"Having the insight and being able to stitch together your technical resources and business decisions together, is the prime place where observability can add value to you,” stated Manesh Tailor, EMEA Field CTO at New Relic.

In this episode of the Tech Transformed podcast, Kevin Petrie, Vice President of Research at BARC, speaks with Manesh Tailor about the intersection of artificial intelligence (AI) and observability, and how this is positively changing business operations.

Tailor emphasises how intelligent observability has changed beyond simple monitoring to provide real-time insights into customer experience and the entire technology stack. This enables informed decisions across engineering, operations, and business domains, directly linking technical performance to strategic business outcomes.

He also discusses the different stages observability has been through and where it's leading to now. The current wave, Observability 3.0, takes advantage of AI to predict issues and even enable self-healing systems.

New Relic has embraced this two-way street, using AI within its platform. This was in an ambition to help users and "AI monitoring" to track the performance of language models alongside traditional metrics. Such a platform provides a holistic view of system health and the cost implications of AI deployments.

Alluding to the management of AI-powered applications, Tailor says collaboration is key between application and data science teams. Not only does it provide real time data but as a result leads to efficient decision making.

Futuristically, the speedy proliferation of AI agents has both pros and cons for observability. This is where New Relic comes in. It addresses the challenges by constructing a platform-centric "AI orchestrator" with a growing library of AI-native agents.

In essence, as AI-powered applications become increasingly integral to business operations, intelligent observability is no longer optional.

• Observability is crucial for understanding unknowns in systems.
• AI enhances observability by providing predictive insights.
• The evolution of observability includes intelligent monitoring.
• Collaboration between technical and business teams is essential.
• Cost efficiency is a key focus in modern observability.
• Real-time data is vital for effective decision-making.
• Self-healing systems represent the future of observability.
• AI and observability must work in tandem for success.
• The complexity of systems is increasing, requiring better tools.
• Observability is applicable across all organizational levels.

00:00 Introduction to AI and Observability

03:10 Defining Observability and Its Evolution

05:49 The Role of AI in Observability

08:46 Navigating AI-Driven Applications

11:52 Target Users and Community for Observability

14:57 Collaboration Across Teams

17:55 Challenges and Opportunities in Observability

20:47 The Future of Observability and AI

23:54 Key Takeaways for CIOs and IT Leaders

The New Relic Intelligent Observability Platform empowers businesses to proactively eliminate disruptions in their digital experiences. As the only AI-enhanced platform that unifies and correlates telemetry data, New...

Takeaways

• #AIagents are #autonomous entities that can perceive and act.
• Human oversight is essential in the initial stages of AI implementation.
• Data quality and trust are critical for effective AI agents.
• Guardrails must be integrated into the design of AI agents.
• Modularity in design allows for flexibility and adaptability.
• #AI should be embedded in data management processes.
• Collaboration between data and application teams is vital.

Summary

In this episode of #TechTransformed, Kevin Petrie, VP of Research at BARC, and Ann Maya, EMEA CTO at Boomi, discuss the transformative potential of AI agents and intelligent automation in business. They explore the definition of agents, their role in automating processes, and the importance of human oversight.

Maya introduces us into the world of AI agents stating that, at its core, it’s an autonomous entity within #AIsystems that can perceive its environment. This creates a deep dive into how they evolved from traditional automation to “observe, think, and act” in novel and autonomous ways.

Maya addresses AI skepticism by acknowledging its growing autonomy while underscoring the current necessity of human oversight. She also highlights data's crucial influence on an agent's perception and decisions, emphasising the need for quality, trustworthy data in effective AI.

Moreover, Maya and Petrie explore AI's practical implications, pointing to Google's agent-to-agent protocol as vital for managing language model interactions and enabling effective communication across diverse agents within complex systems.

"If AI has proven anything, it will change pretty rapidly. Understanding its limitations and not asking too much of it is significant. What’s successful is prototyping tools," said Rob Whiteley, CEO of Coder. "Such tools where AI can create an application, while not the world's most graceful code but will get you to working prototype pretty quickly. That would probably take me days or weeks of research as a developer, but now I have a working prototype so I can socialise it."

