Techsplainers by IBM

Introducing Techsplainers by IBM

Tue, 04 Nov 2025 20:56:45 GMT

Welcome to Techsplainers by IBM, your daily dose of AI and technology insights from Monday to Friday, produced by IBM. Every weekday, we explore a new trending topic, such as generative AI, agentic AI, cybersecurity, data for AI, and more. With hundreds of topics to explore, there’s always something new to learn. Perfect for your commute, workout, or coffee break.

Have a topic that you want techsplained? Let us know in the comments! Tune in every weekday at 6 AM ET to explore new topics, stay ahead, and learn smarter.

Visit the podcast page: https://www.ibm.com/think/podcasts/techsplainers

Learn more about tech topics: https://www.ibm.com/think/topics

What is AI agent orchestration?

Tue, 07 Apr 2026 10:00:03 GMT

This episode of Techsplainers explores AI agent orchestration - the sophisticated process of coordinating multiple specialized AI agents to collaborate effectively on complex tasks. We explain how orchestration systems manage the assignment, communication, and coordination between different AI agents, each with their own capabilities. The episode breaks down the key components of successful orchestration, including the central orchestrator, communication protocols, task decomposition, result aggregation, and conflict resolution mechanisms. We examine different orchestration patterns like sequential, parallel, hierarchical, and collaborative approaches, with special attention to emergent collaboration models where agents dynamically form teams based on evolving needs. The discussion also covers significant challenges in agent orchestration, including communication overhead, dependency management, quality consistency, security concerns, and the risk of cascading failures. Through real-world examples in customer service, software development, research, and content creation, listeners gain insight into how orchestrated AI agents can tackle problems that would be impossible for any single agent.

Find more information at https://www.ibm.com/think/topics/ai-agent-orchestration#228874317

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Narrated by Cole Stryker

What is AgentOps?

Mon, 06 Apr 2026 10:00:01 GMT

This episode of Techsplainers explores AgentOps, the emerging operational framework for managing and optimizing AI agents throughout their lifecycle. Similar to how DevOps manages software development and MLOps handles machine learning models, AgentOps provides the essential practices, tools, and methodologies for deploying, monitoring, evaluating, governing, and continuously improving autonomous AI systems. We examine the five key areas of AgentOps: deployment, monitoring and observability, evaluation and testing, governance and safety, and continuous improvement. The episode also addresses the unique challenges of managing AI agents, including their unpredictability, complexity, tool integration requirements, performance drift, and multi-agent coordination needs. Listeners will gain insight into how organizations can implement effective AgentOps through clear metrics, specialized monitoring tools, robust testing frameworks, and comprehensive governance systems to maximize the reliability and performance of their AI agent investments.

Find more information at https://www.ibm.com/think/topics/agentops#1083937705

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Narrated by Cole Stryker

What is financial management?

Fri, 03 Apr 2026 09:00:01 GMT

This episode of Techsplainers explores financial management, the comprehensive framework organizations use to guide resource allocation, investment decisions, and operational efficiency. As the final installment in our finance operations week, we connect financial management to previously discussed concepts like enterprise performance management, extended planning and analysis, AI in FP&A, and scenario planning. The episode examines the four core responsibilities of financial teams: planning, budgeting, risk management, and procedure setting, while highlighting key components including investment evaluation, reporting, and cash flow monitoring. We dive into the three main types of financial management decisions—working capital management, capital budgeting, and capital structure—with practical examples of each. The discussion also covers how modern organizations leverage AI and automation for real-time insights, with a case study of Landmark Retail's transformation that reduced employee time investment by 75% while improving governance and transparency across their global operations.

Find more information at https://www.ibm.com/think/topics/financial-management

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Narrated by Teaganne Finn

What is scenario planning?

Thu, 02 Apr 2026 09:00:02 GMT

This episode of Techsplainers explores scenario planning, a strategic approach where organizations create multiple potential future scenarios to prepare for uncertainties. Building on previous episodes about enterprise performance management, extended planning and analysis, and AI in financial planning, we examine how scenario planning helps businesses proactively manage risk through a five-step process: defining objectives, analyzing outcomes, recognizing influential factors, evaluating scenarios, and developing indicators. The episode highlights Shell's pioneering use of scenario planning, explains different methodologies (quantitative, normative, exploratory, operational, and strategic), and showcases applications across finance, supply chain, sustainability, and sales. We conclude with best practices including team building, data management, simplification, and leveraging advanced technology to gain competitive advantage in an uncertain business environment.

Find more information at https://www.ibm.com/think/topics/scenario-planning

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Narrated by Teaganne Finn

AI in financial planning & analysis

Wed, 01 Apr 2026 09:00:00 GMT

This episode of Techsplainers explores how artificial intelligence is transforming Financial Planning and Analysis (FP&A), building on our previous discussions of Enterprise Performance Management and Extended Planning. We examine how AI-powered tools—particularly generative AI applications—are automating routine tasks while providing deeper financial insights through machine learning, natural language processing, and predictive analytics. The episode details four key applications: predictive forecasting that adapts to market changes, multi-variable scenario planning for risk management, anomaly detection for financial integrity, and productivity enhancements like automated report generation. We also discuss how AI is reshaping finance roles, requiring new skill sets, and supporting sustainable practices. The conversation concludes with five implementation steps and a vision of how AI enables the integrated business planning approach needed for finance teams to evolve from reactive reporters to proactive strategic partners.

Find more information at https://www.ibm.com/think/topics/ai-in-financial-planning-and-analysis

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Narrated by Teaganne Finn

What is extended planning analysis (xP&A)

Tue, 31 Mar 2026 09:00:00 GMT

This episode of Techsplainers explores Extended Planning and Analysis (xP&A), building on our previous discussion of Enterprise Performance Management. We examine how xP&A expands traditional financial planning across the entire organization, breaking down data silos by connecting financial and operational planning. The episode covers six key features of effective xP&A solutions—from automation and AI-driven insights to scenario planning and scalability—and outlines a seven-step implementation process. We also highlight the benefits of this approach, including centralized data, enhanced forecasting, better collaboration, and increased visibility. The discussion includes a real-world success story from Landmark Retail, which achieved a 75% reduction in budgeting time, before concluding with five emerging trends shaping the future of xP&A, from AI advancements to increased focus on sustainability and compliance.

Find more information at https://www.ibm.com/think/topics/extended-planning-analysis

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Narrated by Teaganne Finn

What is enterprise performance management (EPM)?

Mon, 30 Mar 2026 10:00:01 GMT

This episode of Techsplainers explores Enterprise Performance Management (EPM), the business practices and tools organizations use to plan, forecast, and improve overall performance. We examine how EPM differs from ERP systems, trace its evolution from manual spreadsheets to AI-powered platforms, and break down key features like unified dashboards, predictive analytics, and scenario modeling. The episode also covers the tangible benefits of EPM implementation—from cost insights to streamlined account reconciliation—along with best practices for successful deployment. Finally, we look at the AI-driven future of EPM through IBM's own transformation story, which achieved 95% fewer tools and 40% productivity gains through an integrated approach powered by IBM Cognos Analytics and watsonx.

Find more information at https://www.ibm.com/think/topics/enterprise-performance-management

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Narrated by Teaganne Finn

Modern ETL: The brainstem of enterprise AI

Fri, 27 Mar 2026 10:00:01 GMT

This episode of Techsplainers explores modern ETL (Extract, Transform, Load), examining how traditional data integration has evolved into a more agile, cloud-native approach. Building on our previous discussion of ETL versus ELT, we detail six key characteristics that define modern ETL: cloud-based architecture, real-time ingestion capabilities, unified data sources, automated orchestration, scalability, and AI-ready pipelines. The podcast covers popular platforms powering these solutions including Snowflake, BigQuery, and Apache Kafka, along with implementation strategies and emerging trends like low-code tools and serverless integration. Through a retail flash sale example, we demonstrate how modern ETL enables businesses to process data in real-time, transforming potential operational challenges into competitive advantages by ensuring information flows instantly to where it's needed most.

Find more information at https://www.ibm.com/think/insights/modern-etl

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Narrated by Matt Finio

What is change data capture?

Thu, 26 Mar 2026 10:00:00 GMT

This episode of Techsplainers explores Change Data Capture (CDC), a powerful technique for identifying and synchronizing database changes across multiple systems in real-time. Host Matt Finio explains how CDC works by monitoring databases and transferring only the modified data—insertions, updates, or deletions—to target systems like data warehouses, data lakes, or streaming platforms. The discussion covers three primary CDC methods: log-based (monitoring transaction logs), timestamp-based (using modification timestamps), and trigger-based (executing stored procedures when changes occur). The episode highlights CDC's significant benefits, including enabling real-time analytics, facilitating cloud migrations, optimizing ETL processes, and improving AI performance through continuously updated data. Practical applications span various industries—from detecting fraudulent financial transactions and processing IoT device data to managing inventory and ensuring regulatory compliance.

Find more information at https://www.ibm.com/think/topics/change-data-capture

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Narrated by Matt Finio

What is real-time data integration?

Wed, 25 Mar 2026 10:00:01 GMT

This episode of Techsplainers explores real-time data integration—the process of capturing and processing data from multiple sources the instant it becomes available. Host Matt Finio explains why millisecond-level data access has become critical in today's business landscape, where the global datasphere will reach 394 zettabytes by 2028 and generative AI demands fresh, high-quality information. The episode distinguishes between streaming data (continuous flows from IoT devices or financial markets) and event data (specific changes like transactions or threshold triggers), while examining four key integration methods: Stream Data Integration (SDI), Change Data Capture (CDC), Application Integration, and Data Virtualization. Listeners will discover practical applications in operational intelligence, customer hyper-personalization, fraud detection, and artificial intelligence—all requiring the immediate insights that only real-time integration can provide.

Find more information at https://www.ibm.com/think/topics/real-time-data-integration

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Narrated by Matt Finio

ELT vs. ETL: What's the difference?

Tue, 24 Mar 2026 10:00:04 GMT

This episode of Techsplainers compares two fundamental data integration approaches: ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). We break down how these similar-sounding acronyms represent significantly different data processing workflows. ETL, the traditional approach dating back to the 1970s, extracts data from sources, transforms it in a staging area, and then loads clean data into the target system—prioritizing data quality and structure. In contrast, ELT first loads raw data directly into the target system before transforming it there, leveraging the processing power of modern data warehouses for faster implementation and real-time capabilities. The episode explores the benefits and ideal use cases for each method, helping listeners understand when to apply ETL for carefully synchronized data integration versus when ELT might better serve high-volume, real-time data needs.

