Organizations across the world are using Automation Anywhere Enterprise A2019 to drive some of their digital transformation efforts...but what different types of bots are they creating? Let's examine 4 different types of bots to understand what types your organization may already be working on, as well as some types that you may look to create in the future.

1. Stuff We're Doing Today Bots

   • This bot type is the most common type of bot that organizations are creating - the bots that automate the heavily manual, mundane, and "robotic" tasks that humans are being asked to do.

   • This category of bot includes automating the repetitive, high-volume, rule-based tasks where humans are often tasked with playing the bridge between applications that don't otherwise have automated methods of interfacing with one another.

     • Of note - these types of tasks have lower job satisfaction with employees as the tasks require no real human input (reasoning, decision making, emotional intelligence, etc).

   • These bots typically have the highest ROI and help an organization most quickly reach many of their goals related to automation objectives.

2. Stuff We Should be Doing Bots

   • We all have them - the tasks we as individuals, or we as teams should be doing - but are not - typically due to a lack of time, lack of headcount, or lack of capacity in our days.

   • This bot type enables us to automate those things we should be doing - to give us better insights into how our teams/organization are performing, more regular feedback on how we're doing handling customer transactions, and more regular reporting to highlight the efforts of the team/organization.

   • These bot types often are the level 2 type of automation as they are explored once some of the high-volume, rule-based work has been knocked out.

   • The stuff we should be doing bots also enable teams to start creatively exploring how they might be able to provide additional value to the organization or other teams who may rely on their efforts - through better communication of task status, improved visibility into transactions/bottlenecks, and the more regular generation of reconciliation/SLA reports.

3. Data Manipulation Bots

   • This bot type starts to move away from things that are already being done (or should be done) and into the realm of exploring how joining data from different systems and applications can allow teams and organizations to make more informed, data-driven decisions.

   • This bot type requires a good level of creativity and imaginative thinking to come up with the type of data relationships across systems that may help to make more informed decisions.

   • An example in real estate investing:

     • Typically single-family-home real estate investors look for properties that meet the 1% rule (where the rent that can be charged each month is equal to 1% of the total purchase price of the home).

       • Example: A home that costs $100,000 would need to be able to support a $1,000/month rent for it to be a good return on investment for the investor.

     • Unfortunately, the data to calculate this 1% doesn't always exist in the same application - and searching for "1% homes" isn't a thing in most real estate listing websites.

     • In this case, a bot could scrape all of the listings of homes in realtor.com/zillow/MLS, then make a call to any number of rent-estimating applications (Zillow supports this in their API) to return the expected rent for each property.

     • In this way, a potential investor could quickly have a list of ONLY the homes that met the 1% or greater rule, without having to dig through pages and pages of real estate listings...which could be the difference between quickly finding a diamond in the rough vs getting burned out looking through endless pages to try to find something that works.

4. Cognitive Automation Bots

   • This bot type starts to explore how cognitive capabilities (artificial intelligence, machine learning, computer vision, etc) can be leveraged within a bot - also called intelligent automation.

   • With most of the strictly rule-based tasks already automated, many organizations find value in starting to explore what can be done with RPA when cognitive capabilities can help to give the automation process a lift.

   • Some examples:

     • This could mean using IQ Bot to extract key-pair values from invoices or other forms that the organization is receiving.

     • This could mean doing things like task assignment optimization to improve the way that workflow tasks are being assigned to staff based on their capabilities, availability, and permissions.

     • This could mean taking some of the data exhaust (data collected during bot processing) of other tasks to build and consume a machine learning model that can automate the approval of loans or other transactions.

     • This could mean consuming some of the cognitive capabilities provided by cloud services like AWS, Azure, and Google.

     • With cognitive automation, the benefits of robotic process automation can be fully explored alongside the benefits of artificial intelligence/machine learning

Conclusion

As organizations continue to mature in their use of Enterprise A2019, there's no doubt that they will begin to explore bots in all of these different bot types. If you haven't started exploring some of these areas yet, hopefully, this breakdown gave you some food-for-thought on different ways your organization may be able to scale RPA into new areas. Admittedly, this list of bot types is surely not exhaustive - and it's very possible that your organization is creating unique bots that go even beyond the categories presented. What types of bots are you creating?