How to Think About Voting For Uncertainties in a Scenario Planning Project

How to Think About Voting For Uncertainties in a Scenario Planning Project

Voting for uncertainties in a scenario planning project is counterintuitive and sometimes overwhelming. The list of items arrives for consideration from interviews and research that the people voting likely didn’t participate in. That means that people voting need to familiarize themselves with the items on the list.

Outside-In Thinking

More importantly, there is a key conceptual issue to keep in mind as people vote: what scenario planners call the “outside-in.” The “outside-in” asks participants to consider uncertainties as a series of layers. The outside layer is broad, global considerations over which the participants have little or no influence. Many of these big uncertainties may not have any bearing on the focal question (such as “what is the nature of learning in 2030?”). But to create good, expansive scenario stories, the uncertainties that drive the scenario matrix need to be broader than the topic at hand.

In a well-run scenario planning project, all of the uncertainties on the list should already be vetted as to being uncertain. The voting isn’t about if something is uncertain or not, but how uncertain, and more critically, how important and uncertain (combined) any one item is relative to all other items under consideration within the framework of the focal question.

The next layer of uncertainties concerns the industry, and finally, the inner circle contains those things that are uncertain about the institution or its people. That inner circle should be excluded from scenario planning at the outset because the decisions and control points in the middle drive the project. They are also the decisions and ideas over which the organization has the most control; therefore, even if they are uncertain, internal decisions can turn them into certainties with little or no influence from higher levels. Examples include when a project ships, how many people a division will employ, and how quickly a new facility will get online.

If the organization or institution can make future decisions that will turn uncertainty into certainty, this will be explored during the implications and actions portion of the process, when scenarios are available to inform those decisions. So, even though these decisions are uncertain, they are controllable within the organization.

Global and industry uncertainties are critical because they are independent, and while the organization may attempt to influence their outcomes, it does not have singular control over the shape of those uncertainties, if it has any control at all.

The way to think about voting then is to consider which items on the outer layer could influence those on the second layer. The outer-layer items should then be considered more uncertain than those at the next level because their uncertainty is amplified by their influence on those in the inner circle.

A Worked Example

Assume the focal question for a scenario planning project is:

How will adults learn in 2036?

After interviews and research, the scenario team identifies dozens of uncertainties. Before voting begins, the team removes uncertainties that are mostly internal choices, such as “our organization’s AI adoption plan,” “our budget allocation,” or “our hiring model.” Those issues matter, but they are better handled later as strategic responses.

The voting list should now contain only external uncertainties: forces the organization cannot control but must understand.

Example uncertainty set

Layer	Uncertainty	One extreme	Other extreme	Why it matters
Global / societal	Public trust in institutions	Trust rebounds; schools, governments, and employers are seen as legitimate and useful	Trust fragments; people rely more on peer networks, influencers, private platforms, or local communities	Trust affects policy acceptance, credential value, data sharing, and willingness to participate in formal education systems
Global / technological	Governance of AI and data	Strong, coherent rules create confidence, interoperability, and responsible experimentation	Fragmented rules produce uneven adoption, liability concerns, and data silos	AI governance influences assessment, tutoring, student privacy, procurement, and platform choice
Global / economic	Household economic security	Families have stable income and time to support learning	Economic volatility forces learners to prioritize work, caregiving, and low-cost options	Economic security affects enrollment, persistence, lifelong learning, and willingness to pay
Industry / education	Recognition of alternative credentials	Employers widely accept microcredentials, portfolios, and skills records	Degrees remain the dominant trusted signal	Credential recognition changes the role of colleges, bootcamps, employers, and learning platforms
Industry/education	Role of human educators	Teachers become high-touch coaches, mentors, and designers of learning	Automation absorbs many instructional, assessment, and advising functions	This uncertainty shapes staffing models, professional development, learner experience, and cost structures
Industry / education	Learning venue	Learning is mostly place-based and community-centered	Learning is mostly distributed across home, work, and digital environments	Venue affects campus investment, technology infrastructure, equity, and social development

How participants might vote

A participant should not simply vote for the uncertainty closest to their role. A faculty member may gravitate toward the role of human educators. An employer representative may care most about recognition of alternative credentials. A technology leader may focus on governance of AI and data.

