Avoiding setbacks in the intelligent automation race

New study reveals most organizations’ low readiness to deploy artificial intelligence technologies

Many traditional businesses with legacy approaches risk falling behind digital-first companies if they stay with the status quo. It takes a comprehensive transformation of business and operating models to compete in their own market at the level at which a Tesla or Amazon do in theirs.
Cliff Justice, KPMG Partner, Innovation & Enterprise Solutions, and leader of Cognitive Automation initiatives

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.

It's not clear whether most companies understand that intelligent automation is about changing business processes, and then restructuring the organization around those new processes now driven by technologies that didn't exist before. This means shifting the business and operating model from one of people supported by technology to one of technology supported by people. It's a digital-first operating model.                      

KPMG recently undertook a study to understand the reasons for and implications of deploying IA and what it takes to scale. KPMG professionals interviewed executives from numerous industries and geographies worldwide about their experiences with deployment and their perspectives on the future. Most emphasized that IA is poised to digitally transform their companies and industries and profoundly impact their employees' roles.                                                                             

At the same time, executives highlighted several challenges. In addition to 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 research considered three main areas of intelligent automation -- basic or robotic process automation (RPA), enhanced automation and cognitive automation.

These results underscore the need to not only act quickly but to plan deployments strategically with scale in mind. Most companies' executives acknowledged they are still experimenting only with RPA, applied to legacy applications and processes. With such a narrow focus and a bottom-up approach, they have not positioned themselves to transform their business and operating models so they can become and remain competitive with digital-first companies.                                                                 

As intelligent automation use accelerates across industries and organizations worldwide, digital-first companies already have a distinct competitive advantage. Not all companies can emulate Amazon's one-click experience with its complexity and checks-and-balance built into a digital supply chain. Companies can, however, close these gaps if they act quickly, understand the urgency, and define and execute a comprehensive IA strategy–one that looks not just at technology but at business and operating model opportunities and constraints.

This report summarizes KPMG's research into how IA is currently impacting business and operating models. It provides recommendations for how companies can plan for and implement an IA strategy that will help enable them to complete with digital-first competitors and thrive in a digitally-driven world.

KPMG research concludes that companies recognize intelligent automation’s potential value but are moving slowly toward using artificial intelligence technologies and still in the early stages of deploying software robots.

High expectations but little readiness to deploy IA overall and deficient change management and government capabilities

High expectations but little readiness to deploy IA overall and deficient change management and government capabilities

Opportunities across all functional areas and all sectors, though certain industries, characterized as being more aggressive with adopting new technologies

Opportunities across all functional areas and all sectors, though certain industries, characterized as being more aggressive with adopting new technologies

IA is part of overall digital transformation but lack of in-house talent and organizational focus may hinder initiatives

IA is part of overall digital transformation but lack of in-house talent and organizational focus may hinder initiatives

Investment spending increases are significant over the next five years but may not prove enough to support deployment expectations

Investment spending increases are significant over the next five years but may not prove enough to support deployment expectations

Culture change is paramount to success with few exceptions of job losses to IA

Culture change is paramount to success with few exceptions of job losses to IA

Respondents demonstrated high hopes but little readiness to drive IA deployment at scale and use it as a vehicle for organizational transformation. Overall, they:

  • Predicted significant investment increases in automation platforms over the next three to five years
  • Emphasized the importance of IA implementation in the context of an overall digital transformation within companies that want to compete successfully
  • Indicated opportunities across all functional areas and industry sectors
  • Emphasized the need to combine technological capacity building, structural workforce adjustment and cultural change as vital for success

Keep up and lose out: The study indicates that companies that successfully embrace emerging IA technologies will be able to differentiate themselves to compete successfully with digitally advanced enterprises.

Lack of vision, lack of commitment: As they seek to accelerate their IA efforts, companies’ executives encounter a variety of challenges across their organizations.

Big investments: Respondents indicated plans to steadily increase direct and indirect investment in IA solutions of all types over the next three years.

Fragmented focus = missed opportunities: Companies err in focusing solely on automation legacy processes and applications, which only improves efficiency incrementally and may have little impact on enterprise effectiveness and overall competitiveness.

A boundary-less organization?: An overriding issue is the need to grapple with the organizational impact automation will have on operating models

The “HAL” in the next cube: The people side of the business is of great concern when companies think about automating tasks that had been performed manually.

