Tech Transformed

For years, observability sat quietly in the background of enterprise technology, an operational tool for engineers, something to keep the lights on and costs down. As systems became more intelligent and automated, observability has stepped into a far more strategic role. It now acts as the connective tissue between business intent and technical execution, helping organizations understand not only what is happening inside their systems, but why it’s happening and what it means.

This shift forms the core of a recent Tech Transformed podcast episode between host Dana Gardner and Pejman Tabassomi, Field CTO for EMEA at Datadog. Together, they explore how observability has changed into what Tabassomi calls the “nervous system of AI”, a framework that allows enterprises to translate complexity into clarity and automation into measurable outcomes.

AI models make decisions that can affect everything from customer experiences to financial forecasting. It's important to understand that without observability, those decisions remain obscure.

“Visibility into how models behave is crucial,” Tabassomi notes. True observability allows teams to see beyond outputs and into the reasoning of their systems, even if a model is drifting, automation is adapting effectively, and results align with strategic goals. This transparency builds trust. It also ensures accountability, giving organizations the confidence to scale AI responsibly without losing sight of the outcomes that matter most.

Observability is not merely about monitoring; it is about decision-making. It provides the insight required to manage complex systems, optimize outcomes, and act with agility. For organizations relying on AI and automation, observability becomes the differentiator between being merely efficient and achieving a sustainable competitive edge. In short, observability is no longer optional; it is central to translating technology into strategy and strategy into advantage.

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• Observability has evolved from cost efficiency to a strategic role in AI.
• Integration with automation ensures explainable and predictable outcomes.
• Standards like OpenTelemetry unify observability efforts.
• Observability enhances security and governance in AI applications.
• Real-world examples show observability's impact on business processes.
• Strategic steps for leaders to leverage observability are provided.
• Observability provides visibility and governance for AI-driven businesses.
• Automation and observability integration are crucial for business success.
• Observability aids in enhancing security across AI applications.

00:00:00 Introduction to Observability and AI

00:03:00 The Evolution of Observability

00:06:00 Observability as a Governance Tool

00:09:00 Automation and Observability

00:12:00 Standardization and Open Telemetry

00:15:00 Security and Observability

00:18:00 Practical Applications and Case Studies

00:21:00 Strategic Steps for Leaders

00:24:00 Conclusion and Future Outlook

Datadog is a SaaS platform that integrates and automates infrastructure monitoring, application performance monitoring, log management, real-user monitoring, and more—delivering unified, real-time observability and security across the entire technology stack. Used by organizations of all sizes and industries, Datadog enables digital transformation and cloud migration, fosters collaboration among development, operations, security, and business teams, accelerates time to market, reduces problem resolution time, and provides insight into user behavior and business performance.

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““Without healthy employees, you don’t have healthy customers. And without healthy customers, you don’t have a healthy bottom line.” — Kate Visconti, Founder and CEO, Five to Flow.

While artificial intelligence (AI) has hastened development and made enterprises more efficient, it also comes with more deadlines. The deadlines often merge into after-hours messaging. Burnout has become a default result of productivity, especially in the tech industry. 

In this episode of the Tech Transformed podcast, Shubhangi Dua, Podcast Host, Producer and B2B Tech Journalist, speaks with Kate Visconti, Founder and CEO of Five to Flow, about the critical issues of burnout and disengagement in the workplace. They discuss the five core elements of change management, the financial implications of employee wellness, and strategies for enhancing productivity through flow optimisation. 

Also Watch: Fixing the Gender Gap in STEM

Visconti stresses the importance of creating a supportive work environment and implementing effective change management practices to improve organisational performance. The conversation also highlights the role of technology in productivity and the need for leaders to prioritise employee well-being to drive business success.

With an ambition to change the way organisations define true performance, VIsconti developed a system – a data-driven framework called The Wellness Wave. As per the official Five to Flow website, The Wellness Wave is “a proprietary diagnostic that measures sentiment and business performance across five core elements.”