All three may be important. The voting question is different:

Which uncertainties are both highly uncertain and highly consequential for the future being explored?

In this example, public trust in institutions may deserve many votes because it shapes several other uncertainties. If trust is high, schools and employers may have more room to experiment with AI-enabled learning, learner records, alternative credentials, and cross-sector partnerships. If trust is low, even technically sound innovations may face resistance from students, parents, regulators, faculty, or employers.

Likewise, governance of AI and data may receive many votes because it affects multiple education-specific outcomes: whether AI tutors are trusted, whether student data can move across systems, whether automated assessment is accepted, and whether institutions can responsibly personalize learning.

Sample voting outcome

If participants each receive 10 votes, with no more than 5 votes on one item, one plausible result might look like this:

Uncertainty	Total votes	Interpretation
Public trust in institutions	42	High-impact external uncertainty; likely to shape many education futures
Governance of AI and data	39	High-impact technological and regulatory uncertainty
Recognition of alternative credentials	31	Directly shapes education business models and learner pathways
Household economic security	24	Important context for affordability, access, and persistence
Role of human educators	18	Important, but partly shaped by AI governance, economics, and credential expectations
Learning venue	13	Relevant, but may be more an outcome of other uncertainties than a primary driver

The voting does not reveal the “correct” future. It helps the group see which uncertainties appear most powerful for scenario development. In this worked example, the team might select Public trust in institutions and Governance of AI and data as the two primary scenario axes because they are broad, external, uncertain, and likely to shape multiple downstream outcomes.

That choice gives the team a strong foundation for building four distinct scenario worlds, each shaped by a different combination of trust and AI/data governance.

Other Voting Considerations

Considering Magnitude

The magnitude of uncertainty, as well as the relative impact of the uncertainties, is expressed by the weight of the vote, be it the number of stickers applied in a workshop or the number of stars in an online survey. A typical voting scheme involves distributing 10 votes across the field of uncertainties, with no more than 5 votes allocated to any one item.

The Personal Perspective

Good scenario planning projects include a diverse group of participants. Each participant brings their own perspective to the voting process, and that is how it should be. Each person should consider uncertainties not only in relation to the focal question and the industry context, but also by drawing on their personal and functional knowledge and experience to identify the uncertainties most important to them. They should, however, be aware of the scenario planning process and its expectations for creating broad stories about the future. While participants can be made aware of the negative impacts of personal bias, voting does not eliminate it; hopefully, it spreads it across votes, so the items most critical and uncertain become the driving factors in the work.

In most voting schemes, importance and uncertainty are considered together. The number of votes assigned to any item then weights the vote in its favor. This permits individuals to influence the shape of the vote so that, in the case of students or hourly staff, their concerns, vs. those of “experts” and organizational leaders, aren’t easily ignored.

Executive Voting

Executives and other leaders should vote last. This avoids the issue of staff members voting to support their leaders’ ideas. Conversely, if leaders wait until the end, they are likely to be influenced by the extant votes, which will likely temper their desire to shape the outcome, as much of what is important will already be apparent. This does not stop leaders, for instance, from placing large numbers of votes on uncertainties they think need to be shored up in the voting. It does make that behavior transparent, and therefore less like to occur.

Summary

Individuals involved in voting then need to consider the following:

• The potential influence of uncertainties in one layer against uncertainties in the next layer (global vs. industry).
• Which, if any, of the uncertainties related to the industry are independent of more global influences?
• Determine how the uncertainties stack up against each other in order to assert which are the most important and most critical.
• Use personal experience and functional expectations to represent a function’s perspective on which uncertainties are most likely to affect them, while still maintaining an outside-in perspective in their thinking.

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