Tech Transformed

In a time when the world is run by data and real-time actions, edge computing is quickly becoming a must-have in enterprise technology. In the recent episode of the Tech Transformed podcast, hosted by Shubhangi Dua, a Podcast Producer and B2B Tech Journalist, discusses the complexities of this distributed future with guest Dmitry Panenkov, Founder and CEO of emma.

The conversation dives into how latency is the driving force behind edge adoption. Applications like autonomous vehicles and real-time analytics cannot afford to wait on a round trip to a centralised data centre. They need to compute where the data is generated.

Rather than viewing edge as a rival to the cloud, the discussion highlights it as a natural extension. Edge environments bring speed, resilience and data control, all necessary capabilities for modern applications. 

For organisations looking to adopt edge computing, this episode lays out a practical step-by-step approach. The skills necessary in multi-cloud environments – automation, infrastructure as code, and observability – translate well to edge deployments. These capabilities are essential for managing the unique challenges of edge devices, which may be disconnected, have lower power, or be located in hard-to-reach areas. Without this level of operational maturity, Panenkov warns of a "zombie apocalypse" of unmanaged devices.

Managing different APIs, SDKs, and vendor lock-ins across a distributed network can be a challenging task, and this is where platforms like emma become crucial.

Alluding to emma’s mission, Panenkov explains, "We're building a unified platform that simplifies the way people interact with different cloud and computer environments, whether these are in a public setting or private data centres or even at the edge."

Overall, emma creates a unified API layer and user interface, which simplifies the complexity. It helps businesses manage, automate, and scale their workloads from a singular perspective and reduces the burden on IT teams. They also reduce the need for a large team of highly skilled professionals leads to substantial cost savings. 

emma’s customers have experienced that their cloud bills went down significantly and updates could be rolled out much faster using the platform.

• Edge computing is becoming a reality for more organisations.
• Latency-sensitive applications drive the need for edge computing.
• Real-time analytics and industry automation benefit from edge computing.
• Edge computing enhances resilience, cost efficiency, and data sovereignty.
• Integrating edge into cloud strategies requires automation and observability.
• Maturity in operational practices, like automation and observability, is essential for edge readiness.
• Standardisation of APIs and devices is crucial for future edge solutions.
• The market demand will drive the evolution of edge computing platforms.

• 00:00 Introduction to Edge Computing
• 02:28 Driving Forces Behind Edge Computing
• 04:30 Integrating Edge into Cloud Strategies
• 06:41 The Role of Android in Edge Computing
• 08:38 Preparing for Edge Computing Challenges
• 11:30 Elevator Pitch for Emma
• 13:09 Managing Complexities in Edge Computing
• 15:30 Building Operational Maturity for Edge
• 17:54 Use Case: Autonomous Vehicles
• 21:07 Future of Platforms in Edge Computing
• 23:44 Edge Computing in Different Scenarios

"The real challenge that many manufacturers have dealt with for a long time and will keep facing is the shift from mass manufacturing to mass customisation," stated Daniel Joseph Barry, VP of Product Marketing at Configit. 

In a world that has moved from mass manufacturing to mass customisation, makers of complex products like cars and medical devices face a hidden problem. For more than a century, since the time of Henry Ford, manufacturers have worked in a separate, mass-production mindset. This method in the recent industrial scenario has caused a lot of friction and frustration.

In this episode of the Tech Transformed podcast, Christina Stathopoulos, Dare To Data Founder, talks with Daniel Joseph Barry, VP of Product Marketing at Configit. They talk about Configuration Lifecycle Management (CLM) and its importance in tackling the challenges that manufacturers of complex products face recurrently.

The speakers discuss the move from mass manufacturing to mass customisation, the various choices available to consumers, and the need to connect sales and engineering teams. Barry emphasises the value of working together to tackle these challenges. He points out that using CLM can make processes easier and enhance customer experiences (CX).

According to Barry, Configuration Lifecycle Management (CLM) is an approach that involves managing product configurations throughout their lifecycle. He describes it as an extension of Product Lifecycle Management (PLM) that focuses specifically on configurations. 

