Tech Transformed

• Setting Up for Success: Why Enterprises Need to Harness Real-Time AI to Ensure Survival

  The issue is data fragmentation, where untrustworthy data is siloed across different databases, SaaS applications, warehouses, and on-premise systems,” Vladimir Jandreski, Chief Product Officer at Ververica, tells Christina Stathopoulos, the Founder of Dare to Data. “Simply, there is no single view of the truth that exists. With governance and data quality checks, these are often inconsistent, AI systems end up consuming incomplete or conflicting signals,” he added, setting the stage for the podcast.In this episode of the Don't Panic, It's Just Data podcast, Stathopoulos speaks with Jandreski about the vital role of unified streaming data platforms in facilitating real-time AI. They discuss the difficulties businesses encounter when implementing AI, the significance of going beyond batch processing, and the skills necessary for a successful streaming data platform. Applications in the real world, especially in e-commerce and fraud detection, show how real-time data can revolutionise AI strategies.Your AI Could Be a Step Behind Jandreski says that most organisations continue to be engineered on batch-first data systems. That means, they still process information in chunks—often hours or even days later. “It's fine for reporting, but it means your AI is always going to be one step behind.”However, “the unified streaming platform flips that model from data at rest to data in motion.” A unified platform will “continuously capture the pulse” of the business and feed it directly to AI for automated real-time decision making. Challenges of Agentic AI Considering that the world is moving toward the era of agentic AI, there are some key challenges that still need to be addressed. Agentic AI means autonomous agents make real-time decisions, maintain memory, use tools and collaborate among themselves. Because they act on their own decisions, regulating them is necessary. Building agents is not the main challenge, but the real challenge is “actually giving them the right infrastructure.” Jandreski highlights. Alluding to an example of AI prototyping frameworks such as Longchain or Lama Index, he further explained that those frameworks work for demos. In reality, however, they can’t support a long-running system trigger workflows that demand high availability, fault tolerance, and deep integration with the enterprise data. This is because enterprises have multiple systems, and many of them are not connected. This way, the data forms into silos. When data is in silos, a unified streaming data platform becomes the key solution. “It provides a real-time event-driven contextual runtime where AI agents need to move from the lab experiments to production reality.”TakeawaysUnified streaming data platforms are essential for real-time AI.Batch processing creates lag, hindering AI effectiveness.Data fragmentation leads to unreliable AI decisions.A unified platform ensures data is fresh and trustworthy.Real-time AI requires a robust data infrastructure.Organisations must move beyond legacy batch systems.Governance and data quality are critical for AI success.Real-world applications...

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• How Can AI Bridge the Gap from Observability to Understandability?

  "The tools we make are observability tools today. But it can never be the goal of our business to provide observability. The goal of our business as a vendor and as a partner with our customers is to give them understandability,” stated Nic Benders, the Chief Technical Strategist at New Relic.In this episode of the Don't Panic It's Just Data podcast, host Christina Stathopoulos, the Founder of Dare to Data, speaks with Benders about where observability is headed in IT systems. They discuss how AI is transforming observability into a more comprehensive understanding of complex systems, moving beyond traditional monitoring to achieve true understandability. Benders explained the importance of merging various data types to provide a complete picture of system performance and user experience. He believes AI can bridge the gap between mere observation of systems and a deeper understanding of their functionality. This could ultimately lead to enhanced incident response and operational efficiency. With maturing technology, complexity is expected to grow, too. The straightforward act of “observing” those complexities is like watching a green light on a machine. This is not enough. The major challenge is to “understand” the inside operations of the machine. This is the difference between simply seeing the data and knowing the "why."Observability to UnderstandabilityAs per Benders, the term observability "leaves a lot to be desired." While it’s the industry’s common label, it only describes seeing a system. The real goal, he argues, is to understand it.Alluding to an analogy, the technical strategist asks Stathopoulos to imagine a nuclear power plant full of a million blinking lights and screens. “You can have all the observability available, but if you're not an expert, you won't grasp what’s actually happening,” says Benders. Typically, software has been developed by a single person who knows every inch of it. However, today, technology has become more perplexing. AI, alongside teamwork and collaboration, provides the tools to solve this problem. An engineer might manage code they didn’t write, making a dashboard full of charts unhelpful. Understandability means moving beyond raw data to give context and meaning.Ultimately, Benders advises IT leaders to embrace change. The tech industry is constantly changing and advancing. Instead of fearing new tools, organizations should focus on what they need to grasp the unknown. As he puts it, "a lot of unknown is coming over the next few decades."TakeawaysObservability is not enough; understanding is crucial.AI can enhance the understanding of complex systems.The shift from observing to understanding is essential for modern IT.AI presents both challenges and opportunities in software development.New interfaces powered by AI can improve user interaction with data.AI can help reduce incident response times significantly.Collaboration with AI is becoming the norm in software development.Real-world applications show measurable benefits of AI in observability.IT decision-makers must prepare for ongoing changes in technology.Understanding the unknown is key to navigating future challenges.Chapters00:00 Introduction to Observability and Understandability05:00...

