If there was a top takeaway from this week’s Momentum AI conference in New York, it was this: AI literacy is now essential for competitive advantage — both for companies and individuals.
Karin Kimbrough, Chief Economist at LinkedIn, laid out the numbers:
- 70 percent of executives now expect AI skills from entry-level hires
- 20 percent of US jobs today didn’t exist in 2000
- AI-related skills have grown 6x over the past few years
- Finance is leading AI adoption; education is lagging behind.
She warned: “The future of AI isn’t in 2030. It’s happening now.”
But we’re not ready.
China is mandating AI education, but its literacy rate remains around 30 percent. In contrast, the US scores closer to 70 percent, yet half of American adults read below a sixth-grade level. Access isn’t enough. Comprehension and application are the true gap.
And this gap has deeper consequences. If women, underrepresented groups, and marginalized voices aren’t in the training data or on the build teams, they’re left out of the future entirely.
Even when access is there, trust takes longer.
Geddes Munson of Affirm noted:
“Machines need to be ten times more accurate than humans to be trusted at the same level.”
Scaling AI Requires Culture, Not Just Code
Katya Andresen, SVP at Mastercard, delivered one of the sharpest insights of the event:
“It’s a fail if we have to go through change management. That means the culture wasn’t ready to begin with.”
Scaling AI isn’t about rolling out new tools, it’s about embedding a mindset of experimentation, learning, and resilience across the organization.
Leaders across industries are proving this:
- Mary Alice Vuicic, Chief People Officer at Thomson Reuters, launched AI learning paths for 27,000 employees.
- Sydney Klein, Chief Information Security Officer at Bristol Myers Squibb built an internal “storefront” for hands-on AI use.
- Ali Keshavarz, Chief Data, Analytics & AI Officer at CVS Health involved 300,000 frontline employees in co-creating use cases, especially in clinical operations.
In every success story, the frontline wasn’t just informed they were architects of co-creating change.
Matt Studney, SVP at Merck Research Labs from the organizational design panel, didn’t mince words: “Failure creates headlines. Scaling creates value.”
There’s enormous temptation to celebrate early AI pilots. But real value comes from repeatable, scalable systems tied to business outcomes.
Examples:
- Destination Canada accelerated tourism marketing by cutting data lag from 18 to 6 months, increasing media efficiency by 40 percent.
- Oura used 13.5 billion hours of biometric data to shift from consumer wearables to enterprise healthcare.
- American Express reported a 10 percent productivity boost among 7,000 software engineers using generative AI — proof that high-scale adoption can move the needle.
The takeaway: move fast, but move with purpose.
The New Org Chart: Flat, Agile, and Cross-Functional
AI isn’t just automating tasks. It’s reorganizing companies.
The most forward-looking firms are:
- Flattening hierarchies
- Building AI product teams across business functions
- Prioritizing emotional intelligence, not just technical credentials.
Katya Andresen, Chief Digital & Analytics at The Cigna Group added that system level thinking, seeing patterns, designing workflows, rethinking incentives is the skill that will separate leaders from laggards.
The Rise of AI Agents — and the Need for Real Human Integration
AI isn’t just automating tasks; it’s evolving into collaborative agents that reason, learn, and interact across workflows.
In a session on AI in financial services, executives from TIAA, Sari, and Bank of America outlined real-world wins, including:
- Service Level Agreement (SLA) times cut
- Millions in annual cross-functional savings, and
- Streamlined operations in customer service, lending, and fraud detection.
Aditya Bhasin, Chief Technology and Information Officer at Bank of America, framed the challenge this way: “Systems do repetition well. People do empathy well. You have to start there.”
The bigger reflection? If AI systems only perform simple, repetitive tasks, they’ll never evolve. Like Neo, the humanoid robot who is learning through immersion, AI must be exposed to complex, human-driven environments to grow meaningfully not just faster, but smarter.
The Stakes are Human, Not Just Technical
The future of AI in large organizations isn’t pre-ordained; it’s actively being shaped right now by the decisions leaders are making about who gets trained, who gets a seat at the table, and how AI systems are designed, deployed, and governed.
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