In this episode of the Tech Transformed podcast, Dana Gardner, a thought leader, speaks with Rob Whiteley, CEO of Coder, about the transformative impact of agentic AI on software development. They discuss how AI is changing the roles of developers, the cultural shifts required in development teams, and the integration of AI agents in cloud development environments.

Agentic AI is seemingly set up for favourable outcomes. Or is it? Agentic AI is believed to shake-up enterprise IT, offering a productivity boost similar to the iPhone's impact.

This isn't about replacing developers but amplifying their output tenfold. It aims to allow the implementation of rapidly created solutions and iteration that has been unimaginable in the past. This shift requires valuing "soft skills" like communication and collaboration over pure coding proficiency, as developers guide AI "pair programmers."

The synergy of AI agents, human intellect, and Cloud Development Environments (CDEs) is key. CDEs provide secure, governed, and scalable platforms for this collaboration, allowing developers to focus on business logic and innovation while AI handles the coding groundwork. This requires a move from rigid "gates" in development processes to flexible "guardrails" within CDEs. Such a move fosters innovation with built-in control and security.

Flexibility and choice are vital in this constantly advancing AI space. CDEs enable organisations to select the best AI agents for specific tasks, avoiding vendor lock-in by expressing the development environment as code. This leads to practical applications like faster prototyping, enhanced code development, and automated testing, significantly boosting code output. Furthermore, agentic AI democratises development, empowering non-engineers to build solutions.

Preparing for this future requires proactive experimentation through AI labs, engaging early adopters, and viewing AI as an augmentation of human skills. Watch the podcast for more insights on CDEs and the impact of AI agents on enterprise cloud development.

Agentic AI is a transformative technology for software development.

The role of developers is shifting from hard skills to soft skills.

AI agents can significantly increase productivity in coding tasks.

Organizations need to rethink their development strategies to integrate AI.

Cloud development environments are essential for safely using AI agents.

Choosing the right AI agent is crucial for effective development.

Security and governance are critical when integrating AI into development.

AI can empower non-developers to create applications.

Guardrails are more effective than gates in managing AI development.

Organisations should experiment with AI to find the best fit for their needs.

00:00 Introduction to Agentic AI and Developer Roles

03:20 Transformative Impact of AI on Development

06:50 Cultural Shifts in Development Teams

10:30 Integrating AI Agents in Cloud Development Environments

12:49 Choosing the Right AI Agents

15:21 Security and Governance in AI...

Can customer success be your secret growth weapon? This 'TechTransformed' episode explores its evolution from a cost center to a revenue driver. Marilee Bear, Chief Revenue Officer at Gainsight, and Christina Stathopoulos, founder of Dare to Data, discuss AI's role and how strong customer relationships boost your bottom line.

Are Agentic AI systems the next big leap in business technology? Christina Stathopoulos (Dare to Data) and Jeff DeVerter discuss the real-world impact on 'Tech Transformed.' From data infrastructure to ethical dilemmas and workforce transformation, this episode answers critical questions for CIOs facing the AI revolution. What are the practical steps to prepare your organisation? Tune in to find out.

From the integrities of the human workforce embracing enhancing soft skills over hard skills in the enterprise tech space to the adoption of artificial intelligence (AI) agents in customer service, this conversation covers it all.

In this episode of the Tech Transformed podcast, Shubhangi Dua speaks with Nikhil Nandagopal, co-founder and CPO of Appsmith, about the metamorphological impact of AI agents in the workplace. He particularly emphasises the need for organisations to hone in on the advancing capabilities of agentic AI while still maintaining a focus on human collaboration and security.

