Tech Transformed

• How To Maintain Human Connection in an AI World

  For CISOs and technology leaders, AI is reshaping business process management and daily operations. It can automate routine tasks and analyse data, but the human element remains critical for workforce oversight, customer interactions, and strategic decision-making.In this episode of Tech Transformed, Trisha Pillay talks with Anshuman Singh, CEO of HGS UK, about AI in the workplace. They discuss how AI can support employees, improve customer service, and require careful oversight. Singh also shares insights on preparing organisations for AI integration and trends leaders should watch in the coming years.Questions or comments? Email [email protected] or follow us on YouTube, Instagram, and Twitter @EM360Tech.TakeawaysAI is reshaping workforce needs, not just replacing jobs.Routine tasks are increasingly being automated by AI.AI can free up capacity for more meaningful work.The narrative around productivity is changing with AI.AI will create new job opportunities, often better-paying.Human oversight is crucial in AI decision-making.AI can assist in customer service, enhancing empathy.Organisations should not wait for perfect AI solutions.Training and hands-on experience with AI are essential.A psychological safety net is necessary for AI experimentation.Chapters00:00 Introduction to AI and Human Element03:03 AI's Impact on Workforce Dynamics08:29 The Role of Human Oversight in AI10:46 AI Innovations in Customer Service16:34 Positioning for Growth in Business Process Management20:01 Preparing the Workforce for AI Integration25:35 Emerging Trends in AI and Workforce29:19 Final Thoughts on AI and Ethics

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• AI-Powered Canvases: The Future of Visual Collaboration and Innovation

  AI-Powered Canvases: The Future of Visual Collaboration and InnovationAs hybrid and remote work become the standard, organizations are rethinking how teams brainstorm, align, and innovate. Traditional whiteboards and digital tools often fall short in keeping pace with today’s complex business challenges. This is where AI-powered canvases are transforming visual collaboration.In this episode of Tech Transformed, Kevin Petrie, VP of Research at BARC, joins Elaina O’Mahoney, Chief Product Officer at Mural, to explore how AI collaboration tools are reshaping teamwork in off-site locations. From customer journey mapping to process design, AI-powered canvases give teams the ability to visualize ideas, surface insights faster, and make better decisions—while keeping human creativity at the centre.AI-Powered Canvases, Visuals, and CollaborationA central theme in the conversation is the distinction between automation and augmentation. While AI can recommend activities, map processes, and identify participation patterns, decision-making remains a human responsibility.As O’Mahoney explains:“In the Mural canvas experience, we’re looking to draw out the ability of a skilled facilitator and give it to participants without them having to learn that skill over the years.”This balance ensures that while AI-powered canvases streamline collaboration, teams still rely on human judgment, creativity, and contextual knowledge. One of the most powerful contributions is in AI-driven visuals, which can translate raw data or unstructured input into clear diagrams, journey maps, or process flows. These visuals not only accelerate understanding but also help teams spot gaps and opportunities more effectively.For example:In customer journey mapping, AI can quickly generate visual flows that highlight pain points and opportunities that would take much longer to uncover manually.In manufacturing, AI-powered canvases can create dynamic visuals of workflows, showing how new technologies might disrupt established processes.The Role of Visual Tools in Hybrid WorkIn blended work environments, teams often lack the in-person cues that guide effective collaboration. Visual canvases bring those cues into the digital workspace, showing where ideas are concentrated, highlighting gaps in participation, and enabling alignment across dispersed teams. By combining intuitive design with AI-driven support, platforms like Mural help organisations adapt to the demands of hybrid work while keeping human creativity at the centre.TakeawaysAI is reshaping visual collaboration in distributed teams.Visual elements enhance understanding and decision-making.AI can augment workflows but requires human oversight.There is no universal playbook for AI integration in businesses.Hybrid work necessitates effective digital collaboration tools.AI can help visualize complex customer experiences.Human intuition and creativity remain essential in AI applications.Training and guidance are crucial for effective AI use.Collaboration tools must adapt to diverse work environments.AI should be seen as a partner in the creative process.Chapters00:00 The Evolution of Visual Collaboration05:15 Augmenting vs Automating: The Role of AI10:36...

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• 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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