How Slack can Improve Slackbot (with AI?)

Top Takeaways for Product Managers

• Context is King: Understand user intent and channel discussions to provide hyper-relevant suggestions and assistance.

• Anticipate Needs, Not Just Tasks: Go beyond reminders - automate workflows and suggest actions before users need to ask.

• Natural is Best: Ditch the cryptic commands! Make Slackbot understand natural language for seamless conversation.

• Personalization Pays Off: Machine learning allows Slackbot to adapt to each user's work style and preferences.

Hey there 👋🤖!

It’s been a couple of years since I first used Slack. I think it was around 2019 when I was in my BMS second year. I enrolled for a Social Media Marketing internship. The startup that hired me was using Slack.

My first time with Slack was terrible! The small channel buttons all hung to the left side of my screen made me go crazy! I really don’t know why but I would often go lost 😭.

Looking back, I believe it was the poor mobile app design.

Within all this clutter and chaos, I think the Slackbot was somewhat helpful. At least, it let me know each time someone shared a file with me or made changes to my file 😮‍💨. Except it was (and still is) very annoying when it was too much.

Here at Product Monk, we use Slack - sadly - yes. Hate me all you want, but I hate Slack 😭 - and love Teams 😍!

When we decided we needed a better communication platform, I remember I almost went on my knees (virtually) begging Angad not to, when he chose Slack! He did try setting up Teams after all my whining, but their onboarding was a heck of an experience. We had to ditch it :(

Well fast forward to today, we still use Slack.

And I always notice this guy Slackbot sitting there in the lowest corner of my screen - sad and lonely. Although he still has an awkward smile plastered on his face - thanks to the Slack team for the nice picture!

Today, I am going to try and add more meaning and value to Mr. Slackbot’s life. I am also going to see if we can, in anyway, leverage AI to enhance this underutilized assistant in Slack.

Let’s go!

How does the Slackbot function today?

Turns out the Slackbot does a LOT of things that most users aren’t even aware of!

Here's a detailed breakdown of its key features:

1. Internal Knowledge Base:

• /help and /faq: These commands provide users with quick access to helpful pointers and frequently asked questions (FAQs) regarding Slack itself. This empowers users to find solutions to common issues independently.

2. Targeted Information Retrieval:

• Simple keyword queries: Slackbot can answer basic questions for which it has been programmed. This can save time and effort compared to searching through external resources.

3. Advanced Channel Search:

• /find [@channel] "keyword": This command allows users to search for specific keywords within a designated channel. This functionality proves invaluable for retrieving past discussions or locating critical information shared within a channel.

4. User Management within Channels:

• /members: This command provides a list of members currently associated with a particular channel. This is useful for quickly identifying team members who are actively engaged within a specific channel.

5. Third-Party App Integration:

• Slash commands: Slackbot acts as a bridge between Slack and various third-party applications. Through the use of specific slash commands (e.g., /trello or /drive), users can leverage functionalities offered by these integrated services without leaving the Slack interface. The availability of these integrations and their specific functionalities may vary depending on the workspace and admin settings.

6. Reminder and Scheduling:

• /remind: This command allows users to set reminders for themselves or others. This helps ensure that important tasks and deadlines are not overlooked.

• /schedule: This functionality enables users to schedule messages to be delivered at a specific time in the future. This can be useful for sending messages outside of regular working hours or for ensuring that critical information is delivered at an opportune moment.

Why don’t users use Slackbot as much?

So the next question that comes to mind is why still users are not using the Slackbot.

Limited Functionality

• Repetitive Tasks: Slackbot excels at basic functions, but repetitive tasks like setting reminders or basic searches make users feel limited compared to more feature-rich integrations.

• Lack of Awareness: Many users may not be fully aware of Slackbot's capabilities, especially its ability to integrate with third-party apps.

Usability Issues

• Unintuitive Commands: Relying on slash commands feels clunky and unintuitive compared to a more natural conversational interface.

• Limited Context Understanding: Slackbot struggles to understand the context of conversations, making its responses generic and potentially irrelevant.

Privacy Concerns

• Data Usage Transparency: Users might be hesitant to interact with Slackbot due to concerns about how their data is being used.