Takeaways

• AI agents are autonomous entities designed to achieve specific goals.
• The centralisation of data through AI agents simplifies workflows.
• Conversational interfaces are becoming the norm for accessing information.
• Humans remain integral to AI workflows, acting as moderators.
• Job roles will evolve, requiring new skills and adaptability.
• Critical thinking is essential when interacting with AI outputs.
• Cybersecurity is a major concern with centralised AI systems.
• Self-hosting AI solutions can mitigate cybersecurity risks.
• The future of work will reward soft skills over hard skills

Chapters

00:00 Introduction to AI Agents and Their Impact

03:34 The Shift Towards Conversational Interfaces

05:07 Assisted Workflows and Human-AI Collaboration

10:05 Job Market Evolution in the Age of AI

13:23 Critical Thinking in the Age of AI

15:29 Cybersecurity Concerns with AI

20:31 Preparing for Cyber Threats in AI Systems

22:51 The Future of AI Agents in the Workplace

In this episode of the Tech Transformed Podcast, Jon Arnold, Principal of J Arnold Associates speaks with Nikola Mrksic, CEO of PolyAI, discussing all things AI, specifically in contact centres. From the benefits of automation to the emergence of the most trending subject of the year – Agentic AI.

Mrksic particularly spotlights some underutilised capabilities of AI such as how it can manage up to 90% of repetitive duties, allowing human agents to concentrate on other complex tasks. The conversation also explores the transition from basic service to a broader, more holistic customer experience, necessitating the need for rapid adaptation and experimentation.

AI in contact centers isn't just about cutting costs. This conversation shows how it can truly make a difference – giving agents the tools to shine, providing customers with better, more quality experiences, and even letting AI take care of tasks behind the scenes securely, so humans can focus on what truly matters.

Takeaways

• AI is a dominant force shaping technology today.
• Contact centers have a high volume of repetitive tasks suitable for AI.
• AI can automate up to 90% of tasks in contact centers.
• The role of AI is not just cost-cutting but improving service quality.
• Agentic AI can perform tasks on behalf of users asynchronously.
• Customer experience is now a key focus beyond just service.
• Companies must adapt quickly to avoid falling behind competitors.
• Failing fast and experimenting is crucial for success with AI.
• AI can provide insights that traditional methods miss.
• Investing in AI should be about solving problems, not just keeping up with trends.

Chapters

00:00 Introduction to AI in Contact Centers

02:01 Benefits of AI in Contact Centers

07:37 Transforming Customer Experience with AI

15:42 Understanding Agentic AI

21:27 The Shift from Customer Service to Customer Experience

30:25 Advice for Business and CX Leaders

The world is changing faster than ever. Businesses are drowning in data, yet struggling to extract the insights they need to stay ahead. Artificial intelligence (AI) holds the key, but traditional AI models are too slow, too static, and too disconnected from the real world. This is where real-time AI comes in.

Real-time AI empowers businesses to make decisions in milliseconds, reacting to changing conditions and seizing fleeting opportunities. It's about more than just analysing historical data; it's about understanding the present and predicting the future, all in the blink of an eye.

Imagine a world where customer service agents have access to the most up-to-the-minute information, resolving issues before they escalate. Envision supply chains that dynamically adjust to disruptions, ensuring products are always available. Envision marketing campaigns that personalise experiences in real time, maximising engagement and driving conversions.

But real-time AI isn't just about speed; it's also about accuracy. The time to embrace real-time AI is now. Businesses that fail to adapt risk falling behind in an increasingly competitive world. By harnessing the power of real-time data and intelligent agents, enterprises can tap into new levels of performance, innovation, and growth.

In this episode, Shubhangi Dua, an editor and tech journalist at EM360Tech, speaks to Madhukar Kumar, the Chief Marketing Officer at SingleStore, about the transformative potential of real-time AI for enterprises.

Takeaways

• Real-time AI is essential for modern enterprises.
• The evolution from generative AI to real-time AI is significant.
• Data accuracy and freshness are critical for AI success.
• AI agents will collaborate to enhance business processes.
• Enterprises must manage data silos to improve efficiency.
• Smaller companies can leverage AI to create innovative solutions.
• Data governance is crucial for protecting sensitive information.
• Real-time AI can significantly improve user experience.
• AI will enable professionals to focus on higher-value tasks.
• Harnessing data effectively will be a key differentiator for businesses.

Chapters

00:00 Introduction to Real-Time AI and Its Importance

03:03 The Evolution of AI: From Generative to Real-Time

05:54 Real-Time AI in Enterprises: Advantages and Examples

11:01 The Future of AI Agents and Their Collaboration

16:47 Preparing Enterprises for AI: Data Management and Security

20:47 Business Advantages of Real-Time AI and Future Opportunities