dealerships, giving managers the visibility needed to ensure interactions were handled correctly. Today, communication extends far beyond the phone. SMS, MMS, instant messaging, and other channels allow customers to engage in multiple ways.

Sean explains how integrating these channels into a single technology platform provides managers with a clear view of all interactions, ensuring employees follow policies and customers receive the attention they deserve. This approach strengthens trust and improves the overall customer experience.

Compliance and Data Privacy in Automotive Communication

Alongside multi-channel communication, compliance and data privacy are critical. Regulations like GDPR and UN R155 require dealerships to protect customer data while maintaining seamless communication. Transparent practices, combined with adherence to regional rules, help build trust and protect both customers and the dealership’s reputation. Observing patterns in customer interactions also allows dealerships to make informed decisions, improve processes, and enhance service quality. Using these data insights, dealerships can make communication more effective and meaningful for every customer.

Infrastructure That Keeps Dealerships Operational

Reliable infrastructure underpins all communication efforts. Sean shares how dealerships can prepare for unexpected disruptions with geo-redundant systems, cloud-based platforms, and layered internet backups, including options like Starlink or fibre connections. These measures ensure dealerships stay operational, customers can reach them without interruption, and business continuity is maintained.

Preparing for Emerging Communication Channels

As new channels emerge, proactive preparation is key. Dealerships that view communication as an investment, rather than a cost, position themselves for long-term success. Monitoring trends, adapting quickly, and fostering transparency help maintain strong customer relationships even as expectations evolve.

Training and Staff Development

Staff development is a critical component of a communication strategy. By using insights from technology platforms, dealerships can guide employee training, build accountability, and create a culture of learning. Confident, well-trained teams contribute to consistent, high-quality interactions that enhance customer trust.

Success in automotive communication isn’t just about adopting the latest tools—it’s about building systems and practices that protect customers, support employees, and foster trust at every touchpoint. Sean Barrett’s insights provide a roadmap for dealerships aiming to elevate communication strategies, improve customer satisfaction, and maintain resilience in an increasingly connected world.

Learn more: callrevu.com

Takeaways

1. Trust and transparency are essential for customer relationships.
2. The evolution of communication systems has introduced multi-channel interactions.
3. Compliance challenges vary across regions and regulations.
4. AI can provide deeper insights into customer interactions.
5. Training with AI can enhance employee performance and accountability.
6. Dealerships should view communication as an investment, not an expense.
7. Data insights are crucial for improving customer experience.
8. Emerging communication channels require proactive preparation.
9. A risk-free training environment can foster employee growth.
10. Customer lifetime value is realised through effective data utilisation.

Chapters

00:00 Introduction to Trust and Transparency in Automotive Communication

03:06 Evolution of Dealership Communication Systems

05:02 Building Reliable Communication Infrastructure

07:13 Navigating Compliance Challenges in Automotive

09:12 Preparing for Emerging Communication Channels

11:15 The Role of AI in Automotive Communication

14:10 Bridging the Gap Between AI and Human Interaction

16:35 Success Stories and Training Innovations

18:22 Practical Actions for Dealerships

22:14 Conclusion and Key Takeaways

About CallRevu

CallRevu is the leading communication intelligence platform built for automotive retail, empowering dealerships to take control of every conversation, from the first ring to the final result. Its unified solution combines a hosted phone system, call monitoring, performance training, and reputation management fueled by AI-powered analytics that turn every customer interaction into actionable intelligence.

Founded in a dealership in 2008, CallRevu was created by the industry, for the industry. We deliver the tools dealerships need to drive revenue, improve operations, and deliver exceptional customer experiences.

In a world where customer expectations evolve faster than ever, organisations are rethinking how they manage and leverage data. Legacy, monolithic Customer Data Platforms (CDPs) are increasingly challenged by rigidity, slow adaptability, and regulatory pressures. In this episode of Tech Transformed, Christina Stathopoulos, Founder of Dare to Data, speaks with Joe Pulickal, Director of Product Management at Uniphore, about the shift to composable CDPs and what it means for modern marketing technology.

