This is the second in what will inevitably be a series of AI-related posts. See my first post, Artificial Intelligence, UX and the Future of Findability.
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| Source: Pixabay |
Every organization has dirty jobs that it should be doing, but that no one wants to do. Work that is so big and daunting and tedious, that it gets shelved for a rainy day, or started dozens of times but never finished, or worse that keeps getting re-invented into yet another un-scalable pilot project.
In our organization, we have a lot of dirty jobs related to Information Management: classifying documents, cleaning the metadata on web pages, defining and assigning taxonomies for search… Lots of these projects start but they never seem to end: frameworks are developed but applying them is just too time consuming, especially since we generate and hold so much data.
Retroactive work on existing records is difficult and slow. With budget cuts, it’s hard to justify taking resources away from day-to-day business to clean up old data. So, maybe we just work differently from today forward. Now we have disparity between the old and the new.
Worse, no one changes how they work to incorporate these IM practices into their day-to-day (in fact we were trained not to assign metadata in the new document management system) so everything just gets messier and messier as time goes on.
Over time, the problem gets bigger and more unwieldy, making it difficult to find information, making us less productive, making our sites less usable, and making us generally inefficient. The idealistic frameworks become dusty and outdated. And then someone decides we should just start anew, kicking off some initiative to define a framework to be used from this point forward. And here we go again.
What if we stopped trying to redefine ourselves and innovate, and instead turned our focus to solving the problems we are ignoring? What if we used AI to just make things work? Could that be the innovation?
We can use tech to do the dirty work for us. We can assign AI to do the work that needs to get done, but that we aren’t actually doing.
Earlier this year, during the business case phase for my project, I realized that there was important work that would be so time consuming, it might never actually get done during the project. We wouldn’t have the time or the resources to do the work by hand, but it was necessary for our success:
- We need to create an ontology to improve the web search for a specific type of information;
- We need to help users figure out where to direct their request, among 240+ institutions;
- We need to provide users with a “simple” way of accessing a service (yes, our mandate actually includes the word “simple”).
- mapping out a subject-specific ontology using existing data and semantic analysis;
- providing recommendations using that ontology and natural language processing; and,
- providing support to users using both of the above and a chatbot interface to help them provide all the data we need to deliver the service and fulfill their request.
In the past, we tried to this kind of work by hand and we failed. Mapping out relationships between different data sources, creating new taxonomies, applying them to data, making constant improvements… things got really big, really quickly and it all became overwhelming. To meet deadlines, we scoped down to a minimum sustainable product but the end result wasn’t as good or as usable as it could have been if we’d completed the work. We just didn’t have the time or the capacity, no matter how many humans we could throw at the problem.