Let’s start with an axiom: In a modern business chat is, as much as email, where business gets done. Every company I have worked with or for in the past decade has come to increasingly rely on chat to coordinate activities within and across teams. This is great, as chat provides a convenient, asynchronous way to work together. It fits nicely in that niche between a phone call and an email in terms of urgency of response and formality. It is no surprise then, that the uptake of chat tools for business has been high.
I’m a big proponent of using chat for our teams, but using it has uncovered a few challenges. One of them is managing the content that comes out of chats. Just about every chat tool has a content sharing facility for sending docs back and forth. That’s great, but what happens to that content once it is pasted into a chat? Often that content becomes somewhat ephemeral, maybe viewed when it is dropped into the chat but then forgotten. What happens when you have segments of a chat that are valuable and should be saved? If you are using chat as a support channel, for example, that chat content may well form the foundation for knowledge that you want to capture and reuse. If the chat is related to a deal that sales is working, you might want to capture it as part of your sales process so others know what is happening.
This sort of “knowledge leakage” in chat can be partially solved by the search functions in chat, but that is often limited. Some tools can only go back a certain amount of time or a certain number of messages with the baked in search functions. The quality of this search depends on which tool you are using and whether you are using a paid or free version of that tool. Frustratingly, chat tools do not typically index the content of documents shared via chat. This means you can probably search for the title of the document or the context in which it was shared, but not the content of the document itself. Regardless of the quality of search, it only solves part of the problem. Content in chat may be discoverable, but it isn’t easily shareable or captured in a form that is useful for attaching or including in other processes. In short, chat content creates chaos. How can we tame this and make chat a better channel for sharing, capturing, curating and finding real knowledge?
Within our Customer Success team at Alfresco we have done some small research projects into this problem, and have even solved it to a certain extent. Our first crack at this came in the form of an application that captures certain chats from our teams and saves those chat logs into an Alfresco repository. This is a great start, as it partially solves one of our problems. Chat logs are no longer ephemeral, they are captured as documents and saved to Alfresco’s Content Services platform. From there they are indexed, taggable and linkable, so we can easily share something that came up in the chat with others, in context. This approach is great for capturing whole chats, but what about saving selected segments, or capturing documents that are attached to chats?
Solving both of these problems is straightforward with Alfresco’s content services platform, a good chat tool with a great API, and a little glue. For this solution I have set out a few simple goals:
- The solution should automatically capture documents added to a chat, and save those documents to Alfresco.
- The solution should post a link to the saved document in the chat in which the document originated so that it is easy to find in the content repository. This also ensures that captured chat logs will have a valid link to the content. We could ask people to post a doc somewhere and then share a link in the chat, but why do that when we can make it frictionless?
- The solution should allow chats or segments of chats to be captured as a text document and saved to Alfresco.
- The solution should allow for searching for content without leaving the chat, with search results posted to the chat.
Looking at the goals above, a chat bot seems like a fairly obvious solution. Chat bots can listen in on a chat channel and act automatically when certain things happen in the chat or can be called to action discretely as needed. A simple chat bot that speaks Alfresco could meet all of the requirements. Such a bot would be added to a chat channel and listen for documents being uploaded to the chat. When that happens the bot can retrieve the document from the chat, upload it to an Alfresco repository, and then post the link back to the chat. The bot would also need to listen for itself to be called upon to archive part of a chat, at which point it retrieves the specified part of the chat from the provider’s API, saves it to Alfresco and posts a link back to the chat. Finally, the bot would need to listen for itself to be invoked to perform a search, fetch the search terms from the chat, execute a search in Alfresco and post the formatted results back to the chat channel. This gives us the tools we need to make content and knowledge capture possible in chat without putting a bunch of extra work on the plate of the chat users.
I’ve put together a little proof of concept for this idea, and released it as an open source project on Github (side note, thank you Andreas Steffan for Dockerizing it!). It’s implemented as a chatbot for Slack that uses the BotKit framework for the interaction bits. It’s a simplistic implementation, but it gets the point across. Upon startup the bot connects to your slack and can be added to a chat just like any other bot / user. Once it is there, it will listen for its name and for file upload events, responding more or less as described above. Check out the readme for more info.
I’d love to see this improve a bit and get better. A few things that I’d like to do right away:
- Get smarter about the way content gets stored in Alfresco. Right now it’s just a little logic that builds a folder structure by channel, this could be better.
- Improve the way metadata is handled for saved documents / chats so they are easier to find in the future. Maybe a field that stores chat participant names?
- Some NLP smarts. Perhaps run a chat excerpt through AWS Comprehend and tag the chat excerpt with the extracted topics?
- Workflow integration. I’d love to see the ability to post a document to the chat and request a review which then triggers an Alfresco Process Services process.
If you want to work on this together, pull requests are always welcome!