Visconti sheds light on the original framework of the company. She says, “The original was adopted when we first kicked off as part of our consulting, and it's called the Wellness Wave diagnostic. It’s literally looking across the five core elements — people, culture, process, technology, and analytics.”

This framework helps companies identify and fix their profit leaks, which are the hidden financial losses caused by employee burnout, disengagement, and distraction. 

In her conversation with Dua, host of the Tech Transformed podcast episode, Visconti shares how understanding human behaviour can lead to significant improvements in business performance.

According to Five to Flow’s global diagnostics, only 13 per cent of flow triggers work at their best. For tech leaders, that means most teams are functioning well below their potential.

Kate’s top tip is to create flow blocks. “It’s about designing uninterrupted time for peak focus. This is when your brain isn’t in a stress state. For me, it’s mornings with my coffee. For others, it might be in the afternoon. Communicate those times to your team and protect them like meetings.”

These flow blocks aren’t just productivity tricks; they show that focus is more important than frantic multitasking. “Multitasking is a fallacy,” Kate says. “You’re just rapidly switching tasks and burning through mental energy.”

This episode of the Tech Transformed podcast is a must-listen for CEOs, CIOs, and IT decision-makers who want to regain performance from burnout.

Tune in for a discussion that redefines productivity, from managing time to mastering flow.

Listen at em360tech.com/podcasts

Explore more at fivetoflow.com

Follow the conversation on X and LinkedIn: @EM360Tech

• Burnout and disengagement are significant profit leaks for organisations.
• Understanding the five core elements of change management is crucial for success.
• Employee wellness directly impacts productivity and organisational performance.
• Distraction management is a key area needing improvement in workplaces.
• Creating flow blocks can enhance focus and productivity.
• Multitasking is ineffective; task switching reduces overall efficiency.
• Technology should be leveraged to support, not hinder, productivity.
• The financial cost of burnout is staggering, reaching $8.9 trillion globally.
• Quality of work is more important than quantity; focus on meaningful productivity.
• Leaders must prioritise their own well-being to effectively support their teams.

• 00:00 Introduction to Tech Transformed Podcast
• 01:05 Understanding Burnout and Disengagement
• 02:33 The Five Core Elements of Change Management
• 04:52 The Business Value of Employee Wellness
• 08:05 Assessing Financial Impact of Burnout
• 10:55 Strategies for Enhancing Focus and Productivity
• 14:56 Flow Optimisation vs. Time Management
• 19:08 Creating Flow Blocks for Peak Performance
• 22:58 The Role of Technology in Productivity
• 26:58 Implementing Change for Organisational Success
• 30:12 Case Studies and Real-World Applications
• 35:49 Key Takeaways for Leaders

“Cyber resilience isn’t just about protection, it’s about preparation.”

Every business in this day and age lives in the cloud. Our operations, data, and collaboration tools are powered by servers located invisibly around the world. But here’s the question we often overlook: what happens when the cloud falters?

In this episode of Tech Transformed, Trisha Pillay sits down with Jan Ursi, Vice President of Global Channels at Keepit, to uncover the real meaning of cyber resilience in a cloud-first world. Are you putting all your trust in hyperscale cloud providers? Think again. Trisha and Jan explore why relying solely on giants like Microsoft or Amazon can put your data at risk and how independent infrastructure gives organisations control, faster recovery, and true digital sovereignty.

• The importance of cyber resilience in a cloud-first world
• How independent cloud infrastructure protects your SaaS applications
• Common shared responsibility misconceptions that can cost organisations data
• Strategies for quick recovery from ransomware and cyberattacks
• Why digital sovereignty ensures control and compliance

00:00 – Introduction to Cyber Resilience and Cloud Strategy

05:00 – The Importance of Independent Infrastructure

10:00 – Shared Responsibility and Misconceptions

15:00 – Digital Sovereignty and Compliance

20:00 – Practical Tips for CISOs and CIOs

22:00 – Conclusion

Jan Ursi leads Keepit’s global partnerships, helping organisations embrace the AI-powered cyber resilience era. Keepit is the world’s only independent cloud dedicated to SaaS data protection, security, and recovery. Jan has previously built and scaled businesses at Rubrik, UiPath, Nutanix, Infoblox, and Juniper, shaping the future of enterprise cloud, hyper-automation, and data protection.

