AI for core process transformation

The various forms of artificial intelligence (AI) are indisputably more accessible to the enterprise than they’ve ever been. Yet for the vast majority of organizations deploying them, these technologies are only implemented in isolated, fringe use cases that rarely produce any substantial effect to justify their investments.

Enterprise-scale AI will only achieve its full potential when it empowers core process transformation, improving the quantity and quality of workflows directly impacting strategic business objectives. Until its technologies are ingrained in these mission critical use cases, it’ll just remain a novelty for fringe deployments without demonstrable business value.

However, justifying its value to upper-level management and articulating how simplistic, rules-based chatbots for customer-facing applications will impact the bottom line can be difficult. What needs to be conveyed is that by automating and accelerating fundamental business processes in industry-specific use cases, such as claims adjudication, medical underwriting and policy automation (in finance or insurance), AI becomes vital to increasing revenues while decreasing costs.

When implemented via utilitarian, omnichannel AI bots, technologies such as deep learning and natural language processing not only create these results, but also do so with a quantifiable ROI resonating at the executive level. As a result, users can demonstrate potentially millions in savings and higher revenues, oftentimes before investments are made. 

At the Fringe

The chief issue with deploying AI for fringe use cases is a pronounced difficulty in justifying its long-term value proposition. Cautious AI initiatives usually take one of two forms. In the first, AI supports virtual assistants, chatbots or some other means of automating external, customer-facing applications. In this instance, the technologies simply involve the capture of relatively minor amounts of data. The agent or bot is used to begin different processes, yet almost always fails to significantly improve them.

In the second most common use of AI throughout the enterprise, different technologies are employed for back-end process automation. In these instances, AI is tasked with helping different aspects of data preparation, such as hastening the steps to data discovery or quickening what’s required for transforming data. Typically, the rationale for these fringe use cases is that it enables users to work on challenges that are more difficult and rewarding. Nevertheless, such advantages aren’t easily quantifiable, making it difficult to gain – and keep – executive level support for these initiatives, resulting in organization-wide disillusionment of AI. 

AI’s Core Process Transformation Benefits

By leveraging AI at the epicenter of prime business functions, however, organizations realize a number of benefits that are easily quantifiable. The most immediate of these are the temporal advantages of expediting business tasks with these technologies. For example, the automotive insurance claims adjudication process is a lengthy one that regularly spans upward of two months. Commonly involving more than 200 time-consuming steps including accepting or declining declarations, reviewing police reports and analyzing varying healthcare documents, this monolithic procedure is quickly partitioned into separate tasks by AI-bots. By using this method to empower respective tools for underwriting, policy automation and claims processing, AI can reduce the time for completing adjudication from more than eight weeks to under a minute.

Tantamount to these temporal boons are their financial repercussions. For instance, insurance companies usually spend 30 cents of each premium dollar for claims processing. Even if they shave just three to five cents off this figure, at scale, that difference translates to millions of dollars for this core process transformation. Customer onboarding in this vertical provides another tangible illustration of AI’s pecuniary and temporal boon when transforming core business processes.

Facilitating a complex life insurance policy, for example, can take insurance companies a month to vet potential customers and solidify policies suitable to the latter. During this time, organizations must perform prospect analysis, underwriting, risk analysis and multiple interviews. The trouble is, once customers have to wait more than four or five weeks for the result, they may lose interest or begin again with a different provider. The same intelligent bot methodology can reduce this month-long task to one taking approximately 15 minutes, improving customer satisfaction, efficiency and overall organizational effectiveness. 

Quantifiable Value

Most of all, when employed in core process transformation, AI delivers the greatest of advantages to upper level management: quantifiable value via ROI. Organizations can gauge how much time they’ll be able to save on these essential business functions, then evaluate them in terms of their monetary value. Insurance companies that are able to save even a penny on each claim in their claims processing will realize a considerable return. When calculating this ROI at scale for a $10 billion insurance market, a conservative estimate of this return is approximately a million dollars. This expedient understanding of the financial impact of transforming core business processes with AI isn’t possible when deploying it on fringe use cases and represents the most viable applications of these technologies.