Many businesses are looking to automation to increase productivity, save costs and improve customer and employee experiences.
For example, imagine a large insurance company that processes millions of claims a year. Around 60 percent of its claims are automatically processed, but it’d like to automate 85 percent of claims and reduce error rates, reduce costs and increase topline revenue. To make these improvements, the company is considering artificial intelligence (AI) to extend its automation capabilities.
While AI technology has the potential to make automation truly intelligent, there are barriers to adopting AI across operations that could limit early success. Three barriers we see most often are:
Business people don’t know how and where AI can be best applied to their problems. In a recent McKinsey survey, only 17 percent of respondents knew the areas of their operations where AI could have the biggest impact.
AI algorithms are often disconnected from daily business operations. The same survey found that for business processes where AI has been adopted, only 26 percent have been integrated into daily business operations. Furthermore, only 6 percent of organizations that have adopted AI use it for decision making with front line employees.
AI is difficult for business people to trust, control and monitor. Only 16 percent of respondents in the survey said that employees trust AI-generated insights. Since the nature of learning systems means they evolve over time, unless business people are empowered to measure and manage the performance of AI doing work on their behalf, they will not trust the system to do a good job.
Introducing IBM Business Automation Intelligence with Watson
To help eliminate these barriers, IBM is designing a learning system to help business managers improve productivity and customer experiences using AI in their daily business operations. IBM Business Automation Intelligence with Watson is an automation capability for creating, managing and governing AI across the enterprise and applying it to operations using Watson. It will be able to access and act on the operational data generated by the IBM Automation Platform for Digital Business.
With Business Automation Intelligence, business leaders will be able to automate work from the mundane to the complex while measuring the impact of AI on business outcomes. Users will be able to apply AI to existing apps to capture the necessary data; run analytics at scale; and deliver continuous, AI-enabled operational improvements.
Overcoming AI barriers
Here’s how Business Automation Intelligence could address each barrier to AI adoption using the hypothetical insurance company example above:
1. Business people don’t know how and where AI can be best applied to their problem.
Business Automation Intelligence will enable business users to identify opportunities for automation by seeing where automation agents (bots that handle specific tasks or functions, with or without intelligence) could potentially have the most impact. Business Automation Intelligence will provide built-in analysis using process mining to find hot spots for automation.
For example, imagine Lisa, an employee at the insurance company. As the business owner of the claims processing system, she uses Business Automation Intelligence to analyze the claims processing operational data and finds her employees are spending a lot of time extracting information from claims documents and entering it into their claims processing system. Based on this data, she could prioritize automating this part of the workflow.
2. AI algorithms are often disconnected from daily business operations.
Business Automation Intelligence is designed to enable you to apply AI at scale to a wide range of styles of work, from the mundane clerical to complex knowledge work. Our goal is to help clients move past one-off AI experiments and use Business Automation Intelligence to methodically discover, create, manage, govern and apply AI to automated business operations across the enterprise, delivering continuous, AI-enabled operational improvements. It will do this with built-in connectivity to the IBM Automation Platform for Digital Business, as well as with several Watson capabilities.
For example, Lisa’s knowledge workers must analyze every claim that isn’t automatically processed and then manually route it to the right claims processor based on complexity. With Business Automation Intelligence, built-in machine learning evaluates the complexity of the claim and automatically routes it to the claims specialist with the appropriate level of experience and expertise.
3. AI is too hard for business people to trust, control and monitor.
Business Automation Intelligence will include work guardrails and performance monitoring so business leaders can control and manage the digital workforce initiatives based on business outcomes. Guardrails will use natural-language rules to define and control the conditions under which the automation operates. To monitor the performance of automation agents, prebuilt dashboards will be included with KPIs that the user defines.
For example, some insurance claims require specialized handling. Lisa sets up guardrails in Business Automation Intelligence that define the types of claims that get immediately routed to a specialist instead of being processed automatically. This helps as the company handles specific compliance situations. When these guardrails are embedded alongside the AI algorithm, claims can be managed more comprehensively, ensuring the AI tech is applied consistently.
Learn more about what Business Automation Intelligence can do, or request an invitation to the early access program.