WIRE)--CLARA Analytics ("CLARA"), the leading provider of artificial intelligence (AI) technology in the commercial insurance industry, today announced that it has hired Tyler Jones to be its Chief Customer and Marketing Officer. Jones will be responsible for directing the company's complete relationship with its customers and driving efforts to assess and elevate experiences at each touchpoint across the customer journey, ensuring that commercial insurers are achieving optimal value from CLARA's AI platform. Reporting to CLARA CEO Heather H. Wilson, Jones will apply his unique blend of customer-focused innovation, technology and insurance industry expertise to CLARA's ongoing pursuit of superior customer value, continuous improvement, and exceptional service to its clients. Tyler Jones is a seasoned executive with nearly two decades of experience in the insurance and banking industries. During his tenure at Kaiser Permanente, he was accountable for technology strategies to support the company's revenue management operations.

"What's dangerous is not to evolve." Let me ask a loaded question that might ruffle a few feathers: How successful do you think Amazon would be if it only responded to 40-50 percent of the orders customers placed online? It may sound absurd, but this is exactly the predicament many commercial insurance brokers and agents are put in every day when they submit an application to a carrier on their customer's behalf. A lack of automation in the submission intake process means that many carriers can only respond to less than half of the submissions they receive simply because they get too many. For business that the carrier doesn't want to write, it's not uncommon for distribution partners to receive no response at all. Can you think of another industry where this would be acceptable?

For data-complex and risk-adverse industries like insurance, being able to access data locked away in file stores and data lakes is critical for effective decision making. Data collection and analysis is at the heart of insurance business processes. Real-time data extraction enables insurers to automate and standardize time-consuming labor-intensive processes. With insurers being under pressure to deliver a better customer experience, they are being forced to examine existing processes and adopt new methods of doing business. But given the plethora of technology available, it can be difficult to understand what it is and how to use it.

Since its establishment 100 years ago in 1919, the UK Government Actuary's Department (GAD) has been at the heart of actuarial advice, serving the public interest by providing specialist risk and finance advice to public policymakers. This is directly related to a requirement of the IFoA Royal Charter, under which the profession has a duty to put the public interest first. GAD's role has changed significantly since its inception. In the early years, it advised the National Health Insurance Joint Committee, providing advice to support the financial management of the newly introduced old age pension and health insurance systems. Over time, its influence widened as it provided advice on public service pensions, expanding social security benefits and population projections.

A few weeks ago, I had the opportunity to demo Chisel AI's Submission Triage and Policy Check solutions at Digital Insurance's Dig In conference in Austin. In speaking with commercial insurance brokers and carriers at the event, a common theme was how to get artificial intelligence implemented. Insurers understand the necessity of innovation in their industry and are excited about the potential benefits of AI in streamlining workflows and improving the digital customer experience. But they have valid questions about how to deploy an AI solution in production, and what a successful AI rollout looks like in the real world. According to a new report conducted by CompTIA, a mere 19 percent of companies say that they have expert knowledge around AI.

Last time, we discussed some of the potential benefits of AI in commercial insurance. Now, let's talk making the business case. Many insurers are hesitant to invest in AI without proof that these theoretically smart systems will yield real-world returns. A mature AI vendor will have the foresight to develop a team within its organization that's dedicated to value analytics. This team -- made up of data scientists and actuarial experts -- will use the company's own AI solution to run a simulation that can quantify potential savings the solution could provide.