Episodes
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"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.
Scaling Up for AIThe 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.
Takeaways90% 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.
Chapters00:00 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...
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Takeaways#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.
SummaryThis 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
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"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.
What is Zero Ticket IT?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.
AI Automation to Resolve Substantial IT RequestsAutomation 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...
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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.”
‘Android is the key’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.
Key Takeaways 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.
Chapters00:00 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
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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."
TakeawaysAgentic 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.
Chapters00:00 Introduction to Agentic AI and Data Governance
02:41 Understanding Data Quality
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“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.
TakeawaysCuriosity 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.
Chapters00:00 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
About Red HatRed 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...
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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
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"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.
TakeawaysObservability 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.
Chapters00:00 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
About New RelicThe 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...
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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
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"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.
TakeawaysAgentic 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.
Chapters00:00 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...
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Takeaways
#Customersuccess is about helping customers get value from products.The perception of customer success is shifting from a cost centre to a revenue generator.#AI can enhance customer success by providing predictive insights and automating processes.Customer success teams are uniquely positioned to identify upsell and cross-sell opportunities.Collaboration between customer success and sales teams is essential for maximising revenue.The integration of AI tools can streamline customer success operations.Summary
Ever wonder how to transform existing customers into your company’s most powerful growth engine? According to Gainsight’s Chief Revenue Officer, Marilee Bear, it starts with one deceptively simple principle: “helping customers get value from your product.”
This #TechTransformed episode features Christina Stathopoulos, Founder of Dare to Data, in conversation with Marilee about the dynamic role of customer success – a powerful avenue for building deeper customer bonds, boosting retention rates, and ultimately achieving significant revenue gains.
Tune in to learn how to unlock the potential of customer success as a catalyst for cross-selling and upselling opportunities. Explore how to equip your customer success teams with commercial acumen and harness their potential as a secret weapon for business growth.
Whether you're a #CRO looking to optimise revenue or a business leader navigating the AI revolution, this episode offers invaluable insights into the future of customer success.
For the latest tech insights visit: EM360Tech.com
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Takeaways
#AgenticAI systems can string tasks together for efficiency.Real-world applications include supply chain optimisation and knowledge worker augmentation.#Dataquality is crucial for effective AI implementation.Education and understanding of AI's potential are essential for organisations.Governance is key to ethical AI deployment.Leadership is critical in adopting AI technologies.AI will create new job opportunities, not just displace existing ones.Summary
Jeff DeVerter, Field Chief Technology Officer at Pythian, describes agentic AI as “little workers that are going to go off and all do a job. You're now a manager of these AIs that are going to go off and do some work and come back and give you that work product.”
In this episode of the #TechTransformed podcast, Christina Stathopoulos, Founder at Dare to Data, and Jeff DeVerter explore this concept, revealing how agentic systems are re-shaping real-world business scenarios.
Imagine these agentic AIs as powerful “personal assistants” when empowering leaders to manage data, streamline workflows and drive commercial acumen.
The discussion goes beyond the possibilities addressing how to prepare your data infrastructure, navigate ethical considerations, and understand AI’s impact on employees, delivering crucial takeaways for CIOs.
For the latest tech insights visit: EM360Tech.com
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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 skillsChapters
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
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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
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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
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In this conversation, Ryan Worobel shares his extensive experience in the technology sector, discussing the evolution from traditional monitoring to observability. He highlights the cultural and technical challenges organizations face during this transition, emphasizing the importance of collaboration and data management. Ryan also explores the role of AI in enhancing IT operations, advocating for a balance between automation and human expertise. He provides insights on implementing AI in organizations, the risks and opportunities associated with it, and the necessity of understanding company culture for successful adoption.
Key Takeaways
Observability requires a cultural shift towards collaboration.Data management is crucial to avoid overwhelming teams.AI is transforming IT from reactive to proactive approaches.Organizations must start small when implementing AI.Understanding company culture is key to AI adoption.Uptime is essential; downtime is no longer acceptable.AI should supplement human expertise, not replace it.Effective data sorting can reduce noise in decision-making.Innovation is necessary to maintain a competitive edge.Organizations need to establish governance around AI usage.Chapters
00:00 Introduction to Ryan Worobel and His Journey
07:37 Proactive vs Reactive Approaches in IT
13:31 Implementing AI in Organizations
19:31 Conclusion and How to Connect with Ryan
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In this conversation, Sam Page explores the evolving landscape of digital experiences, emphasizing the shift from the information age to the experience age, driven by advancements in AI.
