Tech Transformed

"Certainly an exciting time for data centers, private and public alike, isn't it?" This opening remark from Tom Croll of Lionfish Tech Advisors set the stage for a compelling discussion with Ryan Mallory, President and COO of Flexential, on the recent episode of the Tech Transformed podcast. 

The speakers discuss the current AI scenario's impact on data centers, high-density computing, and cloud infrastructure. This is where Flexential comes in. Mallory stresses the importance of trust and verification in AI deployment, especially regarding security and data privacy, which Flexential has established a reputation for.

“How to adapt to the AI boom?” is one question everyone’s asking, Mallory says. From a service provider perspective, it's a "proverbial gold rush" for powered land. This is essential for building the relevant AI infrastructure that will serve as early entry points. 

Flexential's survey reveals that a staggering "90% of people surveyed are contemplating an AI strategy." The number spotlights the widespread interest and impending demand. “This isn't a short-term trend,” says Mallory. He also projects a "12-year" development cycle for AI infrastructure, emphasizing the long-term commitment required from the industry.

The unprecedented growth in AI demands specialized infrastructure, especially concerning the sustainable use of AI and running data centers, and strong strategies for scalability, reliability, and cost-effectiveness. 

Flexential is uniquely positioned to meet this challenge. "We've been developing high-performance compute facilities for over 10 years," he states. Their "Gen 4 and Gen 5 sites can cool 50 kilowatts per cabinet air-cooled." 

This information has allowed them to readily support the requirements of H100 and H200 type deployments, not just for service providers, but also for ramping deployments in the healthcare and financial sectors.

Looking ahead, the data center industry is preparing for even higher-density racks and the widespread adoption of liquid cooling. While "all of our sites are liquid-cooled ready," Mallory says, thorough airflow studies and CFD analysis show liquid cooling is genuinely necessary. 

Flexential’s air-cooled solutions are already handling "high-dense pods for some of the companies that have recently gone public and other companies that are out there that you hear about in this AI service provider realm,” Mallory added. 

• 90% of surveyed companies are considering an AI strategy.
• The AI industry is experiencing a gold rush for infrastructure.
• Data centers must adapt to high-density computing demands.
• Liquid cooling is essential for high-performance AI deployments.
• AI regulations are shaping how data centers operate.
• Trust but verify is crucial for AI deployment.
• AI democratization is vital for businesses of all sizes.
• Flexential is focused on providing scalable AI infrastructure.
• Security policies are essential for protecting sensitive data.
• AI can enhance productivity, but it requires human oversight.

00:00 The Impact of AI on Data Centers

02:51 Infrastructure Challenges and Solutions

05:59 Navigating AI Regulations and Security

08:59 Democratization of AI for Businesses

12:03 Key Takeaways for CIOs

Flexential enhances organizational operations by providing customized hybrid IT solutions through its FlexAnywhere platform. This platform offers integrated colocation, cloud, connectivity, data protection, and managed and professional services. Backed by three million square feet of data center space across 19 highly connected markets, Flexential helps customers navigate complex IT journeys.

The FlexAnywhere solutions empower customers to address their most intricate hybrid IT infrastructure needs, including reliability, performance, agility, scalability, and seamless application and user interconnection.

• #AI #analysts are crucial for integrating AI into business processes.
• Organisations need to rethink their data management strategies for AI.
• AI #data clearinghouse concepts help manage data access and security.
• Cross-functional collaboration is essential for successful AI integration.
• AI can enhance employee effectiveness rather than replace jobs.
• The future of work will see AI analysts in various business functions.
• Companies must adapt quickly to remain competitive in the AI landscape.

This episode of the #TechTransformedPodcast explores the role of AI Analysts. Host Keyari Page is joined by guest speaker Andy MacMillan, CEO of Alteryx.

We learn that the term AI Analyst refers to an emerging role of #professionals who help organisations rethink their processes, workflows, and employee capabilities through the use of AI.

This new role bridges the gap between AI systems and tangible #businessoutcomes. In the podcast, we also cover the importance of managing data for AI through an “AI data clearing house”. This helps business analysts prepare data for AI projects.

Through this system, analysts and business owners are able to ensure that compliance and security measures are met.

