Promptly Speaking is your guide to navigating the AI revolution without losing your mind—or your job. We bridge the gap between complex technology and real-world application for professionals, creatives, and business leaders.
If you are asking questions like:
🤔 "Will AI replace my job?"
🤔 "Is AI making us dumber?"
🤔 "How do I lead a team through this disruption?" ...then this channel is for you.
We move beyond the "Tool of the Week" to provide deep dives into AI Strategy, Cognitive Offloading, AI Ethics, and the Future of Work. Whether you are a corporate executive, a solopreneur, or a parent concerned about screen time, we provide the frameworks you need to stay relevant and resilient.
New episodes every Tuesday.
Six months ago we were told to use AI constantly, for everything. Now companies are capping it, and we have to relearn how to work. This episode of our ai podcast looks at the cognitive overload that comes with juggling AI across five workflows at once, why output can rise while understanding quietly drops, and what that means for the future of work.
Key takeaways:
Model providers were subsidizing token costs, and usage-based pricing is changing how companies let employees use AI
Managing multiple AI-assisted workflows at once creates a different, heavier kind of context switching
Output can go up while depth and understanding quietly go down
Executives and knowledge workers alike are skipping the deep internalization step AI makes easy to skip
Speed and quality are in real tension, and planning time before a task may matter more than the task itself
The fix may be small, deliberate, AI-free planning time before diving in
Follow Sara & Dan:
Sara: https://www.linkedin.com/in/saralynneroberts/
Dan: https://www.linkedin.com/in/danroberts27/
Email the show: hello@promptlyspeakingpod.com (mailto:hello@promptlyspeakingpod.com)
00:00 Cold open: retraining after AI limits
00:41 Welcome back to the studio
01:20 Six months of "just use it"
02:03 How company token caps happen
02:48 Models got better, usage exploded
03:35 A friend's company caps his AI use
04:34 Companies rolling back heavy spend
04:58 Choosing the right model, not just the biggest
05:38 Running ten tasks at once
06:33 Context switching gets harder
08:15 Juggling searches, Slack, and tools
10:09 New research on context switching and output
11:46 Cognitive load and exhausted brains
12:20 Prepping executives before AI
13:46 Shallow understanding as a side effect
14:21 Balancing speed and quality
15:23 More capacity just means more expected of you
17:52 What happens when your AI gets cut in half
18:43 A simple fix: plan before you prompt
29 Sept 2026
Inside the Heated Debate Over AI on College Campuses
Universities are basically small cities, and getting everyone to agree on AI is nearly impossible. Beth Myers, CIO at the University of Colorado Denver, joins Sara and Dan to talk about rolling out AI across a massive, opinionated institution. She covers the heated debates between faculty and students, why some disciplines are all in while others resist, what the early research says about AI and learning, the mental health concerns showing up on campus, and why she still worries about the environmental cost of it all.
Connect with Beth Myers: https://www.linkedin.com/in/beth-myers/ (https://www.linkedin.com/in/beth-myers/)
Follow Sara & Dan:
Sara: https://www.linkedin.com/in/saralynneroberts/ (https://www.linkedin.com/in/saralynneroberts/)
Dan: https://www.linkedin.com/in/danroberts27/ (https://www.linkedin.com/in/danroberts27/)
Email the show: hello@promptlyspeakingpod.com (mailto:hello@promptlyspeakingpod.com)
00:00 Cold open, AI and complex organizations
00:54 Beth's title and 27 years at CU
01:31 What a vice chancellor actually does
02:11 Why bringing AI to a university is complex
04:11 Losing the ability to have hard conversations
04:31 Who gravitates toward AI, who resists
05:43 Talking to the AI student society
06:36 The computer lab techs' hesitancy
07:23 How heated does it get
09:21 How AI is changing how people learn
11:32 Why students need clearer AI guidelines
11:54 AI and entrepreneurship in students
14:13 The risks of vibe coding without knowing security
15:47 AI literacy and parenting teenagers
17:15 The parental controls Beth actually uses
19:56 AI, loneliness, and student mental health
21:08 Bringing AI into a university's shared governance
23:08 Why the ChatGPT rollout didn't go smoothly
24:10 Required training before getting AI access
26:14 Students who don't want AI in their education
27:22 What early research says about learning and AI
33:39 The environmental cost of AI
35:52 AI subsidies and what happens when prices rise
41:26 Closing thoughts and thanks
15 Sept 2026
The AI Habit That's Quietly Wrecking Customer Success Teams
A fluent AI answer isn't the same thing as a correct one, and that gap is costing companies more than they realize. Andrew Marks, co-founder of Success Hacker, joins Sara and Dan to unpack why AI's confident tone tricks even smart people into skipping the fact-check. They dig into persona validation, confidence calibration, and why Andrew's team built their own small language model instead of leaning on a chatbot. Along the way: why customer success teams are selling every day whether they admit it or not, why "easy button" layoffs are a mistake, and how to actually put a number on value.
Key Takeaways:
AI sounds confident even when it's wrong, and that fluency is exactly what makes it dangerous to trust blindly
Persona validation (feeding AI a stakeholder's real background) surfaces angles a person might miss
Confidence calibration forces AI to rate its own certainty, which helps catch hallucination before it spreads
Customer success teams often assume customers understand the value they're getting, when they don't
Value looks different depending on who you're talking to: end user, middle manager, or C-suite
Customer success professionals are selling constantly (renewals, expansions, retention) even when they resist the label
Cutting customer-facing teams because AI can "do it all" is a mistake companies will regret
Small, domain-specific language models may end up mattering more than giant general-purpose ones
Real value has to be translated into numbers stakeholders care about, like budget or revenue impact
00:00 Cold open: the easy button myth
00:43 Welcome and introducing Andrew Marks
01:10 Success Hacker's accreditation and university ties
02:11 What customer success actually is
04:34 Defining value across stakeholders
06:23 The biggest AI mistake teams make
07:19 AI hallucinations in the real world
08:23 What persona validation really means
09:08 Using AI on a real RFP response
13:27 A better way to prompt and iterate
17:32 Building a knowledge graph and trust layer
19:16 Four years building an AI platform
21:25 Who actually owns customer value
23:43 How this reshapes the customer journey
25:11 Naming "bot shitting"
26:10 Why layoffs based on AI are a mistake
27:58 AI should work with you, not for you
29:28 Teaching critical thinking in the AI era
32:47 Small language models vs. large ones
36:39 What conventional CS wisdom got wrong
39:10 The $150 million case for selling
42:04 Why comp models haven't caught up
44:41 Making value measurable and portable
48:35 The Project Lead the Way story
50:51 Advice for ethical AI adoption
53:52 What to do with 30 minutes and AI
References & Links:
Project Lead the Way, STEM curriculum nonprofit (www.pltw.org)
Success Hacker's AI for Customer Success training program (www.successcoaching.co/ai-for-customer-success)
University of San Francisco School of Executive Management (www.usfca.edu/management/programs/executive-education)
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