AI Over 40 Series - Week 9: Literacy Before Agency

Why we keep making the same mistakes

How a room full of leaders convinced me we’re repeating 50 years of hype

Last week, I joined a panel of seasoned executives buzzing about “agentic AI” and its promise to transform every facet of business—from client engagement to workforce structure. The big ask? Hire AI specialists. But when I probed, “What exactly will these experts build?” the room fell silent. “AI stuff,” someone finally mumbled.

We realized we were recruiting hammer experts without an architectural plan—like hiring Excel gurus to redefine financial strategy. And when I asked who in the room had felt a personal transformation from AI beyond routine tasks—email drafts, slide decks, RFP responses—not one hand went up. Yet everyone expected AI agents to revolutionize everyone else’s jobs.

The 50-year automation pattern

History shows us this script repeats every decade:

• 1970s, Mainframes: Promise to eliminate clerical and middle-management roles
• 1980s, PCs & Spreadsheets: Forecasts of obsolete accountants and analysts
• 1990s, ERP: Predictions of end-to-end business process automation
• 2000s, BPM & Workflow: Hype around eradicating back-office roles
• 2010s, Cloud & SaaS: Warnings of vanishing IT departments
• 2015+, RPA: Expectations that bots would replace routine knowledge work
• 2024, AI Agents: Bold claims that knowledge workers are next

Each wave follows the same arc—wild enthusiasm, sobering reality check, human adaptation, then enhancement rather than outright replacement.

Why this time feels different (but isn’t)

Agentic AI’s capabilities are genuinely striking—you can prototype in hours what once took weeks. But every leap in productivity has required more upfront design, strategic thinking, and human expertise. AI saves time on execution, but it intensifies the need for thoughtful problem definition and change management.

Even if AI were free, organizations still wrestle with:

• Identifying true automation opportunities vs. redesigning flawed processes
• Aligning stakeholders around new workflows
• Allocating resources for experimentation and training

Those challenges never went away. They just wear a new label: “AI agency.”

The enhancement opportunity we’re overlooking

When I reflect on AI’s real value, it’s in amplifying human capabilities, not replacing them. Think of all the strategic initiatives, customer-experience improvements, and innovation pilots that languish for lack of bandwidth. AI can power through the legwork—data synthesis, pattern recognition, initial drafts—freeing people to focus on creativity, judgment, and relationship-building. The true revolution is a workforce supercharged by AI, not supplanted by it.

Building AI literacy: a four-phase framework

To avoid the “hire AI experts for AI stuff” trap, I advocate starting with broad AI literacy—fostering fluency in how to collaborate with these tools—before leaping to full autonomy.

Phase 1: Cultivate Innovators

• Assemble a pilot cohort of early adopters.
• Encourage them to tackle real business challenges with AI—no scripts, just exploration.

Phase 2: Set Minimum Expectations

• Define baseline AI-usage skills for key roles (e.g., proposal drafting for sales, data aggregation for analysts).
• Recognize and reward those who meet or exceed these expectations.

Phase 3: Unboxing Workshops

• Host company-wide “unboxings” where every team member signs up for ChatGPT, Claude, Gemini, etc.
• Run synchronized exercises—same prompt, same data—and share surprising outputs.

Phase 4: Measure the Productivity Gap

• Track performance differences between AI-trained and non-AI-trained employees.
• Use metrics (e.g., turnaround time, error rates, creative output) to highlight the tangible impact of AI fluency.

This progression creates a foundation of skilled collaborators primed to design and oversee agentic systems, rather than blindly delegate to them.

Why agency without literacy is risky

Jumping straight to autonomous AI agents risks:

• Misaligned objectives: Agents executing flawed instructions at scale.
• Unintended consequences: Automated processes that clash with human workflows.
• Knowledge silos: Expertise confined to tech teams, leaving business units in the dark.

History shows every major technology rollout—from ERP to cloud migration—failed when organizations neglected the human side first. AI will be no different.

Your week 9 challenge: build AI literacy first

1. Assess your starting point.
   • Survey how many team members use AI beyond trivial tasks.
2. Launch a pilot group.
   • Recruit 2–3 volunteers to experiment with AI on real projects.
3. Define minimum skills.
   • Specify the simplest meaningful AI task for each role.
4. Host a collective unboxing.
   • Get everyone on the same platform with shared prompts and live demos.
5. Quantify the gap.
   • Establish metrics to compare AI-enabled vs. non-AI outcomes.

Document lessons learned in an “AI Literacy Playbook” that your organization can iterate on.

The real transformation ahead

We face a choice: repeat the past—or learn from it. The companies that win with AI will be those that invest in human fluency first, then layer in agency. They’ll amplify their people’s creativity, judgment, and relationship skills—rather than chasing an illusion of full automation.

The future isn’t humans versus AI. It’s humans with AI. But we must master collaboration before we hand off control.

This post is part of our “AI Over 40” series. It first appeared on LinkedIn: AI for the Over 40 – Week 9: Literacy Before Agency.

Next Week: We’ll explore how AI literacy drives genuine workflow transformation—and why the results often surprise even the most skeptical leaders.

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