ServiceNow Podcasts

Hannes Mühleisen, creator of DuckDB, joins Juan and Tim to discuss why tables keep coming back and how the pendulum always swings. They cover the background of DuckDB, how it started, dive into the internals, when to use it and when not to use it. And of course, a bit of discussion on AI. Topics discussed

• Tables as the basic abstraction
• The pendulum from schemaless back to schema
• Physics and performance
• Install experience drives adoption
• Scale up versus scale out
• When to use DuckDB
• Graphs
• Governance of local compute
• Semantics belongs to other layers
• AI and agents
• Building a database company
• Do we need more database systems?

See omnystudio.com/listener for privacy information.

This is the takeaway episode with Hannes Mühleisen, creator of DuckDB who joined Juan and Tim to discuss why tables keep coming back and how the pendulum always swings. They cover the background of DuckDB, and so much more

See omnystudio.com/listener for privacy information.

Richard Masters, Senior Managing Director and Head of AI Strategy at Ancora AI by Design and former VP of Data at Virgin Atlantic joins Juan and Tim to discuss how to tie data and AI work to business strategy.

The first basic question: what does the organization want to look like in three to five years, and what has to happen to get there? Platforms and models only matter if they serve that answer. And how do you measure? Setting a baseline is hard, slow work, and teams often skip it to start building. Without one, it is hard to tell whether the work is worth doing, and harder to ask that question once the build is underway.

We cover: starting with AI instead of a workflow or metric, running a pre-mortem, tracking evaluation scores instead of the business outcomes, governance engineering, and the roles that change as AI takes over work that used to be outsourced. 

See omnystudio.com/listener for privacy information.

This is the takeaway episode with Richard Masters, Senior Managing Director and Head of AI Strategy at Ancora AI by Design and former VP of Data at Virgin Atlantic where he joins Juan and Tim to discuss how to tie data and AI work to business strategy.

We cover: starting with AI instead of a workflow or metric, running a pre-mortem, tracking evaluation scores instead of the business outcomes, governance engineering, and the roles that change as AI takes over work that used to be outsourced, and so much more. 

See omnystudio.com/listener for privacy information.

When AI can answer almost anything, how do you know you're actually learning? Senior Staff Software Engineer Tommy Ryan and former ServiceNow intern Hannah Kuo share how they tackle hard problems, check AI's work, and know when to ask for help.

#ServiceNow #EngineeringNOWUnlocked #AI #SoftwareEngineering #EarlyCareer

With school back in session, hosts Jorden Shelton and Cynthia Mathenge sit down with Tommy and Hannah to talk about learning and building with AI, and where teammates and mentors still fit in. ServiceNow Insights host Bobby Brill opens and closes the episode.

In this episode:
• How a senior engineer reviews your solution: back it up like you're in court, and run a pre-mortem • Why "correct and complete" matters, and why AI years are dog years
• Using output styles to make AI explain, quiz, and mentor you
• Hannah's time-boxing rule for deciding when to keep going, use AI, or ask a teammate
• Tommy's half-day rule, and why a walk can beat another hour at the keyboard
• Rubber ducking, explained with help from Woody • Advice for students: stay curious, learn fast, and automate the boring stuff

Guests:
Tommy Ryan, Senior Staff Software Engineer, Developer Productivity, ServiceNow
Hannah Kuo, Software Engineer and former intern, ServiceNow

Hosts:
Jorden Shelton, Technical Program Manager, AI Engineering Delivery, ServiceNow
Cynthia Mathenge, Business Operations Manager, Engineering, ServiceNow

Chapters:
0:00 Intro
1:32 Welcome to Engineering NOW Unlocked
2:22 Meet Tommy Ryan and Hannah Kuo
3:06 What Hannah wishes she knew before her internship
3:38 How a senior engineer reviews your solution
4:40 Checking AI's work: correct and complete
5:43 Make AI teach you with output styles
6:46 Breaking down a hard problem
9:11 Architecture isn't set in stone
10:17 Time-boxing: when to ask for help
11:32 The half-day rule and stepping away
12:42 Making it safe to ask questions 13:36 Imposter syndrome and work sessions
15:16 What is rubber ducking?
16:09 Rapid fire
18:18 Advice for students
20:10 Wrap-up