Tune in for insights on how AI is reshaping roles, boosting efficiency, and transforming customer experiences in the evolving business landscape.

For more tech insights visit: em360tech.com

"The concept of Zero Ticket IT is that instead of reacting to the ticket and trying to solve the ticket, you go directly to the source of the issue." This statement by Sean Heuer, CEO of Resolve Systems, sets the stage for this episode of the Tech Transformed podcast.

Shubhangi Dua, podcast host and producer at EM360Tech, , sits down with Heuer to unpack the ambitious yet achievable vision of Zero Ticket IT and how both agentic AI and intelligent automation are poised to change IT operations.

Traditional IT ticketing systems, with their reactive nature and reliance on human intervention, are facing an overdue overhaul. Heuer shares a path towards a more efficient, proactive, and ultimately frictionless IT experience.

Zero Ticket IT shifts the focus from reacting to individual tickets to directly addressing the source of the issue. As Heuer explains, a major portion (roughly 70 per cent) of IT tickets originate from employee requests and range from password resets to connectivity problems. Another substantial chunk comes from machine alerts, often leading to "alert storms" where a single underlying issue triggers a cascade of notifications.

For instance, imagine an AI-powered conversational interface that can understand an employee's problem. Now this problem can be resolved using a vast knowledge base and service catalog. 

"There's no reason for a human to intervene if you locked your account. If you need to reset your password. There's no reason for a human to have to handle that. It should happen instantaneously," Heuer elaborates. This self-service approach immediately reduces ticket volume by a significant margin.

Automation can also solve challenges head-on by integrating AI operations (AIOps) solutions to analyse countless machine alerts, thereby identifying correlations and spotting the root cause. 

"Instead of getting a thousand incidents you have to manage, you get one incident," Heuer states, allowing for precise and rapid resolution.

Heuer highlights that by implementing these two layers — direct interfaces for employees and intelligent automation for machine alerts — organisations can achieve a 60 per cent to 70 per cent reduction in total ticket volume.. 

Some Resolve Systems customers have even seen up to an 80 per cent reduction, with Heuer noting: "We have a telco customer that's gotten to 80 per cent reduction of incidents in their network and infrastructure. We have a retail company that has gotten to 75 per cent reduction. Auto-remediation is the solution for all employee requests."

Heuer envisions a future where the core role of an IT technician evolves from reactive ticket-solving to proactively managing and optimizing AI and automation systems. The focus will be on identifying patterns, improving knowledge articles, and developing new automations. 

The Resolve System CEO said that IT organisations must shift their focus from traditional metrics like agent utilisation and first-touch resolution to employee productivity and digital employee experience. 

Forrester Research suggests that for every 1,000 employees, companies can lose $1.5 million to $3 million in productivity due to IT-related downtime.

"IT's job isn't to work tickets. Its job is to make the business productive, to keep it productive," Heuer says. 

"Start now. Stop waiting, stop with the FUD, stop with the experimenting. Instead, apply AI to a problem. This is real. This isn't a future fantasy world but it’s something you could do today, right now,” the CEO added.

To learn more about Resolve Systems and their work in this space, visit resolve.io and follow them on social media: Resolve Systems LinkedIn and Resolve Systems YouTube.

• Zero Ticket IT aims to solve IT issues at the source.
• Agentic AI can significantly reduce ticket volume.
• The focus of IT should shift from ticket management to employee productivity.
• AI can help eliminate human error in IT operations.
• Education and transparency are crucial for building trust in AI.
• Employee experience directly impacts productivity and engagement.
• IT organisations should measure success through employee productivity metrics.
• Automation can lead to significant cost savings for businesses.
• The future of IT roles will involve managing AI and automation.
• AI's impact on the workforce raises important economic questions.

00:00 Introduction to Zero Ticket IT

06:52 Understanding Agentic AI and Automation

13:50 Shifting Focus: From Tickets to Employee Productivity

18:55 Challenges and Trust in AI

26:00 Future Skills for IT Teams

30:59 The Future of Work and AI

Resolve is redefining IT and network operations with an agentic automation and orchestration platform built for a Zero Ticket future. Our AI agents don’t just assist—they autonomously detect, diagnose, and resolve incidents before they impact the business. By transforming reactive, manual workflows into proactive, self-healing systems, Resolve slashes ticket and alert noise by up to 90 per cent, reduces MTTR from hours to minutes, and empowers IT teams to scale without increasing headcount.

