The limitations that shaped 35 years of corporate strategy are dissolving. The strategy built on top of them hasn't caught up.
In September, Eli Lilly released over $1 billion of proprietary drug discovery data — free — through their TuneLab platform for biotech partners.
That isn't a loss. That's a declaration.
Lilly's bet: the data isn't the advantage anymore. Judgment about what to do with the data is. And they'd rather build ecosystem gravity than hoard information that's about to become abundant anyway.
For 35 years, corporate strategy has been built on the opposite assumption. Go deep. Protect your core. Don't diversify into adjacent markets — the learning cost is too high and the failure rate is brutal. That framework, introduced by Prahalad and Hamel in 1990, became the default doctrine. It was right for its time.
The economics underneath it just changed.
The knowledge collapse
The cost of acquiring deep domain knowledge is in freefall across every industry.
88% drop in time-to-insight after implementing AI analytics.
70-80% overlap between AI-synthesized research and human expert analysis.
Drug discovery timelines compressed from 3-4 years to 13 months.
Market research studies that cost $50,000+ now largely replicable in a single afternoon.
Modern AI research tools can scan and synthesize across 220 million peer-reviewed publications in hours, not months. Expert networks source specialists in 24-48 hours, not weeks. The exploration economics aren't adjusted — they're inverted.
Why core competency is quietly becoming obsolete
The historical failure rate of diversification moves wasn't random. It came from a specific cost structure: eighteen months and roughly $2 million in consulting, regulatory analysis, formulation studies, and expert interviews — just to get directionally oriented in a new domain.
When learning was that expensive, "stick to your core" was financially correct.
When learning costs collapse, the opposite becomes true. Companies can now achieve meaningful understanding of adjacent markets before they commit significant capital. The constraint was never inherent to expansion. It was the economics of learning.
And the competitive threat runs both ways. The same AI that lets you explore into new territories lets competitors explore into yours. "Stick to your core" has quietly shifted from a defensible position to an exposed one.
The new strategic metric: initiative velocity
Here's the KPI that should emerge on your exec dashboard:
Initiative velocity — how many new frontiers can your organization meaningfully explore at the same time?
Meaningful meaning: research conducted, experts consulted, feasibility assessed, directional decisions made. Not a dashboard full of zombie pilots. Actual exploration.
For decades, headcount and budget capped most organizations at two or three simultaneous initiatives. That constraint is gone. The new constraint is your organization's processing capacity and decision quality — not your research capacity.
Most leadership teams haven't noticed the shift yet.
The compounding advantage
Exploring multiple domains at once doesn't just add — it compounds.
A company exploring one domain gets information. A company exploring ten gets pattern recognition.
Cross-domain insights generate moves that single-lane competitors can't see. New adjacencies. New customer segments. New business model patterns lifted from industries you weren't previously watching. The value isn't the ten individual explorations. It's the intersections between them.
3M: what this looks like at scale
3M — 122 years old, historically known for adhesives — committed $3.5 billion to R&D over three years, with one-third dedicated to AI-powered research and simulation tools.
The early returns:
64 new products in Q2 2025 alone (70% increase year-over-year).
On track to launch 1,000+ products by 2027 across aerospace, defense, and advanced infrastructure — domains that would have required years of specialized learning under the old model.
That's not incremental. That's a different strategy.
What comes next
Today: human-assisted AI research. Cheap, fast exploration.
Next: autonomous research agents that scan literature, identify experts, synthesize findings, and surface opportunities without human direction.
After that: autonomous execution agents that build prototypes, run simulations, test hypotheses, and report findings — closing a continuous loop from exploration to testing to validation. At that point, "initiative velocity" stops being a human-capped metric altogether.
If you're not building the operating muscle for a wider surface area now, you're going to be catching up to both phases at once.
What to do this week
Revisit the strategic decisions where you rejected a domain because you "didn't have the expertise" or "it would take years to learn."
For each one, honestly assess: What would 70% understanding of that domain require today?
The answer is usually: a week of AI research tools, three targeted expert conversations, and a few thousand dollars. If the original rejection was financial, it's no longer financially defensible.
Core competency was a cage that looked like a strategy.
The toll booths between lanes are gone. The companies still framing single-lane focus as discipline are often just exhibiting the caution that used to be correct.
The question has changed. It used to be: "where are we best in the world?"
Now it's: "How fast can your organization learn something it's never done before?"
From the portfolio
Three resources powering this shift inside HauerX companies:
NotedSource (notedsource.io) — connects organizations with 50,000+ vetted researchers and domain experts. Their Research Compass synthesizes 220 million publications to deliver expert-level understanding without building a new team.
Board of Innovation (boardofinnovation.com) — AI Transformation Studio redesigning revenue, operations, and AI-native business models for mid-market and Fortune 500.
AlignAI (getalignai.com) — governance infrastructure to track experiments, research sprints, and new initiatives as your exploration surface expands.
Which domain did you reject because it would "take years to learn"?
Reach out. I'd love to think it through with you.
Jason Hauer CEO, HauerX Holdings jason@hauerx.com
