generative AI

The integration of AI systems in consumer markets, especially in retail settings, introduces new uncertainties, risks, and challenges. The assortment of digital tools available to retailers has surged in recent years, with advancements in Artificial Intelligence (AI) software becoming readily accessible and cost-efficient to deploy. As retailers integrate these digital capabilities into their settings, the systems they create are increasingly free to change their behavior in informative, interpretative, or generative ways. This ability to create social artifacts enables new competitive capabilities for companies—their AI systems can proactively shape the market context in which they operate and the role of their organizational customers within it, radically transforming the competitive environment that has persisted over recent decades. These technological and social developments go hand-in-hand, shaping the design of the AI systems that are to be deployed and their business context. Data capabilities enable retailers to launch and scale data-driven AI capabilities that respond to changes in the market context. However, such systems are difficult to design and necessitate improvements in organizational sense-making and designing collaborative working practices. New practices that ground technical systems in human expertise, enable end-users in the business units to shape the target of AI systems, and operationalize expectations and predictions are required. Practitioners need procedures for analyzing how AI capabilities influence the end-users under their organizational responsibilities and the attended channels in their business market. Data operations that address both the NTY and sustain capabilities of AI systems are matters of a new kind of collective impact. The design of practices and the allocation of roles and responsibilities across units are of concern to corporate boards and the highest levels of management in the organization. Adopting and growing data-driven AI capabilities require substantive position and/or infrastructure changes at the highest organizational levels. Long-term commodification of data precepts into business decisions and practices is necessary to leverage the investment and risk that companies take on in new black-boxed AI capabilities.

Workers compensation claim processing is extraor￾dinarily inefficient and causes claimants and employers to suffer. In a typical Australian workers compensation insurer, the Claim Establishment Process can require up to 28 manual steps performed by claim adjuster. Claims departments follow strict business rules that stipulate if a claimant is eligible for certain claim benefits as well as the appropriate medical certificates, certificates of capacity, statutory reserves and claim flags to allocate to the claimant. These are often automated or semi-automated in complexity, however in practice, require considerable agentic decision-making to complete the processing. Claim adjusters perform these decisions, which can be expensive and introduce wait times that delay claimants and employers from receiving their benefits. This feasibility study presents a deep learning framework designed to automate the workers compensation claim process using generative AI. A proof of concept application, entitled ClaimGPT, was implemented using the OpenAI API and Salesforce Einstein GPT, providing agentic AI capabilities for claim generation and decision-making. These capabilities were combined with natural language processing models for claim textual data and image generation for claim documentation. The framework was implemented in two existing workers compensation insurers, focusing on the Claim Establish￾ment and Claim Decision Process. Both implementations were successful in accelerating the claim processing, while reducing the size of the claim reserves allocated to each claim established. The image generation models for claim documentation synthesised novel claim scenes and demonstrated claim simulation capability on a specific insurance use case.