Trusted by some of the largest global enterprises, Resolve empowers IT teams to move from reactive firefighting to proactive transformation. Learn more at resolve.io

Are you a CIO, CTO, CISO, or IT decision maker in the restaurant or retail industry, grappling with rising costs and tariffs to keep up with the rapid pace of technological change? The pressure to create high-quality solutions with AI while managing existing infrastructure can be challenging.

In this episode of the Tech Transformed Podcast, Shubhangi Dua, podcast host and producer at EM360Tech sits down with Keith Szot, SVP Chief Evangelist at Esper, to talk about how enterprises can extend the lifespan of their current edge devices. They also discuss how such enterprises can easily integrate Edge AI solutions without a complete hardware overhaul.

"Starting out with IT Ops is a tough endeavor. If you are running revenue producing key business operating systems that are out in the field, it's not an easy job,” stated Szot. “With everything that's going on these days, it's not getting any easier. Now, considering tariffs, the impact of AI in terms of how you look at your hardware refresh cycle, it's really tough.”

Specifically alluding to edge devices in restaurant and retail, Szot reflecting on the limitations of traditional operating systems says Android is the key. "If you look at the ISV and the solution provider community, the best and latest solutions for these markets are built on Android.”

“You look at enterprise developers, if you're developing in-house, arguably it's a lot easier to find an Android developer and build a team to focus on creating Android applications than it is in Windows,” he added. “Android is the biggest developer ecosystem in the world in the history of humankind.

Unlike operating systems tied to rapid consumer hardware upgrades, Android offers the flexibility of an open-source project. It allows for greater control over updates and longevity. 

Szot believes that the Android UX is more intuitive. He says that people use phones all the time. “And even if you're an iOS user, the paradigm, if you go to Android, still has a familiarity where the bar to understand how to use the software in the device arguably is lower." This minimises training needs and improves operational efficiency.

In a nutshell, the conversation touches on extending hardware lifecycles for edge devices in the restaurant and retail industry, primarily through the Android flip, to enable the integration of AI at the edge and prepare for future trends like robotics and 5G.

• Extending hardware lifecycles is crucial for cost management.
• Android is becoming the preferred OS for enterprise solutions.
• AI can enhance customer experiences in retail environments.
• Robust hardware design is essential for longevity.
• Transitioning from Windows to Android can save costs.
• Edge AI allows for on-premise processing without latency issues.
• Physical AI will revolutionize the retail and restaurant sectors.
• Quantum computing poses both opportunities and challenges for security.
• Device management is key to maintaining operational efficiency.
• Innovative hardware designs will create new customer experiences.

00:00 Introduction to Edge Devices and AI

03:12 Extending Hardware Lifecycles in Retail

06:06 Transitioning from Windows to Android

08:56 Practical Applications of Edge AI

11:47 AI Integration in Restaurant Kiosks

14:55 Managing Existing Hardware with Software Solutions

18:01 The Role of Edge AI in Future Technologies

20:51 Physical AI and Robotics in Retail

24:03 Future Trends: Quantum Computing and Device Management

27:07 Innovations in Hardware for Enhanced Experiences

30:14 Key Takeaways for IT Decision Makers

Founded in 2017, Esper is a company known for its modern device management platform, going beyond traditional MDM to power exceptional device experiences with a focus on automation. They aim to redefine how businesses manage their company-managed hardware, particularly Android devices, driving towards a DevOps future.

Esper's core offerings provide advanced automation and unmatched operational efficiency, eliminating re-provisioning and enabling exception-based management. Their powerful and scalable platform features kiosk mode, remote control, software deployment, and device grouping, with tailored industry solutions for sectors like healthcare, retail, hospitality, and logistics. They also maintain strong partnerships with key players such as Zebra, Lenovo, and Intel.

The ability to effectively manage and optimise data is key in an organisation today. But with the sheer volume and complexity of enterprise data, traditional methods are struggling to keep up with the change. This is where the agentic AI approach has swooped in to transform how organisations handle their most valuable resource. 

