EP 271 - How to Quantify AI ROI Beyond ‘Time Saved’
Release Date: 12/25/2025
EP281 Your Team's Nervous System Is Infrastructure - Sira Laurel on Load-Bearing Trust
AI Literacy for Entrepreneurs
Most of the leaders Sira Laurel works with look like they're winning. Inside, they're exhausted. Sira is an executive coach and facilitator who works on leadership, organizational design, cognitive load, trust and nervous system capacity. She has watched organizations get very good at making work faster while getting worse at deciding what the speed is for. AI raises the stakes on that question. Her starting point is that capacity covers far more than time. It includes attention, decision-making, emotional bandwidth and creative energy. A tool can hand back hours on the calendar while draining...
info_outline
EP280 Faster Than a Generation - Ian Alden Russell on Change That Outruns Us
AI Literacy for Entrepreneurs
This episode skips the tools, the prompts, and the latest model release. It goes somewhere more philosophical. started out as an archaeologist and anthropologist. He was the kid who loved museums and Indiana Jones. Since then he has worked across history, contemporary art and culture in Dublin, Istanbul, Beijing, Hong Kong, Pittsburgh and Doha. Now he works as a strategist helping organizations make sense of complexity and line up around change. His starting point is that change itself is old news. Humanity has absorbed huge technological shifts again and again. The new part is the speed....
info_outline
EP279 How higher ed moves from AI interest to governed use with James Hutson
AI Literacy for Entrepreneurs
A previous president from Lindenwood University (a former IBM executive) stands up in town halls and tells people he uses Claude - that it helped him organize the financial report. HR follows and says they used Gemini for an employee questionnaire. Then James Hutson PhD. goes and asks employees whether they understood that as permission. The answer? 'No. The policy says I'll get fired if I use it.' That gap is the subject of this episode. James is Senior Professor and Director of AI-Enabled Academic Transformation at Lindenwood University, with two PhDs - the first in art history, the second...
info_outline
EP278 AI Governance in Higher Ed with Dr. Eugene Chan
AI Literacy for Entrepreneurs
Most organizations govern AI the same way cities govern speeding. They set a rule, then punish people who break it. Dr. Eugene Chan thinks that's the least interesting tool available. He's a behavioural scientist - founder of the consultancy Behavieural and Professor of Business at Tyndale University - and his argument is that punishment is a lagging response to a problem you could have designed out. His analogy is a speed camera versus a speed bump. Both reduce speeding. The camera does it by fining you after the fact, and it needs maintenance forever. The speed bump does it by making the...
info_outline
EP277 Is AI Going to Take the Gift of Failure Away? asks Julie Cole
AI Literacy for Entrepreneurs
Episode description Julie Cole and her co-founders started Mabel's Labels in a Hamilton basement 23 years ago, back when customers were still nervous about typing a credit card number into a computer. They figured out early that they weren't really running a label company - they were running a tech company that happened to make labels. Nothing off the shelf did what they needed, so they built it themselves. Which is why her read on this moment carries some weight. She's watched every technology wave since. She's not a skeptic and she's not a cheerleader. She uses AI, has a clear personal rule...
info_outline
EP276 Your AI Strategy Is a Hammer. Where's the Nail? - with David Cohen
AI Literacy for Entrepreneurs
"We need AI" is the most common brief in business right now. David Cohen's read on what it means: usually nothing in particular. David is the founder of Superposition, a consultancy for data and AI consultancies - a meta consultancy, as he calls it. He's spent his career inside both big and boutique consulting shops, which makes him refreshingly blunt about what's actually happening in the services world right now. His diagnosis is that we're still in the solution-in-search-of-a-problem stage. Everybody's curious. Everybody's excited. There's enormous movement, enormous hype, and very few real...
info_outline
EP 275 "Do I Trust Myself?" Grace Gravestock on the Real Reason AI Adoption Stalls
AI Literacy for Entrepreneurs
Episode description Everyone's arguing about whether AI can be trusted. Almost nobody's asking the harder question underneath it. Grace Gravestock has spent more than twenty years leading change management on some of the highest-profile technology projects around - including the team that set the stage for the $67B Dell EMC integration. Her verdict on why AI pilots stall is unsentimental: it's the same reason ERP stalled, and CRM before that. People aren't using the technology the way it was intended. You can have the best tools in the world, and if people don't use them, it doesn't matter....
info_outline
EP 274 The Human OS - AI Adoption With Curiosity, Safety, and Monday Ease ft. Melissa Penton
AI Literacy for Entrepreneurs
In the final episode of the Podcast-to-Book series, host sits down with change leader and AI education lead (Sun Life) for a human-first conversation about what actually makes AI adoption work. They talk productivity vs room-for-life, why one-prompt culture is snake oil, the shift from prompt engineering to context engineering, and the simplest enterprise question that changes everything: “What would make Monday easier for employees?” Episode summary Susan closes out the Podcast-to-Book sprint with a conversation that feels like the point of the whole series: AI isn’t a tool problem....