In today's highly bespoke world, customers are buying configurations of products instead of just the products themselves. The answer isn't to work harder within existing teams but to adopt a new, collaborative approach. This is where Configuration Lifecycle Management (CLM) comes in. 

CLM creates a single, shared source of truth for all product configuration information. It combines data from engineering, sales, and manufacturing. Configit’s patented Virtual Tabulation® (VT™) technology pre-computes all the different options, so there’s no longer a need for slow, real-time calculations. 

Barry says, "It's just a lookup, so it's lightning fast.” This represents a prominent shift that removes the delays and dead ends, frustrating customers and sales staff. Such a centralised system makes sure that every department uses the same, verified information, stopping errors from happening later on. 

One such company, and Configit’s customer, Vestas, a wind power company, automated its configuration process for complex wind turbines that have 160,000 options. By adopting a CLM approach, they cut the time to configure a solution from 60 minutes to just five.

Tune into the podcast for more information on the transformational impact of Configuration Lifecycle Management (CLM). 

• Manufacturers are transitioning from mass manufacturing to mass customisation.
• Customisation leads to complexity and challenges in manufacturing.
• Siloed systems create inefficiencies and reliance on experienced employees.
• Configuration Lifecycle Management (CLM) can automate and streamline processes.
• Aligning sales and engineering is crucial for successful product delivery.
• Digital threads and AI are becoming essential in manufacturing.
• A cross-functional approach is necessary to tackle modern manufacturing challenges.
• Understanding product configurations is key to meeting customer demands.
• Companies must adapt to avoid being left behind in the industry.
• The future of manufacturing relies on integrated solutions and data management.

• 00:00 Introduction to Configuration Lifecycle Management
• 02:47 Challenges in Manufacturing Complex Products
• 07:55 The Impact of Customisation on Manufacturing
• 14:33 The Role of Heroes in Manufacturing
• 18:05 Aligning Sales and Engineering
• 26:11 The Benefits of Configuration Lifecycle Management
• 35:49 Conclusion and Key Takeaways

Configit builds configuration solutions for manufacturing companies to master the challenges of getting configurable products to market faster, and to sell, manufacture, and service them more effectively.

Trusted by the world’s largest manufacturing companies for their mission-critical functions, Configit’s advanced configuration platform, built on Virtual Tabulation® technology, handles the most complex products in the world.

As global industries face mounting pressure to operate more efficiently and sustainably, many are turning to the combined power of artificial intelligence (AI) and the Internet of Things (IoT). From optimising energy usage to enabling real-time decision-making, these technologies are reshaping how businesses think about infrastructure, impact, and innovation. But the road to adoption isn’t without its challenges, from data literacy to greenwashing.

In this episode of Tech Transformed, Em360Tech host Trisha Pillay talks with Akanksha Sharma, Senior Director at the GSMA Foundation, about how these emerging technologies are creating tangible value, especially for small and medium-sized enterprises (SMEs) and industries with legacy systems like utilities. 

Sharma highlights that the 2020s will be remembered as the decade when IoT experiences exponential growth, supported by data from GSMA Intelligence projecting over 37 billion IoT connections worldwide by 2030, more than doubling the number recorded in 2021. She notes that, unlike previous technological waves, AI adoption is accelerating rapidly, moving from niche awareness to mainstream use within just a few years.

When discussing climate action and carbon markets, Sharma stresses the need for transparent, data-backed verification mechanisms. She warns against superficial greenwashing practices and advocates for AI systems that prioritise accuracy and ethical standards to ensure genuine environmental benefits.

• Data-driven infrastructure can turn sustainability into reality.
• AI and IoT are set to scale in the 2020s.
• Small and medium enterprises face unique operational challenges.
• Digital solutions can enhance the accuracy of carbon credits.
• Greenwashing misleads consumers about environmental benefits.
• Digital literacy is a major barrier to technology adoption.
• Start with the 'why' when adopting new technologies.
• Ethics in AI must be prioritised to avoid negative consequences.
• The world is changing due to climate change and technology.
• Collaboration is key to effective climate action.

