The first four themes above address user experience and adoption issues vs. technology and data considerations—how to integrate algorithmic forecasting into new ways of working and the most salient, people-centric challenges.

Human-centered design focuses on taking a holistic approach to developing a forecasting capability by incorporating individual requirements of the key user groups and the interactions across them. In this approach, user groups, also known as personas, are not defined by their function or business unit but by their business objectives. For example, while a vice president and an analyst may both sit in FP&A, their goals will be different. The former will need the means to toggle effectively between scenarios that model different business decisions, while the latter will seek the functionality to build those scenarios, assign values, set relevant thresholds, and complete their work accordingly.

For that reason, promoting successful adoption of a solution depends on asking two vital questions: Did we account for the right user groups or personas when designing the capability, or were the personas defined too broadly? And did we successfully connect the dots between each user group to create a comprehensive solution?

Adoption challenges could signal a missed or misaligned persona during the initial design. It’s critical to define meaningful personas through comprehensive discovery and research. Doing so involves working closely with users to understand their role within the forecast process, intended outputs, and interdependencies with other groups. The personas then become the basis of the solution design, creating a unique experience for each user group while addressing any cross-persona interactions.

A persona-agnostic design approach for an algorithmic forecasting solution can lead to an overly complex user interface. If every feature and function is available to all, users may not be able to navigate the solution easily and may be inclined to prepare their forecast outside of it. As a result, aggregation can’t be conducted systematically without the proper inputs. Even if only one stakeholder group is directly affected, rejection of the capability could cause a chain reaction, leading to widespread failed adoption.

Another consequence could be a lack of governance around sequencing, handoffs, and workflow visibility, elements that are critical to collaboration and aggregation within the forecast process. For example, without proper user groups in the workflow, tax rates could be applied to an aggregate forecast while adjustments are made at a lower level, resulting in unnecessary reconciliation issues and the loss of a key feature of the forecasting process. As algorithmic forecasting introduces a new way of working, adding persona-based embedded workflow into a forecasting solution can also support change management and training, which aids in the adoption of the solution.