Weekly Business Insights 12/31/2024
CPOs steering GenAI in procurement through uncharted waters. AI in Healthcare: How It’s Used and Future Use Cases. How AI-Enabled Fraud, Waste, and Abuse Detection Can Put an End to “Pay and Chase”.
CPOs steering GenAI in procurement through uncharted waters
Generative AI in procurement allows organizations to not only "do things differently," but also to "do different things." Imagine a world where procurement transcends routine transactions, driven by intelligent technology-enabling strategic decisions. With just a few prompts, RFX documents and contract summaries are created. A conversational interface automates requisition creation. Companies can steer innovation with the transformative potential of Generative AI—reimagining operations, making sourcing and procurement smarter and more proactive, and setting new efficiency standards.
Generative AI can revolutionize spend analytics, forming the foundation for future-proof procurement organizations. By automating categorization, gathering supply market insights, analyzing spend patterns and identifying cost-saving opportunities, Generative AI has the potential to create category strategies, minimize value leakages, improve spend visibility and eliminate data redundancies. Generative AI's advanced algorithms supplement the current AI/ML capabilities to provide deeper insights, uncover hidden spending trends and deliver predictive analytics, enabling more informed decision-making.
The effectiveness of Generative AI in procurement generally hinges on high-quality spend data, as actionable outcomes depend on data quality. The spend analytics market is advancing as existing end-to-end suite solutions are enhancing their AI capabilities in spend analytics and niche next-generation solutions are emerging.
Generative AI in Procurement | Deloitte US
AI in Healthcare: How It’s Used and Future Use Cases
The healthcare industry is using artificial intelligence to elevate patient care and alleviate administrative burdens on a wide scale, with 79% of healthcare organizations reporting that they have adopted AI technology in some capacity, according to a study commissioned by Microsoft. It’s important to remember that the idea of a sentient and self-aware AI capable of offering healthcare on its own is still science fiction. While current AI applications in healthcare can help improve clinical efficiency, advance research efforts and aid in precision surgery, they are tools designed to enhance human work.
Data suggests the use of AI in healthcare will expand substantially over the next decade. Researchers project it will grow from a global marketplace value of almost $27 billion in 2024 to more than $613 billion by 2034. AI will help bolster medical education and specialty training. We will see medical students being certified sooner and having the tools to be more productive in healthcare. Some predict AI applications could be used to strengthen patient data security. “To train these AI models and detect patterns, they need a lot of cloud-based data, and there are questions about how we safely manage it all, and AI will fix that.”
AI in Healthcare: How It’s Used & Future Use Cases | HealthTech Magazine
How AI-Enabled Fraud, Waste, and Abuse Detection Can Put an End to “Pay and Chase”
Healthcare Fraud, Waste, and Abuse (FWA) costs are out of control. The National Health Care Anti-Fraud Association estimates that healthcare fraud costs the US approximately $68 billion each year. Further, the Centers for Medicare and Medicaid Services (CMS) reported that improper payments made by Medicare and Medicaid accounted for $31.46 billion in 2022. AI-enabled pre-payment prevention solutions can improve detection of fraudulent or abusive claims by up to 10x over antiquated rules-based engine detection and claims editing systems.
According to a report by Transparency Market Research, the healthcare claims audit and recovery market in the US was approximately $1.36 billion in 2019, projected to grow to $3.1 billion by 2027—a figure that does not include those internal recovery effort costs incurred by the healthcare payer.
IImplementing AI-based FWA solutions is a key factor toward delivering on the promise of intelligent operations in the Healthcare Sector. Using intelligent automation can significantly reduce FWA costs, improve accuracy, and alleviate dependency on third-party after-the-fact auditing to support recovery efforts. Delivering more accurate, prescriptive, and predictive results creates a better healthcare ecosystem for all—less waste, more knowledge, and most crucially, enhanced overall patient care.