00:00 – Transforming Sustainability with Data-Driven Infrastructure

03:05 – The Role of AI and IoT in Enterprises

09:10 – Challenges in Operational Efficiency and Sustainability

13:42 – Real-World Impact of AI and IoT

16:57 – Carbon Markets and Digital Solutions

21:08 – Understanding Greenwashing

23:30 – Barriers to Technology Adoption

26:17 – Key Takeaways and Predictions

Akanksha Sharma leads the ClimateTech and Digital Utilities programmes at GSMA, where she drives innovation at the intersection of mobile technology and sustainability. Since joining GSMA in 2012, she has specialised in leveraging mobile and digital solutions for social impact, with extensive experience in research and data-driven insights through her roles at GSMA Intelligence and Mobile for Development’s Central Insights Unit.

Before GSMA, Akanksha worked as a Power and Utilities Analyst at GlobalData in India, bringing deep sector knowledge to her current focus on transforming utility services through digital technologies. She holds an MBA from IBS Hyderabad and a Bachelor of Science degree in Psychology from Rajasthan University, India.

Many companies spend a lot on data technology, but often forget about the importance of data and AI literacy. Without the right skills, even the best platforms can fail to deliver results. Teams need to understand how to work with data and AI to make any strategy successful.

In this episode of Tech Transformed, EM360Tech’s Trisha Pillay chats with Greg Freeman, the founder of Data Literacy Academy about why knowing data and AI matters for anyone building a digital strategy.

Freeman points out that many data strategies end up as technical documents rather than actionable roadmaps. He explains that organisations often spend heavily on infrastructure, expecting better tools to solve their problems but without employees who understand how to work with data and why it matters, these investments rarely deliver results.

Freeman explains that data strategies often fail because only a small portion of employees less than 20 per cent are truly enthusiastic about data. Most strategies are designed with this minority in mind, creating an echo chamber that leaves the majority behind. As a result, data stays siloed, and business decisions don’t improve. 

The Data Literacy Academy founder stresses that unless organisations engage the 80 per cent of employees who aren’t already invested, their strategies are unlikely to succeed. When the focus is on tools rather than people, adoption falls behind.

• Data and AI literacy are key to turning strategy into value.
• Tools alone don’t work; people need confidence and context.
• Focus on engaging the data-hesitant majority, not just the enthusiasts.
• Cultural change, not just technical change, is what drives ROI

• 00:00 – Introduction
• 02:07 – Beyond the Tech Stack
• 04:41 – Why Strategies Fail
• 08:41 – Literacy Barriers
• 12:08 – Success in the Real World
• 17:17 – Building Lasting Literacy
• 22:20 – AI Needs Literacy Too
• 26:33 – Final Takeaways

Greg Freeman is the founder and CEO of Data Literacy Academy, where he works with CDOs, CIOs, and business leaders to drive real cultural change around data. His mission is to help organisations tackle data illiteracy by building confidence and capability from the ground up, especially for employees who feel disengaged or anxious about data.

With a background in sales leadership and tech startups, Greg brings both strategic insight and real-world experience.

"As agentic AI spreads across industries,” states Rishi Rana, the Chief Executive Officer at Cyara. “Everybody is curious to understand how that is going to transform customer experience across all the channels?"

In this episode of the Tech Transformed podcast, Shubhangi Dua, the Host and Podcast Producer at EM360Tech, talks with Rishi Rana, the CEO of Cyara, about how agentic AI is changing customer experience (CX). 

They look at how AI has developed from simple chatbots to advanced systems that can understand and predict customer needs. Rana spotlights the need for ongoing testing and monitoring to make sure AI solutions work well and follow the regulations. 

They also discuss the obstacles businesses encounter when implementing AI, the importance of good data, and the future of AI agents in improving customer interactions.

Customer experience (CX) is changing quickly and significantly, thanks to the rise of agentic AI. These advanced systems go beyond the basic chatbots of the past. 

While such a change may offer a future equipped with a smart, proactive customer journey, it doesn't come without its challenges. These obstacles require organisations to thoughtfully plan and carefully execute strategies.

For years, chatbots provided a basic type of automated customer support. However, Rana explains that the evolution of AI is pushing boundaries. "AI in customer experience (CX) is changing from a basic level of chatbots that have been present for the last five or 10 years. Now they are turning into fully agentic systems that operate across voice, digital and human-assisted channels," said Rana. 

