Have you ever needed input from a diverse group of stakeholders, only to find that organising a meeting became harder than solving the actual problem? In this episode, we explore how the Delphi method can help us collect and refine insights from a diverse group of people and help us make collaborative decisions without actually meeting.
As enablers of change we tend to work in environments where decisions need to be informed by multiple perspectives. The trouble is that finding a time that works for everyone, means geography and competing priorities can make in person workshops difficult. That’s where asynchronous approaches can help. As we discussed in our recent episode The Learning Design Dilemma, asynchronous engagement allows people to contribute in their own time, meaning we don’t miss out on their perspectives and experience when making decisions.
The Delphi method is a structured way for groups to share specialist knowledge, review feedback, and help create understanding or consensus. This occurs through multiple rounds of surveys. Participants complete a survey independently, the facilitator summarises and aggregates the responses, and then another round is distributed so participants can reflect on the collective feedback before responding again.
A key feature of the Delphi method is that participants remain anonymous throughout the process. Contributors don’t know who gave which response, and discussions don’t happen face-to-face. This anonymity is important because it reduces a whole lot of things that could influence our feedback and perspectives. We mean things like hierarchies, dominant personalities, organisational politics, or just group think! Instead of reacting to individuals, participants respond to the ideas and trends that emerge from the group.
This process is designed to be iterative, and improve the quality of group judgement over time. Rather than trying to force quick agreement in a workshop, Delphi encourages participants to reflect, reconsider assumptions, and revise their responses as they see the group feedback over time. The process is focused on valuing refinement rather than immediate answers. It can also reveal where there is still disagreement.
The Delphi method was originally developed by the RAND Corporation in the 1950s. This was at the beginning of the Cold War and the process was used to forecast the impact of technology on warfare. It was a way to generate reliable consensus in situations where hard evidence was limited. They named it after the ancient Greek Oracle of Delphi, a priestess at the Temple of Apollo who was believed to provide prophecies and guidance about the future. They chose the name partly because it sounded memorable and also that it suggested forecasting. One of the first published journal articles on the method was by Dalkey and Helmer (1963). They described an experimental application of it in Management Science.
The Delphi method later became widely used across health, education, and foresight research because it provided a practical way to gather informed judgements from a range of participants. The four defining features of Delphi are anonymity, iterative data collection, participant feedback, and statistical aggregation of group responses. These characteristics make the method particularly well suited to extension where complex decisions benefit from multiple perspectives, but trying to get everyone we’d like involved at the same time can be difficult.
For enablers of change, this approach can be valuable because it reduces some of the barriers we see in group discussions. Strong personalities are less likely to dominate, quieter voices can contribute more confidently, and participants have time to reflect before responding. In agricultural extension, where complex problems rarely have a single ‘right’ answer, this can help create more balanced and thoughtful outcomes.
Imagine a regional drought adaptation project involving agronomists, farmers, climate scientists, and local government staff. A traditional workshop may be difficult to coordinate, and some participants may feel hesitant challenging expert voices. Using a Delphi process, participants can independently rank priorities, identify risks, and suggest practical responses over several rounds of feedback. Over time, the group develops a clearer understanding of where agreement exists and where uncertainty remains.
Recently, researchers have begun adapting the Delphi method to better suit digital environments. One emerging variation is the Durvey method (Steiner, 2026), a modified Delphi process that incorporates continuous real-time data aggregation and visualisation. The online Durvey platform has many of Delphi’s strengths, including anonymity, iterative reflection, and controlled feedback, but changes how and when participants see group results.
For enablers of change interested in trying the Delphi approach, starting small is often the best option. Begin with a clearly defined question, select participants with varied experience, and keep surveys concise. Think carefully about what type of feedback participants will receive between rounds and how you will communicate uncertainty or disagreement. Most importantly, remember that these methods are not only about reaching consensus. They’re also about surfacing assumptions and exploring differences.
Asynchronous engagement tools means methods like Delphi are really useful for extension. This tool offers ways to include more perspectives, reduce meeting fatigue, and helps us create spaces for collective learning. We think that they offer a different engagement approach that can be a very useful tool in our toolbox.
We began by asking how we can gather meaningful perspectives from a range of people without relying on everyone being available at the same time. The Delphi method shows that asynchronous collaboration can be both rigorous and practical. We think extension design and implementation could be improved by using this tool to combine structured reflection with flexible participation, and help us navigate uncertainty while still allowing a range of perspectives to contribute to the problem.
We’ve shared our thoughts, now it’s your turn! Drop a comment below with your experiences, tips, and ideas on using Delphi in extension. We’d love this to be a conversation!
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Resources
Dalkey, N., & Helmer, O. (1963). An experimental application of the DELPHI method to the use of experts. Management Science, 9(3), 458–467. https://doi.org/10.1287/mnsc.9.3.458
Durvey. (2026). What is the Delphi method? A beginner’s guide. https://www.durvey.org/blog/what-is-delphi-method-beginners-guide
Khodyakov, D. (2023, October 17). Generating evidence using the Delphi method. Integration and Implementation Insights. https://i2insights.org/2023/10/17/delphi-method/
Steiner, B. (2026, May 19). Modifying the Delphi method with continuous real-time data analysis. Integration and Implementation Insights. https://i2insights.org/2026/05/19/modified-delphi-method-durvey/
Such a versatile method – we used it during the pandemic to run this study: https://pubmed.ncbi.nlm.nih.gov/35598314/ It was about the information that patients with a specific syndrome need vs. what clinicians think their patients need. I’ve recreated a similar process for my project on understanding the golden rules for extensions, and it turned out very useful!
Great, thanks for sharing that Valeria!
Thank you – really useful. I’m keen to try the Durvey method in my research.
Good to hear thanks Janet, and let us know how Durvey goes for you!
Definitely an efficient method to find consensus of opinions.
Delphi technique is straightforward, the most difficult part of the process is finding willing specialist participants! Starting with an open ended question to gather perspectives allows us to identify a range of problems/issues; solution(s) and so on towards prioritizing a critical issue/problem to solution amongst people who live or work in a specific environment.
Thanks for the updates!
Adelle
Thanks for your positive feedback Adelle! I agree that it can sometimes be difficult to find suitable participants but often specialists can be intrigued by the topic being studied, and can then be quite generous with their time commitment.