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Consortium for Service Innovation

Tools Considerations

All the functionality to support Intelligent Swarming exists today, however, it is not integrated into a single platform at the time of this writing. Intelligent Swarming adoptions to date have used a collection of tools to serve specific swarming functions and manage the integration of those tools or used tools in a non-integrated way. 

We continue to collect evidence that initial success requires a focus on people: the knowledge workers' understanding of why we are doing this, their buy-in to the concepts and processes, and most importantly, their behaviors. Long-term success is enabled through continuous improvement in the required functionality, automation, and integration.  As we mention in the adoption section, we want to start with the people and the process: get some experience using our current collaboration functionality and build some manual processes for People Profiles. It is important that we have some experience with the process, even if it is largely manual, before we go shopping for technology. Shopping for the tools before we have experience with the process is like shopping for clothes without knowing what size we wear. 

The information below should be considered a starting point. Each organization will have unique requirements and must do its own due diligence when evaluating technology options. And, each organization will have to invest in the integration of the various functions needed for success.   

Functional Requirements

The following is a list of the typical functional requirements that emerge from an Intelligent Swarming Design Session:

  • People Profiles
    • Reflect my identity, skills/competencies, interests, preferences, reputation
    • Automated skills profile that is derived and maintained from the content I interact with (requests, knowledge articles, documentation)
  • Ability to see all requests/work that are relevant to me (based on my profile)
    • From any "queue"
    • Owned or un-owned (available)
  • Ability to request help/raise my hand
  • Ability to ask a specific person or small group of people a question 
  • Ability to join groups
  • Ability to see and respond to requests for help that are relevant to me
  • Ability to offer unsolicited help on issues/work that are relevant to me
  • Ability to see others’ availability and status
  • Ability to see open requests for help and aged requests for help
    • Configurable by team
  • Options on how to interact
  • Ability to create “collaboration space” for two or more people to work on an issue and collect/store documentation (including the requestor)
  • Enable measurements
    • Time: incident open to request help
    • Frequency of requesting help
    • Frequency of offering help: unsolicited and requested  
    • Frequency of being requested (by name)
    • Age of unanswered requests
    • Number of unanswered requests
    • Feedback from requestor about the help received
    • Feedback from the responder about the requestor
    • Who is interacting with who and how often (input to ONA)

Having the above functionality is important, but equally important is the integration of this functionality into the primary user interface for the knowledge worker, whether that's a CRM or other work management tool. An important part of sustaining the benefits of swarming is making it easy for the knowledge worker to do the right thing.

Emerging Digital Automation

We see huge opportunities to leverage emerging digital automation capabilities to automate some of these functions as well as opportunities to augment knowledge worker capability. Digital automation is a term we use to describe a broad set of digital capabilities including automated data classification, recommendations, prediction, and optimization. In an effort to capitalize on these capabilities, members are engaging data scientists as key players in their infrastructure design. Data scientists understand the tools, models, and techniques associated with sophisticated text analytics, machine learning, neural networks, and artificial intelligence (to name a few). Two things we have observed that are necessary to leverage data scientist expertise are a clear business objective (or problem statement), and good data. The KCS methodology is often cited as a critical asset by Members who have successfully implemented digital automation; the knowledge created and maintained through the KCS processes are how we get good data.

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