The integration of Generative AI (GenAI) into diverse digital workplace applications is advancing at a rapid pace. Digital workplace leaders need to leverage “productivity zones” to identify what aspects of productivity can be improved for information-centric work.
Employees engaged in information-centric work do not generally work in strict, task-driven, or rigid workflow structures where productivity is easily measured. Also, they often organize their work in a flexible manner, frequently switch activities, and interact with many people. These interactions may only be tangentially related to their tasks, but they still impact productivity.
Measuring and improving information workers’ productivity needs to factor in experiences that go beyond the task itself, such as the choices the staff make when sequencing and prioritizing their work or how much they depend on the input of others. Additionally, treating work patterns as zones can help identify where technologies like GenAI can help employees to be more productive.
What are productivity zones?
Productivity zones represent a way to better understand information work employee experiences beyond task-driven productivity approaches. Digital workplace leaders can consider the following nine zones to help segment the different digital experiences employees encounter as they perform information-centric work.
- Planning: Organizing and self-managing the work
- Creating: Composing, designing, and refining knowledge artifacts
- Meeting: Gathering people to share information, make decisions, document actions, and grant individual perspectives and a shared vision
- Analyzing: Assessing the effort, artifacts, data, and content needed for work
- Collaborating: Conducting conversations and joint efforts to execute the work
- Coordinating: Interacting with team members and others to refine work efforts, align timelines, schedule deliverables, and reevaluate assignments
- Networking: Connecting with colleagues to gain and share expertise, community feedback, or information
- Decision-making: Reaching an authoritative judgment or conclusion that affects individual and team actions for a given work activity
- Finding: Discovering and utilizing the content, people, or other data related to the work at hand
There isn’t a uniform route through these zones. It’s possible for employees to navigate through the zones multiple times for a specific activity. Digital workplace leaders must analyze and understand what zones to “weigh” more than others. The areas where GenAI might have a more profound impact and productivity value are those zones with more weight attached to them.
With GenAI, the lack of historical insights necessitates identifying areas of potential benefit.
Read more: 29 percent of organizations deployed GenAI as leading AI solution, reveals new survey
The role of GenAI
While Generative AI appears to offer a general uplift in work efficiency for all types of work, questions remain as to whether GenAI will deliver improvements to business results beyond personal productivity. Personal productivity might be improved, but if the work is dependent on synchronizing tasks and results with team members, then individual productivity gains can be negated — unless the productivity gain is felt across the team.
For instance, GenAI in collaboration tools like Microsoft Teams and Slack can help summarize chat threads and highlight key conversations, saving time compared to scanning lengthy chat histories.
Applying the concept of productivity zones to use cases proves beneficial in equipping business stakeholders and GenAI advocates with deeper insights. These use cases allow digital workplace leaders to frame GenAI’s potential business value and productivity impact for line-of-business leaders. They can also guide decisions on whether to implement GenAI broadly across the organization for overall productivity enhancement or restrict it to specific work activities and employee groups.
Ultimately, use cases can distinguish the capabilities and impact of numerous digital workplace application vendors, all of whom are integrating Generative AI into their products. Moreover, these accumulated insights form a feedback loop to fine-tune deployment scenarios.
Mike Gotta is a VP analyst at Gartner.
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