Moving Away from Monolithic CDPs

Organisations are moving away from rigid, all-in-one CDPs as regulations around data privacy, consent, and cross-border data flows intensify. Joe explains that companies can no longer rely on systems that lock them into a single architecture or make compliance retrofitting difficult. Data governance, consent management, and data sovereignty have become critical considerations in every technology decision, forcing leaders to rethink the underlying structure of their CDPs.

Challenges in Composable Systems

While composable CDPs offer flexibility, they introduce new challenges. Organisations must define ownership and accountability within modular systems to prevent fragmentation and ensure consistent data quality. Leadership must consider how compute, storage, and access are distributed across modules while maintaining compliance and security standards. Joe notes that without clarity on ownership, organisations risk operational inefficiency and weakened governance.

Flexibility and Modularity in Data Management

The core advantage of composable architectures lies in modularity. By decoupling components from data ingestion to activation, organisations gain the freedom to innovate without being constrained by a monolithic platform. Joe emphasises: “You need flexibility in where data lives, how compute happens, ultimately doubling down on sovereignty, security, and that composable idea that initially started with data.” This approach allows teams to adopt new tools, scale selectively, and respond to changing business or regulatory requirements with agility.

Embracing First-Party Data Strategies

The shift to first-party data strategies is essential in today’s marketing landscape. With third-party cookies being phased out and privacy regulations tightening, companies must rely on direct, trusted data from their customers. Composable CDPs provide the framework to centralise first-party data while giving teams the ability to personalise experiences, maintain compliance, and safeguard trust. Joe highlights that organisations need to view data not just as an asset, but as a responsibility, balancing customer value with ethical management.

Here are what leaders can do:

1. Rethink data architecture: Move from monolithic to composable systems to gain flexibility, scalability, and regulatory alignment.
2. Prioritise governance: Define ownership, consent management, and security practices across modular components.
3. Focus on first-party data: Build direct customer relationships and leverage trusted data responsibly.
4. Embrace modularity: Enable innovation, adaptability, and resilience in data management through composable design.

This episode offers practical insights for leaders navigating the transition from traditional CDPs to composable architectures. It highlights how thoughtful design, governance, and first-party data strategies empower organisations to act with agility, comply with regulations, and deliver better customer experiences.

For more information, book a demo with Uniphore.

Takeaways

• Organisations are moving away from rigid monolithic CDPs due to regulatory pressures.
• Composable architectures offer flexibility and modularity in data management.
• The shift to first-party data strategies is essential in the current landscape.
• Data governance and consent management are critical in modern marketing.
• Organisations face challenges in defining ownership within composable systems.
• Uniphore supports hybrid deployment options for data management.
• AI integration is crucial for enhancing CDP functionalities.
• Flexibility in architecture helps avoid vendor lock-in.
• People and processes are as important as technology in CDP implementation.
• Clear alignment on objectives is necessary for successful transitions.

Chapters

00:00 The Shift from Monolithic CDPs to Composable Architectures

08:18 Understanding the Limitations of Monolithic CDPs

10:59 Rethinking First-Party Data Strategies

17:44 Challenges in Implementing Composable CDPs

21:18 Uniphore Role in Composable Marketing Intelligence

25:28 Future Considerations for CDP Ecosystems

About Uniphore

Uniphore is a B2B artificial intelligence (AI) company that provides a full-stack Business AI Cloud platform for enterprises to manage customer interactions, sales, marketing, and internal operations. Founded in 2008 and incubated at IIT Madras, Uniphore has dual headquarters in Palo Alto, California, and Chennai, India, and reached unicorn status with a $2.5 billion valuation in 2022. The platform is designed to be sovereign, composable, and secure, allowing businesses to connect diverse data sources, leverage AI models, and deploy AI agents across the enterprise. Uniphore’s offerings include Customer Service AI for agent guidance and analytics, Sales AI for real-time insights, Marketing AI with CDP capabilities, and People AI for HR automation.