Find more information at https://www.ibm.com/think/topics/elt-vs-etl

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Narrated by Matt Finio

What is data integration?

Mon, 23 Mar 2026 10:00:00 GMT

This episode of Techsplainers introduces data integration, the essential process of combining information from disparate sources into a unified, usable format. Host Matt Finio explains the step-by-step integration process, from data source identification through extraction, mapping, validation, transformation, and loading. We explore different integration approaches including ETL, ELT, real-time integration, and data virtualization, while highlighting key benefits like reduced data silos, improved quality, increased efficiency, and faster insights. The episode also examines practical use cases across industries, from building data warehouses to creating comprehensive customer views and processing IoT data. Whether you're dealing with scattered spreadsheets or complex enterprise systems, understanding data integration is crucial for making informed business decisions.

Find more information at https://www.ibm.com/think/topics/data-integration

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Narrated by Matt Finio

What is identity threat detection and response (ITDR)?

Fri, 20 Mar 2026 10:00:00 GMT

This episode of Techsplainers explores identity threat detection and response (ITDR) tools— proactive cybersecurity solutions that discover and remediate identity-based threats and vulnerabilities.

Building on our previous discussions of digital identities, nonhuman identities and identity fabric, we explain how ITDR protects identity infrastructure. We examine how ITDR works through data collection, continuous monitoring and automated incident response, addressing threats like privilege escalation, lateral movement and phishing attacks.

The episode highlights why ITDR has become essential in today's complex hybrid cloud environments where traditional network boundaries have dissolved.

Find more information at https://www.ibm.com/think/topics/identity-threat-detection-response

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For more on cybersecurity, listen to the Security Intelligence podcast at https://ibm.biz/Bdp2aH

Narrated by Bryan Clark

What is an identity fabric?

Thu, 19 Mar 2026 10:00:00 GMT

This episode of Techsplainers explores identity fabric: the architectural framework that integrates multiple identity and access management systems into a unified whole.

Building on our previous discussions of identity security, digital identities and nonhuman identities, we examine how identity fabrics help organizations manage identities across increasingly complex hybrid and multicloud environments.

The podcast explains how identity silos create security gaps that malicious actors exploit, and we break down the key components of an identity fabric, including identity orchestration, threat detection and response, directory synchronization, risk-based authentication and privileged access management. Listeners learn how identity fabrics enable consistent security policies, simplified compliance and support for modern approaches like zero trust architecture.

Find more information at https://www.ibm.com/think/topics/identity-fabric

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For more on cybersecurity, listen to the Security Intelligence podcast at https://ibm.biz/Bdp2aH

Narrated by Bryan Clark

What is nonhuman identity?

Wed, 18 Mar 2026 10:00:02 GMT

This episode of Techsplainers explores nonhuman identities—the digital identities associated with bots, AI agents, applications, services and devices.

Building on our previous episodes about identity security and digital identities, we examine why nonhuman identities are critical for automation yet pose unique security challenges. The podcast breaks down the main types of nonhuman identities and explains how cloud computing, DevOps practices and AI advances are driving an explosion of nonhuman identities in enterprise networks.

We also address key security challenges including overprivileging, credential theft and lack of visibility, with special attention to the unique risks posed by AI agents.

The episode concludes with essential security strategies for managing nonhuman identities, from continuous monitoring and lifecycle management to zero trust principles and separation of duties.

Find more information at https://www.ibm.com/think/topics/non-human-identity

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Narrated by Bryan Clark

What is digital identity?

Tue, 17 Mar 2026 10:00:00 GMT

This episode of Techsplainers explores the concept of digital identity—the collection of attributes and information that uniquely identifies users, machines and other entities in IT systems.

Building on our previous discussion of identity security, we examine how digital identities serve as the foundation for authentication and authorization processes.

The episode breaks down different types of digital identities (human, machine, and federated), explains their critical role in identity and access management (IAM) systems and highlights benefits like enhanced cybersecurity and regulatory compliance.

With account theft involved in 32% of cyber incidents according to IBM's X-Force Threat Intelligence Index, understanding how digital identities function has never been more important for overall security strategy.

Find more information at https://www.ibm.com/think/topics/digital-identity

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Narrated by Bryan Clark

What is identity security?

Mon, 16 Mar 2026 10:00:03 GMT

This episode of Techsplainers introduces identity security, explaining how it's become the crucial paradigm for protecting modern distributed environments. As traditional network perimeters dissolve with cloud adoption and remote work, digital identities have become the new security boundary. We explore why compromised credentials are a leading breach vector and how identity security addresses this risk.

The episode breaks down the key components of identity security—from IAM and identity governance to privileged access management and identity threat detection and response—and provides a practical five-step implementation playbook. We also discuss architectural approaches like identity fabrics and examine real-world challenges of balancing security with user experience. Whether you're strengthening your organization's security posture or preparing for zero trust, this episode offers essential insights into making identity your first line of defense.

Find more information at https://www.ibm.com/think/topics/identity-security

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For more on cybersecurity, listen to the Security Intelligence podcast at https://ibm.biz/Bdp2aH

Narrated by Bryan Clark

What is asset tracking?

Fri, 13 Mar 2026 10:00:00 GMT

This episode of Techsplainers explores asset tracking, the practice of monitoring an organization's physical assets to maximize efficiency and minimize loss. We examine how asset tracking fits within broader asset management strategies and why visibility into assets can improve productivity while reducing maintenance costs. The episode covers key tracking technologies including barcodes, RFID tags, Bluetooth Low Energy, GPS systems, and LPWAN networks, highlighting their different capabilities and use cases. We also discuss the evolution from manual tracking methods to sophisticated enterprise asset management systems that leverage AI, IoT, and predictive analytics to optimize the entire asset lifecycle. Whether managing vehicle fleets, IT equipment, or manufacturing machinery, effective asset tracking provides the visibility and control organizations need for operational excellence.

Find more information at https://www.ibm.com/think/topics/asset-tracking

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Narrated by Ian Smalley

What is a CMMS?

Thu, 12 Mar 2026 10:00:02 GMT

This episode of Techsplainers explores Computerized Maintenance Management Systems (CMMS), the software solutions that help organizations automate and enhance maintenance operations. We examine how modern CMMS platforms leverage AI, machine learning, and IoT to transform maintenance from reactive to proactive, extending asset lifecycles and reducing costly downtime. The discussion covers core CMMS functions including work order management, inventory tracking, preventive maintenance scheduling, and data visualization through dashboards. We highlight key benefits like reduced downtime, enhanced maintenance processes, cost savings, and improved decision-making capabilities. The episode also explores CMMS applications across manufacturing, healthcare, facility management, energy, and government sectors, while looking ahead to innovations like machine learning-enhanced predictive maintenance, augmented reality for repairs, cloud-based solutions for smaller businesses, and the integration of generative AI for physical asset management.

Find more information at https://www.ibm.com/think/topics/what-is-a-cmms

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Narrated by Ian Smalley

What is preventive maintenance?

Wed, 11 Mar 2026 10:00:01 GMT

This episode of Techsplainers explores the five types of preventive maintenance strategies that help organizations avoid costly equipment failures. We dive into usage-based, time-based, condition-based, predictive, and prescriptive maintenance approaches, highlighting how each provides unique benefits for different operational priorities. The discussion covers major advantages of preventive maintenance including extended asset lifespans, significant cost savings, reduced downtime, improved workplace safety, and enhanced sustainability. We also examine how modern technologies like IoT sensors, AI analytics, and computerized maintenance management systems are transforming maintenance practices, enabling more sophisticated approaches that optimize equipment performance while minimizing costs. This episode builds on our previous discussion of preventive maintenance fundamentals to provide a comprehensive understanding of this essential facilities management practice.

Find more information at https://www.ibm.com/think/topics/what-is-preventive-maintenance

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Narrated by Ian Smalley

What is enterprise asset management (EAM)?

Tue, 10 Mar 2026 10:00:00 GMT

This episode of Techsplainers explores Energy Asset Management (EAM), the specialized practice of maintaining, monitoring, and optimizing energy infrastructure throughout its entire lifecycle. We examine how organizations track and manage critical assets like power plants, electrical grids, and renewable installations to extend equipment lifespan, increase efficiency, and reduce operational costs. The discussion covers the five key elements of effective EAM: asset tracking, performance management, data management, risk management, and lifecycle management. We highlight the major challenges facing the energy sector, including growing demand (4% annually), aging infrastructure, sustainability goals, security threats, and market volatility. The episode also provides historical context on energy's critical importance—from deciding the outcome of World War II to fueling modern geopolitical conflicts—and explains how advanced technologies like IoT sensors and AI are transforming asset management in our increasingly electrified world.

Find more information at https://www.ibm.com/think/topics/enterprise-asset-management

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Narrated by Ian Smalley

What is asset lifecycle management (ALM)?

Mon, 09 Mar 2026 10:00:01 GMT

This episode of Techsplainers explores asset lifecycle management (ALM), the comprehensive process that helps organizations maximize the value and lifespan of their valuable resources. We break down the four key stages of ALM—planning, procurement, usage, and disposal—and explain how modern technologies like IoT sensors, digital twins, and enterprise asset management systems enable real-time monitoring and preventive maintenance. The discussion covers various asset tracking methods including RFID tags, GPS monitoring, and QR codes, while highlighting the major benefits of effective ALM: extended asset lifespan, reduced costs, minimized downtime, and increased operational efficiency. We also examine how cutting-edge technologies like artificial intelligence, augmented reality, and robotics are transforming asset management by enabling predictive maintenance, remote inspections, and enhanced worker capabilities.

Find more information at https://www.ibm.com/think/topics/asset-lifecycle-management

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Narrated by Ian Smalley

What is dark data?

Fri, 06 Mar 2026 11:00:00 GMT

This episode of Techsplainers explores dark data - the information assets organizations accumulate but fail to use for analytics or business purposes. We examine how prevalent this issue is, with surveys showing 60% of organizations reporting half or more of their data remains unused. The discussion covers why dark data accumulates (inexpensive storage, "just in case" mentality), the various causes (lack of awareness, data silos, poor governance), and the three types of dark data (structured, unstructured, and semi-structured.) We also detail the substantial costs beyond storage, including liability, missed opportunities, inefficiency, and risks. The episode concludes with practical strategies for managing dark data through improved data governance, breaking down silos, and leveraging AI/ML tools to uncover valuable insights from previously hidden information.

Find more information at https://www.ibm.biz/techsplainers-podcast

Narrated by Mimi Sun Longo

What is data observability?

