There’s no doubt artificial intelligence is transforming the very concept of work and, when combined with effective collaboration, the possibilities are endless. In this five-part blog series, I use my nearly 20 years of industry expertise to dive into these possibilities, imagining a world where AI meets collaboration—and work is never quite the same.
Previously, I asked what happens when artificial intelligence meets collaboration, focusing on some of the problems organizations encounter when starting a project. In a world of contextual collaboration, the question of whether work has been done is answered, so that’s where we’ll start.
Writer and philosopher George Santayana is believed to have said, “Those who cannot remember the past are condemned to repeat it.” Unless your role at work is leading repeatable projects, or if you’ve been at your company long enough to remember the “why” behind the odd ways it operates, chances are your project kickoff is happening without full knowledge of your forbearers’ tribulations or successes.
Uncovering Tribal Knowledge
When you decide to host a kickoff meeting for a project, imagine if the collaboration environment automatically looked in the company repository for projects with similar descriptions and offered you contact information of project managers who might be able to give insight on what they learned. Maybe you could even see a summary of their output and have access to the templates they used, presentations they created and the approvals given at each milestone.
Given the turnover we see in corporations, the people with relevant tribal knowledge – information and experience gained by colleagues – may no longer be part of the organization, so the lasting evidence of their work provides valuable trail markers for the future. Project documents that have been archived for years or wiped from the computer of a departing colleague don’t have to be gone forever in a world were AI indexes the information immediately.
Beginning with the End in Mind
Here’s a different take on Steven Covey’s Successful Habit #2: Begin with the End in Mind.
With contextual collaboration, project kickoff meetings start with a review of similar projects. Relevant project managers are invited to provide a synopsis of their learnings and potential issues. The team can ask clarifying questions before starting the project and assess any materials created from the original project. Undoubtedly, considering the end results from previous projects and their impact on the current project means a higher chance of success.
Workflows Are the Killer App
When it comes to collaboration, workflows are the killer app. Repeatable processes can be optimized for efficiency and, over time, they can be studied to identify opportunities for improvement. That’s why this first dimension of contextual collaboration is so powerful. In the bespoke world of the unique project, studying the history of similar projects builds the foundation for how a type of project should be run. Then, with enough interactions, we can see what they all have in common and template it.
What’s the difference between an organizational message, a marketing message and an upgrade notification? The audiences, of course―and the departments who would craft them. However, the workflows of how each of these messages are created are strikingly similar. Running this through a contextual collaboration environment powered by an AI module may bring in representatives from marketing and HR to your IT upgrade notification kickoff meeting. Their insights on crafting an honest, cogent message may save you time, and colleagues creating future IT messages can consult you instead of those other departments.
This organic construction of workflows is a powerful tool that only exists when we can see the artifacts available to us in the organization. By having a tool that taps into tribal knowledge, we find that in most endeavors, someone has blazed a trail for us to follow. AI simply tells us where to look.
Where To Go From Here
It’s always useful to understand what work has been done before starting a new project, but then we must build a team to complete the tasks at hand. Next, we’ll look at how AI can help us build teams based on what we’ve learned through mining tribal knowledge. I’ll also delve into what our HR systems can share about available skillsets, what calendars can tell us about availability and how we can use exceptions to project participation.