info_outline
273 - Future-proofing your organization through continuing AI literacy
AI Literacy for Entrepreneurs
Most companies do a few AI trainings, run some pilots, and then stall. In this episode, host argues the only real future-proofing strategy is continuous AI literacy. She breaks down what “continuous literacy” actually includes (skill, judgment, workflow, norms), the predictable failure modes of the AI literacy divide, and a simple flywheel you can run monthly so capability keeps compounding. Episode summary Susan opens with a familiar pattern: a burst of AI excitement, a deck called “AI Strategy 2025” a few clever workflows… and then reality hits. Tools change. Policies shift....
info_outline
EP 272 - Mindset, Sales, and AI That Actually Helps with Gazzy Amin
AI Literacy for Entrepreneurs
Host sits down with sales strategist , founder of Sales Beyond Scripts, to talk about the real ways AI is changing revenue, planning, and scale. They cover AI as a thinking partner, how to use it across departments in a small business, why audits matter more than hype, and how mindset quietly determines whether you treat AI as a threat or an advantage. Episode summary This episode is part of Susan’s 30-episodes-in-30-days “podcast to book” sprint for Swan Dive Backwards. Susan and Gazzy zoom in on the selling process first. Then they zoom out to the whole business. They talk about three...
info_outlineIf you’re measuring AI success by “hours saved” you’re playing the easiest game in the room. In this episode, Host Susan Diaz explains why time saved is weak and sometimes harmful, then shares a better “AI ROI stack” with five metrics that map to real business value and help you build dashboards that actually persuade leadership.
Episode summary
Time saved is fine.
It’s also table stakes.
Susan breaks down why “we saved 200 hours” is the least persuasive AI metric, and why it can backfire by punishing your early adopters with more work. She then introduces a smarter approach: a set of five metrics that connect AI usage to quality, risk, growth, decision-making, and compounding capability.
If you want your AI work funded, supported, and taken seriously, you need to move the conversation from cost to investment. This episode shows you how.
Key takeaways
Time saved doesn’t automatically convert to value.
If no one reinvests the saved time, you just made busy work faster.
Hours saved can punish high performers.
Early adopters save time first.
They often get “rewarded” with more work.
Time saved misses the second-order benefits.
AI’s biggest wins often show up as fewer mistakes, better decisions, faster learning, and faster response to opportunity.
Susan’s “AI ROI stack” has five stronger metrics:
-
Quality lift
Is the output better?
Track error rate, revision cycles, internal stakeholder satisfaction, customer satisfaction, and fewer rounds of revisions (e.g., proposals going from four rounds to two). -
Risk reduction
AI can reduce risk, not only create it.
Track compliance exceptions, security incidents tied to content/data handling, legal escalations/load, and “near misses” caught before becoming problems. -
Speed to opportunity
Measure time from idea → first draft → customer touch.
Track sales cycle speed, launch time, time to assemble POV/brief/competitive responses, and responsiveness to RFPs (the “game-changing” kind of speed). -
Decision velocity
AI can reduce drag by improving clarity.
Track time-to-decision in recurring meetings, stuck work/aging reports, decisions per cycle, and decision confidence. -
Learning velocity
This is the compounding one.
Track adoption curves, playbooks/workflows created per month, time from new capability introduced → used in production, and how many documented workflows are adopted by 10+ people.
Dashboards should show three layers:
Leading indicators (adoption, workflow usage, learning velocity).
Operational indicators (cycle time).
Business outcomes (pipeline influence, time to market, cost of service).
You’re not investing in AI to save hours.
You’re building a system that produces better work, faster, with lower risk, and gets smarter every month.
Timestamps
00:01 — “If you’re measuring AI success by hours saved… that’s table stakes.”
00:51 — Why time saved doesn’t translate cleanly into value
01:12 — Time saved doesn’t become value unless reinvested
01:29 — Hours saved can punish high performers (they get more work)
02:10 — Time saved misses second-order benefits (mistakes, decisions, learning)
02:45 — Introducing the “AI ROI stack” (five better metrics)
02:59 — Metric 1: Quality lift (error rate, revision cycles, satisfaction)
03:31 — Example: proposal revisions drop from four rounds to two
04:14 — Metric 2: Risk reduction (compliance, incidents, legal load, near misses)
05:19 — Metric 3: Speed to opportunity (idea to customer touch, sales cycle, launches)
06:11 — Example: RFP response in 24 hours vs five days
06:34 — Metric 4: Decision velocity (time to decision, stuck work, confidence)
07:30 — Metric 5: Learning velocity (adoption curve, workflows, time to production)
08:57 — Dashboards: leading indicators vs lagging indicators
09:15 — Dashboards should include business outcomes (pipeline, time to market, cost)
09:32 — Reframe: AI as a system that improves monthly
10:08 — “Time saved is the doorway. Quality/risk/speed/decisions/learning is the house.”
10:36 — Closing + review request
If your AI dashboard is only “hours saved” keep it - but don’t stop there.
Add one metric from the ROI stack this month.
Start with quality lift or speed to opportunity.
Then watch how fast the conversation shifts from cost to investment.
Connect with Susan Diaz on LinkedIn to get a conversation started.
Agile teams move fast. Grab our 10 AI Deep Research Prompts to see how proven frameworks can unlock clarity in hours, not months. Find the prompt pack here.