Thu, 05 Mar 2026 11:00:00 GMT

This episode of Techsplainers introduces data observability, the practice of monitoring data health across an organization. We explore why data observability matters, with 80% of executives distrusting their data and companies like Unity Software losing $110 million due to bad data. The discussion covers the three stages of the DataOps cycle (detection, awareness, and iteration), the five pillars of data observability (freshness, distribution, volume, schema, and lineage), and how data observability differs from data quality and governance. We also examine the hierarchy of data observability and provide a practical roadmap for implementing a data observability framework to ensure reliable, trustworthy data for better business decisions.

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Narrated by Mimi Sun Longo

What is data reliability?

Wed, 04 Mar 2026 11:00:02 GMT

This episode of Techsplainers explains data reliability—the completeness and accuracy of data across time and sources. We explore how reliability is measured through validity, completeness, and uniqueness, and distinguish it from related concepts like data quality and validity. The discussion covers common challenges organizations face with data reliability, from collection methods and human error to changing sources and duplication issues. We provide practical steps for ensuring reliable data, including standardized collection, proper training, regular audits, and strong governance. Finally, we examine how data observability transforms reliability management by enabling real-time issue identification and resolution before bad data impacts decision-making. For organizations seeking competitive advantage through data-driven decisions, establishing robust reliability practices is no longer optional but essential.

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Narrated by Mimi Sun Longo

What are data quality dimensions?

Tue, 03 Mar 2026 11:00:00 GMT

This episode of Techsplainers explores data quality dimensions – which provide the structured framework for measuring and evaluating data trustworthiness. We explain the six core dimensions: accuracy (correctness of data), completeness (presence of all required values), consistency (uniformity across systems), timeliness (currency of information), validity (conformity to rules), and uniqueness (absence of duplicates). The episode delves into why these dimensions matter – with poor data quality costing organizations millions annually – and outlines a three-step implementation process: assessment, measurement, and continuous improvement. We also highlight key benefits, including better decision-making, regulatory compliance, workflow optimization, customer satisfaction, and risk reduction. These dimensions provide the foundation for trusted data that powers reliable insights and automation.

Find more information at https://www.ibm.biz/techsplainers-podcast

Narrated by Mimi Sun Longo

What is data quality?

Mon, 02 Mar 2026 11:00:02 GMT

This episode of Techsplainers explores data quality—the measure of how well datasets meet criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose. We examine the seven key dimensions of data quality and explain how they impact business decisions, processes, and customer satisfaction. The discussion highlights the critical distinction between data quality, data integrity, and data profiling, while explaining why poor quality data costs organizations an average of USD 12.9 million annually (according to Gartner research). We also explore the growing importance of data quality for AI and machine learning systems, where the "garbage in, garbage out" principle directly affects outcomes. Whether you're in marketing, supply chain management, or healthcare, understanding data quality fundamentals is essential for making reliable, data-driven decisions.

Find more information at https://www.ibm.biz/techsplainers-podcast

Narrated by Mimi Sun Longo

What code LLMs mean for the future of software development

Fri, 27 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores Code LLMs, specialized AI models that are transforming software development by understanding, generating, and explaining code. We examine how these models differ from general-purpose LLMs through their extensive training on programming languages, documentation, and code repositories, giving them a deeper understanding of software development concepts and patterns. The discussion covers how Code LLMs work through transformer architecture and reinforcement learning from human feedback, as well as their substantial benefits for developers, including increased productivity, enhanced learning opportunities, improved documentation, and democratized access to coding. We highlight practical applications such as code generation, bug fixing, refactoring, and test creation, while also addressing important limitations including potential security vulnerabilities, challenges with complex code, intellectual property concerns, and the risk of over-reliance. The episode concludes with insights into how these tools are reshaping the development landscape, with developers increasingly shifting toward a supervisory role focused on architecture and design decisions.

Find more information at https://www.ibm.biz/techsplainers-podcast

Narrated by Erika Russi

What is COBOL modernization?

Thu, 26 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores COBOL modernization strategies for organizations managing critical legacy systems written in this 60-year-old programming language. We examine why COBOL remains vital today—processing 95% of ATM transactions and 90% of global financial transactions—while highlighting the pressing challenges of maintaining these systems amid a growing skills gap, integration difficulties, and scalability concerns. The discussion covers various modernization approaches, from less disruptive strategies like rehosting and refactoring to more transformative methods like rearchitecting and replacement. We share key best practices for successful modernization, including thorough application documentation, business-value prioritization, preservation of essential business logic, and incremental implementation. The episode emphasizes that effective COBOL modernization isn't about wholesale replacement but thoughtful evolution that preserves decades of refined business logic while enabling future innovation.

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Narrated by Erika Russi

What is legacy code?

Wed, 25 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores legacy code—software that continues to deliver value despite being inherited, outdated, or difficult to modify. We examine how these systems, though challenging, often represent significant business value and intellectual property, running critical operations across industries from banking to government. The discussion covers the main challenges legacy code presents: knowledge gaps from departed developers, accumulated technical debt, outdated technologies, poor documentation, and integration difficulties. We explore practical approaches to managing legacy systems, including incremental modernization through refactoring, the service wrapper approach, complete rewrites when necessary, and hybrid strategies. The episode concludes with best practices for working effectively with legacy code, emphasizing the importance of documentation, testing, incremental changes, and maintaining respect for systems that have successfully powered business operations for years.

Find more information at https://www.ibm.biz/techsplainers-podcast

Narrated by Erika Russi

AI code documentation: Benefits and top tips

Tue, 24 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores how artificial intelligence is transforming one of developers' most dreaded tasks: code documentation. We examine how AI-powered tools can automatically generate human-readable explanations of code, bringing consistency, efficiency, and improved maintenance to software development teams. The discussion covers key benefits of AI documentation, including time savings, comprehensive coverage, and knowledge preservation, while providing practical tips for effectively implementing these tools. We highlight the importance of using AI as a starting point rather than a complete solution, establishing clear documentation standards, and integrating tools directly into development workflows. The episode also addresses current limitations of AI documentation and the evolving landscape of tools that are making documentation more interactive and contextually relevant. Whether you're a seasoned developer or new to programming, this episode offers valuable insights into how AI is solving one of software development's persistent challenges.

Find more information at https://www.ibm.biz/techsplainers-podcast

Narrated by Erika Russi

What is code refactoring?

Mon, 23 Feb 2026 11:00:01 GMT

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Narrated by Erika Russi

iPaaS examples and use cases

Fri, 20 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores Integration Platform as a Service (iPaaS), a cloud-native solution that connects disparate business systems and applications. We examine how iPaaS differs from traditional middleware approaches and note key use cases including data synchronization, streamlined automations, AI-powered optimization, governance, and B2B integration.

The episode highlights industry-specific applications in healthcare, banking, and manufacturing, along with significant benefits like operational efficiency, improved accessibility through no-code tools, and enhanced security. We also discuss future trends, including how iPaaS helps combat SaaS sprawl and leverage unstructured data for AI training and autonomous agent development. With organizations achieving up to 345% ROI after implementation, iPaaS is becoming an essential component of modern digital transformation strategies.

Find more information at https://www.ibm.biz/techsplainers-podcast

Narrated by Dan Segal

What is iPaaS (integration platform as a service)?

Thu, 19 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores Integration Platform as a Service (iPaaS), a cloud-based solution that connects applications, systems, and data sources across diverse IT environments. We explain how iPaaS emerged to address the challenge of SaaS sprawl—where organizations use hundreds of different applications—and how it offers pre-built connectors, low-code interfaces, and centralized monitoring. The episode walks through how iPaaS works, how it compares to traditional approaches like Enterprise Service Buses and API management, and its various use cases from app-to-app integration to AI-powered workflows. Listeners will learn about the benefits of iPaaS, including reduced complexity, lower costs, improved data accessibility, and increased scalability, all of which help organizations streamline operations and break down data silos in increasingly complex IT ecosystems.

Find more information at https://www.ibm.biz/techsplainers-podcast

Narrated by Dan Segal

What is enterprise application integration?

Wed, 18 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores enterprise application integration (EAI), the crucial technology that connects disparate business systems and software applications across organizations. We explain how EAI works through both synchronous and asynchronous processing methods, and breaks down five key architectural patterns including point-to-point, hub and spoke, service-oriented architecture, microservices, and event-driven approaches.

The discussion covers how EAI compares to related technologies like iPaaS, EDI, and ERP systems, while highlighting major benefits including legacy system integration, elimination of data silos, and increased business agility. The episode also addresses challenges like security vulnerabilities, migration issues, and performance limitations, before concluding with a look at how modern innovations like AI-powered integration and low-code tools are transforming EAI for today's enterprise environments.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Dan Segal

What is EDI integration?

Tue, 17 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores EDI integration, the critical process that connects electronic data interchange platforms with an organization's internal systems. We examine how EDI integration creates automated data highways that eliminate manual processes by transforming standardized digital documents like purchase orders and shipping notices between different systems.

The discussion covers the key benefits of EDI integration, including operational efficiency gains, cost reductions and improved data quality, along with various architectural patterns from centralized hub-and-spoke to hybrid API-EDI approaches. We also explore connectivity considerations, from direct point-to-point integration to value-added networks, and look at emerging trends like AI-enhanced integration, deeper ERP connectivity, and self-service options that are making EDI more accessible across industries.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Dan Segal

What is electronic data interchange (EDI)?

Mon, 16 Feb 2026 11:00:01 GMT

This episode of Techsplainers explores electronic data interchange (EDI), the standardized system for computer-to-computer exchange of business documents like invoices and purchase orders. We explain how EDI works through specialized translator software and transmission protocols, and details the major standards including ANSI ASC X12, HIPAA, and EDIFACT. The discussion covers EDI's substantial benefits: time and cost savings, error reduction, and improved business analysis capabilities. The episode also examines how EDI is evolving through AI integration for fraud detection and autonomous processing, while comparing EDI with APIs to show how these technologies complement each other for different business needs. Despite being decades old, EDI continues to process trillions of dollars in commerce annually across major industries worldwide.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Dan Segal

What are hierarchical AI agents?

Fri, 13 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores hierarchical AI agents, sophisticated systems where multiple AI components work together in a structured, tiered fashion to tackle complex problems. We examine the three levels of agents—high-level strategic planners, mid-level coordinators, and specialized low-level executors—and how they communicate to accomplish goals efficiently. The episode details key features like agent hierarchy, task decomposition, specialization, and feedback-driven coordination that make these systems effective. We also investigate real-world applications in supply chain management, manufacturing, cybersecurity, and autonomous vehicles, while discussing the benefits of modularity, efficiency, scalability, and fault tolerance alongside challenges like complexity, rigidity, and communication bottlenecks that organizations must navigate when implementing these powerful AI systems.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Matt Finio

What is a utility-based agent?