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"5G is becoming a great enabler for industries, enterprises, in-building connectivity and a variety of use cases, because now we can provide both the lowest latency and the highest bandwidth possible,” states Ganesh Shenbagaraman, Radisys Head of Standards, Regulatory Affairs & Ecosystems.

In the recent episode of the Tech Transformed podcast, Shubhangi Dua, Podcast Host, Producer, and Tech Journalist at EM360Tech, speaks to Shenbagaraman about 5G and edge computing and how they power private networks for various industries, from manufacturing, national security to space.

The Radisys’ Head of Standards believes in the idea of combining 5G with edge computing for transformative enterprise connectivity. If you’re a CEO, CIO, CTO, or CISO facing challenges of keeping up the pace with capacity, security and quality, this episode is for you. The speakers provide a guide on how to achieve next-gen private networks and prepare for the 6G future.

The growing need for real-time applications, such as high-quality live video streams and small industrial sensors with instant responses, demands data processing to occur closer to the source than ever before. Alluding to the technical solution that provides near-zero latency and ensures data security, Shenbagaraman says:

"By placing the 5G User Plane Function (UPF) next to local radios, we achieve near-zero latency between wireless and application processing. This keeps sensitive data secure within the enterprise network."

Such a strategy has now become imperative in handling both high-volume and mission-critical low-latency data all at the same time. Radisys addresses key compliance and confidentiality issues by storing the data within a private network. Essentially, they create a safe security framework that yields near-zero latency to guarantee utmost data security.

The real-world benefit of this zero-latency setup is the power it gives to edge computing applications. As the user plane function is the network's final data exit point, positioning the processing application near it assures prompt perspicuity and action.

"The devices could be sending very domain-specific data,” said Shenbagaraman. “The user plane function immediately transfers it to the application, the edge application, where it can be processed in real time."

It reduces errors and improves the efficiency of tasks through the Radisys platform, with the results meeting all essential requirements, including compliance needs.

One such successful use case spotlighted in the podcast is the Radisys work with Lockheed Martin’s defence applications. "We enabled sophisticated use cases for Lockheed Martin by leveraging the underlying flexibility of 5G,” the Radisys speaker exemplified.

Radisys team customised 5G connectivity for the US defence sector. It incorporated temporary, ad-hoc networks in challenging terrains using Internet Access Backhaul. It also covered isolated, permanent private networks for locations such as maintenance hangars.

Intelligence comes from the RAN Intelligent Controller (RIC), Shenbagaraman explained on the podcast. These small, AI and machine learning-enabled network applications run directly on the RIC. The smart applications allow functions like prioritising mission-critical traffic in real time.

• 5G enables low latency and high bandwidth for various applications.
• Edge computing co-located with 5G can achieve near-zero latency.
• Private networks ensure data security and compliance for enterprises.
• RADIS provides solutions compatible with multiple hardware platforms.
• Lockheed Martin's 5G MIL stack showcases practical applications of edge computing.
• 5G technology is evolving and will continue into 6G.
• RADISYS is positioned to play a key role in future connectivity solutions.
• 5G can address the needs of industries like manufacturing and national security.
• The integration of satellite communication with 5G expands connectivity options.
• Enterprises can leverage RADISYS for secure and differentiated connectivity solutions.