Look well beyond the back office: Ultimately, developing technologies will also enable companies to accelerate their automation journeys. As one example, a vendor that provides robotics as a service has introduced “Bot Farm,” a service enabling companies to scale a bot on-demand in the cloud. Expect more forward-looking technologies to emerge as IA becomes mainstream.

33% of respondents indicated that management concerns over IA’s impact on employees was the biggest obstacle.

Case Study: Virtual assistants make customer service even easier

Regulation and competition drive this industry, pushing banks to provide a better customer experience—especially with a growing millennial population. This bank jumped ahead early by developing a virtual assistant. We helped the bank assess its technology capabilities and limitations. Then we designed and implemented a virtual assistant framework that could meet its customers’ current needs for common transactions like scheduling payments and future capabilities to proactively receive financial guidance.

As a significant part of the bank’s commitment to customer service and digital transformation, when launched, customers can access this voice and text-driven virtual assistant platform 24x7 to get an even better banking experience. 

We deliver what your customers want.

Case Study: Insurer picks up speed with digital project

An insurance company sought to lower costs, improve quality and efficiency, and free up employees to work with customers on more complex issues. In early 2017, the company launched an IA project to identify and automate administrative tasks in one business line.

The team started by prioritizing a preliminary list of processes for automation, based on cost and benefit. Within four months, the insurer had a road map for automating core business processes, such a customer requests and back-office tasks. Judgments were made over whether to revise the process, automate it, or turn to a managed service. The team selected the automation software platform Blue Prism as the primary technology service provider. While management started slowly, the company’s executives are fully engaged and picking up speed on the automation journey, changing organizationally and digitizing data on the front end to drive further improvement.

A transformational framework: Four stages of IA progress

KPMG has developed a framework that describes an organization’s progress with IA along a continuum, from static to incremental, disruptive and transformative.

At the center of the framework is the organization’s progress with transforming its core operating models and using new and existing data as part of an overall IA design and implementation.

The survey suggests that most companies fall within static and incremental/fragmented categories. All-digital and born-in-cloud companies are already in the disruptive and transformative stages, but these companies make up a very small percentage of all major enterprises today. Differing stages of adoption can exist across an organization – transformative in one part of the business and incremental in another, sometimes depending on industry issues. Large banks and insurance companies offer real-world examples of this splintered approach. Financial services and retail lead all others in diving into disruptive and transformative approaches.

In any organization, each of these four stages may exist simultaneously across specific parts of the enterprise, with the adoption of IA resembling a financial portfolio of different opportunities with specific returns. A portfolio approach can be effective as long as it emanates from a defined strategy, with organizations carefully considering where to push for more aggressive, transformational efforts based on that strategy. Understanding that organizations’ experiences will vary when adopting IA should help guide decision makers in narrowing the gaps between expectations and realities.

Here’s how

How can companies improve their readiness to meet high marketplace expectations for IA? Here are some recommendations:

  1. Recognize that the use of IA is transformative and built on the use of new machines and data sources. As a result, companies will need entirely new blueprints and architectures for operating models and business models. Such a transformation requires long-term planning with a sequence of steps, starting with prioritizing projects that can realize scale in one or two years. C-level buy in and sponsorship is critical.
  2. Formulate a comprehensive approach to automating the service delivery model including centers of expertise, shared service centers and business partnering, self service, and outsourcing providers. Approach IA spending holistically across all technology platforms, with data and analytics. Develop solid business cases to ensure investment value and maintain expectations between deployment promises and investment capacity.
  3. Design 2 by 2 structures on automation activities that show the trade-offs between preserving value and reducing risk compared to those that are creating value and improving product and service quality. Desired outcomes will dictate which technologies and processes to choose and the speed with which to deploy them against specific business objectives.
  4. Consider the “operating model” in all of its forms: operational and technology infrastructure, organizational structure and governance, and people culture are all critical to IA deployment, especially its impact on core business processes. Measurement and incentive systems will change to align with operating model disruption.
  5. Think about ways to disrupt business from within while maintaining uninterrupted business operations. Some companies such as fintechs create different entity structures to help continue to play in their industry and disrupt in a very different way.

At KPMG, we understand that intelligent automation is far from simply a technology challenge to be handled in siloes by individual department heads. We are seeing IA’s impacts within companies. We are also helping leaders develop and implement the strategies they need to optimize their business and operating models and transform our own business. We can help you make the most of the opportunities IA provides.