Thu, 12 Feb 2026 11:00:03 GMT

This episode of Techsplainers explores utility-based agents, sophisticated AI systems that use mathematical utility functions to make optimal decisions by weighing multiple competing objectives. We examine the five key components of these agents: utility functions, sensors, internal models, action selection mechanisms, and actuators. The episode walks through their decision-making workflow and highlights applications in smart homes, self-driving cars, healthcare, and e-commerce. While utility-based agents offer advantages in adaptability, flexibility, and reliability over simpler AI systems, they also present challenges in computational requirements and the ethical considerations of translating human values into mathematical formulas. Understanding these advanced agents provides insight into how AI can make complex trade-offs in uncertain environments.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Matt Finio

What is a goal-based agent?

Wed, 11 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores goal-based agents, which sit in the middle of the AI agent complexity hierarchy. These agents go beyond simple reflexes by incorporating planning capabilities that consider future states when making decisions. The podcast explains how goal-based agents work through four stages: goal definition, planning, action selection, and execution. We examine a real-world example of warehouse automation robots that plan efficient paths rather than simply reacting to obstacles. The episode also discusses when to use goal-based agents versus more complex types like utility-based agents, and how different agent types can work together in multi-agent systems, as illustrated through a healthcare example where five specialized agents handle different aspects of hospital management based on their complexity requirements.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Matt Finio

What is a model-based reflex agent?

Tue, 10 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores model-based reflex agents, a type of AI that makes decisions using both current input and an internal model of its environment. Unlike simple reflex agents that only react to immediate stimuli, model-based agents maintain memory of past perceptions and can predict how their actions might affect their surroundings. We examine the four key components—sensors, internal model, reasoning component, and actuators—and the four-stage behavioral loop these agents follow: sensing, internal modeling, decision-making, and action. The discussion highlights use cases in autonomous vehicles, robotics, gaming, and enterprise automation, while comparing them with other agent types including goal-based, utility-based, learning, and hierarchical agents. Finally, we address the limitations of model-based reflex agents, from computational requirements to their inability to adapt their rulesets over time.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Matt Finio

What is a simple reflex agent?

Mon, 09 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores simple reflex agents, the most basic type of AI agents that operate on straightforward "if-this-then-that" logic. We examine how these agents directly respond to their environment based on predefined rules, without considering past experiences or future consequences. The discussion covers real-world examples like thermostats, factory safety systems, and quality control monitors, highlighting the benefits of these agents: computational efficiency, instantaneous response times, predictable behavior, and cost-effectiveness. We also address their limitations, including lack of memory, inability to handle uncertainty, and inflexibility when facing new situations. Finally, we demonstrate how simple reflex agents can work effectively as part of multi-agent systems, providing critical safety backstops while more sophisticated agents handle complex decision-making.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Matt Finio

What is cloud storage?

Fri, 06 Feb 2026 11:00:01 GMT

This episode of Techsplainers explores cloud storage—a service that allows data and files to be stored offsite by third-party providers and accessed via the internet or private networks. We examine how cloud storage works through virtual servers in massive data centers, with data replicated across multiple machines for redundancy. The discussion covers four different cloud storage environments (public, private, hybrid, and multicloud) and three main types of storage solutions (file, block, and object). We also highlight the significant benefits of cloud storage, including offsite management, fast implementation, cost-effectiveness, and virtually unlimited scalability. Security considerations, compliance tools, and pricing models are explained, along with common use cases ranging from team collaboration to AI and data analytics. With the cloud storage market projected to grow from $108.7 billion in 2023 to $665 billion by 2032, this technology continues to transform how organizations of all sizes manage their ever-increasing data volumes.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Daniela Baez

What is multicloud?

Thu, 05 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores the concept of multicloud—the strategic use of cloud services from more than one provider. We examine how organizations leverage multicloud to optimize performance, control costs, and avoid vendor lock-in while maintaining flexibility to adopt the best technologies as they emerge. The discussion covers the differences between simple SaaS usage and more complex enterprise multicloud scenarios using PaaS and IaaS from major providers like AWS, Google Cloud, IBM Cloud, and Microsoft Azure. We also address the challenges of multicloud management and how organizations use centralized platforms with AI capabilities to maintain consistent security, compliance, and operational efficiency across diverse cloud environments. Finally, we clarify the relationship between multicloud and hybrid cloud, explaining how most enterprise environments today are actually hybrid multiclouds that combine the benefits of both approaches for maximum business value.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Daniela Baez

What is virtualization?

Wed, 04 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores virtualization—the foundational technology that enables the creation of multiple virtual environments from a single physical machine. We trace its evolution from IBM's early experiments in 1964 to today's $85 billion industry powering cloud computing worldwide. The episode explains how virtualization works through hypervisors—software that creates and manages virtual machines—and dives into its many benefits, including resource efficiency, easier management, minimal downtime, and cost savings. We also explore the various types of virtualization beyond servers, including desktop, network, storage, and application virtualization, while comparing traditional VM-based virtualization with newer containerization approaches. Whether you're running a massive data center or simply want to run multiple operating systems on your laptop, this episode provides a comprehensive overview of this essential technology that makes modern computing more efficient, flexible, and resilient.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Daniela Baez

What is cloud infrastructure?

Tue, 03 Feb 2026 11:00:00 GMT

This episode of Techsplainers explores cloud infrastructure—the foundational hardware and software components that make cloud computing possible. We dive into the four key elements of cloud infrastructure: servers (both physical and virtual), storage solutions for various data types, networking components that enable communication between resources, and management software that ties everything together. The discussion covers virtualization technology and hypervisors that create multiple virtual machines from physical hardware, as well as modern cloud-native approaches using containers and microservices. We also examine different deployment models, including public, private, hybrid, and multicloud, along with service delivery options like IaaS, PaaS, SaaS, and serverless computing. Finally, we highlight the significant benefits cloud infrastructure offers: reliability through redundancy, agility for rapid deployment, elasticity to handle variable workloads, cost optimization through pay-as-you-go models, and robust disaster recovery capabilities.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Daniela Baez

What is hybrid cloud?

Mon, 02 Feb 2026 11:00:00 GMT

This episode of Techsplainers introduces hybrid cloud—a flexible IT approach that combines public cloud, private cloud, and on-premises infrastructure into a unified environment. We explore the core components of hybrid cloud architecture including network connectivity, virtualization, containerization, and management platforms, while tracing its evolution from traditional physical connections to modern workload portability across environments. The discussion highlights how businesses leverage hybrid multicloud to improve developer productivity, optimize infrastructure spending, enhance security compliance, and accelerate innovation. You'll learn about real-world applications, including regulatory compliance, scalability, legacy app enhancement, and disaster recovery. We also examine how hybrid cloud is enabling next-generation technologies like generative AI, with insights into the explosive market growth projected to reach $558.6 billion by 2032.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Daniela Baez

What is AI agent communication and AI agent learning?

Fri, 30 Jan 2026 11:00:01 GMT

This episode of Techsplainers explores two fundamental capabilities of AI agents: communication and learning. We examine how AI agents exchange information with each other and humans, including agent-to-agent protocols like KQML and FIPA-ACL. We also look at the challenges they face with standardization, ambiguity, latency, and security. The discussion then shifts to how agents learn and improve over time, covering supervised learning with labeled data, unsupervised learning that finds patterns without human oversight, and reinforcement learning through trial and error with rewards. We also explore continuous learning, where agents adapt to new information without forgetting previous knowledge, and how these capabilities combine in multi-agent systems to create collaborative intelligence that can solve complex problems across various industries.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Selma Pacheco Jimenez

What is tool calling?

Thu, 29 Jan 2026 11:00:01 GMT

This episode of Techsplainers explores the concept of tool calling in artificial intelligence, explaining how it enables AI models to interact with external tools, APIs, and systems beyond their native capabilities. We walk through how tool calling works, from recognizing when external assistance is needed to selecting appropriate tools and processing responses. The episode highlights the powerful combination of tool calling with retrieval augmented generation (RAG) and examines real-world applications in information retrieval, code execution, process automation, IoT device control, and personalized recommendations. By bridging the gap between AI reasoning and action, tool calling is transforming passive AI assistants into proactive digital agents capable of completing complex, multi-step tasks through dynamic access to external resources.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Selma Pacheco Jimenez

What is AI agent memory and agentic reasoning?

Wed, 28 Jan 2026 11:00:00 GMT

This episode of Techsplainers explores the crucial components of AI agent memory and agentic reasoning. We delve into how AI agents store and recall information through different memory types—including short-term, long-term, episodic, semantic, and procedural memory—and how frameworks like LangChain and LangGraph implement these capabilities. The episode also examines various reasoning paradigms that power AI decision-making, from simple conditional logic to sophisticated approaches like ReAct, ReWOO, and multiagent reasoning. By understanding these complementary components, listeners gain insight into how modern AI systems transform from passive models into intelligent agents that can maintain context across interactions, learn from past experiences, and make autonomous decisions to achieve complex goals.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Selma Pacheco Jimenez

What is AI agent perception and AI agent planning?

Tue, 27 Jan 2026 11:00:03 GMT

This episode of Techsplainers explores two fundamental capabilities of AI agents: perception and planning. We examine how agents perceive their environment through visual, auditory, textual, environmental, and predictive means, breaking down the four-step perception process from sensory input collection to decision-making. The discussion then shifts to how agents use this perceived information to plan their actions, covering goal definition, state representation, action sequencing, and optimization techniques like heuristic search and reinforcement learning. We also explore how different planning frameworks operate and how planning becomes more complex in multi-agent systems where coordination is essential. By understanding these interconnected components, listeners gain insight into what makes AI agents truly intelligent and capable of operating autonomously in complex environments.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Selma Pacheco Jimenez

What are the components of AI agents?

Mon, 26 Jan 2026 11:00:00 GMT

This episode of Techsplainers explores the essential components that make AI agents function, breaking down the "brain" of these intelligent systems. We examine how perception enables agents to understand their environment through various inputs, while planning allows them to map out complex task sequences. The discussion covers memory systems that provide both short-term context and long-term learning, reasoning modules that power decision-making, and action capabilities that execute tasks through tool calling. We also investigate how communication facilitates interaction with humans and other agents and how learning capabilities enable continuous improvement over time. By understanding these interconnected components, listeners gain insight into how AI agents operate across various industries and applications.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Selma Pacheco Jimenez

What is reinforcement learning?

