In a recent KPMG study the professional services organization published a report called "Ready, Set, Fail?: Avoiding setbacks in the intelligent automation race", which projects rapid growth of the intelligent automation (IA) domain. The report suggests that the overall spend will reach $232 billion by 2025 compared to $12.4 billion that is spent today. But, this expected growth comes with many challenges, including tool maturity, skilled labor, organizational change management, governance and a lack of clarity involving return on investment.
Intelligent automation is an emerging set of new technology tools that mimic the actions a user would ordinarily perform to complete a task. Federico Berruti, a partner at McKinsey & Company, defines intelligent automation as:
A suite of business-process improvements and next-generation tools that assists the knowledge worker by removing repetitive, replicable, and routine tasks. And it can radically improve customer journeys by simplifying interactions and speeding up processes.
As market conditions continue to place profit margin pressures on many sectors, organizations are looking for ways to reduce costs while improving productivity. McKinsey has witnessed that organizations that use intelligent automation can:
Automate between 50% and 70% of tasks, which has translated into 20% to 35% percent annual run-rate cost efficiencies
While the projected size of the IA market is explosive and some early evidence suggests there are tangible savings available for companies who take this plunge, there are many challenges along this journey. In the KPMG report, they cite many challenges for organizations including:
Grappling with the extraordinary pace of change, they are faced with understanding and choosing among hundreds of technology options, the need for effective data and analytics, prioritizing automation focus, and defining their future workforce.
KPMG also reports additional challenges exist when it comes to managing expectations, and the extent in which it is deployed within an organization:
Executives have high expectations for the impact of intelligent automation, but they're not yet ready to implement it from the top down and at scale. They'll struggle to get adequate ROI until they recognize two critical issues: 1) intelligent automation investment decisions need to be C-level strategy imperatives, 2) intelligent automation is about business and operating model transformation not simply technology deployment.
While many of these challenges imply there are a lot of organizational change management issues, a recent HFS Top 10 Robotic Automation Products (RPA) report provides some additional insights into the maturity of IA technology. In particular, getting automated processes to production isn't as predictable as promised:
The client experience with the amount of coding/configuration required is rated amongst the lowest [in product satisfaction]. Management of version control, upgrades, training and support offered by RPA providers was also sub-par.
Another criticism from the HFS report, challenged how "smart" these intelligent solutions were:
The dimension around embedding intelligence in RPA was rated amongst the lowest by clients. There is considerable confidence in RPA's ability to process structured data, but drops down significantly when asked about unstructured or even semi-structured data. Clients are not convinced about the Artificial Intelligence (AI) capabilities of their RPA products.
Even though these challenges exist today, KPMG was able to share some case studies that include promising results that support the growth of IA solutions over the next seven years. One particular case study described a bank providing virtual assistants that improve customer service and address the needs of a growing millennial population. The solution included providing access to voice and text-driven conversational interfaces that allowed the bank to provide a 24x7 banking experience.
Currently, many of these initiatives are running as experiments within organizations. KPMG believe that until companies prioritize IA across the entire organization, their business and operating models will struggle to remain competitive against digital-first companies.
