Cate Hall’s surface-area thesis is simple: serendipity = doing × telling. The more experiments you run and the more publicly you share the lessons, the more good luck finds you. (usefulfictions.substack.com)
Generative AI is the ultimate surface-area amplifier. Models get cheaper, new use cases emerge weekly, and early wins snowball once word spreads. Below is a playbook, rooted in real-world data, for technology leaders who want to stay ahead of the AI wave and translate that edge into concrete gains for their organizations and their own careers.
1. Run More (and Smaller) Experiments
| Tactic | Recent proof-point |
|---|---|
| Quarterly hack-days with a “ship in 24 hours” rule. | Google Cloud’s Agentic AI Day gathered 2,000+ developers who built 700 prototypes in 30 hours, earning a Guinness World Record and seeding multiple production pilots. (blog.google, The Times of India) |
| 30-day “two-pizza” squads on nagging pain points. | Walmart’s internal “Associate” and “Developer” super-agents started as 30-day tiger-teams and are now rolling out across stores and supply-chain tools. (Reuters, Forbes) |
Organizational upside: frequent, low-cost trials de-risk big bets and surface unexpected wins early.
Career upside: you become the executive who can reliably turn “weekend hacks” into measurable ROI.
2. Create an Adoption Flywheel
“AI is only as powerful as the people behind it.” – Telstra AI team
Levers
- Default-on pilots. Telstra rolled out “Ask Telstra” and “One Sentence Summary” to every frontline agent; 90% report time-savings and 20% fewer follow-up calls. (Microsoft)
- Communities of practice. Weekly show-and-tell sessions let power users demo recipes, prompts, or dashboards.
- Transparent metrics. Publish adoption, satisfaction, and hours-saved to neutralise fear and spark healthy competition.
Organizational upside: time-to-value shrinks, shadow-IT falls, and culture shifts from permission-based to experiment-by-default.
Career upside: you gain a track record for change management, a board-level differentiator.
3. Build Platforms, Not One-Offs
| Platform move | Result |
|---|---|
| Expose reusable agent frameworks via internal APIs. | Walmart’s “Sparky” customer agent is just one of four AI “super-agents” that share common services, accelerating new use-case launches and supporting a target of 50% online sales within five years. (Reuters) |
| Offer no-code tooling to frontline staff. | Telstra’s agents let 10k+ service reps mine CRM history in seconds, boosting first-contact resolution and agent NPS. (Telstra.com, Microsoft) |
Organizational upside: every new bot enriches a shared knowledge graph, compounding value.
Career upside: platform thinking signals enterprise-scale vision, which is catnip for CEO succession committees.
4. Broadcast Wins Relentlessly
“Doing” is only half the surface-area equation; the other half is telling:
- Internal road-shows. Add Ten-minute demos into your team meetings.
- External storytelling. Publish case studies or open-source prompt libraries to attract talent and partners.
- Metric snapshots. Microsoft found Copilot adoption surged once leaders shared that 85% of employees use it daily and save up to 30% of analyst time. (Microsoft, The Official Microsoft Blog)
Organizational upside: shared vocabulary and proof accelerate cross-team reuse.
Career upside: your public narrative positions you as an industry voice, opening doors to keynote slots, advisory boards, and premium talent pipelines.
5. Quantify the Payoff
| Outcome | Evidence you can quote tomorrow |
|---|---|
| Productivity | UK government Copilot trial: 26 minutes saved per employee per day across 14,500 staff. (Barron’s) |
| Client speed | Morgan Stanley advisors auto-generate meeting summaries and email drafts, freeing prep time for higher-margin advice. (Morgan Stanley) |
| Revenue | Walmart expects agentic commerce to accelerate its push to $300 B online revenue. (Reuters) |
Use numbers like these to build cost-benefit cases and secure funding.
6. Personal Career Playbook
| Focus Area | Action | Why It Pays Off |
|---|---|---|
| Public Credibility | Share what you learn, whether on LinkedIn, Github, YouTube, or other channel. | Consistently sharing insights brands you as a thought leader and attracts high-caliber talent. |
| Hands-On Insight | Pair with an engineer or data scientist for one sprint each quarter. | Staying close to the build process sharpens your intuition about real-world AI capabilities and constraints. |
| Continuous Learning | Commit to one AI-focused certification or course each year. | Ongoing education signals a growth mindset and keeps your expertise relevant in a fast-moving field. |
Make your own luck
Boosting your AI surface area is not about chasing shiny tools. It is a disciplined loop of many small bets + aggressive storytelling. Organizations reap faster innovation, richer data moats, and happier talent. Leaders who orchestrate that loop accrue reputational capital that outlives any single technology cycle.
Start widening your surface area today, before the next wave passes you by.
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