Fri, 23 Jan 2026 11:00:00 GMT

This episode of Techsplainers explores reinforcement learning, a machine learning approach where AI agents learn to make decisions through trial and error by interacting with their environment. Unlike supervised learning's labeled data or unsupervised learning's pattern discovery, reinforcement learning teaches through reward signals—similar to how we might train a pet with treats. The episode breaks down the core components of this approach, including the Markov decision process framework, the critical exploration-exploitation tradeoff, and key elements like policy, reward signals, and value functions. We also examine major reinforcement learning methods, such as dynamic programming, Monte Carlo techniques, and temporal difference learning. The discussion covers real-world applications in robotics and natural language processing, highlighting both impressive successes like AlphaGo and ongoing challenges in creating effective learning environments with meaningful reward systems.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Anna Gutowska

What is semi-supervised learning?

Thu, 22 Jan 2026 11:00:01 GMT

This episode of Techsplainers explores semi-supervised learning, the machine learning approach that bridges supervised and unsupervised techniques by combining small amounts of labeled data with larger volumes of unlabeled information. The episode explains why this method is crucial when obtaining fully labeled datasets is prohibitively expensive or time-consuming, such as in medical imaging or genetic analysis. We break down the key assumptions that make semi-supervised learning work—including the cluster assumption, smoothness assumption, low-density assumption, and manifold assumption—and how they help models generalize beyond limited labeled examples. The discussion covers major implementation approaches, including transductive methods like label propagation, and inductive methods like wrapper techniques, unsupervised pre-processing, and intrinsically semi-supervised algorithms. Real-world applications and challenges are also examined, providing listeners with a comprehensive understanding of this practical machine learning technique.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Anna Gutowska

What is unsupervised learning?

Wed, 21 Jan 2026 11:00:01 GMT

This episode of Techsplainers explores unsupervised learning, the branch of machine learning where algorithms discover hidden patterns in data without human guidance or labeled examples. The discussion covers the three main tasks of unsupervised learning: clustering (grouping similar data points), association rules (finding relationships between variables), and dimensionality reduction (simplifying data while preserving essential information). We examine popular algorithms like K-means clustering, hierarchical clustering, the Apriori algorithm for market basket analysis, and techniques like Principal Component Analysis and autoencoders. The episode highlights real-world applications including news aggregation, recommendation engines, medical imaging, and customer segmentation. The conversation also compares unsupervised learning with supervised approaches and addresses challenges such as computational complexity, validation difficulties, and interpretation of results, offering listeners a comprehensive understanding of how AI can extract valuable insights from unlabeled data.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Anna Gutowska

What is supervised learning?

Tue, 20 Jan 2026 11:00:00 GMT

This episode of Techsplainers explores supervised learning, the most widely used approach in machine learning, where AI models are trained using labeled data with known correct answers. The episode explains how supervised learning uses ground truth data to teach models to recognize patterns and make accurate predictions on new information. We break down the two main categories of supervised learning tasks—classification for sorting data into categories and regression for predicting numerical values—and examine popular algorithms, including linear regression, decision trees, random forests, and neural networks. The discussion also covers how supervised learning differs from other approaches like unsupervised, semi-supervised, self-supervised, and reinforcement learning, along with real-world applications ranging from image recognition to fraud detection. While highlighting supervised learning's effectiveness for many AI applications, the episode acknowledges its limitations, including data labeling requirements and potential for bias.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Anna Gutowska

What is machine learning?

Mon, 19 Jan 2026 11:00:00 GMT

This episode of Techsplainers explores machine learning—the subset of artificial intelligence that enables computers to learn patterns from data without explicit programming. The episode explains how machine learning models are trained on datasets to recognize patterns and make predictions on new information, breaking down the three main approaches: supervised learning (using labeled data with correct answers), unsupervised learning (discovering patterns in unlabeled data), and reinforcement learning (learning through trial and error with rewards). The discussion also covers deep learning and neural networks, explaining how these powerful systems can automatically extract features from raw data, powering breakthroughs in computer vision, natural language processing, and more. From transformers to the newest Mamba models, the episode provides a comprehensive overview of how machine learning works and its wide-ranging applications across industries.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Anna Gutowska

What is continuous testing?

Fri, 16 Jan 2026 11:00:01 GMT

This episode of Techsplainers explores continuous testing—a critical component of modern software development that integrates automated feedback throughout the development lifecycle. Host Dan explains how continuous testing works alongside CI/CD pipelines to accelerate development while maintaining quality. The episode breaks down various testing methodologies including shift-left, shift-right, smoke tests, unit testing, integration testing, and more. Listeners will learn how continuous testing helps teams identify errors early, reduce costs, and improve user experiences through automation. The episode also addresses the unique challenges of testing in today's distributed, multi-region IT systems and explains how continuous testing frameworks provide consistency across modules, connectors, platforms, and infrastructure to ensure reliable results.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Dan Segal

What is continuous delivery?

Thu, 15 Jan 2026 11:00:00 GMT

This episode of Techsplainers explores continuous delivery, the software development practice that automates the movement of code changes through testing and eventual release into production. We examine how continuous delivery transforms traditional infrequent, high-risk software releases into smaller, regular updates that can be deployed quickly and reliably. The podcast details key benefits, including reduced deployment time, decreased costs, improved scalability, and automated code deployment through development phases. We cover essential best practices such as making every change releasable, embracing trunk-based development, building automated pipelines, and aiming for zero-downtime deployments. The episode also clarifies the important distinction between continuous delivery (which prepares code for release with manual approval) and continuous deployment (which automatically releases code to production). Finally, we discuss how continuous delivery integrates with Agile and DevOps methodologies to create more efficient, reliable software development processes.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Dan Segal

What is continuous integration?

Wed, 14 Jan 2026 11:00:01 GMT

This episode of Techsplainers explores continuous integration (CI), a fundamental software development practice where developers regularly merge code changes into a central repository. We explain how CI works by automatically building and testing code with each submission, dramatically improving upon traditional development, where infrequent integrations caused painful conflicts and delays. The podcast covers key CI components, including central repositories, CI servers, and automated testing suites, while highlighting how testing forms the backbone of effective CI implementations. We distinguish between continuous integration, delivery, and deployment in the CI/CD pipeline, and examine CI's critical role in both DevOps and agile methodologies. The episode concludes by detailing CI's main benefits: earlier error detection, improved team collaboration, accelerated development cycles, and reduced risk through incremental changes.

Find more information at: https://www.ibm.com/think/podcasts/techsplainers

Narrated by Dan Segal

Part 2: What is DevOps?

Tue, 13 Jan 2026 11:00:03 GMT

This episode of Techsplainers explores the key benefits of DevOps methodology and how it's transforming software development and delivery. We examine five major advantages: better collaboration between development and operations teams, accelerated delivery through microservices and CI/CD pipelines, greater reliability via automated testing, quicker scaling capabilities, and enhanced security with DevSecOps practices. The podcast also covers essential DevOps tools, including version control systems, containerization platforms, and monitoring solutions that enable automation throughout the software lifecycle. We discuss how DevOps complements Site Reliability Engineering (SRE) to balance rapid development with system reliability, and explore how AI is boosting DevOps productivity through improved troubleshooting, security, monitoring, and testing. Finally, we look at emerging trends shaping the future of DevOps, including platform engineering, observability, and low-code/no-code development.

Find more information at: https://www.ibm.com/think/podcasts/techsplainers

Narrated by Dan Segal

Part 1: What is DevOps?

Mon, 12 Jan 2026 11:00:00 GMT

This episode of Techsplainers introduces DevOps, explaining how this approach revolutionized software development by breaking down traditional silos between development and operations teams. The discussion traces DevOps' evolution from agile methodologies and CI/CD practices, detailing the eight core steps of the DevOps lifecycle: planning, coding, building, testing, release, deployment, operation, and monitoring. We explore how organizations implement DevOps through both technical workflows and cultural transformation, emphasizing automation, collaboration, and continuous feedback. The episode also addresses DevSecOps and how integrating security throughout the development process leads to more secure applications. We highlight real-world benefits of DevOps adoption, including faster delivery cycles, higher quality software, improved team collaboration, and better security posture, while acknowledging the challenges of implementation. Whether you're new to DevOps or looking to optimize existing practices, this episode provides valuable insights into this essential approach to modern software development.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Dan Segal

Part 2: What is a qubit?

Fri, 09 Jan 2026 11:00:00 GMT

This episode of Techsplainers explores the diverse world of qubits—the fundamental units of quantum computing. The discussion examines various qubit implementations, including superconducting qubits (used in IBM's quantum computers), trapped ion qubits, quantum dots, photon qubits, and neutral atoms, with each offering unique advantages for different quantum computing applications. The episode then delves into quantum entanglement, the phenomenon Einstein called "spooky action at a distance," where measuring one qubit instantaneously affects its entangled partner regardless of distance. This remarkable property dramatically increases quantum computing power by enabling massively parallel computations. The conversation also addresses the significant challenge of quantum decoherence—how even tiny disturbances can disrupt qubits' delicate quantum states—and highlights promising advances in quantum error correction that may help overcome these obstacles as the field rapidly evolves.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Ian Smalley

Part 1: What is a qubit?

Thu, 08 Jan 2026 11:00:00 GMT

This episode of Techsplainers explores qubits, the fundamental building blocks of quantum computing. Unlike classical bits that can only be 0 or 1, qubits can exist in superposition, representing both states simultaneously until measured. The episode explains how qubits harness quantum mechanics to potentially solve complex problems that would take classical computers thousands of years. We learn how qubits work through quantum superposition, why they can process multiple possibilities at once, and their applications in fields like cancer research, climate modeling, and drug discovery. The discussion also touches on the extreme conditions required to maintain qubit stability, setting the stage for future episodes about different types of qubits and quantum entanglement.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Ian Smalley

Part 3: What is quantum computing?

