robotic process automation

From RPA to Cognitive Computing: Finding the right robot for the job

Artificial Intelligence. Digital Labor. And Robotic process automation. Finding the right answer for your business. (1).png

Digital Labor.  Artificial Intelligence.  Cognitive Computing.  Robotic Process automation (RPA). The possibilities of new A.I. business model are increasing by the month, as is the hype.  Finding the right platform, adoption process, and long term value can be a challenging journey.

Insights on what works and why are becoming more clear, and sharable.  Earlier this month, I participated in a panel on robotic process automation and cognitive computing sharing such experiences, as part of the DePaul University Digital Innovation conference. I was joined by several business leaders with deep practical experience, including Dwayne Prosko from Deloitte, John Stiber from Mondelez International, and Steven Pyke from W.W. Grainger.  The panel discussion was wide-ranging, and provided the assembled audience much to consider with the challenges and benefits of digital automation.  

6 key insights from the panel discussion included:  

  1. Automation, of any sort - is a process transformation before it is anything else. Realize you need to focus on the process(es) you wish to change, understand the why (business and mechanical), consider the human factors (job / performance changes and differences in work ‘speed’), and evaluate which platform(s) help achieve the business / process goals. Do not start with an automation 'hammer' and look for 'business problem nails' to hit.
  2. There are many layers (and complexities) to digital automation.  While the business process itself may jump off the page needing to be improved - deeper thought on the underlying systems, processes, and connective tissue of a business function need thought.  Something as simple as a Windows software patch, normally done without thought to the impact in a company - can completely disrupt the performance of an automated platform that is handling thousands of returns an hour. Third parties are also bringing their own automation tools to the table now - consider how your automation solution will interface with third parties for joint automation (e.g. procure to pay, or payroll processing / outsourcing). You might be surprised which platforms do and do not work well together.
  3. Digital automation does not mean digitally unattended.  The ability to automate a business process or event does not preclude the need for human supervision.  Sampling for accuracy in the automation, review of exceptions, and performance tuning are all part of the journey of digital automation. Ensure you have a clear vision who will be 'managing the digital labor / platform' (both from IT and from the business) as part of your change journey. And be ready for surprising adjustments / changes to the situation as you accelerate the pace of performance.
  4. Crawl, Walk, Run, then Fly.  Many times, businesses respond to the hype that can proceed a digital automation investment. The better advice - be clear on the why, take it at pace and go patiently forward. Focus on a single function / department business process change you want to transform as a starting point to prove out the benefits, and increase organizational buy in. Learn how the business responses to faster, increased accuracy, or differences in efficiency. Ensure your culture can sustain the change and grow on the knowledge gained from the automation experience (vs. rebel and resist - see the next point).  
  5. Human / cultural alignment is key to success. Putting a human face on the automation process increases the acceptance and utilization / leverage of digital automation in a business.  Something as simple as ‘naming’ the automated platform, including in work schedules, and ensure team members who are working with the digital colleague’ understand the ‘why’ and the ‘WIFM’ - improve the chances of cultural adoption long term.   If you do not address - myths and worries will manifest around the 'real reasons' for the automation change- something which can become difficult to overcome in a culture that relies on the automation process.
  6. The idiom - “Horses for courses” certainly applies to digital automation.  A key insights agreed: No A.I. platform can do it all in the area of digital automation. Companies are now finding that ‘moonshot’ A.I. programs that focus on becoming a digital game-changer can quickly miss the value target (and revenue / ROI expectation) if implemented in an eco-system or scenario that fails to integrate the digital automation itself.    

And last - a better automation model:

Consider having a 'automation eco-system' vs. a singular platform. Instead of relying on a singular answer - consider how you can optimize the benefits of multiple platforms to your digital advantage (and do this over time, learning and absorbing in your culture).

An automation eco-system model: an RPA solution gathering normalized data and performing entry / validations at record speed, a digital autonomics platformevaluating and managing exceptions and acts as an ‘engagement agent’ to support high volume customer interactions, while a cognitive computing solution provides analytics, guidance, and exception ideation that helps surface new value in the business.

Leveraging these insights above, achieving a digital automation platform (or eco-system) for your company can be a smoother transformation in the digital world. Focusing on process, the change journey (and the human factors), and finding the right digital 'horses for courses' will improve your chances for digital success!