Alternatives and Feature Creep

• Third-Party Integrations: Many organizations have adopted robust third-party integrations within Slack, potentially rendering some of Slackbot's functionalities redundant.

• Human Interaction Preference: For complex questions or discussions, users might prefer the nuance and clarity of direct human interaction over interacting with a bot.

Overall Perception

• Simple Tool, Not an Assistant: Slackbot is often perceived as a simple tool for basic tasks, not a powerful assistant that can anticipate needs and proactively offer solutions.

Rethinking Slackbot with AI

1. Understanding User Context

Imagine a Slackbot that doesn't just answer questions, but anticipates them. AI can analyze various data points to grasp the user's current situation:

• Past Interactions: By analyzing past interactions with Slackbot and within channels, AI can understand a user's typical behavior and communication style.

• Channel Activity: Monitoring discussions within relevant channels allows AI to glean context about ongoing projects, deadlines, and team focus areas.

• Sentiment Analysis: Natural Language Processing (NLP) can detect the sentiment of user messages. Is the user stressed about a deadline? Frustrated with a technical issue? Understanding user sentiment allows Slackbot to tailor its responses and assistance accordingly.

With this contextual awareness, Slackbot can transform from a reactive tool to a proactive partner. Here are some possibilities:

• Suggesting Relevant Tasks: Based on project discussions and deadlines, Slackbot can proactively suggest relevant tasks or to-do items for a user.

• Recommending Documents: Is a user discussing a specific topic? Slackbot can analyze past document shares and user behavior to recommend relevant documents or resources within the channel.

• Identifying Integration Needs: Slackbot can monitor conversations for keywords that indicate a need for a specific third-party integration. It can then suggest the relevant integration and guide the user on how to utilize it.

2. Proactive Assistance: Freeing Up Mental Space

Moving beyond basic reminders, AI can empower Slackbot to anticipate user needs and automate repetitive tasks:

• Deadline Assistant: By analyzing project discussions and calendars, Slackbot can identify upcoming deadlines and proactively suggest actions, like creating tasks or scheduling meetings.

• Workflow Automation: Slackbot can learn from past user workflows and automate repetitive tasks. For instance, if a user frequently needs to generate reports after a meeting, Slackbot could automate the report generation process based on meeting notes and relevant data.

This proactive approach streamlines workflows and frees up valuable mental space for users. They can focus on strategic tasks and creative problem-solving, while Slackbot handles the administrative burden.

3. Conversational Intelligence

Imagine having a fluid conversation with Slackbot, just like you would with a colleague. NLP empowers AI to understand complex questions, follow the flow of conversation, and offer solutions tailored to the user's intent. This eliminates the need for remembering cryptic slash commands:

• Natural Language Understanding: Slackbot can understand the nuances of human language, including slang, idioms, and context-specific terminology. This allows users to interact with Slackbot in a natural and intuitive way.

• Conversational Flow: By following the conversation thread, Slackbot can understand the user's intent and provide relevant responses. This eliminates the frustration of irrelevant or generic responses.

• Tailored Solutions: Slackbot can leverage its understanding of the user's intent and current context to offer personalized solutions. This could involve suggesting relevant integrations, providing summaries of past discussions, or connecting the user with a human expert.

4. Learning and Adapting: A Personalized Assistant

The beauty of AI is its ability to learn and adapt. Machine learning allows Slackbot to continuously improve its performance based on user interactions and preferences:

• Personalized Suggestions: Over time, Slackbot learns a user's work style and preferences. This allows it to personalize its suggestions for tasks, documents, and integrations, becoming a truly individualized digital assistant.

• Evolving Responses: As Slackbot interacts with users, it learns the most effective ways to communicate and respond to their needs. This can involve adjusting its communication style (formal vs informal) or the level of detail provided in its responses.

Summary

Slackbot holds immense potential to transform from a basic utility into a powerful AI assistant.

By leveraging features like understanding user context, offering proactive assistance, enabling natural conversations, and continuously learning, Slackbot can become an indispensable partner in the digital workplace.

This case study explores how AI can empower Slackbot to streamline workflows, anticipate user needs, and foster a more intuitive and personalized work experience.