Potential US Economic Impact by 2034
$4.8–$6.6 Trillion
AI augmentation could add between $4.8 trillion and $6.6 trillion to the US economy by 2034 (around 15% of its current size at the lower range). But only if learning keeps pace.
Ten percent cost savings are nice, but that's not what excites businesses the most. It takes reengineering the workflows to get to significant growth.
Realizing AI’s productivity promise through augmentation
AI delivers the greatest value when paired with human capability. To capture its full economic impact, businesses must commit to equipping workers with the skills and confidence to use AI effectively.
Estimated impact of AI worker augmentation on US economy (GVA) by 2034, (US$, 2024 prices)
The percentage change shows the potential increase in GVA that augmentation can bring to our cohort of knowledge workers
Closing the learning gap: A priority for every board
The bridge to productivity
AI’s potential is unlocked by closing the learning gap. Human-centric learning helps organizations boost productivity and growth.
Rethinking learning at work
Success with AI requires a radical new approach to learning. Learning and augmentation must happen together, from the start.
Investing in skills for the future
Supporting people in building the skills to thrive alongside intelligent systems is a strategic investment to power innovation and ensure wellbeing.
The pace and direction of progress will depend on how effectively we invest in human learning. Every positive scenario for an AI-enabled future is built on human development. Every negative one stems from neglecting it.
We thank the experts who contributed to this work
Beena Amanath, Thirumala Arohi, Jacqui Canney, Adrian Clamp, James Cook, Eleanor Cooper, Thomas H Davenport, Mark Esposito, Maria Flynn, David Garza, Dr Philippa Hardman, Jayney Howson, Jeana Jorgensen, Nick Kind, Stephanie Kneisler, Sudeep Kunnumal, Joseph Lin, Sandra Loughlin, Andrew Ng, Michael Osborne, Erin Rifkin, Matt Sigelman, Mark Williamson and Joshua Wohle.
Cognizant
Learning teams should shape future ways of learning and connect these models and experiences to business outcomes.
EPAM Systems
No one knows exactly how to use AI in their job right now—everyone’s just figuring it out. The trick is moving from initial training to use case-specific training by role.
Goodnotes
We’ve created a series of internal trainings that we call AI Foundations… Using human networking to put a human face onto all of this is how you get things going.
Because skills are evolving so quickly, we need better ways to assess those skills and validate them in a real-world scenario of how people actually use those skills.
IBM
We’ve gone further, using the skills data embedded in badges to build a connected system that continuously links learning, skill development, and career advancement.
Jobs for the Future
As a leader, I share how I use AI in my day to day, where it works and where it doesn’t, to lead by example. I support employees in training, testing, iterating, and sharing with each other.
KPMG Consulting
Modern AI-driven learning isn’t just about delivering content—it’s designed to continuously assess your progress, identifying how quickly you master concepts and tailoring the experience to your pace.
Microsoft
The agentic coach is helping our sellers feel like they’re learning directly from Microsoft leaders. This has created greater psychological safety in ways we didn’t anticipate.
Mindstone
When companies run synchronous formal training… they should keep that hour blocked on employees’ calendars for the rest of the year as ‘experimentation time'.
Pathstream
To shift frontline teams to higher value work, make hard work feel fulfilling—fuel progress, confidence, agency, and impact.
Pearson
Communication Coach accelerates learning in the flow of work, amplifying human expertise with AI. At Pearson, technology and people grow together, driving real transformation.
ServiceNow
Especially for base-level AI work, AI knowledge is ‘caught, not taught.’ People will hear other people talking about it and see other people doing it.
Tata Consultancy Services (TCS)
Our senior leaders have a ‘human copilot’ and these are the Gen Z employees who are AI natives, and they have really made a phenomenal difference.
Tecnológico de Monterrey
Teams of what I call ‘shakers,’ specialized teams fully dedicated to disruption… that's where I've seen many of the good use cases come from early on.