AI Today Podcast

Kathleen Walch speaks with Jürgen Eckhart, Engineering Program Manager at Google in Munich, lecturer in project management, and PMI-CPMAI certified practitioner. Jürgen shares how CPMAI expanded his “toolbox” for managing AI projects, grounding them in business value, quality data, and realistic stakeholder expectations.
He explains why AI must be treated as iterative and probabilistic, not deterministic “magic,” and how project managers can shift from delivery to discovery—offloading routine tasks to AI while focusing on orchestration, collaboration, and trust. Jürgen likens today’s AI era to the “fax machine state,” a transitional phase in which AI is already relieving administrative burdens, yet its ultimate potential is still unfolding.
You’ll gain insights on: 
Why project management foundations matter for AI success
How CPMAI enables continuous reassessment in fast-changing environments
What practical methods integrate AI into daily workflows, from meeting notes to coding support
How to balance automation and creativity in stakeholder management
How ethical standards and frameworks build trust in AI adoption
Jürgen also reflects on misconceptions fueled by hype, shares real-world use cases like AI-assisted SQL scripting and portfolio reporting, and emphasizes the importance of ethics as AI accelerates global problem-solving. Practical and inspiring, this episode is a must-listen for project managers, AI practitioners, and leaders ready to strengthen their foundations and harness AI responsibly.

Kathleen Walch speaks with Vinay Babu Tarala, Agile and AI Transformation Leader at AT&T, PMI Pearl City Hyderabad Chapter General Secretary, and author of AI Meets Agile. With passion and candor, Vinay shares how early AI project failures led him to PMI-CPMAI certification, transforming his approach to managing AI initiatives.
He illustrates how CPMAI’s structured framework reduced software development cycle times from 2.5 months to 1.5 months by embedding AI across the SDLC—from user story creation to coding and testing. Vinay emphasizes that success comes not from chasing tools, but from solving real business problems with quality data and strong governance.
You’ll gain insights on: 
Why AI projects fail without a structured methodology 
How CPMAI enables measurable outcomes and cycle-time reduction 
Why AI is probabilistic, iterative, and experimental—not deterministic “magic”
How project managers must shift from delivery to discovery, fostering cross-functional collaboration, and a common language 
Practical steps for project managers to start using AI in daily workflows
Vinay also tackles misconceptions about AI, the rise of agentic workflows, and why human creativity and critical thinking remain irreplaceable. Practical and inspiring, this episode is a must-listen for project managers, AI practitioners, and leaders ready to move beyond hype and harness AI as a transformative force in project management.

Kathleen Walch speaks with Johan Sporre, Engineering Manager at IKEA Retail (Ingka Group) and CPMAI-certified professional, about how AI is transforming translation and language services at scale. 
Johan explains how his team supports 230,000 coworkers across 64 markets, with 90% of translations now AI-driven, and how CPMAI ensures alignment, quality data, and measurable outcomes.
An IKEA case study shows how redefining translation as “text generation” in IKEA’s voice, powered by a graph database enriched with metadata, created a scalable foundation across dozens of languages. Johan highlights the challenges of balancing linguistic nuance with algorithms, misconceptions about “out-of-the-box” LLMs, and why operationalization—not deployment—defines AI success.
You’ll gain insights on:
Why CPMAI’s structured phases prevent costly failures
How IKEA scaled translation from 6 to 46 languages iteratively
Why tone, grammar, and cultural context matter in AI
How sustainability and resource use shape AI’s future
How project managers are evolving as orchestrators of AI-enabled workflows
Practical and rich with real-world lessons, this episode is invaluable for project managers, AI practitioners, and leaders navigating language, technology, and business transformation.

In this episode of AI Today, host Kathleen Walch speaks with Michael Joyce, Enterprise Service Delivery and AI Transformation Leader at Cisco, about what it really takes to move AI from experimentation to operational impact in enterprise project delivery. 
Drawing on his experience leading technical program management teams across multiple customers, Michael shares how Cisco is applying generative AI to accelerate service delivery, improve decision quality, and empower project managers. He also founded Cisco’s internal AI Innovator Hub for PMs and helped create an AI for PM playbook, giving teams a repeatable way to apply AI under delivery pressure. 
Michael explains why AI projects differ fundamentally from traditional software initiatives, and why treating AI like a new team member through proper onboarding, context engineering, and iterative coaching turns it into a powerful "co-pilot" rather than just another tool. He and Kathleen discuss common pitfalls for AI projects like “shiny AI features” that fail to deliver, and how the CPMAI framework helps teams ask the right questions early, using Business Understanding and the AI Go/No-Go checklist to avoid wasted effort. 
A standout story from the podcast is how a high-stakes hospital system cutover illustrates how organizing critical knowledge into AI-supported playbooks enabled faster responses, smoother transitions, and stronger stakeholder confidence. They also explore PMI’s M.O.R.E. vision, the importance of relentlessly reassessing AI initiatives as conditions change, and how project managers can balance speed with accountability to deliver trustworthy, responsible AI. 
You’ll hear insights on: 
How PMI-CPMAI establishes a common language between business and technical teams 
Why chasing “shiny” AI features without understanding why often fails  
How treating AI like a teammate leads to stronger results 
How project leaders can balance speed with accountability in responsible AI 
How project managers are evolving into orchestrators of AI-enabled workflows 
Practical, grounded, and enterprise-focused, this episode offers valuable perspectives for project managers, technology leaders, and decision-makers working to turn AI ambition into dependable business results.

In this episode of AI Today, host Kathleen Walch speaks with CP Richardson who is co-founder of Agile in Color, an Agile Alliance Board Member, and is also PMI- CPMAI certified to discuss what it really takes to bring AI into day-to-day work with discipline, intention, and the right framework.

CP draws on more than 15 years of experience across software development, operations, marketing, and AI, including early work with machine learning in fraud detection. He also shares lessons learned from experimenting with AI to help run a small wine import business and how earning his PMI-CPMAI certification gave him a shared language and a structured, repeatable approach to AI, replacing instinct-driven decisions with clearer business alignment.  That hands-on context provides some of the episode’s most practical and relatable insights.

CP shares how he built an AI agent to help do prospecting for his wine import business. While the agent failed to deliver on the desired outcome, the lessons learned were valuable: building the right solution technically does not guarantee business success. Without CPMAI’s Phase One Business Understanding and the AI Go/No Go checklist, teams risk investing in solutions that work in theory but fail in practice.

Kathleen and CP also discuss the importance of stakeholder engagement, the PMI M.O.R.E. vision of moving project managers beyond outputs to outcomes, and how combining agile principles with a data-centric methodology enables faster learning, better decisions, and more effective iteration.

You'll hear insights on: 
Why Business Understanding is such a critical phase of any AI project 
How the AI Go/No Go checklist surfaces critical questions before starting your AI project 
What a real-world AI failure teaches about context, behavior, and business objectives 
Why agile practitioners need "chops" — and how AI raises the ceiling for those who have them 
How small businesses can punch above their weight with AI, if they're disciplined about it 
Practical, grounded, and anti-hype — for project managers, agile coaches, engineers stepping into AI leadership, or founders figuring out what AI can do for their business.

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