Ready, Set, Fail - Avoiding Setbacks in the Intelligent Automation race - A new major study by KPMG
KPMG recently undertook a study to understand the reasons for and implications of deploying IA and what it takes to scale. This survey was conducted via interviews between KPMG professionals and leading executives across numerous industries all over the world. While nearly all were in consensus that IA is poised to digitally transform the landscape of business today, I want to point out 5 key take-aways from the report and tell you what they mean for you.
"Ready, Set, Fail?: Avoiding Setbacks in the Intelligent Automation race." A link to the report can be found here.
Executives have high expectations but their organizations are unlikely prepared to meet them: Within three years, 74% of executives plan on using RPA at scale, and 49% think they’ll be utilizing cognitive/AI/machine learning at scale. This is unlikely to be the case, however, with most organizations lacking in one way or another to reach these goals. When discussing potential challenges that would get in the way, 63% indicated talent as a major issue with 45% responding that lack of organizational accountability and direction would play a major role. Others talked about lack of data and company culture. All of these tie into the next major point…
It’s time to let the past die and look towards the future: 28% of executives believe that between 26-50% of roles will be significantly altered by IA in the next three years. In order to keep up, that number is probably low. Organizations are going to have to make significant changes to their structure and culture. A model where machines operate around humans must change to one where humans operate around machines. Silos within an organization will be brought down as the company begins to think bigger picture. Technical skills for programming and soft skills that can contribute to presentation and driving strategy will come at a premium in the future job market. A majority-millennial workforce will challenge company culture in ways that previous generations have not. These changes could prove significantly more difficult for legacy companies than digital ones because…
Legacy companies are starting behind the 8-ball in comparison to their digital-first peers: It’s no secret that new-age born-in-the-cloud companies have had the advantage over legacy organizations for a while now. That’s why Netflix killed Blockbuster, Uber and Lyft have beaten the Taxi companies, and Amazon has dealt a crushing blow to retail chains everywhere. Despite a somewhat grim history, these organizations cannot just wait it out or pick up their ball and go home. They must compete. KPMG has developed a 4-quadrant framework that operates on a spectrum, describing an organization’s progress with IA. The categories are static, incremental/fragmented, disruptive and transformative. While a majority of companies fall within static and incremental, the digital companies’ head starts already have them operating in disruptive and transformative. How do we close those gaps? Well…
‘Scared money don’t make none’ and the spending floodgates are opening: Currently, enterprise IA investment (AI, ML, and RPA) is estimated to come in at around $12.4b. Incredibly, that number is expected to grow by almost 2000% to $231.9b by 2025, less than a decade away. The IA arms race is already on, and it’s only going to grow bigger as time goes on. The executives we interviewed seem to be prepared for that with 40% expecting their cognitive investments to increase by 20% or more, 32% planning on a similar jump in RPA spending, and 24% and 30% respectively increasing their investments in data/analytics and customer-facing initiatives at that same 20%+ benchmark. All of these investments will occur over the next three years if things go expected. Investing in the right technology and creating the right organizational structure are important because…
Technological capability runs on a spectrum that’s getting more sophisticated: In this digital revolution, the capabilities of machines are improving at a rate that we haven’t seen before. The technology spectrum ranges from task automation to knowledge augmentation. In other terms, machine functions are ranging from acting like humans to thinking like humans. The spectrum starts with rules and basic process automation. Think of this like a macro from an excel document or a self-executing function. The next rung on the ladder is learning and enhanced automation. Examples of this are pattern recognition and natural language processing. The machines can recognize certain things and structure the data accordingly. Finally, you get to reasoning and cognitive automation. At this level you have sophisticated predictive analytics and Artificial Intelligence.