"The promise of AI and agentic AI is that we're now building very meaningful automation into the platform such that these teams of 10 are now able to basically actually capture all of the metadata about all of the data cataloged across their entire company," stated Corey Keyser, the head of artificial intelligence (AI) at Ataccama.

In this episode of the Tech Transformed podcast, Shubhangi Dua, a B2B tech journalist and Podcast host at EM360Tech speaks with Keyser from Ataccama, about agentic AI, data quality, and data governance. 

They explore how intelligent automation is shaping enterprise data management, the role of AI in improving data quality, and the importance of trust in AI systems. Additionally, Keyser shares significant insights on Ataccama's unique approach to data governance, practical applications of their AI agent, and how they are keeping pace with the constantly changing AI regulations. 

While the speed and efficiency of AI are undeniable, the question of trust remains. Keyser addressed this directly: "The short answer is you can never fully trust these automations, right? 

“That's why it's really critical to always have data stewards that we will serve. We will always have data engineers that we will serve. We're just looking to improve their productivity. We always assume that there will be humans in the loop who are verifying the tasks orchestrated by AI agents."

Ataccama's One AI Agent exemplifies the practical application of these principles. Keyser added that the AI agent can go and create data quality rules in bulk. “Go through the evaluation and testing of those quality rules in bulk, and then also assign the rules in bulk. Something that would take potentially weeks, can now actually kind of take hours depending on the person."

• Agentic AI is about dynamic planning and semi-autonomous task execution.
• Data governance involves cataloging and managing organisational data.
• Data quality assessment is crucial for ensuring high trust in data.
• AI can significantly speed up the creation of data quality rules.
• Human oversight is essential in AI-driven automation processes.
• Atacama's AI agent improves productivity for data management teams.
• Regulatory compliance is a growing concern for AI applications.
• User experience is key to successful AI integration in organisations.
• The relationship between data and AI is symbiotic and essential.
• Organisations must adapt to evolving AI regulations and standards.

00:00 Introduction to Agentic AI and Data Governance

02:41 Understanding Data Quality and Governance

06:30 The Role of AI in Data Management

11:14 Practical Applications of One AI Agent

15:46 Differentiating Atacama in the AI Landscape

19:37 Case Studies of Transformation

20:05 Integrating Data Quality and Governance

22:07 Navigating Regulatory Changes

24:19 Enhancing User Experience with AI

25:29 Key Takeaway for CIOs

“Before starting a new AI project, it is really worthwhile defining the business priority first,” asserts Joanna Hodgson, the UK and Ireland regional leader at Red Hat.

“What specific problem are you trying to solve with AI? Do we need a general purpose AI application or would a more focused model be better? How will we manage security, compliance and governance of that model? This process can help to reveal where AI adoption makes sense and where it doesn't," she added. 

In this episode of the Tech Transformed podcast, host Shubhangi Dua, podcast producer at EM360Tech speaks with Hodgson, a seasoned business and technical leader with over 25 years of experience at IBM and Red Hat. They talk about the challenges of scaling AI projects, the importance of open source in compliance with GDPR, and the geopolitical aspects of AI innovation. 

They also discuss the role of small language models (SLMs) in enterprise applications and the collaboration between IBM and Red Hat in advancing AI technology. Joanna emphasises the need for a strategic approach to AI and the importance of data quality for sustainable business practices. While large language models (LLMs) dominate headlines, SLMs offer a cost-effective and efficient alternative for specific tasks.

The podcast answers key questions, like ‘how do businesses balance ethical considerations, moral obligations, and even patriotism with the drive for AI advancement?’ Hodgson shares her perspective on how open source can facilitate this balance, ensuring AI works for everyone, not just those with the deepest pockets.

Hodgson also provides her vision on the future of AI. It comprises interconnected small AI models, agentic AI, and a world where AI frees up teams to create personal connections and exceptional customer experiences.

• Curiosity is a strength in technology.
• AI is becoming embedded in existing applications.
• Regulatory compliance is crucial for AI systems.
• Open source can enhance trust and transparency.
• Small language models are efficient for specific tasks.
• AI should free teams to create personal connections.
• A strategic AI platform is essential for businesses.
• Data quality is key for sustainable business success.
• Collaboration in open source accelerates innovation.
• AI can be used for both good and bad outcomes.