• 00:00 Introduction to 5G and Edge Computing
• 01:20 The Role of 5G in Various Industries
• 04:26 Understanding Edge Computing and Zero Latency
• 08:16 Private Networks and Their Importance
• 12:19 Making Software Compatible with Hardware
• 15:53 Use Case: Lockheed Martin and 5G MIL Stack
• 20:19 Future of 5G, Satellite Communication, and Edge Computing
• 24:52 Looking Ahead: The Role of RADISYS in 6G 

Now that companies have begun leaping into AI applications and adopting agentic automation, new architectural challenges are bound to emerge. With every new technology comes high responsibility, consequences and challenges. 

To help face and overcome some of these challenges, Temporal introduced the concept of “durable execution.” This concept has quickly become an integral part of building AI systems that are not just scalable but also reliable, observable and manageable.

In this episode of the Tech Transformed podcast, host Kevin Petrie, VP of Research at BARC, sits down with Samar Abbas, Co-founder and CEO of Temporal Technologies. They talk about durable execution and its critical role in driving AI innovation within enterprises. 

They discuss Abbas’s extensive background in software resilience, the development of application architectures, and the importance of managing state and reliability in AI workflows. The conversation also touches on the collaboration between developers, data teams, and data scientists, emphasising how durable execution can enhance productivity and governance in AI initiatives.

Also Watch: Developer Productivity 5X to 10X: Is Durable Execution the Answer to AI Orchestration Challenges?

“AI agents are going to get more and more mission critical, more and more longer lived, and more asynchronous," Abbas tells Petrie. “They’ll require more human interaction, and you need a very stable foundation to build these kinds of application architectures.”

AI not just fuels chatbots today. Enterprises are increasingly experimenting with agentic workflows—autonomous AI agents that carry out complex background tasks independently. For example, agents can assign, solve, and submit software issues using GitHub pull requests. 

Such a setup isn’t just a distant vision; the Temporal co-founder pointed to OpenAI’s Codex as a real-world case. With this approach, AI becomes a system that can handle hundreds of tasks at once, potentially achieving "100x orders of magnitude velocity," as Abbas described.

However, there are some architectural difficulties to stay mindful of. The AI agents are non-deterministic by nature and often depend on large language models (LLMs) like OpenAI’s GPT, Anthropic’s Claude, or Google’s Gemini. They reason based on probabilities, and they improvise. They often make decisions that are hard to trace or manage.

This is where Temporal comes in. It becomes the executioner that keeps the system cohesive and in alignment. “What we are trying to solve with Temporal and durable execution more generally is that we tackle challenging distributed systems problems," said Abbas.

Rather than developers stressing over queues, retries, or building their own reliability layers, Temporal allows them to write their AI workflows as simple code. Temporal takes care of everything else—reliable state management, retrying failed tasks, orchestrating asynchronous services, and ensuring uptime regardless of what fails below the surface.

As agent-based architectures become more common, the demand for this kind of system-level orchestration will only increase.

Listen to the full conversation on the Tech Transformed podcast, and discover how Temporal is shaping the next era of durable, autonomous, AI-powered applications.

Learn More at Temporal.io

• Durable execution is essential for maintaining software uptime.
• Samar Abbas has over 25 years of experience in software development.
• AI agents are becoming increasingly mission-critical in applications.
• Organisations need a stable foundation for AI architectures.
• Durable execution provides full visibility into AI decision-making.
• The future of AI involves more autonomous agents performing tasks.
• Context is crucial for maximising the value of AI models.
• Durable execution helps manage scalability and reliability in AI workflows.
• Cross-functional collaboration is key to successful AI initiatives.
• Investing in durable execution can unlock higher productivity and quality.

• 00:00 Introduction to Durable Execution and AI Innovation
• 07:06 Samar Abbas's Journey and Insights on Software Resilience
• 12:54 The Role of Durable Execution in AI Workflows
• 18:52 Cross-Functional Collaboration in AI Initiatives
• 24:38 The Importance of Durable Execution for Business Leaders

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.

• AI 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.

