Managing Uncertainty Effectively

Managing uncertainty is where many business cases either gain credibility or fall apart entirely. Raymond Sheen emphasizes in the HBR Guide to Building Your Business Case that you're dealing with estimates and forecasts, not guarantees, and stakeholders understand this reality. What they won't tolerate, however, is sloppy documentation or obvious bias in your projections. Throughout this unit, you'll transform uncertainty from a weakness into a strength by learning to meticulously track every assumption, systematically test variations to expose hidden biases, and properly handle the thorny issue of sunk costs that can derail even well-constructed cases.

The foundation of managing uncertainty lies in creating an assumption fortress, or a comprehensive documentation system that captures not just your numbers but the reasoning and sources behind them. As you'll discover, tracking assumptions in dedicated spreadsheet columns isn't bureaucratic overhead but rather your defense against the inevitable "Where did this number come from?" questions during review meetings. Moreover, when you test your assumptions with ±20% variations, you'll often uncover emotional attachments to certain projections that signal dangerous bias.

Stakeholders who react emotionally to sensitivity questions are often protecting unrealistic estimates, whether they realize it or not. Finally, you'll master the delicate art of excluding sunk costs from ROI calculations while keeping them visible for transparency, a balance that prevents the all-too-common mistake of throwing good money after bad simply because "we've already invested so much."

Every number in your business case needs a clear source. If you can’t explain where a figure came from, your credibility suffers. An assumption tracking spreadsheet helps turn rough estimates into defensible projections. This isn’t just for finance teams; it also helps you feel confident when your numbers are questioned.

Your spreadsheet should list each key cost or benefit, along with columns for the assumption, its source (who provided it), the rationale (why it makes sense), and your confidence level. For example, if you estimate "$400,000 for licenses," your source might be "Victoria from Procurement, based on three vendor quotes," and your rationale could be "Average of three proposals, excluding an outlier." This detail saves you from scrambling for answers during presentations.

This documentation is your defense when stakeholders challenge your numbers. If the CFO asks about your "$8 million in Year 2" revenue projection, you can point to "Sales VP Jake, based on current pipeline plus 15% growth, validated against recent launches." Without this, you’re left guessing, which weakens your case.

Tracking sources also helps spot bias. If optimistic estimates come from one team and conservative ones from another, you can adjust for balance. The confidence level column is important too: mark high confidence for solid data, and low confidence for rough guesses. Being open about uncertainty shows transparency and invites others to help improve your estimates, building trust rather than skepticism.

Testing your assumptions with 20% variations up and down isn't just a mathematical exercise—it's a powerful technique for exposing hidden biases and emotional attachments to certain projections. How people react to these sensitivity questions tells you more about the reliability of their estimates than the numbers themselves. When someone becomes defensive about a variation test, it often signals they've already committed to that number emotionally or politically, making the estimate suspect regardless of its initial justification.

The technique works straightforwardly: take any critical assumption and ask your source what would happen if the number were 20% higher or lower. If sales projections show "$10 million revenue," ask about $8 million and $12 million scenarios. The rational response involves calmly discussing what conditions would lead to each outcome—perhaps "$8 million if we lose our key account" or "$12 million if the competitor's product fails." However, watch for emotional responses like "That's impossible!" or "We'd never let that happen!" or "That would make the whole project worthless!" These reactions indicate the person has a stake in the number beyond its analytical merit.

Here's how this technique plays out in practice:

• Victoria: Ryan, you're projecting 35% productivity improvement from the new system. What if we only achieve 28%?
• Ryan: Twenty-eight percent? That's ridiculous! We'd be complete failures if we only hit 28%.
• Victoria: OK, but help me understand what specific factors would prevent us from dropping below 35%.
• Ryan: Look, I've already promised the board 35%. We can't go backward now.
• Victoria: I see. And what about achieving 42% improvement? What would that require?
• Ryan: Well... that would mean getting buy-in from all three regional offices simultaneously, which honestly hasn't happened before. Plus, we'd need the premium training package, which isn't in the budget.
• Victoria: So it sounds like 35% already assumes perfect conditions?
• Ryan: I guess when you put it that way... maybe 30% is more realistic if we hit normal delays.

Notice how Ryan's initial emotional response—calling 28% "ridiculous" and mentioning his promise to the board—immediately signals that the 35% estimate is more political than analytical. When Victoria probes the upside scenario, Ryan inadvertently reveals that his baseline already assumes best-case conditions, making it unreliable for planning purposes.

What makes this technique particularly valuable is how it reveals the difference between genuine confidence and wishful thinking. A facilities manager might project "50% reduction in emergency repairs" with absolute certainty, but when asked about a 40% reduction, suddenly admits "Well, that assumes we get the premium service contract, which isn't guaranteed." This revelation shows the original estimate included unstated assumptions that need explicit documentation. Similarly, an IT director projecting "six-month implementation" might react angrily to an eight-month scenario, not because it's unrealistic but because he's already promised the CEO a six-month delivery. The emotional response signals political pressure rather than analytical assessment.

Conduct these variation tests in one-on-one conversations rather than group settings, as people are more likely to be honest without an audience. Start with less threatening variations on minor assumptions to establish a pattern of analytical discussion, then move to critical estimates where bias is more likely.

Money already spent (sunk costs) should never be included in your ROI calculations. Including sunk costs can make good projects look bad and lead to poor decisions like continuing just because of past investment. For example, if you’ve already spent "$3 million on consultants", that amount is gone whether you move forward or not—it shouldn’t affect your go/no-go decision.

However, stakeholders will want to know the total investment, so don’t hide sunk costs. Use a dual tracking system: exclude sunk costs from your main ROI analysis, but show them separately for transparency. For instance: "The go-forward investment of $2 million generates a 35% IRR. We’ve already invested $800,000 in pilots, so total project investment is $2.8 million."

This approach is especially important for projects needing extra funding. If you’ve spent "$5 million of a $6 million budget" and now need $3 million more, the right question is whether the new $3 million will deliver enough value—not whether it saves the $5 million already spent. Track and report sunk costs, but don’t let them drive your decision to continue. This discipline helps avoid escalating commitment to failing projects.

Through the upcoming role-play exercises, you'll practice building comprehensive assumption spreadsheets that can withstand CFO scrutiny, testing projections to uncover hidden biases in revenue forecasts, and navigating the political minefield of sunk costs in real-world scenarios. These hands-on sessions will prepare you to manage uncertainty with the transparency and rigor that builds stakeholder confidence, even when dealing with wide-ranging estimates and inherited project complications.