00:00 Introduction to the Tech Transform Podcast

01:35 Pivotal Moments in Joanna's Career

05:12 Challenges in Scaling AI Projects

09:15 Open Source and GDPR Compliance

13:11 Regulatory Compliance and Data Security

17:30 Geopolitical Aspects of AI Innovation

22:31 Collaboration Between IBM and Red Hat

23:58 Understanding Small Language Models

29:54 Future Trends in AI and Sustainability

Red Hat is a leading provider of enterprise open source solutions, using a community-powered approach to deliver high-performing Linux, hybrid cloud, edge, and Kubernetes technologies. The company is known for Enterprise Linux.

They offer a wide range of hybrid cloud platforms and open source technologies—including AI, virtualisation, edge, app development, and automation solutions. The hybrid cloud solutions are built to run their customers’ most important, most complex projects—from the datacenter to clouds to the edge. Their open ecosystem of partners and communities aim to ensure existing investments stay protected, providing unmatched freedom and choices to change, flex, reinvent, or stay the course.

Open source software (OSS) refers to the source code of software that is relatively liberally available for use, modification, and distribution. The source code knowledge is accessible with few restrictions. 

Enterprise open source software is supported by a vendor, usually via a subscription fee. For example, the vendor stabilises and quality-assures the software, certifies it works with an ecosystem of hardware and software, secures it and provides technical support.

OSS is a driver of collaborative development of AI models, fostering transparency, team-backed innovations. This often results in highly adaptable and cost-effective solutions. The open nature allows for peer review, potentially leading to enhanced security and reliability, making it a cornerstone of modern technology.

Takeaways

• #Satellitecommunications are essential for remote field teams.
• They provide safety and tracking for workers in isolated areas.
• Disaster preparedness involves proactive planning and communication.
• User-friendly #technology is crucial for effective utilisation.
• Reliable communication impacts day-to-day operations positively.
• Employers show care for employee safety through #satellitetechnology investment.

Summary

In this episode of #TechTransformed, Jonathan Care discusses the importance of satellite communications for field teams. He is joined by Mark O'Connell, EMEA & Asia Pacific General Manager at Globalstar, and Grace Finn, Senior Account Manager at Peoplesafe. Together, they explore how satellite technology enhances safety, disaster preparedness, and operational efficiency for remote, field workers.

We learn the differences between satellite and cell communication. O’Connell emphasises the importance of satellite communication, stating that due to field workers, there is a requirement to not be reliant on cellphone towers.

O’Connell further clarifies that field teams who are working remotely, and are away from terrestrial communications, need access to constant communication.

Finn also expresses the importance of field worker safety, sharing: “If the unforeseen does happen, they’re protected.” She emphasises that organisations should invest in satellite communications to ensure their teams' wellbeing & security.

Tune in to learn the user-friendly aspects of the technology, its features for challenging environments, and the critical role it plays in ensuring reliable communication.

For the latest tech insights and content visit: EM360Tech.com

"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 Relic provides comprehensive clarity across your entire digital landscape. 

By processing the optimal data at the right moment, we shift your problem-solving capabilities beyond proactive measures to a predictive stance, maximizing value and controlling expenditures. This is why global enterprises like Adidas Runtastic, American Red Cross, Domino’s, GoTo Group, Ryanair, Topgolf, and William Hill rely on New Relic to fuel innovation, enhance reliability, and deliver outstanding customer experiences that drive growth.

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.

For the latest tech insights visit: EM360Tech.com

"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 Development

17:29 Coder's Role in the AI Development Landscape

20:14 Expanding Development Beyond Traditional Roles

23:27 Guardrails vs. Gates in Development

26:56 Practical Applications of AI in Code Generation

29:58 Acquiring AI Agents for Development

32:34 Preparing for the Future of Development with AI

Coder is a remote-first company with products used by many of the world’s largest enterprises. Coder provides an enterprise platform and open source tools that make it easier than ever to configure, secure, and manage software development environments.

Leave hardware limitations behind and welcome accelerated code execution, providing you and your developers with unmatched efficiency and elevated productivity. This enables an organisation to innovate rapidly and maintain a competitive edge.