Wed, 07 Jan 2026 11:00:00 GMT

This episode of Techsplainers explores the revolutionary applications of quantum computing across diverse industries and disciplines. We dive into how quantum computers could transform pharmaceutical development by simulating molecular interactions digitally, potentially reducing drug discovery timelines from 15 years to just months. The discussion extends to quantum computing's applications in materials science, climate change mitigation, artificial intelligence, and financial modeling. We'll look at the critical distinction between "quantum utility" (already achieved) and "quantum advantage" (expected by 2026), while addressing the significant challenges facing the field, including qubit scaling and quantum error correction. The episode highlights how industries from healthcare to logistics to energy management are already investing in quantum research, with companies like Moderna, HSBC, and FedEx exploring quantum solutions for complex optimization problems. Listeners gain insight into IBM's quantum roadmap, which aims for 2,000 logical qubits by 2033, and learn how quantum-centric supercomputing—the strategic combination of quantum and classical systems—represents the most promising path forward. Rather than merely offering incremental improvements, quantum computing promises to solve problems that are currently impossible, potentially revolutionizing our approach to some of humanity's most complex challenges.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Ian Smalley

Part 2: What is quantum computing?

Tue, 06 Jan 2026 11:00:00 GMT

This episode of Techsplainers explores the inner workings of quantum computers, diving deep into the physical mechanisms and infrastructure that make quantum computing possible. We break down the fundamental concept of qubits and explain how their ability to exist in superpositions creates exponential computational power. The episode examines different qubit types, including superconducting, trapped ion, quantum dots, and photonic qubits, while explaining why quantum computers require massive cooling systems operating at temperatures colder than space. Listeners will gain insights into how quantum computers differ fundamentally from classical computers in their approach to problem-solving, the emerging field of quantum-centric supercomputing, and the development of accessible quantum programming tools like IBM's Qiskit. The discussion highlights that quantum computers won't replace classical systems but will complement them by tackling previously impossible calculations, with quantum technology advancing rapidly toward systems with thousands of qubits and improved error rates.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Ian Smalley

Part 1: What is quantum computing?

Mon, 05 Jan 2026 11:00:00 GMT

This episode of Techsplainers introduces quantum computing, a revolutionary technology that harnesses the principles of quantum mechanics to solve problems beyond the capabilities of classical computers. We explain the four foundational principles of quantum computing: superposition, entanglement, interference, and decoherence, breaking down complex concepts with accessible analogies. The episode explores how quantum computers differ fundamentally from classical computers by using qubits rather than binary bits, allowing them to process multiple possibilities simultaneously. Listeners will learn about practical applications in pharmaceuticals, materials science, and artificial intelligence, while gaining insight into the current state of quantum technology, including IBM's roadmap for scaling to 2,000 logical qubits by 2033. The episode also addresses common misconceptions, clarifying that quantum computers will complement rather than replace classical computers for specific complex computational challenges.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Ian Smalley

What is AutoML?

Fri, 02 Jan 2026 11:00:00 GMT

This episode of Techsplainers explores automated machine learning (AutoML), a transformative approach that automates the end-to-end development of machine learning models. We explain how AutoML democratizes AI by enabling non-experts to implement intelligent systems while allowing data scientists to focus on more complex challenges rather than routine tasks. The podcast walks through how AutoML solutions streamline the entire machine learning pipeline—from data preparation and preprocessing to feature engineering, model selection, hyperparameter tuning, validation, and deployment. Particularly valuable is our discussion of automated feature engineering, which can reduce development time from days to minutes while increasing model explainability. We explore four major use cases where AutoML excels: classification tasks like fraud detection, regression problems for forecasting, computer vision applications for image processing, and natural language processing for text analysis. The episode concludes by acknowledging AutoML's limitations, including potentially high costs for complex models, challenges with interpretability, risks of overfitting, limited control over model design, and continued dependence on high-quality training data.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Ian Smalley

What is data labeling?

Thu, 01 Jan 2026 11:00:01 GMT

This episode of Techsplainers explores data labeling, the critical preprocessing stage where raw data is assigned contextual tags to make it intelligible for machine learning models. We examine how this process combines software tools with human-in-the-loop participation to create the foundation for AI applications like computer vision and natural language processing. The podcast compares five distinct approaches to data labeling: internal labeling (using in-house experts), synthetic labeling (generating new data from existing datasets), programmatic labeling (automating the process through scripts), outsourcing (leveraging external specialists), and crowdsourcing (distributing micro-tasks across many contributors). We also discuss the tradeoffs involved—while proper labeling significantly improves model accuracy and performance, it's often expensive and time-consuming. The episode concludes by sharing best practices like consensus measurement, label auditing, and active learning techniques that help organizations optimize their data labeling processes for maximum efficiency and accuracy across various use cases from image recognition to sentiment analysis.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Ian Smalley

What is access management?

Wed, 31 Dec 2025 12:00:00 GMT

This episode of Techsplainers explores Identity and Access Management (IAM), the cybersecurity discipline that controls who can access what in digital systems. We examine IAM's four foundational pillars—administration, authentication, authorization, and auditing—and how they work together to secure modern organizations. The episode details essential IAM capabilities, including directory services, authentication tools like multi-factor authentication and single sign-on, various access control methods, and specialized functions for privileged accounts and non-human users. With 30% of cyber attacks involving stolen credentials and non-human identities now outnumbering human users 10:1 in enterprises, IAM has evolved from basic IT functionality to a critical security foundation. The discussion concludes by examining emerging trends like identity fabrics that unite disparate systems and how AI is both challenging and enhancing IAM capabilities.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Bryan Clark

What is authentication?

Tue, 30 Dec 2025 12:00:00 GMT

This episode of Techsplainers introduces authentication, the cybersecurity process that verifies a user's identity before granting access to systems or data. The episode distinguishes authentication (proving who you are) from authorization (determining what you're allowed to do) and explores the four main authentication factors: something you know (passwords), something you have (security tokens), something you are (biometrics), and something you do (behavioral patterns). Modern authentication approaches are examined, including single sign-on (SSO), multi-factor authentication (MFA), adaptive authentication that uses AI to assess risk in real-time, and passwordless authentication using cryptographic passkeys. Technical standards like SAML, OAuth, and Kerberos are also explained. With account hijacking involved in 30% of cyber attacks, according to IBM's X-Force Threat Intelligence Index, strong authentication proves critical for security, access control, and regulatory compliance across industries like healthcare and finance.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Bryan Clark

What is full-stack observability?

Mon, 29 Dec 2025 11:00:01 GMT

In this episode of Techsplainers, we dive into full-stack observability, a comprehensive approach that unifies telemetry across infrastructure, applications, and user experiences. Unlike siloed monitoring, full-stack observability provides a single source of truth for system health, enabling faster incident resolution, predictive optimization, and improved operational efficiency. We discuss how it works, including automated service discovery, leading factor analysis, unified dashboards, and AI-driven analytics. You will also learn about its benefits for performance, security, compliance, and business outcomes, as well as challenges like data scale, integration, and privacy. Finally, we explore how machine learning and natural language processing are shaping the future of observability. No matter your role, episode offers a complete guide to why full-stack observability is essential in today’s complex digital environments.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by PJ Hagerty

What is SRE observability?

Fri, 26 Dec 2025 11:00:00 GMT

In this episode of Techsplainers, we dive into SRE observability, a critical practice for ensuring site reliability in today’s dynamic, cloud-native environments. Discover how SRE observability goes beyond traditional monitoring by using telemetry data—metrics, logs, and traces—to provide deep visibility into complex systems. We explain how it supports proactive issue detection, faster incident response, and data-driven decision-making. You will also learn about real-world use cases in ecommerce, finance, logistics, and healthcare, as well as emerging trends like AI-driven observability and causal AI. Whether you are an engineer, IT professional, or tech enthusiast, this episode will help you understand how SRE observability optimizes performance, enhances user experience, and drives better business outcomes.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by PJ Hagerty

What is data accuracy?

Thu, 25 Dec 2025 11:00:02 GMT

This episode of Techsplainers explains what data accuracy is, why it matters, and how organizations can achieve it. We explore its role as a core dimension of data quality, the benefits of accurate data for decision-making, compliance, AI, and customer satisfaction, and the common causes of inaccuracies—from human error to outdated information and biased data.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Matt Finio

What is data integrity?

Wed, 24 Dec 2025 11:00:00 GMT

This episode of Techsplainers explains what data integrity is, why it matters, and how organizations can maintain it. We cover the processes and security measures that ensure data remains accurate, complete, and consistent throughout its lifecycle. Learn why data integrity is critical for analytics, compliance, and trust, and explore the five key types of data integrity.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Matt Finio

What is multi-agent collaboration?

Tue, 23 Dec 2025 11:00:02 GMT

This episode of "Techsplainers" explains the concept of multi-agent collaboration. It discusses how multi-agent systems, comprising multiple AI agents, coordinate actions in a distributed system to achieve complex tasks. These tasks, once handled only by large language models, now include customer service triage, financial analysis, technical troubleshooting, and more. The podcast details how agents communicate via established protocols to exchange information, assign responsibilities, and coordinate actions. It also highlights the benefits of multi-agent collaboration, such as scalability, fault tolerance, and emergent cooperative behavior, using examples like a fleet of drones searching a disaster site.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Alice Gomstyn

What is a multi-agent system?

Mon, 22 Dec 2025 11:00:00 GMT

This episode of Techsplainers introduces listeners to the concept of agentic architecture, a framework used for structuring AI agents to automate complex tasks. The podcast explains that agentic architecture is crucial for creating AI agents capable of autonomous decision-making and adapting to dynamic environments. It delves into the four core factors of agency: intentionality (planning), forethought, self-reactiveness, and self-reflectiveness. These four factors underpin AI agents' autonomy. The discussion also contrasts agentic and non-agentic architectures, highlighting the advantages of agentic architectures in supporting agentic behavior in AI agents. The podcast further breaks down different types of agentic architectures – single-agent, multi-agent, and hybrid – detailing their structures, strengths, weaknesses, and best use cases. Finally, it covers three types of agentic frameworks—reactive, deliberative, and cognitive—concluding with a detailed explanation of BDI architectures, a model for rational decision-making in intelligent agents.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Alice Gomstyn

What is vibe coding?

Fri, 19 Dec 2025 11:00:00 GMT

This episode of Techsplainers introduces vibe coding, the practice of using AI tools to generate software code through natural language prompts rather than manual coding. We explore how this approach follows a "code first, refine later" philosophy that prioritizes experimentation and rapid prototyping. The podcast walks through the four-step implementation process: choosing an AI coding assistant platform, defining requirements through clear prompts, refining the generated code, and reviewing before deployment. While highlighting vibe coding's ability to accelerate development and free human creativity, we also examine its limitations—including challenges with technical complexity, code quality, debugging, maintenance, and security concerns. The discussion concludes by examining how vibe coding is driving paradigm shifts in software development through quick prototyping, problem-first approaches, reduced risk with maximized impact, and multimodal interfaces that combine voice, visual, and text-based coding methods to create more intuitive development environments.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Amanda Downie

What is retrieval augmented generation (RAG)?

