Scheduler Bots – Meeting Planning Automated

March 1, 2019

Scheduler Bots – Meeting Planning Automated

We all have a few hours of our day dedicated to working, and everyone is playing to catch up. Every second is precious and invested in the growth of organizations. Amidst this busy scenario, did you know that scheduling meetings via calls or e-mails can take up about 4.8 hours per week?

Here is an example of the confusion and frustration that you might be familiar with:

Kevin: Hi! Would like to go through our plans next week. What day and time would suit you?
Anne: (Flicking through her calendar) I am pretty busy next week. How about this week, Friday?
Kevin: (Flicking through his calendar) Friday is a bit dicey. How about Thursday?
Anne: Sure. Thursday works well. 5 pm?
Kevin: That works for me. Let me see if Caleb is available.

And the process doesn’t seem to end.

The human mind is as busy as it is fickle and due to the diversity in routines of every team member, scheduling activities undergoes many different, taking a substantial time and effort to fix a meeting in the first place!

Hope Is Not A Plan

Given the complexity of scheduling, technology is finally offering solutions to assist people with bots.  Scheduler bots coordinate meetings between two or more people. These bots integrate with other calendar systems, pulling data from the back-end and providing options to a user looking to plan a meeting.

Scheduler bots are intelligent and straightforward versions of long drawn mail & calendar invites. Based on their previous encounters with humans, these bots can narrow down the conversations and break them into:

  • Singular segments like time, place (if any), and date.
  • Determine if a person is looking to schedule a meeting at a different location or choose a series of timelines like 2:00 PM to 3:00 PM.

By segregating each element of the request, scheduler bots can gather insight & high-quality data that can be later used to automate the scheduling process with higher precision.

This trial-and-error concept helps developers create different use case scenarios leading to a variety of machine learning model to further refine the interaction between the bot and the human mind.

Time Is Money

Backed by the AI model, scheduler bots are still quite a ways off from being a model digital employee.
It has yet to get a good grasp over designing the right AI capabilities that can handle the myriad of back and forth communication between employees, sorting through the required data, and eventually merging them for the desired results.

Moreover, like a good virtual assistant, scheduler bots must also be prompt in reminding the user about the upcoming engagements. The corporate world is a tough crowd.

But their use in closed-boxed conversations allows scheduling to be a highly effective use case for automating appointments.  The biggest practical examples to date lie in customer & service environments.

Doctor-patient relationships, guest restaurant reservations, and even applicant recruiter interviews gain the most value from scheduling bots.  The bot’s ability to save time and money in automating scheduling is invaluable to these industries.

The Future Is Already Here. Just Unevenly Distributed
Scheduler bots are on the road of constant learning that will soon negate the intervention of a human for its processing. Like two sides of a coin, scheduler bots also have the pros of being effective, and the cons of still being in the learning stage, which creates a dilemma.

These are the predictions considering scheduling bots:

  • These bots might lead to increased meetings & interactions.
  • Being time-efficient, it will help focus on productivity.
  • Many people might not be comfortable responding to a bot for something as important as meetings.

Scheduler bots are a melting pot of innovations and as per the true nature of AI, they will grow as they learn.

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