Originally published on Financial Tech Times
Your grandparents watched rural electrification transform night into day. Your parents saw color television and cable reshape how the world connected. Many leaders built their careers during the rise of the internet and the smartphone.
Rick Inatome has lived at the center of those inflection points. As an architect of the digital age throughout his career and today, a Managing Director of Collegio Partners, he continues to operate at the intersection of innovation, leadership, and long-term impact.
Now, artificial intelligence is rewriting that curve entirely, rising faster than anything before it.
Recent research estimates that AI tools have reached over a billion users in under three years, making AI the fastest-diffusing technology in history. ChatGPT alone reached 100 million users in roughly two months.
What makes this curve especially significant is not just its steepness, but its impact. Earlier technologies primarily changed how we communicate or access information. AI changes who can create value.
From Energy and Information to Intelligence
Electricity enabled industry to redesign production processes. Radio and television created mass culture and connectivity. The internet facilitated global digital commerce.
Smartphones turned every pocket into a worldwide networked communication device.
AI, by contrast, offers a general-purpose capability that touches any task involving language, images, numbers, or decisions. It can draft contracts, analyze data, design interfaces, suggest code, simulate scenarios, brainstorm, and perform a dazzling array of other functions. In effect, intelligence has become infrastructure — a shared capability, delivered through models and APIs that anyone can plug into.
In this environment, the question for leaders is no longer “Do we use AI?” It is “How quickly can we learn to build with it, and what uniquely human value can we create?”
The Skills Signal Behind the Curve
The World Economic Forum’s Future of Jobs report provides an important companion chart. Employers around the world rank analytical thinking, creative thinking, and AI/big data as the top skills rising in importance. Leadership, social influence, curiosity, lifelong learning, resilience, and technological literacy follow close behind.
WEF also finds that six in ten workers will need training by 2027, yet only about half can expect adequate access to upskilling. The two charts—AI’s near-vertical adoption and the skills outlook—send a clear message to leaders that the primary constraint on AI value no longer is access to technology. It is the learning velocity of their people.
Rethinking Talent Strategy for an AI Infrastructure World
What does it mean to manage talent when intelligence is abundant?
1. Make AI fluency the new baseline literacy
Just as previous waves required basic digital skills, the AI era demands fluency in prompts, workflows, and model strengths and limitations. The need is not about turning everyone into data scientists. It is about ensuring every knowledge worker can:
- Frame problems in ways AI can help solve
- Critically evaluate AI outputs
- Combine multiple tools into effective workflows
It is clear that short, practice-based learning sprints—“AI in your job in 30 days”—will outperform general theory courses that sit far from daily work.
2. Design roles around human differentiation
If AI is good at pattern recognition, summarization, and first drafts, humans must double down on:
- Judgment and ethical reasoning
- Relationship-building and trust
- Cross-domain creativity and systems thinking
- Role descriptions, performance reviews
Leadership development must reflect this emerging division of labor to avoid paying human wages for tasks that machines can do better and cheaper.
3. Build internal “AI Fellowships” for employees
Elite universities and innovative schools are experimenting with AI/entrepreneur fellowships that give students structured time to build real projects with AI tools. The corporate equivalent might include programs such as:
- Multi-month internal entrepreneur programs in which cross-functional teams use AI to deliver a 10x improvement in a real process
- Rotational “AI builder” roles that sit at the edge of business units and technology
- Recognition and career pathways for employees who become AI force multipliers
These types of programs can signal that the organization values initiative and learning velocity as much as existing credentials.
4. Close the access gap
If only a subset of employees gains meaningful exposure to AI tools and training, firms will assume the risk of inequality within their ranks. The very people who most need leverage—frontline staff, early-career employees, workers in lower-margin regions—are the ones who can least afford being left behind. The basic principle should be that if a task can be significantly augmented by AI, the people doing that task should have both the tools and the training.
Leading When the Curve Is Steep
Executives might ask, “Is this AI moment more like electricity, the internet, or the smartphone?” The discomforting answer is “all of the above, but faster.”
AI is a general-purpose capability, delivered on existing digital rails, diffusing at unprecedented speed, and directly reshaping the skill profile of the workforce. You will not slow the curve. You can only influence where your organization sits on it.
This reality, in practical terms, means shifting from a technology-first mindset (“Which models should we buy?”) to a capability-first mindset:
- What problems do we want to be uniquely good at solving?
- What human strengths will we double down on?
- How will we help thousands of people build AI into their daily work safely and creatively?
When you look at the AI adoption chart, you are not just seeing a curve. You are looking at a timer on your talent strategy. Intelligence is now infrastructure, and the organizations that thrive will be those that invest as deliberately in human learning systems as previous generations invested in physical plants and IT.