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• Not just Chatbots: What AI Agents Really Mean for Enterprises

  The phrase “AI agent” still brings to mind chatbots handling customer queries. Fast forward to today - AI agents are far more versatile, representing a new generation of systems capable of perceiving, reasoning, and acting autonomously. These bots are beginning to reshape how enterprises operate, not just in customer service but across software development, data analytics, and operational workflows.In this episode of Tech Transformed, Dare To Data Founder Christina Stathopoulos explores the rapid rise of AI agents with Ben Gilman, CEO of Dualboot Partners. Together, they unpack how AI agents differ from traditional automation and what this shift means for software development, enterprise operations, and the future of productivity.AI Agents vs. Traditional AutomationUnlike traditional automation, which follows strict, deterministic rules, AI agents can adapt to changing inputs, analyze complex data sets, and make autonomous decisions within defined parameters. This allows them to tackle tasks that were previously too intricate or time-consuming for automated systems. Dualboot Partners helps organizations harness these AI agents, integrating them into workflows to deliver real business value through a combination of product, design, and engineering expertise.“The biggest difference with an AI agent, between a standard tool, is that the agent can perceive information and reason about it, providing context and insights you don’t normally get in an algorithm.” — Ben Gilman, CEO, Dual Boot Partners.The Future of AI in EnterpriseOrganisations face several hurdles when integrating AI agents, including defining clear use cases, understanding the probabilistic nature of AI reasoning, and incorporating agents into existing processes and workflows. Despite the challenges, the potential payoff is substantial. AI agents can boost productivity, improve decision-making, and make enterprises more agile. As these systems mature, humans and AI are increasingly collaborating as true partners, reshaping what the workplace and work itself look like.Takeaways:AI Agents vs. Traditional Automation: AI agents can perceive and reason, offering more context and adaptability compared to deterministic systems.Real-World Applications: Examples include virtual vet agents and data analytics tools that enhance productivity and decision-making.Challenges in Adoption: Organizations face hurdles in defining specific use cases and integrating AI agents effectively.Future of AI in Tech: AI agents are expected to significantly boost productivity and innovation in software development and enterprise operations, with AI-first approaches like Dualboot's "DB90" driving structured adoption and accelerating modernization.Chapters0:00 - 3:00: Introduction to AI Agents3:01 - 6:00: Differences from Traditional Automation6:01 - 12:00: Real-World Applications and Examples12:01 - 18:00: Challenges in Adoption18:01 - 22:00: Future Impact on Tech and Operations22:01 - 24:00: Conclusion and Final ThoughtsAbout Dualboot Partners

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• How to Prepare Your Team for Edge Computing?

  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. Adopting Edge ComputingFor 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.Simplifying ComplexityManaging 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.TakeawaysEdge 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...

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• How Can Manufacturers Solve the Mass Customisation Problem?

  "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).What is Configuration Lifecycle Management (CLM)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). TakeawaysManufacturers 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...

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