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Ep 126 – AI for networks and networks for AI (Gabriela Styf Sjöman)
Gabriela Styf Sjöman from BT Group explains why telcos need to stop obsessing over technology and prioritize subscriber-centric business outcomes.
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Ep 127 – The courage to change (Jim Abolt)
This week’s guest
Jim Abolt
Strategic HR and Transformation LeaderIf AI is so awesome, why isn’t it having a bigger impact on your bottom line? I hate to break it to you, but the problem isn’t with AI—it’s with your leadership. Look no further than the quality of your ideas and capability of your execution to figure out why your team is not getting better results with AI.
In this episode, I sit down with strategic HR and transformation leader Jim Abolt. He has decades of experience helping organizations navigate major transformations successfully, and isn’t surprised that AI initiatives are failing to deliver. Listen to our conversation as we discuss the hard truths about “Big C” Change leadership, why you need big ideas and bold execution to be successful with AI, and why you should listen to me, DR, the “Disruptor Who’s Right.” We dive into how I’m delivering real results when it comes to implementing AI in my own organization.
Listen now to hear:
- Why AI ROI failure is a leadership problem, not a technology problem [02:56];
- How Totogi redesigned work to achieve 20x developer productivity [06:28];
- Why telling employees the truth about AI’s impact isn’t reckless—it’s responsible [13:05]; and
- How thinking like a business person rather than a functional expert enables bold AI execution [15:27].
Links and resources
- I have a big idea that needs bold execution: my BSS vision for telcos. I want to use AI to help operators solve their vendor interoperability mess by creating a product that helps them build their own BSS. Learn how Totogi generated over 500,000 lines of production BSS Code in one day—live—at the TelecomTV Forum event to show that BSS Magic is the real deal. Check out our results.
- Watch my latest interview with Ray Le Maistre from TelecomTV where I discuss how coding with LLMs is approaching human-level capability.
- Check out my conversation with Scott Bicheno and Iain Morris on the Telecoms.com podcast. We talk about Totogi’s amazing feat at the recent AI-Native Telco Forum and answer all the questions people have about it.
- Read my blogs to learn why telco is the perfect use case for AI-assisted development, how context engineering enables production-ready AI applications, and why your legacy BSS vendors won’t rebuild as AI-native.
- Read the blog that Jim raved about, To get AI value, shift left (and eliminate jobs), to learn how to maximize the impact of AI in your organization.
- Listen to Jim’s previous episode, “How to build an AI-first organization” where he shares a practical framework for AI transformation and AI’s impact on jobs.
- Listen to the “Invest Like the Best” podcast in which Joe Liemandt calls Jim “one of the most influential people in my career.”
- Jim mentioned that I am always right—with the public cloud and now with AI! Watch this clip from the movie, Groundhog Day, where life insurance agent Ned Ryerson tries to convince Phil that he’s right. “Am I right or am I right or am I right? I’m right.”
- Check out this episode on our YouTube channel.
- You can find the episode transcript here.
Wanna talk AI and public cloud? Telco execs, set up a meeting with our team to learn how to tap the immense business value it can bring.
Guest bio
Jim Abolt is a retired HR executive who is widely regarded as an expert in leadership development, culture shaping, and large-scale organizational change. His career spanned multiple industries, disciplines, and continents. Starting as a labor relations specialist in a Midwest steel mill, he ultimately held global senior management positions with leading companies in the consumer packaged goods, pharmaceutical, and software development industries. Jim holds an A.B. from Dartmouth College, and a J.D. and M.A. from the University of Illinois.
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Podcast credits
- Executive Producer and Host: Danielle Rios, TelcoDR
- Senior Producer: Lindsay Grubb, TillCo Media
- Senior Editor/Brand Manager: Alisa Jenkins, Springboard Marketing
- Audio Editor: Andrew Condell
- Supervising Producer: Amanda Avery
- Associate Producer: Kriselda Dionisio
- Music: Dyami Wilson
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Frequently Asked Questions
1. Why aren’t telcos seeing ROI from their AI initiatives?
According to Jim Abolt and Danielle Rios, telco AI failures stem from leadership problems, not technology issues. Specifically, AI initiatives fail because ideas aren’t big enough and execution isn’t bold enough. Most telcos treat AI as a cost-cutting tool targeting 2-5% improvements instead of a capability multiplier. The key is pursuing 10X transformations that fundamentally redesign work rather than simply augmenting existing processes. Success requires bold leaders who are willing to make major changes that current experts may resist. Learn more in Jim’s previous Telco in 20 episode, “How to build an AI-first organization.”
2. How did Totogi achieve 20x developer productivity with AI?
Totogi’s developers now generate up to 20,000 lines of code daily, compared to several hundred previously. This shift required both technological readiness—advanced LLMs with improved coding capabilities—and cultural transformation through change management. Danielle Rios emphasizes that developers should tell AI what they want rather than writing individual lines of code. Totogi’s breakthrough came from thinking about what will be possible in two to four years and working backward. Watch DR’s interview with Ray Le Maistre from TelecomTV where she discusses how coding with LLMs is approaching human-level capability, and read her blog on why telco is the perfect use case for AI-assisted development.
3. How did Danielle Rios transform customer support at Totogi using AI?
DR redesigned Totogi’s customer support by rejecting the augmentation approach (which would yield only 20% productivity gains) and instead asking, “what if AI solved 100% of tickets?” Starting at 5-6% automation, the team systematically tested AI on different products and problem sets, creating a flywheel effect. They soon reached 85% automation, reducing headcount costs almost one-for-one while improving service. This demonstrates how AI solutions can fundamentally transform operations rather than just enhance them.
4. What does “shift left” mean in the context of AI transformation?
“Shift left” means eliminating work from humans entirely rather than just augmenting them with AI tools. Jim Abolt praised DR’s blog on this concept, calling it the best he had read in years. The approach recognizes that we’re experiencing the biggest work transformation in history where every job will be redesigned. It requires brutal honesty about job elimination while also creating new opportunities. Organizations must be transparent with employees about changes coming so they can prepare and pursue AI training opportunities.
5. Why is being brutally honest about AI’s impact on jobs considered responsible leadership?
Jim Abolt describes DR as “a master of being brutally honest without being brutal.” Warning employees about the biggest work transformation in history isn’t reckless—it’s responsible and helpful. Workers need transparency to plan their futures, volunteer for AI opportunities, attend training, and prepare for transitions. Employees who embrace AI by learning new capabilities will remain valuable whether they stay with their current company or move elsewhere. Hiding the truth prevents people from adapting to inevitable changes.
6. What makes Jim Abolt’s approach to HR unique for AI transformation?
Jim Abolt thinks like a business person who specializes in HR rather than just an HR expert—an approach inspired by Disney CEO Michael Eisner. This business-first mindset makes leaders more relevant, impactful, and effective at driving transformation. Joe Liemandt called Jim’s business advice among the most influential in his career on the “Invest Like the Best” podcast. For AI transformation specifically, this means understanding that leadership challenges—not technology limitations—prevent organizations from achieving the bold execution and big ideas necessary for real ROI from AI initiatives.