Chatbots’ lucrative development lies in the strengths of Large Language Models (LLMs) like Google's Gemini, Meta's Llama, and OpenAI's ChatGPT. This is because the AI-backing models are facilitating "voice bots" and other AI agents to move beyond simple response automation to intelligent orchestration. 

Intelligent orchestration results in anticipating user needs, adjusting in real-time, and guiding customers to hybrid solutions where AI and human agents work together. Ultimately, the goal is to greatly improve the customer experience (CX). Studies suggest that 86 per cent of people are willing to pay more for the same service, no matter what it is, when the customer experience is better.

Advancements don’t come without a price. Rana believes the lack of proper guardrails is a cause for concern. "AI is great, but you need to have guardrails and ensure the intent behind the questions and the objective behind the customer interaction is getting answered." 

This requires ongoing testing and monitoring across all channels to ensure consistency and avoid problems like hallucinations, misuse, or bias. These issues can result in major financial losses and damage to reputation. For instance, Rishi Rana mentioned that over "$10 billion in violations and liabilities due to incorrect information given to customers" occurred in 2024 alone.

To successfully execute agentic AI, enterprises must shift left with AI by starting testing of AI agents early. This means focusing on continuous testing and observability instead of relying on one-time quality checks. This proactive approach is key to identifying AI misbehaviour quickly, before customers notice it.

Cyara, with its AI trust suite, leads this effort by validating LLM-based bots and spotting issues like hallucinations and misuse. Their Pulse 360 provides real-time customer experience visibility across 120+ countries and over 360+ carriers, showcasing a wide reach.

• Agentic AI is revolutionising customer interactions across various channels.
• The evolution from basic chatbots to intelligent systems is crucial for better CX.
• Continuous testing and monitoring are essential for AI success.
• Enterprises must ensure compliance with regulations while implementing AI.
• Understanding customer intent is key to effective AI solutions.
• Friction points in customer interactions can lead to dissatisfaction.
• AI must provide a seamless experience across all channels.
• Data quality is critical in the customer experience lifecycle.
• Companies need to proactively address AI misbehaviour to prevent issues.
• The future of AI in CX involves integrating with existing workflows and systems.

• 00:00 Transforming Customer Experience with Agentic AI
• 01:43 Evolving Beyond Basic Chatbots
• 05:04 Key Elements of a Great CX Platform
• 06:41 Understanding Customer and Enterprise Perspectives
• 08:25 Identifying Friction Points in Customer Interactions
• 10:14 Implementing Guardrails for AI in CX
• 11:02 The Importance of Continuous Testing and Monitoring
• 13:51 Ensuring Scalability and Compliance in AI Solutions
• 15:45 The Future of AI Agents in Customer Experience
• 18:18 Integrating AI into Existing Workflows
• 22:22 Key Takeaways for CIOs and IT Decision Makers

Cyara is the global leader in AI-powered customer experience assurance, committed to eradicating bad CX. As the only unified platform for continuous testing and monitoring across voice, digital, messaging, and conversational AI channels, Cyara empowers hundreds of the world’s leading brands to optimise more than a quarter of a billion customer interactions every year. From full customer journey visibility to AI governance and compliance, Cyara ensures every touchpoint works flawlessly, helping businesses deliver secure, friction-free, and high-quality CX at scale.

"If you are not using it and don't understand it, you are losing big time because you reinvent that wheel of durable execution yourself, and it's hard," reasons Maxim Fateev, Co-Founder and CTO of Temporal Technologies. 

In a recent episode of the Tech Transformed podcast, Fateev explored the concept of durable execution. This approach not only improves software reliability but is also becoming essential for the next generation of AI agents and orchestration.

Durable execution, a concept trademarked by Temporal Technologies, changes how developers build reliable applications. "The idea is simple–we preserve the full state of code execution all the time," he explained. 

Imagine a function that makes a series of API calls. If the process running that function crashes, even days later, "we can bring that function back in exactly the same state, still blocked on the same API call with all the variables and state there, and deliver the response. Then it will continue to the next line of code."

From a programming point of view, the function actually “never crashed. It just seamlessly waited for three days, blocked on that API call, and then continued execution," says Fateev.