Learn about importance of creating meaningful digital interactions, the challenges posed by biases in AI, and the need for transparency and education in navigating these technologies. Sam introduces a framework for brands to understand, connect, and serve their audiences effectively, highlighting the potential of AI to enhance consumer experiences while addressing concerns about job displacement and data biases.
Key Takeaways
The digital experience is rapidly changing with AI at the forefront.Brands must prioritize meaningful digital experiences to connect with consumers.The shift from the information age to the experience age is significant.AI can create unique experiences tailored to individual needs.Concerns about AI include job displacement and biases in data.Transparency and trust are crucial in AI adoption.Education about AI should start from a young age.Brands can leverage AI to enhance customer connections.Spotify's DJ feature exemplifies effective AI use in consumer engagement.Understanding, connecting, and serving are key components for brands in the AI era.Chapters
00:00 The Evolution of Digital Experience
04:01 Transitioning from Information Age to Experience Age
08:03 Addressing AI Concerns and Biases
14:11 Navigating AI: Understand, Connect, and Serve
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In this conversation, Phillip Mortimer discusses the transformative impact of AI on private markets, emphasizing the unique challenges posed by non-standardized data and the importance of balancing quantitative and qualitative insights in investment decisions. He highlights the significance of data privacy in AI applications and the evolving role of generative AI in automating workflows. Mortimer also addresses concerns about the future of AI, arguing against the notion of reaching an inflection point in returns due to existing limitations.
Key Takeaways
AI is essential for navigating the complexities of private markets.Data scarcity and non-standardization make AI a necessity in finance.Human intuition remains crucial in investment decision-making.AI can enhance productivity but should not replace human judgment.Generative AI poses new data privacy challenges that must be addressed.Last mile SaaS can still thrive despite the rise of generalized AI.The future of AI is promising, with ongoing advancements in technology.Type 2 thinking in AI is a key area for development.Investors must consider the return on intelligence, not just ROI.AI's role in finance is to augment human capabilities, not replace them.Chapters
00:00 Introduction to AI in Private Markets
02:56 The Role of AI in Data Analysis
06:00 Balancing Quantitative and Qualitative Insights
08:50 Data Privacy Concerns in AI
11:54 Generative AI and Last Mile SaaS
14:59 Future of AI and Investment Returns
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In this conversation, Michel Spruijt discusses the integration of AI and robotics in various industries, emphasizing the importance of balancing automation with human oversight. He highlights the challenges of designing multifunctional AI systems and the critical role of data interpretation in ensuring ethical use. Michel also shares insights on how organizations can adapt to AI, the significance of curiosity in career development, and the evolving job landscape due to technological advancements.
Key Takeaways
AI and robotics are transforming industries, including cleaning.The balance between automation and human oversight is crucial.Data interpretation is more important than just data collection.Organizations should start small with AI investments.Curiosity is key to identifying unique opportunities in careers.AI will create new jobs while automating others.Human oversight is essential for ethical AI use.Staying curious can lead to career growth and innovation.AI should complement human work, not replace it.Understanding AI's capabilities can enhance productivity.Chapters
00:00 Introduction to AI and Robotics in Industry
03:00 The Balance of Automation and Human Oversight
05:56 Challenges in Designing Multifunctional AI Systems
08:48 Data Interpretation and Ethical Considerations
12:07 Adapting Organizations to AI
16:58 Identifying Unique Opportunities in AI Roles
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James Smith discusses his extensive background in business intelligence and analytics, emphasizing the critical importance of adopting generative AI in organizations. He highlights the risks of delaying adoption, the transformational potential of AI, and the need for alignment between AI and business strategies. James also addresses the importance of measuring the impact of AI, ensuring ethical use, and leveraging AI to anticipate future trends. He concludes by sharing insights on how businesses can effectively implement generative AI to gain a competitive edge.
Key takeaways
Generative AI adoption is crucial for maintaining competitive advantage.Organizations that delay AI adoption risk losing market share.AI can transform the role of data teams in organizations.Effective communication is essential during AI implementation.Aligning AI strategy with business goals is critical for success.Measuring ROI is more important than just tracking user adoption.Keeping humans in the decision-making loop is vital for ethical AI use.Generative AI can empower all employees, not just a few.Organizations should test AI solutions against complex use cases.Chapters
00:00 Introduction to James Smith and His Background
03:12 The Importance of Generative AI Adoption
06:05 Transformational Potential of Generative AI in Organizations
08:54 Measuring the Impact of Generative AI
11:54 Aligning AI Strategy with Business Goals
15:05 Addressing Bias and Ensuring Ethical AI Use
18:09 Leveraging Generative AI for Future Trends
23:51 Conclusion and How to Connect with James Smith
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