Three Ways to Begin Harnessing AI – Now

Featured article by Matt Smith, Associate Vice President, Automation Venture Leader at Cognizant’s Emerging Business Accelerator

Artificial intelligence (AI), cognitive computing, smart machines: The trouble with these terms is they seem, for many in the business world, to refer to a far-off future. Systems that take on traditionally human tasks of “thinking,” “learning” and autonomous decision-making are often seen as something pertinent to the R&D function, not a capability to consider in the here-and-now.

In reality, however, opportunities abound for applying intelligent automation – Cognizant’s term for the spectrum of capabilities enabled by AI. Today, intelligent automation has moved beyond the back office, and into front-line, customer-facing systems that are quickly changing the way businesses and their customers interact.

In fact, we believe 2017 is the year when intelligent technologies will become pervasive across the business ecosystem. In our recent study, artificial intelligence (AI) – along with big data/analytics – was regarded by nearly all respondents as the top driver of business change between now and 2018.

What Exactly Is AI?

We see AI as a continuum encompassing three types of systems:

• “Systems that Do,” which are best represented by robotic process automation (RPA) –frequently the on-ramp for many just getting started with intel­ligent automation. Processes that are high-volume, rules-driven and data-intensive are ideal candidates for this approach. Examples include claims processing, accounts payable/receivable and data consolidation/validation.

• “Systems that Think,” which operate with less defined processes and unstructured data. With the introduction of logic, they are able to make decisions autonomously, even when they encounter exceptions or other variances. Examples include service desk incident resolution, complaint management and network security management.

• “Systems that Learn,” which can analyze vast amounts of dynamic and unstructured input, and execute connected tasks and processes that are both dynamic and non-rules-based. Examples include prescriptive pricing engines, virtual service agents and portfolio/investment services.

Across industries, business are already adopting AI in all three of these forms to improve business processes, accelerate outcomes, increase data quality and enable powerful and predictive analytics. Even more compelling, they are improving the human role of their organizational operations. By collaborating with intelligent systems, people are now more empowered than ever to perform uniquely human activities — which is to think creatively, solve problems, prioritize and interact with clients, partners and coworkers in smarter, more productive ways than ever before possible. In short, when we let machines do what they do well – data crunching, pattern recognition, split-second responses and deep analytics – we enable people to do what they do well – collaborate, make suggestive decisions and think creatively.

Getting Started with AI

To chart their path forward and move toward adopting intelligent automation, organizations should consider the following three approaches:

1. Think Big; Scale Fast

Organizations need to identify intelligent automation as a top strategic initiative across their entire enterprise. This means appointing an experienced executive to assume the role of automation leader, with the responsibility of accelerating adoption simultaneously across both IT and business operations. A life sciences organization we work with, for example, established a joint internal/external team of automation experts, with plans to ultimately formalize its own internal automation practice. The team prioritized three unique processes to get experience across several “do, think and learn” technology categories. The company has also developed an automation roadmap for the next 12 months that will encompass more than three dozen process areas across five different functions.

2. Form a Winning Partnership

In the realm of AI, most businesses are not yet in a position to go it alone. They will need to turn to a partner to help them quickly take advantage of the many emerging technologies that continue to appear. We worked with a multinational financial services company to deploy an intelligent automation product across large volumes of back-office transactions. We benchmarked the vendor’s technology, validated its capabilities and then supported the effort of designing, testing and deploying the solution into the client’s operations. By leveraging a partner’s technology, industry and implementation expertise, businesses can reduce risk and move faster.

3. Automate On-Demand

Some organizations have no intention of becoming automation experts themselves, but still want the benefits of these technologies. We worked with a healthcare payer to implement an as-a-service approach to quickly and accurately process out-of-network claims. In just weeks, the automated claims system was in place, and the “intelligent virtual agents” eliminated a backlog of 8,000 claims in just five days, at 99 percent first-pass accuracy. Today, the solution handles every out-of-network claim for this provider. The always-available automated agents determine who should be reimbursed and complete all necessary documentation.

A Continued Evolution

Business leaders should begin now with their plans to understand both the present and future opportunities of AI. When addressing their business goals, they should lead with an “AI-first” approach. And because intelligent automation can be tried, tested and either fail or scale in very short cycles, organizations should automate first and start capturing the benefits right away. In parallel, they can take the time to consider the broader, more comprehensive opportunities as they learn from past experiences where they want to ultimately build scale and acceleration.

Organizations also need to adjust as the ecosystem continues to evolve. For example, as intelligent technologies become more pervasive across the ecosystem, we believe the “systems that do” horizon will become narrower and less useful. The “systems that think” category will then become the entry tier as learning systems become mainstream. Over the course of 2017, the new ‘horizon three’ will become “systems that adapt,” meaning systems with the self-awareness to autonomously apply their learning to provide smarter, more effective outcomes.

The promise of intelligent automation is real, and it’s here now. Taking a wait-and-see stance is not an option when intelligent systems are already fast at work, providing real outcomes and helping companies transform processes across their organizations.

Matt leads the Automation Venture for Cognizant’s Emerging Business Accelerator organization. His responsibilities include automation strategy, enablement and communications across the company’s vertical and horizontal business units. Matt works closely with internal automation teams as well as external providers of automation, AI and other cognitive technologies. Matt holds a bachelor degree in Business Administration from Stetson University, where he majored in Marketing.