How the Best Companies Use AI
Episode
26 min
Read time
2 min
Topics
Productivity, Fundraising & VC, Leadership
AI-Generated Summary
Key Takeaways
- ✓AI Economic Concentration: PwC research shows 75% of AI's economic gains flow to just 20% of companies. Those leaders are 2-3x more likely to use AI for identifying growth opportunities and 2.6x more likely to report AI enabling business model reinvention — not just efficiency gains.
- ✓Measurable ROI Benchmark: McKinsey's study of 20 AI-leading companies found AI-driven business transformations delivered a 20% EBITDA uplift on average, reaching breakeven within one to two years and generating three dollars of incremental EBITDA for every one dollar invested — a concrete benchmark to hold internal AI programs accountable to.
- ✓Institutional vs. Individual AI: Individual AI productivity gains do not automatically compound into organizational value. Without a coordination layer — shared outputs, defined roles, unified workflows — thousands of employees using AI independently create noise, not leverage. Building institutional systems to align individual AI use is a distinct and separate challenge.
- ✓Ramp's Glass Platform: Ramp built an internal AI workspace where every employee gets a fully configured environment on day one, with SSO-connected integrations, a marketplace of 350-plus reusable agent skills, persistent memory, and scheduled automations. When one employee discovers a better workflow, the entire team inherits it automatically through the shared skill system.
- ✓Don't Limit Employee AI Ceilings: Ramp's design principle rejects segmenting employees into basic versus power AI users. Because AI itself can tutor and coach anyone through complexity in real time, organizations should configure systems that give every employee access to full capability — not simplified, restricted interfaces based on assumed technical limitations.
What It Covers
A PwC study, McKinsey's AI transformation manifesto, and Ramp's internal Glass platform reveal how top-performing companies use AI as a growth and business model reinvention tool rather than a productivity shortcut — and why building institutional AI infrastructure separates leaders from laggards.
Key Questions Answered
- •AI Economic Concentration: PwC research shows 75% of AI's economic gains flow to just 20% of companies. Those leaders are 2-3x more likely to use AI for identifying growth opportunities and 2.6x more likely to report AI enabling business model reinvention — not just efficiency gains.
- •Measurable ROI Benchmark: McKinsey's study of 20 AI-leading companies found AI-driven business transformations delivered a 20% EBITDA uplift on average, reaching breakeven within one to two years and generating three dollars of incremental EBITDA for every one dollar invested — a concrete benchmark to hold internal AI programs accountable to.
- •Institutional vs. Individual AI: Individual AI productivity gains do not automatically compound into organizational value. Without a coordination layer — shared outputs, defined roles, unified workflows — thousands of employees using AI independently create noise, not leverage. Building institutional systems to align individual AI use is a distinct and separate challenge.
- •Ramp's Glass Platform: Ramp built an internal AI workspace where every employee gets a fully configured environment on day one, with SSO-connected integrations, a marketplace of 350-plus reusable agent skills, persistent memory, and scheduled automations. When one employee discovers a better workflow, the entire team inherits it automatically through the shared skill system.
- •Don't Limit Employee AI Ceilings: Ramp's design principle rejects segmenting employees into basic versus power AI users. Because AI itself can tutor and coach anyone through complexity in real time, organizations should configure systems that give every employee access to full capability — not simplified, restricted interfaces based on assumed technical limitations.
Notable Moment
Ramp's internal AI lead describes employees who never used a terminal now running scheduled automations that would have required an engineer six months prior — framing the goal not as lowering the ceiling for advanced users but permanently raising the floor for everyone simultaneously.
Episode Transcript
Today, we are discussing how the best companies at using AI are using AI. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Alright, friends. Quick announcements before we dive in. First of all, thank you to today's sponsors, Blitsy, Granola, Section, and Assembly. To get an ad free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts. Ad free is just $3 a month. To learn more about sponsoring the show or really anything else about the show, you can go to a idailybrief.ai. It's gonna have links to pretty much everything we got cooking from free education programs, to our AI operators community, to the new newsletter that is where you can find all the links that I talk about in the show, to, of course, our companion experience page. We did one of those for our how to use Opus four seven and the new codex app episode on Friday. Today's episode combines a number of the big themes that we've been talking about this year. 2026 kicked off with everyone coming back from the holiday break, really understanding and grappling with just how big an advance we had gotten between the harnesses like Claude Code and Codex, and like the models like Opus four five and the g p t five two five three series that had come out around the end of last year. Ever since then, it has just been a race and it has been very clear that there is a massive difference and a growing difference between the people who are best at using AI and using it most fully and those who are not. Now clearly, a huge amount of that work is running through code even when the ultimate output is not building software. Turns out that when you give AI and agents the ability to write code, it in fact unlocks a huge amount of other capabilities that are relevant for knowledge work way outside of just software engineering. When OpenAI announced their new codex app, they said that a full 50% of the usage is not about coding specifically. Now in many ways, the conversation that we've been having is focused on the individual, what you can as an individual do to get the most out of this new set of tools, how you can build and set up your own OpenClaus, what features of the new codex or Cloud Code app you should be using, things like that. And yet at the same time, another big theme for this year is that there is clearly a growing gap between the companies that are using AI best and those who are farther behind. A great example of this was on display with the PwC study we talked about earlier this week that found that three quarters of AI's economic gains were being captured by just 20% of the companies. And importantly, it wasn't …
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Books, tools, and gear mentioned in this episode
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Tools
“SPONSORS: Section (https://sectionai.com)”
by Ramp
“Ramp built an internal AI workspace where every employee gets a fully configured environment on day one, with SSO-connected integrations, a marketplace of 350-plus reusable agent skills, persistent memory, and scheduled automations.”
“SPONSORS: AssemblyAI (https://assemblyai.com/brief)”
“SPONSORS: Blitzy”
“SPONSORS: Granola (https://granola.ai/aidaily)”
company
“McKinsey's study of 20 AI-leading companies found AI-driven business transformations delivered a 20% EBITDA uplift on average, reaching breakeven within one to two years”
“A PwC study, McKinsey's AI transformation manifesto, and Ramp's internal Glass platform reveal how top-performing companies use AI as a growth and business model reinvention tool rather than a productivity shortcut”
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