Techsplainers by IBM

This episode of Techsplainers explores asset tracking technologies and systems that help organizations monitor and manage their physical assets for better efficiency and reduced losses.

This episode of Techsplainers explores CMMS software—how these systems automate maintenance operations, reduce downtime, and leverage AI to predict equipment failures before they happen.

This episode of Techsplainers examines the five types of preventive maintenance strategies and how modern technologies like IoT and AI are revolutionizing equipment maintenance practices.

This episode of  Techsplainers  explores Energy Asset Management—how organizations track, maintain and optimize critical power infrastructure to ensure reliability while facing growing demand and sustainability challenges.

This episode of Techsplainers explores asset lifecycle management—how organizations track, maintain and optimize valuable resources from purchase through disposal using IoT and AI technologies.

This episode of Techsplainers explores dark data - information organizations collect but never use - and how proper management can transform this hidden liability into valuable business insights.

This episode of Techsplainers explores data observability - how organizations monitor data health to prevent costly errors and maintain quality across systems, pipelines and processes.

This episode of Techsplainers explores data reliability—how organizations measure, maintain and improve the consistency and accuracy of their data to enable trusted insights and better decision-making.

This episode of Techsplainers examines the six core data quality dimensions that form the foundation of trusted data for analytics and AI: accuracy, completeness, consistency, timeliness, validity, and uniqueness.

This episode of Techsplainers explores data quality, examining the seven dimensions that determine if data is fit for purpose and how poor quality data undermines AI systems and business decisions.

This episode of Techsplainers examines Code LLMs—specialized AI models trained on programming languages that help developers write, understand, and improve code through AI-powered assistance.

This episode of Techsplainers examines COBOL modernization approaches—from rehosting and refactoring to complete replacement—helping organizations evolve critical legacy systems while preserving valuable business logic.

This episode of Techsplainers examines legacy code challenges and strategies, from incremental modernization to service wrappers, helping organizations maintain vital systems while evolving their tech landscape.

This episode of Techsplainers examines how AI tools are revolutionizing code documentation, saving developer time while creating more consistent, comprehensive, and maintainable software documentation.

This episode of Techsplainers explores code refactoring—how developers restructure code to improve quality while maintaining functionality, using techniques like Extract Method and removing duplication.

This episode of Techsplainers explores iPaaS use cases across industries, from automating workflows to connecting applications, with real-world examples of how this integration technology drives digital transformation.

This episode of Techsplainers demystifies iPaaS—cloud-based integration platforms that connect diverse apps and data sources, helping organizations break down silos and automate workflows without complex coding.

This episode of Techsplainers explores enterprise application integration (EAI), showing how businesses connect disparate systems using various architectures from point-to-point to microservices.

This episode of Techsplainers explores how EDI integration connects business systems to automate data exchange, examining architecture patterns, connectivity options, and emerging AI trends.

This episode of Techsplainers explains electronic data interchange (EDI) - how standardized digital systems enable businesses to automatically exchange documents like invoices and purchase orders.

This episode of Techsplainers explores hierarchical AI agents - multi-tiered systems where strategic, tactical and specialized AI components work together to solve complex problems efficiently.

This episode of Techsplainers examines utility-based agents - AI systems that make optimal decisions by calculating which actions provide the greatest overall benefit in complex, uncertain environments.

This episode of Techsplainers explores goal-based AI agents, showing how they plan ahead to achieve specific objectives while fitting into the broader spectrum of AI agent capabilities.

This episode of Techsplainers explores model-based reflex agents—AI systems that make decisions using both current perceptions and an internal model of the world for more effective environmental navigation.

This episode of Techsplainers explores simple reflex agents—basic AI systems that respond to environmental inputs with predefined rules, powering everything from thermostats to factory safety systems.