KPMG: a guide to Intelligent Automation implementation in insurance

Insurers need to think about Intelligent Automation (AI) not as a technology implementation, but as an end-to-end business transformation that will impact the business model, operating model and ecosystem, states a recent report launched by KPMG, "The Automated Insurer: Next steps on the journey to intelligent automation."

Operating in a challenging environment, insurers are facing an increasing pressure on multiple fronts, from an avalanche of new technologies and more demanding consumers to the new regulatory features etc. AI technologies promise a radical shift to the insurance value chain, as well as the opportunity to revolutionize the way insurers do business, responding to the market pressures, says the consultancy, defining IA as "a combination of multiple automation technologies, such as Robotic Process Automation (RPA), machine learning and cognitive technologies, used together to solve complex business issues in pursuit of organizational goals."

While IA has become an undisputed "must", it is of utmost importance that insurers choose the right path to automation. One of the first things to be taken into consideration is that IA delivers value far beyond cost savings, enabling insurers to achieve a significant improvement in quality, auditability, employee satisfaction etc. Thus, CEO's must acknowledge that IA is transformational and far beyond being just an IT issue. Technology should be understood on a large scale within each organization and assumed as the cultural reality of the future.

KPMG suggests four lines of action insurers can take to prepare their enterprises to fully benefit from the opportunities of intelligent automation:

  1. Start small, but think big. Even if implementing small projects may be more affordable and quickly rewarding, "piecemeal efforts focused on niche tasks will fail to move the needle over the ling term," the report writes. Thus, any automation project should be approached with scale in mind, as well as with a rather clear idea on how to get successful deployments into more areas of the company faster.
  2. Determine the size and scale. Clearly planning stages is the second important step, so that starting from minimum-viable product to gradually reach a more holistic approach.
  3. Determine the operating model. IA implementation should be seen as an end-to-end process of strategic transformation, with its own proper governance structure to ensure coherence and the process' fluidity and avoid redundant operations.
  4. Assess longer-term impacts to people and process. Any implementation of automation will impact on how jobs across the enterprise work. Implementation teams need to be focused on how end users' day-to-day duties will be impacted by the new technology and prepare employees for the changes, both in professional and cultural terms.
Read the full versions of the KPMG's report "The Automated Insurer: Next steps on the journey to intelligent automation" here

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