This ability to provide "crashless execution" changes how developers approach building reliable software. It allows functions to run for long periods, even a year, without interruption or data loss.

Alluding to Temporal’s use case, Fateev referenced their contribution to OpenAI’s image generation code. He stated, "Every time you generate the image using OpenAI, it uses Temporal behind the scenes." 

"Image generation is orchestration. It's not just like one API call. There are a lot of API calls which need to happen to generate an image, and Temporal’s tech guarantees execution." 

While a strong tool for AI, durable execution has many uses beyond that. Fateev notes that Temporal has also been used for "driving large-scale operations with an added productivity advantage, and it’s also for a startup with two people with a small-scale solution.”

From infrastructure automation, like HashiCorp Cloud, and data handling to key business tasks such as customer onboarding and instant payments, including UPI in India and similar systems in Brazil, Temporal shows its worth in various industries. 

"Every Snapchat story is an important workflow,” Fateev tells Dua. Leading AI companies like Replit, Abridge, and OpenAI are using Temporal to power their workflows.” The main idea stays the same: "Nearly every time you need to run any business logic reliably, it works well."

• Durable execution is a new paradigm that enhances software reliability.
• It can significantly increase developer productivity by 5X to 10X.
• The concept allows for crashless execution of business applications.
• AI agents face reliability challenges that durable execution can address.
• Temporal's architecture ensures data is encrypted and secure.
• Open-source solutions provide a competitive advantage and prevent vendor lock-in.
• Temporal is the only production-ready solution for durable execution.
• The technology is used by major companies like OpenAI, with potential updates like Netflix, Snapchat 
• Temporal can orchestrate complex workflows, including AI and business processes.
• The system is applicable in various industries, including financial services, retail, logistics, and more.

00:00 Introduction to Durable Execution

03:07 The Concept of Durable Execution

06:00 AI Agents and Reliability Challenges

09:07 Cybersecurity and Data Protection

11:53 Open Source Advantage of Temporal

14:57 Temporal's Unique Position in the Market

18:12 Real-World Applications of Temporal

Temporal is changing how modern software is built through its open-source Durable Execution platform. By guaranteeing the execution of complex workflows even in the face of system failures, Temporal allows developers to focus entirely on business logic rather than infrastructure complexities—increasing developer velocity. Its polyglot capabilities allow seamless orchestration across multiple programming languages, making it ideal for both traditional enterprise applications and next-generation AI workloads. Temporal Cloud, the company's managed service backed by the originators of the project, has been adopted by thousands of leading enterprises. Learn more at www.temporal.io.

Enterprise data management is undergoing a fundamental transformation. The traditional data stack built on rigid pipelines, static workflows, and human-led interventions is reaching its breaking point. As data volume, velocity, and variety continue to explode, a new approach is taking shape: agentic data management.

In this episode of Tech Transformed, EM360Tech’s Trisha Pillay sits down with Jay Mishra, Chief Product and Technology Officer at Astera, to explore why agentic systems powered by autonomous AI agents, Large Language Models (LLMs), and semantic search are rapidly being recognised as the next generation of enterprise data architecture.

The conversation explores the drivers behind this shift, real-world applications, the impact on data professionals, challenges faced by agentic platforms, and the future of data stacks. Jay emphasises the importance of starting small and measuring ROI to successfully implement agentic solutions.

At its core, agentic data management is the application of intelligent, autonomous agents that can perceive, decide, and act across complex data environments. Unlike traditional automation, which follows predefined scripts, agentic AI is adaptive and self-directed. These agents are capable of learning from user behaviour, integrating with different systems, and adjusting to changes in context, all without human prompts.

As Jay explains, "An agentic system is one that has the agency to make decisions, solve problems, and orchestrate actions based on real-time data and context, not just on training data.

• Agentic data management is the next evolutionary step in data architecture.
• Agents are autonomous and can make decisions on the fly.
• The demand for agentic solutions is increasing due to data volume and AI strategy needs.
• Maturity of foundation models enables near-human reasoning capabilities.
• Real-world applications of agentic AI include insurance claim processing.
• Data engineers will focus on policy and guardrail creation rather than coding.
• Governance, debt and hallucinations are significant challenges in agentic platforms.
• The future of data stacks will include declarative control plans and enhanced memory layers.
• Analysts will play a crucial role in defining policies for agentic systems.
• Starting small and demonstrating ROI is key to successful agentic implementation.

