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:
- 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.
- 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.
- 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.
- 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).
- 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.
- 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!