Pick one idea from this episode, try it this week!
Subscribe so you never miss an episode:
https://www.youtube.com/playlist?list=PLCOmiTb5WX3qvGq7Cp3o2KkCiplJyqQOK
For general information about ServiceNow, visit: http://www.servicenow.com/

See omnystudio.com/listener for privacy information.

In this special rant episode, Sonny Rivera joins Juan and Tim during a live recording and rant about AI-generated dashboards, semantic layers, and what it takes to trust an answer. The situation: dashboards slop is easy to build which makes foundation matter more, not less. We discussed: 

• Building a dashboard was never the hard part. Being able to explain where a number came from is.
• Speed without a governed foundation produces wrong answers faster. As Sonny put it, “fast and wrong is still wrong.”
• Modeling data is really modeling the business. The job has not changed. How it gets done is changing.
• A single source of truth did not hold up. Favors an interoperable set of semantic layers and context, with shared definitions where people actually care.
• Logic written in the moment, in a dashboard or a pipeline, often never makes it back into the semantic layer. Closing that loop is the gap to fix.
• Friction between teams is a sign that a definition matters. Make the differences visible rather than forbidding them.
• Trust is what data leaders can least afford to lose, which is why testing belongs in dashboards too.
• Writing code is no longer the slow part. Product management and testing are becoming the bottlenecks.

See omnystudio.com/listener for privacy information.

Nick Zervoudis, data product manager, trainer, and consultant at Value from Data & AI, joins Juan and Time to talk about why many data teams end up working like a service desk and what to do about it. The answer is: DO DISCOVERY! In this conversation, we cover how a service desk model breaks down when a request can’t be described in a few words, why dashboards get opened once and then forgotten, and how product thinking differs from project management in data work. We also discuss how to start doing discovery without a full transformation, and what AI does and doesn’t change about that work.

See omnystudio.com/listener for privacy information.

This is the takeaway episode with Nick Zervoudis, data product manager, trainer, and consultant at Value from Data & AI, joins Juan and Time to talk about why many data teams end up working like a service desk and what to do about it. The answer is: DO DISCOVERY! 

See omnystudio.com/listener for privacy information.

Mohammed Aaser, former chief data officer at McKinsey and former chief data strategy officer at Domo, recently visited Juan and shared his framework on talent in the AI era. It was incredibly insightful and we asked if he could come back to the podcast (after 5 years!) His argument is that the potential of AI will be unlocked by humans because humans understand the problems, and are driven/motivated to solve the problems. 

In this episode, Mohammed lays out a framework for talent in the AI era. Automation is already taking over the known, repeatable work. What remains is the unknown, and that takes people who keep learning and who take initiative. The framework plots people on two dimensions, learning and drive. You have Doers, Philosophers, Commanders, Hackers, Strategist, Innovators, Game changers and more. Topics discussed

• Automation and the innovation engine
• The known versus the unknown
• Two dimensions of talent
• The talent quadrants
• The multiplier effect
• Commanders and scaling
• Environments that don’t build initiative
• Rarity of high-potential people
• Investing in human potential
• The human mind as the limit
• Critical thinking through discomfort

See omnystudio.com/listener for privacy information.

This is the takeaway episode with Mohammed Aaser, former chief data officer at McKinsey and former chief data strategy officer at Domo. He recently visited Juan and shared his framework on talent in the AI era. It was incredibly insightful and we asked if he could come back to the podcast (after 5 years!) His argument is that the potential of AI will be unlocked by humans because humans understand the problems, and are driven/motivated to solve the problems. 

See omnystudio.com/listener for privacy information.