00:00 Introduction to Agentic Data Management

02:58 Understanding Agentic Data Management

06:58 Drivers of Change in Data Management

10:03 Real-World Applications of Agentic AI

14:15 Impact on Data Engineers and Analysts

16:43 Challenges and Limitations of Agentic Data Platforms

20:03 Future of Data Stacks

23:31 Final Thoughts on Agentic Data Management

Jay Mishra is the Chief Product and Technology Officer at Astera Software, with over two decades of experience in data architecture and data-centric software innovation. He has led the design and development of transformative solutions for major enterprises, including Wells Fargo, Raymond James, and Farmers Mutual. 

Known for his strategic insight, technical leadership, and passion for empowering organisations, Jay has consistently delivered intelligent, scalable solutions that drive operational excellence and financial success.

For more information, please visit: https://em360tech.com/. Follow our LinkedIn for daily tech insights and YouTube ⁨‪@EM360TechInterviews‬ 

"There's a lot of hype with the AI agents and their productivity and potential outcomes. AI Agents are quite amazing, says Eric Paulsen, EMEA Field CTO at Coder.

In this episode of the Tech Transformed podcast, Shubhangi Dua, Podcast Host and Producer at EM360Tech, talks to Paulsen about the constantly advancing role of AI agents in development environments. 

Paulsen explains how AI agents can help developers by handling simpler tasks, almost like having assistants or junior developers to assist them. Not only would this boost productivity and time efficiency, but the technology will also ensure human oversight. 

The conversation further explores how AI fits into cloud development environments, especially in regulated areas like finance, where security and scalability matter most. Paulsen stresses the value of internal AI models and points out Coder's unique role in offering infrastructure-neutral solutions that meet various enterprise needs.

When people hear "agentic AI" or "coding agents," there's often a misconception about fully autonomous coders. However, Paulsen clarifies, "That's a far stretch from where we currently have been, which is with just AI-assisted IDE extensions such as GitHub, Copilot, Amazon Q Developer and systems of that nature." 

Coder focuses on agentic solutions that have a human developer in the loop, emphasising Paulsen. “Think of an AI agent as a junior engineer working alongside you,” Paulsen explains. 

"If anything, it’s improving the output of the human engineer by having an autonomous or artificial or AI process. In the same development environment, working on other tasks that might not necessarily be as complex," he adds. This means developers can offload simple tasks like bug fixes or dependency updates, freeing them to focus on more complex features.

For large financial institutions that have hundreds and even thousands of software engineers, deploying AI agents at scale requires a consistent and secure approach. Cloud development environments provide the best way to deliver and package these agents for developers.

The main concern for enterprises is ensuring data security in addition to stopping AI agents from "running wild on a laptop." Paulsen stresses the need for agents to work within an "isolated compute," with "boundaries around those agents inside of that isolated compute." 

Such a secure environment provides guardrails to synchronise and boost productivity between humans and AI while preventing sensitive data breaches or "hallucinations" from the AI.

Additionally, financial institutions are now increasingly developing their own internal AI models. Paulsen mentions, "What these institutions need is an AI agent that is trained on the internal dataset and internal LLM that is built within the firm so that it can make those decisions and return the relevant output to the data scientist or software engineer." This move towards self-hosted LLMs and internal AI infrastructure is essential for adopting enterprise-grade AI.

The ultimate message is that cloud development environments should provide the framework where AI agents are running inside an enterprise’s infrastructure. “AI agents have access to the data, and they're observed and governed by a set of security standards that you have internally,” says the EMEA Field CTO at Coder.

AI agents can assist developers by handling simpler tasks.

Human oversight is essential in AI-assisted coding.

Cloud development environments enable high-performance workloads.

Security is critical for AI agents in regulated sectors.

Internal AI models are necessary for financial services.

Coder offers infrastructure-neutral solutions for enterprises.

AI agents can automate maintenance tasks for platform engineers.

