Intelligent Automation: The Phoenix Rising from the Pandemic Fire
Intelligent Automation (IA) technologies, such as Robotic Process Automation (RPA), Machine Learning (ML), Chatbots, and Artificial Intelligence (AI) have been at the forefront of business thought for nearly a decade. Over that time there have been enormous investments made in these technologies, with what could be described, at best, as sporadic success. While a handful of companies have achieved great results with these technologies, they are most notable for being rare, rather than representative.
There were a wide range of reasons that organizations failed in their adoption of IA, but the dominant cause of failure was inappropriate expectations. These technologies, and RPA in particular, were sold as “Cheap, Easy and Good”, which is the first sign that something has been over-sold. Whether mis-sold, mis-designed or misused, the vast majority of companies have been unable to leverage these technologies to the degree they originally had hoped.
We believe this trend will end due to COVID-19. The pandemic has forced organizations to rethink nearly all aspects of their operations. It has forced organizations to view human labor and productivity in entirely new ways, how to view the speed with which technology can and should be adopted, and the power of technology when unfettered by organizational norms, habits, beliefs and fears. The constraints that have prevented success with IA have been deeply undermined by the pandemic, while the pressures to adopt IA have grown a hundred-fold.
This research will collect and integrate some of the new thinking on the successful use of IA from LEF, DXC, and their affiliates. It will explore how organizations are changing their approach to using these tools and their integration and synchronization with their human workforce.
What topics will be addressed?
This research will build upon the five years of work by Research Lead Christopher Surdak, which led to his book, “The Care and Feeding of Bots.” Over those years Surdak both analyzed the practical results of thousands of RPA bot implementations, and dozens of artificial intelligence implementations, he also worked to remediate and redeploy a significant number of these implementations that were challenged to yield positive results.
From these interactions, and the feedback of numerous IA practitioners from a wide range of industries five primary categories of IA “challenges” emerged, in order of descending frequency of occurrence:
- Financial Challenges: IA implementations that did not meet financial expectations
- Governance Challenges: Challenges in properly managing and controlling the population of digital laborers
- Operational Challenges: Challenges in running digital laborers in an effective manner
- Design Challenges: Challenges in designing digital workers appropriately for the tasks at hand
- Technical Challenges: Issues with the underlying technology performing effectively.
In light of these challenges, LEF will present a range of best practices and lessons learned by organizations who have advanced in their adoption of IA. We will place special emphasis on how effective use of these technologies leads to changes in how organizations must operate, in preparation for our subsequent research on Cybernetic Organizations, coming in late 2021. Specifically, this will address how organizations must change their management practices, analysis and metrics, organizational structure, processes, and their use of human labor as they more deeply integrate automation into their operations.
Research Interviews & Benefits of Participation
Please reach out to us to join one of the working groups in the following industries at a time convenient for one of our three regions EMEA, the Americas, or APAC:
• Medical, healthcare, and life sciences
• Government & public services
• Banking, insurance and financial services
• Technology infrastructure/telecoms
• Hardware and manufacturing
• Aerospace and Defense
In these working groups, we will present and peer review challenges each business has faced in using IA in the past, and how their experience with the pandemic has changed their thinking and approach in adopting these tools. We will explore use cases for the successful deployment of IA, particularly as the approach that works in 2021 departs from the approaches used in the past.
In addition to peer expertise throughout the working groups, participants will gain early access to the final research report.
If you would like to participate in this project, please contact us.