Digital Labor and RPA: Part 1 in our series on the benefits of robotic process automation

Blog post by Don Sweeney (don.sweeney@practicallydigital.net) 

Blog post by Don Sweeney (don.sweeney@practicallydigital.net) 

Digital Labor.  Artificial intelligence.  Robotic process automation.  Lately there is a lot to talk about the potentials for digital labor, and the benefits of automation in all companies.   While moon shots like cognitive computing / platforms require heavier investments of time and capital, there are easier and faster ways to generate digital labor benefits.   Specifically, a strong first step is through the use of robotic process automation.   

To help clarify the how and where, we wanted to create a multi-part blog to bring more awareness to the topic and cut through a lot of the noise that is out there.  This first post is defining what is RPA since there are a lot of items that sometimes fall into the overall bucket of RPA.  Our next installment will be why RPA is important and why it matters to you.  Finally, we will  provide steps to focus on for a successful implementation.  

So, what is RPA?

The Institute for RPA and AI  defines Robotic process automation (RPA) as “the application of technology that allows employees in a company to configure computer software or a “robot” to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems.”  So what does that mean?  Simply put, RPA is the automation of a process (or processes) that will remove or reduce the human intervention in that process. 

This can be as simple as removing the redundancy of re-entering data in multiple systems or removing the frequently updated items based on simple logic.  Overall, the benefit of RPA is to reduce the manual intervention in repetitive, routine tasks within a process flow and automate it so it is accurate and predictable going forward but also to free up the human intervention for more significant tasks like analyzing and interpreting the data.

RPA seems to be the next iteration of how organizations have focused on technology improvement.  In the late 80s and though out the 90s and 2000s, organizations implemented enterprise software.

The original goals for implementing enterprise software were to A) improve processes with the “built in” best practices and B) have consistent, accurate data that was integrated with other parts of the organization.  For instance, vendor setup was shared with the purchasing application and accounts payable application – thus limiting the redundant data management in both applications. This was the argument for Enterprise ERP or HCM solutions. 

Cloud computing makes a difference with RPA.  For the last couple years, organizations have been moving their enterprise software to the cloud.  This provides the value of having someone else support the software as organizations realized how cumbersome and costly supporting large enterprise applications can become.  RPA becomes the next iteration of the organization’s focus where an entire process flow becomes seamlessly integrated and can require little or no manual intervention. 

My good friend Paula shares an example on this transformation in the telecommunications industry.   Many years ago, if you wanted to call someone you would call the operator and explain to that person who you wanted to call. They would manually connect your line to the destination’s line (sometimes requiring many operators to get involved if the call was of a significant distance).  You could not call someone without the manual intervention of the operator.  Eventually, the telephone systems were all automated and now you simply dial the number you want and it is connected with no manual intervention.  This is significantly more efficient and cost effective as the use of the telephone exponentially grew. This is the same as RPA, but on organizational processes, in real time.

RPA can include “bots”, which are small pieces of a process that are now automated.  An example would be where an organization that automated the process of answering questions for their staff on how much vacation time they had left or questions about their insurance information.  This “bot” (short for robot) would know the identity of the person asking the question based on their active directory authentication when they logged into the network. 

The employee could ask this Human Resources Bot what amount of vacation they had left and it would go and query the HR database for the balance and display their current balance as well as when the last time they took vacation.  It could also answer simple questions like the contact information for their benefits insurance provider or what benefit plans they were signed up for.  All of this minimized the impact of the internal HR team to answer routine questions and allow the staff to work on more important tasks – yet still answering the questions that the employees needed answered. 

RPA is not new, the capabilities to drive enterprise adoption are.  RPA has actually been around for about 10 years already, but has significantly picked up steam in the last 12-18 months.  It is commonly perceived to be the first stage on the evolution of automation and artificial intelligence.  Those stages are:

·       Robotic Process Automation

·       Autonomics (automation augmented by humans)

·       Cognitive Computing (end to end automation with human oversight)

·       Artificial Intelligence (fully automated with computers “learning” by analyzing trends in repeated processes over large numbers of transactions)

We will address the items on the “A.I. Spectrum” in future blogs where intelligent automation services blurs the lines between RPA and A.I.  In the next blog we will address in more detail RPA and why this is important to you and your organization. 

About the author:

Don Sweeney has over two decades experience as a technology consultant and digital visionary, working with companies to automate business processes with digital solutions, including robotic process automation platforms.   Don has worked at global organizations like Andersen Consulting, Oracle, and most recently at Emtec to enable companies in their digital journey.  Don can be reached at don.sweeney@practicallydigital.net