Scalability of AI agents is vital for large organisations.

Enterprises need to consider their internal data for AI agents.

Deployment of AI agents must be secure and efficient.

00:00 Introduction to AI Agents in Development

02:12 Understanding Agentic AI and Its Role

06:24 Cloud Development Environments and AI Integration

08:47 Ensuring Security and Productivity with AI Agents

11:12 Scaling AI Agents in Regulated Environments

13:10 The Importance of High-Performance Developments

15:52 Internal AI Models in Financial Services

17:47 Coder's Unique Position in the Market

19:27 Key Takeaway for Decision Makers

For more information, please visit: https://em360tech.com/ Follow our LinkedIn for daily tech insights: https://www.linkedin.com/company/2403360/admin/dashboard/ and YouTube  ⁨@EM360TechInterviews⁩ 

Are we heading for a future where AI knows everything but won’t bother explaining it to us?

Advancements in artificial intelligence are rapidly transforming the way industries operate and influencing the future of society as a whole. AI has become a force behind breakthrough technologies such as big data analytics, robotics, and the Internet of Things (IoT). The rise of generative AI has only accelerated its adoption and broadened its impact across multiple sectors. 

In this episode of Tech Transformed, host Trisha Pillay at EM360Tech sits down with Nigel Cannings, author and AI expert, to explore one of the most pressing questions of our time: what happens to human expertise in the age of rapid AI advancement?

Nigel Cannings warns that while technology promises efficiency and faster results, it also encourages dependency. Our patience has run thin, and in our rush for instant answers, we may be undermining the very systems that develop human expertise. “I’m kind of fascinated by the change we’ve seen in how we process information,” Cannings reflects.

He describes the displacement dilemma as the idea that tools meant to democratise knowledge could actually erode the skills and pathways that build true mastery. He worries about people losing jobs or being too dependent on technology to even start careers. “I’m really interested to talk to people who’ve been affected by the displacement dilemma, people who are losing their jobs, people who think they’re going to lose their jobs, people who can see the erosion of expertise and skills,” Cannings explains.

As artificial intelligence evolves at breakneck speed, we face a harsh reality: the gap between human and AI intelligence could become so wide that we might not even understand the systems we build. Worse still, AI itself may have no incentive to help us understand it. At that point, it stops being just a tool and becomes an autonomous entity with its private reality.

In 2025, Chief AI Officers report an average AI ROI of 14 per cent, as many AI programs move beyond pilot programs to larger implementations at scale. This is proof that as AI continues to evolve at an unprecedented pace, understanding its implications is important, both for industries navigating these changes and for society adapting to a new technological landscape.

• AI tools meant to democratize knowledge may erode human expertise.
• The displacement dilemma highlights the need for future experts.
• Information consumption is changing due to AI algorithms.
• AI relies heavily on large GPU cards for processing.
• Current AI models are prediction machines, not truly intelligent.
• Future AI may have limited intelligence in specific areas.
• The race in AI development is driven by financial incentives.
• Legislation is crucial for addressing AI's potential harms.
• Humans will always be needed in certain job roles.

00:00 Introduction to AI and Human Expertise

04:06 The Displacement Dilemma: Erosion of Expertise

06:59 Changing Information Consumption in the AI Era

10:07 Technical Aspects of AI: Data Centres and Encryption

15:42 Limitations of AI in Scientific Discovery

20:22 The Future of Superhuman AI

23:23 The Race in AI Development

28:18 Navigating the Future of AI Leadership

Nigel Cannings is a leading expert in speech technology and AI, specialising in applications within the legal and financial sectors. With over 20 years of experience, he holds more than a dozen patents in Natural Language Processing, Cryptography, AI, and Speech Processing. A former city lawyer, Nigel shifted his focus to technology, pioneering innovations such as the integration of GPU technology into speech recognition and advancing work in Explainable AI, AI Ethics, and Secure Search.

Based in London, Nigel is a respected thought leader exploring the evolving relationship between AI and human expertise.

"AI may be both the driver and the remedy for multi-cloud adoption," says Dmitry Panenkov, Founder & CEO of emma, alluding to the vast potential and possibilities Artificial Intelligence (AI) and multi-cloud strategies offer. 