Thu, 18 Dec 2025 11:00:00 GMT

This episode of Techsplainers explores retrieval augmented generation (RAG), a powerful technique that enhances generative AI by connecting models to external knowledge bases. We examine how RAG addresses critical limitations of large language models—their finite training data and knowledge cutoffs—by allowing them to access up-to-date, domain-specific information in real-time. The podcast breaks down RAG's five-stage process: from receiving a user query to retrieving relevant information, integrating it into an augmented prompt, and generating an informed response. We dissect RAG's four core components—knowledge base, retriever, integration layer, and generator—explaining how they work together to create a more robust AI system. Special attention is given to embedding and chunking processes that transform unstructured data into searchable vector representations. The episode highlights RAG's numerous benefits, including cost efficiency compared to fine-tuning, reduced hallucinations, enhanced user trust through citations, expanded model capabilities, improved developer control, and stronger data security. Finally, we showcase diverse real-world applications across industries, from specialized chatbots and research tools to personalized recommendation engines.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Amanda Downie

What are vision language models (VLMs)?

Wed, 17 Dec 2025 11:00:00 GMT

This episode of Techsplainers explores vision language models (VLMs), the sophisticated AI systems that bridge computer vision and natural language processing. We examine how these multimodal models understand relationships between images and text, allowing them to generate image descriptions, answer visual questions, and even create images from text prompts. The podcast dissects the architecture of VLMs, explaining the critical components of vision encoders (which process visual information into vector embeddings) and language encoders (which interpret textual data). We delve into training strategies, including contrastive learning methods like CLIP, masking techniques, generative approaches, and transfer learning from pretrained models. The discussion highlights real-world applications—from image captioning and generation to visual search, image segmentation, and object detection—while showcasing leading models like DeepSeek-VL2, Google's Gemini 2.0, OpenAI's GPT-4o, Meta's Llama 3.2, and NVIDIA's NVLM. Finally, we address implementation challenges similar to traditional LLMs, including data bias, computational complexity, and the risk of hallucinations.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Amanda Downie

What are large language models (LLMs)?

Tue, 16 Dec 2025 11:00:00 GMT

This episode of Techsplainers explores large language models (LLMs), the powerful AI systems revolutionizing how we interact with technology through human language. We break down how these massive statistical prediction machines are built on transformer architecture, enabling them to understand context and relationships between words far better than previous systems. The podcast walks through the complete development process—from pretraining on trillions of words and tokenization to self-supervised learning and the crucial self-attention mechanism that allows LLMs to capture linguistic relationships. We examine various fine-tuning methods, including supervised fine-tuning, reinforcement learning from human feedback (RLHF), and instruction tuning, that help adapt these models for specific uses. The discussion covers practical aspects like prompt engineering, temperature settings, context windows, and retrieval augmented generation (RAG) while showcasing real-world applications across industries. Finally, we address the significant challenges of LLMs, including hallucinations, biases, and resource demands, alongside governance frameworks and evaluation techniques used to ensure these powerful tools are deployed responsibly.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Amanda Downie

What is generative AI?

Mon, 15 Dec 2025 11:00:00 GMT

This episode of Techsplainers explores generative AI, the revolutionary technology that creates original content like text, images, video, and code in response to user prompts. We walk through how these systems work in three main phases: training foundation models on massive datasets, tuning them for specific applications, and continuously improving their outputs through evaluation. The podcast traces the evolution of key generative AI architectures—from variational autoencoders and generative adversarial networks to diffusion models and transformers—highlighting how each contributes to today's powerful AI tools. We examine generative AI's diverse applications across industries, from enhancing customer experiences and accelerating software development to transforming creative processes and scientific research. The episode also addresses emerging concepts like AI agents and agentic AI while candidly discussing the technology's challenges, including hallucinations, bias, security vulnerabilities, and deepfakes. Despite these concerns, the episode emphasizes how organizations are increasingly adopting generative AI, with analysts predicting 80% implementation by 2026.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Amanda Downie

What is model deployment?

Fri, 12 Dec 2025 11:00:01 GMT

This episode of Techsplainers explores model deployment, the crucial phase that brings machine learning models from development into production environments where they can deliver real business value. We examine why deployment is so critical—according to Gartner, only about 48% of AI projects make it to production—and discuss four primary deployment methods: real-time (for immediate predictions), batch (for offline processing of large datasets), streaming (for continuous data processing), and edge deployment (for running models on devices like smartphones). The podcast walks through the six essential steps of the deployment process: planning (preparing the technical environment), setup (configuring dependencies and security), packaging and deployment (containerizing the model), testing (validating functionality), monitoring (tracking performance metrics), and implementing CI/CD pipelines (for automated updates). We also address key challenges organizations face when deploying models, including high infrastructure costs, technical complexity, integration difficulties with existing systems, and ensuring proper scalability to handle varying workloads.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Ian Smalley

What is AI lifecycle management?

Thu, 11 Dec 2025 11:00:00 GMT

This episode of Techsplainers explores AI Model Lifecycle Management, the comprehensive methodology for managing artificial intelligence models throughout their entire lifecycle. We discuss why a structured approach to AI deployment is critical for enterprise success, especially when decisions made by AI systems can significantly impact business outcomes. The podcast outlines the four main stages of the AI pipeline: collect (making data accessible), organize (creating an analytics foundation), analyze (building AI with trust), and infuse (operationalizing AI across business functions). We also examine the essential components of effective AI lifecycle management, including data governance, quality assurance, fairness evaluation, and explainability. The episode concludes by highlighting the key features needed in AI management tools—from ease of model training and deployment at scale to comprehensive monitoring capabilities—using IBM Cloud Pak for Data as an illustrative example of an end-to-end platform designed to increase the throughput of data science activities and accelerate time to value from AI initiatives.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Ian Smalley

What is a machine learning pipeline?

Wed, 10 Dec 2025 11:00:00 GMT

This episode of Techsplainers explores the machine learning pipeline—the systematic process of designing, developing, and deploying machine learning models. We break down the entire workflow into three distinct stages: data processing (covering ingestion, preprocessing, exploration, and feature engineering), model development (including algorithm selection, hyperparameter tuning, training approaches, and performance evaluation), and model deployment (addressing serialization, integration, architecture, monitoring, updates, and compliance). The podcast also emphasizes the critical "Stage 0" of project commencement, where stakeholders define clear objectives, success metrics, and potential obstacles before starting technical work. Throughout the discussion, we highlight how each stage contributes to creating effective, high-performing ML models while examining various training methodologies—from supervised and unsupervised learning to reinforcement and continual learning approaches. Special attention is given to model monitoring and maintenance, acknowledging that deployment is not the end but rather the beginning of a model's productive life cycle.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Ian Smalley

Part 2: What is MLOps?

Tue, 09 Dec 2025 11:00:00 GMT

This episode of Techsplainers explores the practical implementation of MLOps, diving into the key components that comprise an effective machine learning operations pipeline. We examine the five essential elements: data management (including acquisition, preprocessing, and versioning), model development (covering training, experimentation, and evaluation), model deployment (focusing on packaging and serving), monitoring and optimization (highlighting performance tracking and retraining), and collaboration and governance (emphasizing version control and ethical guidelines). The podcast also investigates how generative AI and large language models are reshaping MLOps practices before explaining the four maturity levels of MLOps implementation—from manual processes to fully automated systems with continuous monitoring and governance. Throughout the episode, we emphasize that organizations should select the appropriate MLOps maturity level based on their specific needs rather than pursuing the most advanced level by default.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Ian Smalley

Part 1: What is MLOps?

Mon, 08 Dec 2025 11:00:00 GMT

This episode of Techsplainers introduces MLOps (machine learning operations), a methodology that creates an efficient assembly line for building and running machine learning models. The podcast explains how MLOps evolved from DevOps principles to address the unique challenges of ML development, including resource intensity, time consumption, and siloed teams. We explore the key benefits of MLOps—increased efficiency through automation, improved model accuracy through continuous monitoring, faster time to market, and enhanced scalability and governance. The episode details eight core principles that define effective MLOps practices: collaboration, continuous improvement, automation, reproducibility, versioning, monitoring and observability, governance and security, and scalability. Finally, we examine the key elements of successful MLOps implementation, including the necessary technical and soft skills, essential tools like ML frameworks and CI/CD pipelines, and best practices for model lifecycle management.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Ian Smalley

Part 2: What is ransomware?

Fri, 05 Dec 2025 11:00:00 GMT

This episode of Techsplainers continues the exploration of ransomware, focusing on notorious variants that have caused billions in damages worldwide. The podcast examines landmark ransomware families, including CryptoLocker, which kickstarted modern ransomware attacks; WannaCry, which infected 200,000 computers across 150 countries; and Darkside, responsible for the Colonial Pipeline attack that disrupted 45% of the US East Coast's fuel supply. Listeners will learn about the evolution of ransomware tactics, from standard encryption to AI-powered "Ransomware 3.0" like PromptLock. The discussion also covers ransom payment trends—noting that 63% of victims now refuse to pay—along with law enforcement's stance against payments and potential legal consequences. The episode concludes with essential prevention strategies, including maintaining offline backups, regular patching, employee training, and establishing formal incident response plans that can save organizations nearly $1 million per attack through faster identification.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Bryan Clark

Part 1: What is ransomware?

Thu, 04 Dec 2025 11:00:01 GMT

This episode of Techsplainers demystifies ransomware—malicious software that encrypts victims' data and demands payment for its release. The podcast explains how ransomware has evolved from simple encryption attacks to sophisticated double and triple extortion tactics that threaten data leaks and attacks on business partners. Listeners will learn about different types of ransomware, including encrypting (crypto) ransomware, screen-locking variants, leakware, mobile ransomware, wipers, and scareware. The discussion covers common infection vectors, such as phishing, software vulnerabilities, and credential theft, along with the growing "Ransomware-as-a-Service" business model that allows criminals without technical skills to deploy attacks. The episode walks through the five stages of a typical ransomware attack, from initial access to the final ransom demand, highlighting why these attacks cost victims an average of USD 5.08 million, according to IBM's research.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Bryan Clark

What is phishing?

