Application Envisioning idea
Examples from three knowledge work domains:
(Illustrated above) An architect’s cursor snaps to the edge of a form that she is trying to enclose in her building modeling application. Since she is familiar with the tool’s behaviors, she anticipates the correction and, as a result, spends less time positioning her cursor accurately.
The term “computer” is famously derived from the specialized job that the technology initially replaced — the now extinct profession of manually computing mathematical problems for science, engineering, and business needs. Since that time, developments in computing have only extended this founding notion of offloading well characterized and predictable operations in knowledge work (A4, A5).
A financial trader is booking a deal in his trading application. As he fills in data, the application predicatively defaults subsequent fields, which he then simply tabs through if he agrees with the values that the system has entered.
A scientist selects a different filter for a graph within her analysis application, and the transformed representation of clinical data instantly appears. Without the application’s automation of the graphing operations needed to update this display, the resulting transformation would have taken significant time and effort to manually complete.
Product teams can envision how their interactive applications might augment specific work practices by performing small, useful, and learnable optimizations in the context of users’ actions. To ensure that these small interventions are visible and understandable (E5), computing tools can provide cues to indicate where automations have occurred, as well as how their effects may be removed (C4, D6, H2). Depending on workers’ expectations of control (E6), these granular automations can be the subject of customization choices (C8, K11).
When product teams do not actively consider how small operations in knowledge work could be usefully automated, opportunities to reduce workers’ efforts (D2, D3) and to prevent certain types of errors (C9, G3) can be lost. Depending on their previous experiences with other computing interactions, workers may see the absence of some small automations as annoying oversights in a product’s design (M1).
Conversely, in many cases, these small automations simply cannot be meaningfully
envisioned due to broad variabilities in targeted work practices (A6, A7, A8). When
misapplied, automation of operations can become a frustrating hindrance to the
experience of directness in computing interactions (D4).
See also: B5, C10, D, E, I, M
Application Envisioning questions:
More specific questions for product teams to consider:
What discrete operations in the work practices that your team is striving to mediate are standard, exacting, and tedious? What do targeted individuals think of these operations?
Where might your team’s sketched functionalities introduce new operations that could also fit the standard, exacting, and tedious description?
Which operations in your concepts for work mediation might be usefully automated under the general goal of reducing users’ efforts?
What larger design and technology trends could influence your team’s ideas about small automations in your computing tool?
What predictive actions, useful suggestions, slight corrections, and refined interface tailoring could your application concepts automatically provide?
How might these automated operations reduce the incidence of predictable errors and corrective interactions? How could the design of these features relate to your products’ larger error prevention and handling approaches?
Could certain small automations benefit from clearly communicated conceptual models, or could some of them provide just as much value if they are typically overlooked?
How might your envisioned automations impact workers’ sense of control?
In what cases might targeted individuals see these automations as unpredictable
or distracting nuisances?
What interaction methods could allow users to recognize and override the effects
of certain automations?
What settings and customization functionality can your team envision to help ensure that automations will operate in accordance with workers’ goals? How
could these settings be clearly and contextually accessed?
Do you have enough information to usefully answer these and other envisioning questions? What additional research, problem space models, and design concepting could valuably inform your team’s application envisioning efforts?
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