00:00 Introduction to AI and Human Element

03:03 AI's Impact on Workforce Dynamics

08:29 The Role of Human Oversight in AI

10:46 AI Innovations in Customer Service

16:34 Positioning for Growth in Business Process Management

20:01 Preparing the Workforce for AI Integration

25:35 Emerging Trends in AI and Workforce

29:19 Final Thoughts on AI and Ethics

As 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.

A 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.

In 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.

• AI 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.

00:00 The Evolution of Visual Collaboration

05:15 Augmenting vs Automating: The Role of AI

10:36 Navigating AI Integration in Business

12:44 Hybrid Work and Team Collaboration

16:08 AI's Role in Visualizing Customer Experiences

17:30 Emphasizing Human-AI Collaboration

Mural is a visual collaboration platform that enhances teamwork and productivity. It provides an interactive space for teams to ideate, align, and execute strategies. The platform combines intuitive visual tools with strong security measures to help companies increase efficiency and tackle challenges effectively. Mural offers a variety of features, including a visual work platform with tools like tables, mind maps, and diagrams. Its AI tools assist in structuring working sessions, generating ideas, and automating tasks such as clustering sticky notes. 

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.

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. 

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.”

• Unified 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 demonstrate the value of streaming data.
• E-commerce and finance are key industries for real-time AI.
• AI strategies need a solid foundation to scale.

• 00:00 Introduction to Unified Streaming Data Platforms
• 03:10 Key Blockers in AI Implementation
• 06:13 The Importance of Unified Streaming Data Platforms
• 08:48 Capabilities of a Unified Streaming Data Platform
• 11:54 Real-World Applications of Real-Time AI
• 15:04 Final Takeaways for IT Decision Makers

Ververica, the original creators of Apache Flink®, empowers businesses with high-performance data streaming and processing solutions. Streamlining operations, developer efficiency, and enabling customers to solve real-time use cases reliably and securely. Ververica’s advanced Streaming Data Platform, powered by its cloud native VERA engine, revolutionises Apache Flink®, making it easy for organisations to harness data insights at scale. With Ververica, customers can meet any business SLA, leveraging advanced data streaming and processing capabilities in real-time or on the lakehouse. Ververica enables businesses to connect, process, govern, and analyse data across infinite use cases, with flexible deployment options, including public cloud, private cloud, or on-premise environments. Discover more at ververica.com.

"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."

As 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."

• Observability 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.

• 00:00 Introduction to Observability and Understandability
• 05:00 The Role of AI in Modern IT
• 10:02 AI's Impact on Observability Solutions
• 14:58 Real-World Applications of AI in Observability
• 19:52 Key Takeaways for IT Decision Makers

The New Relic Intelligent Observability Platform helps businesses eliminate interruptions in digital experiences. New Relic is the only AI-strengthened platform to unify and pair telemetry data to provide clarity over your entire digital estate. We move your problem-solving past proactive to predictive by processing the right data at the right time to maximize value and control costs.

That’s why businesses around the world—including Adidas Runtastic, American Red Cross, Domino’s, GoTo Group, Ryanair, Topgolf, and William Hill—run on New Relic to drive innovation, improve reliability, and deliver exceptional customer experiences to fuel growth. Visit: www.newrelic.com.

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.

Unlike 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.

Organisations 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.

• 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.

0:00 - 3:00: Introduction to AI Agents

3:01 - 6:00: Differences from Traditional Automation

6:01 - 12:00: Real-World Applications and Examples

12:01 - 18:00: Challenges in Adoption

18:01 - 22:00: Future Impact on Tech and Operations

22:01 - 24:00: Conclusion and Final Thoughts

Dualboot Partners is a product-led digital transformation company that builds powerful software designed to drive business results. With a proven AI-driven process, Dualboot combines product strategy, design, and engineering to help organizations modernize systems, accelerate delivery, and create scalable solutions. The company’s diverse team partners with clients across healthcare, financial services, technology, manufacturing, consumer markets, and more. Dualboot is recognized for technical excellence, clear communication, and a focus on outcomes that strengthen the bottom line.