Wed, 03 Dec 2025 11:00:00 GMT

This episode of Techsplainers explores phishing—the deceptive technique cybercriminals use to trick victims into revealing sensitive information or downloading malware through fraudulent communications. The podcast explains why phishing is the most common data breach vector, accounting for 16% of all breaches and costing organizations an average of USD 4.8 million. Listeners will discover various phishing methods, including bulk email phishing, targeted spear phishing, executive-focused whaling, business email compromise, SMS phishing (smishing), voice phishing (vishing), social media attacks, and QR code scams (quishing). The discussion highlights how generative AI has transformed phishing, enabling scammers to create more convincing messages in minutes instead of hours. The episode concludes with practical advice on spotting phishing red flags—like urgency tactics, unsolicited requests, poor grammar, and spoofed URLs—and implementing preventative measures, such as security awareness training, multi-factor authentication, and advanced threat detection tools.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Bryan Clark

What is social engineering?

Tue, 02 Dec 2025 11:00:00 GMT

This episode of Techsplainers explores social engineering—cyber attacks that use psychological manipulation rather than technical hacking to compromise security. The podcast examines how attackers impersonate trusted entities and exploit emotions like fear, greed, and curiosity to trick victims. Listeners will discover various attack methods, including different types of phishing, baiting, tailgating, quid pro quo scams, scareware, and watering hole attacks. The discussion shows how these tactics allow cybercriminals to bypass security controls through human vulnerabilities, illustrated with examples from Nigerian prince scams to fake virus warnings. The episode concludes with practical defense strategies, including security awareness training, multi-factor authentication, and advanced detection technologies to protect against these increasingly sophisticated threats.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Bryan Clark

What is a data breach?

Mon, 01 Dec 2025 11:00:00 GMT

This episode of Techsplainers provides an in-depth exploration of data breaches, explaining what they are and their significant financial impact on organizations worldwide. The podcast clarifies how data breaches differ from other cyberattacks and breaks down the staggering costs—with global averages reaching USD 4.44 million and US breaches costing USD 10.22 million. The discussion examines the common causes of breaches, including innocent mistakes, malicious insiders, and external hackers, with financial gain being the primary motivation. Listeners will learn about the breach attack vectors like stolen credentials, social engineering, ransomware, and system vulnerabilities, as well as the comprehensive notification requirements organizations face across different jurisdictions. The episode concludes with effective prevention strategies, highlighting how AI and automation can reduce breach costs by 34% and expedite resolution by 80 days, alongside practical security measures like incident response planning, employee training, and identity management practices.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Bryan Clark

Part 2: What is network observability?

Fri, 28 Nov 2025 11:00:00 GMT

In this episode of Techsplainers, we dive deeper into network observability, comparing it to network performance monitoring and DevOps observability. Learn how observability goes beyond static thresholds and predefined metrics to provide end-to-end visibility, richer context, and predictive analytics for dynamic network environments. We also explore why network observability is critical for industries like financial services and telecommunications, where performance and reliability are non-negotiable. From real-time latency detection in high-frequency trading to managing 5G networks and edge computing, observability tools enable proactive optimization and self-healing capabilities. This episode highlights how observability helps organizations adapt to evolving technologies and maintain seamless connectivity across hybrid and multi-cloud infrastructures.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by PJ Hagerty

Part 1: What is network observability?

Thu, 27 Nov 2025 11:00:00 GMT

In this episode of Techsplainers, we explore network observability, a proactive approach to understanding and managing complex network environments. Unlike traditional monitoring, which focuses on predefined metrics, network observability provides real-time visibility into network health and performance across on-premises, hybrid, and multicloud infrastructures. We break down its five pillars—metrics, logs, traces, context, and correlation—and explain how they work together to deliver actionable insights. You will also learn about key capabilities, such as intelligent alerting, topology mapping, and continuous performance analysis, as well as the benefits of observability for security, cloud migration, and cost optimization. Whether you are an IT professional or a tech enthusiast, this episode will help you understand why network observability is critical for resilience and efficiency in today’s digital world.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by PJ Hagerty

Observability vs. monitoring

Wed, 26 Nov 2025 11:00:01 GMT

In this episode of Techsplainers, we explore the key differences between observability and monitoring and why both are critical for managing complex IT environments. Monitoring focuses on tracking predefined metrics and alerting teams when something goes wrong, while observability goes further by providing context and insights into why issues occur and how to fix them. We discuss how observability evolved from traditional application performance monitoring, the role of telemetry data (including logs, metrics, and traces), and how these tools work together to optimize performance. You will also learn about the benefits of observability for dynamic, cloud-native architectures and how AI-driven features enable predictive analytics and proactive issue resolution. This episode will help you understand how monitoring and observability create a powerful framework for reliability and scalability.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by PJ Hagerty

Three pillars of observability

Tue, 25 Nov 2025 11:00:02 GMT

In this episode of Techsplainers, we break down the three pillars of observability: metrics, logs, and traces. We'll explain how they provide the foundation for understanding complex cloud-native systems. Discover what each pillar does, why they matter, and how they complement each other to deliver actionable insights for DevOps teams. We also explore system events, distributed tracing, and emerging capabilities like continuous profiling, which offer deeper visibility into application performance. Whether you are a developer, IT professional, or tech enthusiast, this episode will help you understand how observability accelerates troubleshooting, optimizes performance, and supports modern digital transformation.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by PJ Hagerty

What is observability?

Mon, 24 Nov 2025 11:00:01 GMT

This episode of Techsplainers takes a deep dive into observability, a cornerstone of modern DevOps and cloud-native environments. We break down what observability really means—going beyond traditional monitoring to provide full-stack visibility into complex systems. You’ll learn about its three pillars: logs, traces, and metrics, and how they work together to deliver actionable insights. The discussion explores how observability empowers teams to troubleshoot faster, optimize performance, and improve user experience. We also examine cutting-edge innovations like AI-driven observability, predictive analytics, and causal AI, which are transforming how organizations prevent issues before they occur. Real-world benefits, common use cases, and the role of observability in accelerating DevOps pipelines round out this comprehensive guide to one of today’s most critical tech practices.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by PJ Hagerty

What is data quality management?

Fri, 21 Nov 2025 11:00:01 GMT

This episode of Techsplainers explains data quality management (DQM)—a set of practices that ensure data is accurate, complete, consistent, timely, unique, and valid. Learn why high-quality data is critical for business intelligence, regulatory compliance, and AI performance, and explore key techniques like data profiling, cleansing, validation, and monitoring.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Matt Finio

What is a data fabric?

Thu, 20 Nov 2025 11:00:00 GMT

This episode of Techsplainers explains what a data fabric is and why it’s critical for modern enterprises. A data fabric is a data architecture that unifies and democratizes data access across hybrid and multicloud environments. It uses AI, metadata, and automation to break down silos, improve governance, and enable self-service data access—accelerating analytics, decision-making, and AI adoption.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Matt Finio

What is a data lakehouse?

Wed, 19 Nov 2025 11:00:01 GMT

This episode of Techsplainers explains what a data lakehouse is and why it’s transforming modern data management. A data lakehouse combines the low-cost, flexible storage of data lakes with the high-performance analytics of data warehouses, enabling unified data systems for advanced analytics and AI. Learn how lakehouses evolved, their architecture, and how they compare to data lakes and warehouses.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Matt Finio

What is a data lake?

Tue, 18 Nov 2025 11:00:00 GMT

This episode of Techsplainers explains what a data lake is, how it evolved, and why it’s a cornerstone of modern data architecture. We cover its role in storing massive amounts of raw data in any format, its cloud-based foundations, and how it supports AI and machine learning workloads. Plus, learn how data lakes compare to warehouses and lakehouses.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Matt Finio

What is a data architecture?

Mon, 17 Nov 2025 11:00:01 GMT

This episode of Techsplainers explains what data architecture is, why it matters, and how it shapes modern data strategies. From centralized and decentralized models to frameworks like TOGAF, we explore how a well-designed architecture enables scalability, governance, and advanced use cases like AI and real-time analytics. Done right, data architecture turns raw data into a strategic asset.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Matt Finio

What is agentic architecture?

Fri, 14 Nov 2025 11:00:00 GMT

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Alice Gomstyn

Types of AI agents

Thu, 13 Nov 2025 11:00:00 GMT

In this episode of Techsplainers, host Alice explains the five main types of AI agents: simple reflex agents (like thermostats), model-based reflex agents (like robot vacuums), goal-based agents (like navigation robots), utility-based agents (like self-driving cars), and learning agents (like reinforcement learning systems). Each type is discussed in detail, highlighting its capabilities, applications, and limitations. The episode concludes by discussing the benefits of deploying multiple types of agents within a single system, emphasizing their potential in diverse industries for automation, optimization, and improved customer experiences.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Alice Gomstyn

AI agents vs. AI assistants

Wed, 12 Nov 2025 11:00:01 GMT

This episode of Techsplainers provides an in-depth exploration of AI agents and AI assistants, comparing and contrasting their functionalities and capabilities. The podcast explains that AI assistants are reactive, performing tasks based on user commands, while AI agents are proactive, autonomously achieving specific goals. The discussion also delves into the structure and features of both types of AI, including conversational AI, prompts, recommendations, and tuning for assistants, and autonomy, decision-making, connectivity, persistent memory, and adaptive learning for agents. Real-world applications and potential benefits of both technologies are highlighted, along with current limitations and risks.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Alice Gomstyn

What are AI agents?

Tue, 11 Nov 2025 11:00:01 GMT

Join us on today's episode of "Techsplainers as we delve into the world of AI agents, explaining their functions, capabilities, and operational components. We explore how AI agents leverage advanced natural language processing and tool calling to surpass traditional AI limitations, autonomously performing tasks and learning from experiences. Our discussion covers three key stages of AI agent operations: goal initialization and planning, reasoning with available tools, and learning and reflection. We also contrast agentic AI chatbots with nonagentic ones, highlighting the advantages of adaptability and comprehensive responses in agentic systems. Finally, we examine reasoning paradigms, agent types, and various use cases, from enhancing customer experiences to revolutionizing healthcare and finance.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Alice Gomstyn

What is agentic AI?

Mon, 10 Nov 2025 11:00:00 GMT

This episode of Techsplainers introduces the concept of agentic AI, explaining how it differs from traditional AI models. Agentic AI, consisting of AI agents, operates autonomously and adaptively, using LLMs to function in dynamic environments. The episode discusses the benefits of agentic AI, including autonomy, proactivity, specialization, adaptability, and intuitiveness. Despite its potential, there are still some challenges, such as misaligned rewards, self-reinforcing behaviors, and cascading failures. Examples of real-world applications are provided, such as AI-powered trading bots, autonomous vehicles, healthcare chatbots, cybersecurity, and supply chain management.

Find more information at https://www.ibm.com/think/podcasts/techsplainers

Narrated by Alice Gomstyn