In this episode of the Tech Transformed podcast, Tom Croll, a Cybersecurity Industry Analyst and Tech Advisor at Lionfish, speaks to Panenkov. They talk about the intricacies of powering multi-cloud systems with AI, offering valuable insights for businesses aiming to tap into the full potential of both.

They also discuss data fragmentation, interoperability issues, and security concerns. 

Addressing the key challenges of AI adoption in multi-cloud environments, Panenkov spotlights one of the most prominent issues – data fragmentation. 

“AI thrives on unified data sets. But multi-cloud setups often lead to data silos across the different platforms,” the founder of emma, the cloud management platform, explained. 

Data silos creates a disconnect which makes it increasingly challenging for AI models. It makes it harder for AI models to access and process the huge amounts of data needed to function efficiently. 

Instead, Panenkov stresses the potential of AI to drive multi-cloud adoption by optimising workloads and automating policies. 

In addition to data fragmentation, the lack of interoperability and tooling presents another challenge when integrating AI with multi-cloud. This is where Inconsistent APIs, a lack of standardisation, and variations in cloud-native tools create major friction. The difference is evident when building AI pipelines across diverse environments. 

Panenkov also pointed out the impact of latency and performance. He says, "Even Kubernetes is sensitive to latency. When we talk about AI and inference, and I'm not even talking about the training, I'm saying that inference is also sensitive." 

Without proper networking solutions, running AI workloads effectively in multi-cloud environments becomes next to impossible.

Of course, security and compliance are a looming challenge for all enterprises across varying industries. Managing data protection in different jurisdictions and environments adds layers of legal and operational complexity.

Despite these challenges, AI has significant advantages in multi-cloud systems that well surpass any challenges. 

The main topic of the conversation was how AI can actually help overcome the complexities of multi-cloud adoption. As the founder of a cloud management platform, Panenkov believes that AI may be both the driver and the remedy for multi-cloud adoption.

For example, Panenkov describes how AI can orchestrate workload placement and resource allocation. “We can look into and predict the behaviour of the workload, and we can optimise the infrastructure for these workloads, and allocate resources, helping our customers to start scaling.” 

The promise of portability offered by platforms like Kubernetes is often "undercut by vendor-specific customisations," noted Panenkov, and AI can address this through intelligent orchestration.

AI can enhance workload placement, resource allocation, and even latency routing. 

Additionally, AI can automate policies, enforcing security, compliance, and cost rules across various clouds. This "simplification" is critical. Panenkov drew a parallel to the way unified interfaces abstract complexity for users, stating, "All these AI agents have to abstract, evade the complexity for the developers and operators alike."

The impact on business is significant: faster product delivery, accelerated innovation, and the ability to leverage "best of breed" tools without heavy vendor lock-in. This leads to higher agility in scaling and adapting applications to market needs more quickly.

• AI thrives on unified data sets.
• Data fragmentation leads to silos in multi-cloud environments.
• Kubernetes can complicate multi-cloud strategies due to latency issues.
• Edge computing adds complexity but offers low-latency benefits.
• AI can optimise workload placement and resource allocation.
• AI helps enforce security policies across multiple clouds.
• A multi-cloud strategy can reshape how organisations innovate.
• Organisations can mitigate risks by avoiding vendor lock-in.
• Building intelligent abstraction layers is crucial for flexibility.
• Leaders should focus on orchestration and not force-fit clouds. 

00:00 Introduction to AI and Multi-Cloud Challenges

02:31 Key Challenges in Multi-Cloud AI Adoption

11:43 AI as a Driver for Multi-Cloud Adoption

16:33 Impact of Multi-Cloud on Innovation and Risk Management

23:00 Final Thoughts and Advice for Leaders

emma helps organisations use real multi-cloud options and unlimited computing power to get the most from their cloud and AI efforts. With support from various providers, including hyperscale and EU-regional, and environments such as on-prem, private, and public, emma enables organisations to reach their business goals now and in the future.

Unlike solutions that focus on just certain parts, emma takes a complete approach to cloud management. By combining AI-driven performance, cost, and network management with tools for optimisation and governance, emma delivers a smart platform that makes cloud operations easier and boosts efficiency.