As companies rethink how they provide customer experiences (CX), a new form of AI capability, agentic AI, is quickly changing how work is accomplished in contact centres. 

In the recent episode of the Tech Transformed podcast, Dialpad Lead Product Manager Calvin Hohener sits down with host Jon Arnold, Principal at J Arnold & Associates. They discuss the transition from legacy chatbots to more autonomous agents capable of completing tasks and improving customer interactions.

The conversation highlights the importance of understanding the technology's impact on enterprise architecture, the need for clean data, and the strategic implications for C-level executives. Hohener emphasises the importance of starting with clear use cases and working closely with vendors to maximise the potential of AI in business operations.

From Legacy Chatbots to Agentic AI

Most people have used chatbots and found them lacking. Hohener explains why: earlier conversational AI was based on retrieval-augmented generation (RAG). These systems could take user input, search a knowledge base or the internet, and provide an answer. This was helpful for customer service queries, but limited.

“Previous AI models could retrieve and return information, but now we’re moving into a new phase with agentic AI.” Agentic AI can take action rather than just providing information. 

For AI agents to succeed, organisations must first organise their data. “How your internal knowledge is structured is crucial. Even if the data is unorganised, you need to know its location and ensure it’s clean,” stated Hohener.

Agentic systems depend on internal knowledge, including knowledge base articles, CRM notes, and process documentation. If this foundation is disordered, the agent’s output will not be reliable. 

This isn’t about achieving ideal data cleanliness from the start; it’s about knowing what information exists, where it is, and whether it can be trusted. If an AI agent bases its decisions on outdated, conflicting, or incomplete content, it will struggle to perform tasks aptly, regardless of how sophisticated the model is. Enterprises need at least basic clarity about which systems hold which knowledge, who is responsible for them, and whether there is consistency across sources.

Hohener noted that organisations often overlook how quickly conflicting information can undermine an otherwise well-designed agent. A single outdated procedure or mismatched policy in a knowledge repository can lead an AI to produce incorrect results or halt during workflow execution. 

Keeping internal content clean, deduplicated, and consistent gives the agent a reliable, valid source. This reliability becomes crucial when AI starts taking meaningful actions, not just providing answers.

By focusing on data readiness early, enterprises not only reduce deployment obstacles but also set the stage for scaling agentic AI across more complex processes. In many ways, preparing data isn’t just a technical task; it’s an organisational one. 

How Human Agents Work with AI Agents?

The Dialpad Lead Product Manager noted that human roles, too, will evolve with agentic AI entering the contact centre. For instance, human agents will take on more of an advisory role—reviewing conversation traces and helping adjust the models.”

Instead of just resolving customer issues, they will help refine and oversee AI workflows. This includes reviewing conversation logs, noting where an AI agent may have misinterpreted intent, and providing feedback to improve the models.

This mentor-mentee relationship between human and digital agents becomes crucial as organisations increase automation. Human agents bring domain knowledge, contextual judgment, and the ability to handle unique situations, all of which help the AI improve over time. In return, digital agents reduce the repetitive workload and allow humans to engage in higher-level thinking. 

Takeaways

• The time is now for adopting agentic AI solutions.
• Agentic AI represents a significant shift in customer experience technology.
• Legacy chatbots have limitations that agentic AI can overcome.
• Real-world applications of agentic AI include mundane tasks like scheduling and verification.
• Quantifying time savings is crucial for measuring ROI with AI.
• AI agents can improve customer satisfaction metrics when implemented correctly.
• Data organisation is essential for effective AI deployment.
• Human agents will focus on more complex cases as AI handles routine tasks.
• C-level executives should view AI as a strategic investment.
• Collaboration with reputable vendors is key to successful AI implementation.

Chapters

• 00:00 Introduction to Agentic AI
• 05:10 Evolution Beyond Legacy Chatbots
• 11:44 Real-World Applications of Agentic AI
• 15:38 Impact on Enterprise Architecture
• 21:41 Strategic Considerations for C-Level Executives