David R. Longnecker

Picture this: a five-year strategic plan lands on your desk with a satisfying thud. 247 pages of projections, market analysis, and implementation roadmaps. By the time you finish reading it, three of your key assumptions are already invalidated by market shifts.

Does this sound familiar? Most organizations are using decision-making frameworks designed for the 1950s manufacturing era, not today’s fluid reality.

The Strategic Plans That Never Saw It Coming

Consider Disney’s strategic position entering 2020. They’d spent years building a theme park empire generating $26.2 billion annually with steady 6% growth. Their strategic plans centered on physical experiences—new attractions, expanded parks, cruise ships. Then COVID hit. Theme park attendance dropped 68% at Walt Disney World, with losses exceeding $4.7 billion in a single quarter.

What did Disney do? They pivoted to streaming with unprecedented velocity. Disney+ gained 60.5 million subscribers in 10 months—something their pre-2020 strategic plans never imagined. As one analyst noted, this pivot “for a company the size and age of Disney is, for lack of a better word, unprecedented.”

Or take the companies whose 2023-2025 strategic plans got blindsided by ChatGPT. In November 2022, AI was a long-term consideration for most. By early 2023, 75% of executives expected generative AI to cause significant disruption within three years. Microsoft completely reshaped its strategic playbook, integrating AI across Bing, Office 365, and Azure. Google, caught flat-footed, scrambled to rebuild its entire search strategy.

These aren’t edge cases. They’re the new normal.

When Planning Became Theater

According to research from MIT Sloan, 84% of executives feel overwhelmed by traditional strategic planning processes. The symptoms reveal why:

• Six-month planning cycles that produce outdated strategies before implementation
• Detailed five-year roadmaps in industries that transform quarterly
• Investment committees that take longer to approve budgets than startups take to capture markets
• “Strategic pivots” that are really admissions the original plan was fantasy

The real issue isn’t that plans fail. It’s that we’ve confused planning (a useful activity) with plans (often useless artifacts). We’ve emphasized certainty over adaptability, consensus over speed, and comprehensive analysis over rapid learning.

  What Software Development Discovered

Twenty years ago, software development faced this same crisis. Waterfall methodologies assumed we could predict every requirement, design every feature, and plan every milestone years in advance. The failure rate was spectacular.

The shift wasn’t just about “going Agile”—that term has been corrupted by its own ceremonies and cargo cult implementations. The real transformation was deeper: embracing uncertainty as a design constraint rather than fighting it.

Instead of pretending we could eliminate risk through analysis, we learned to manage it through iteration. Instead of seeking perfect plans, we built systems that could adapt. This fundamental reframe hasn’t made it to most boardrooms.

Risk: The Missing Conversation

Watch how your organization approaches strategic decisions. Do leaders ask “How can we be certain?” or “How can we learn fast?” That distinction reveals everything about your true risk tolerance.

Traditional planning tries to eliminate risk through analysis. But research from BCG shows adaptive organizations outperform rigid planners by 2.5x during volatility. Why? Because they’ve shifted from avoiding risk to managing it intelligently.

The paradox: Organizations that acknowledge uncertainty make better long-term decisions than those that pretend to predict the future.

Building Adaptive Decision Systems

The most successful organizations I’ve worked with share a common trait: they’ve replaced rigid planning processes with adaptive decision systems. I’ll admit though, for some organizations, it was by accident, especially in startups who are constantly pivoting and trying to find the right market at the right time. Being flexible isn’t a option, it’s a way of life.

The core concept, however, is an evolution of hypothesis-driven development, the Opportunity Solution Tree, and decades of product thinking applied to strategy.

  Core Principles

1. North Stars Over Detailed Maps You need crystal clarity on destination (where you’re going) without pretending to know every turn (how you’ll get there). Amazon’s “customer obsession” is unchanging; their tactics evolve constantly.

2. Hypothesis Over Certainty Frame strategic choices as testable hypotheses, not permanent commitments. “We believe X will lead to Y” invites learning. “Our strategy is X” invites defensiveness when X stops working.

3. Time-boxed Decisions Parkinson’s Law applies to analysis: it expands to fill available time. Set decision sprints. Not because fast is always better, but because constraints force clarity on what actually matters.

But here’s the crucial distinction: time-boxing decisions doesn’t mean eliminating thinking time. As I’ve written before about thoughtwork, the best decisions come from combining deep reflection with action constraints. The sprint forces you to move from endless analysis (“analysis paralysis”) to focused thinking—quality over quantity.

4. Two-way Doors Jeff Bezos’s framework remains brilliant: most decisions are reversible two-way doors, but we treat them like one-way doors. Ask: “What would it take to reverse this if we’re wrong?”

Practical Implementation

A company I advised faced a classic build-vs-buy decision for their analytics platform. Traditional path would take 6-8 months of vendor evaluations, RFPs, and business cases.

Instead, they ran parallel experiments:

• Weeks 1-2: Define success metrics and key uncertainties
• Weeks 3-6: Proof-of-concept internally + vendor trial with real data
• Week 7: Compare actual results against success metrics
• Week 8: Decision with explicit review points at 30, 60, 90 days

They discovered their “must-have” features weren’t actually used, while unexpected needs emerged from real usage. The 90-day review led to a hybrid approach neither original option would have provided. As a bonus, the team gained not only a solution, but a new perspective on how to solve their problems.

New Measurements for Uncertain Times

Traditional metrics focus on planning accuracy: Did we hit our targets? Did we follow the plan? But these assume predictability.

Adaptive organizations measure differently:

• Decision velocity: Time from question to actionable answer
• Learning rate: How quickly assumptions get validated or invalidated
• Option preservation: Does this choice open or close future possibilities?
• Adaptation frequency: How often do we adjust based on new information?

The Objection Handling

“But we need long-term thinking!” Absolutely. North stars matter. The difference is holding vision constant while letting tactics adapt. Patagonia’s environmental mission hasn’t wavered since 1973 (unchanging), but how they pursue it—from materials innovation to regenerative agriculture—constantly evolves based on what they learn (highly changeable).

“Our board expects detailed plans!” McKinsey research found companies that adapted strategies based on real-time data saw 2.2x better returns. Boards care about results more than plans.

“This is just agile for strategy!” Yes and no. It borrows what worked (iteration, learning, adaptation) while avoiding what didn’t (excessive ceremony, rigid frameworks). The principles matter more than the process.

Starting Your Evolution

1. Pick your longest-running decision. What’s been in analysis paralysis? That’s your first experiment.

2. Set a learning deadline. Not a decision deadline—a learning deadline. What must we know to move forward?

3. Design the smallest valid test. What’s the minimum commitment that generates real data?

4. Pre-define adaptation triggers. What signals would cause us to change course? Define them before you’re emotionally invested.

5. Celebrate course corrections. When teams adapt based on data, they’re succeeding, not failing.

The Planning Paradox

Organizations that plan least often think most strategically. They’ve replaced heavy processes with lightweight systems. They’ve chosen adaptation over prediction, learning over knowing, and progress over perfection.

Your strategic planning was built for a predictable world. That world is gone. The question isn’t whether to adapt your decision-making, but how quickly you can start.

What strategic decision has your organization been analyzing for months that a four-week experiment could illuminate? What’s actually stopping you from starting that experiment tomorrow?