Time: 4:00 PM
Speaker Bio: Nathaniel Whittemore (NLW). Host of “The AI Daily Brief” podcast. Founder of Superintelligent (AI education platform).
Speaker Profile: Full Speaker Profile
Company: Super.ai provides intelligent document processing and workflow automation. Superintelligent teaches practical AI skills.
Focus: Real-world AI consulting patterns and best practices. How businesses are actually adopting AI beyond the hype.
References:
Notes
- Dubious MIT report about how things are working
- Enterprise adoption status
- ROI -> where we are seeing it
- From KPMG agent use survey
- 11% from Q1 to 42% to Q3
- Decrease to the resistance of agents
- Many orgs are stuck inside of pilots and experimental studies
- Only 7% believe that they are fully at scale
- Jagged patterns of adoption
- IT and ops jumping ahead of other use cases
- Big differences between leaders and laggers
- Spends is going to do nothing but increase on this
- Increase of optimism of the deployment of AI over the course of the year
- ROI report from NLW study
- 3500 use cases
- 8 impact categories
- Couple hours a week is multiple work weeks a year
- Different company sizes and different roles are using things differently and there is different impact
- Coding is further ahead
Slides
Slide: 2025-11-20-16-00
Key Point: This is a speaker introduction slide for Zachary Charlop-Powers from Lila Sciences, presenting at the AI Engineer Code Summit in New York 2025, with Google DeepMind as the presenter.
Literal Content:
- AIEngineer CODE SUMMIT logo with New York skyline and business person icon
- “NEW YORK 2025”
- “Zachary Charlop-Powers”
- “Principal Software Engineer, Applied AI”
- “Lila Sciences”
- “PRESENTED BY Google DeepMind” (with logo)
- QR code in bottom right
- Footer badges: “LEADER” and “ENGINEER”
Slide: 2025-11-20-16-10
Key Point: Time savings is by far the most common benefit/impact category from AI adoption, accounting for over one-third of all use cases, followed by increased output and quality improvements.
Literal Content:
- Title: “Impact Categories”
- 8 purple-outlined circular icons with categories and percentages:
- Time Savings (clock icon) - 35.0% of use cases
- Increased Output (up arrow) - 14.5% of use cases
- Quality Improvement (checkmark) - 14.4% of use cases
- New Capabilities (lightbulb) - 12.0% of use cases
- Improved Decision Making (brain icon) - 8.9% of use cases
- Cost Savings (dollar sign) - 7.9% of use cases
- Increased Revenue (chart icon) - 4.2% of use cases
- Risk Reduction (shield icon) - 3.4% of use cases
Slide: 2025-11-20-16-12
Key Point: Most AI-driven time savings are modest (1-10 hours per week) but meaningful - this translates to approximately 7-10 weeks of recovered work time annually, which represents significant value despite not being dramatic hour-for-hour savings.
Literal Content:
- Heading: “Most time savings are between 1-10 hpw”
- Subheading: “Buying back 7-10 weeks worth of work a year ain’t bad”
- Bar chart showing “Distribution of Time Savings (0-100 Hours/Units Focus)”
- Distribution heavily concentrated in the 0-10 range, with a long tail extending to 100
Slide: 2025-11-20-16-13
Key Point: AI benefits vary significantly by organizational role - different job levels and functions realize value from AI in different ways, suggesting that AI adoption strategies should be tailored to specific role categories rather than one-size-fits-all.
Literal Content:
- Title: “Different roles are finding different benefits”
- Stacked horizontal bar chart labeled “ROI Category Distribution by Role Category”
- Shows 6 different roles (Individual Contributor, C-Level/Founder, Director, VP, Other)
- Bars segmented by different colored sections representing: costSavings, improvedDecisionMaking, increasedOutput, increasedRevenue, newCapabilities, qualityImprovement, riskReduction, and timeSavings