In the midst of shifting business patterns, where every thread of innovation weaves the fabric of tomorrow, leaders find themselves at the crossroads of possibility. Within the 96% of organizations engaging in discussions about generative AI at the boardroom level, 59% of executives express robust support from their leadership for generative AI, a sentiment observed just six months after the technology entered the mainstream. This figure soars even higher in the high-tech sector, reaching an impressive 84%. Against such a backdrop, the quest to harness the transformative power of artificial intelligence has never been more critical.
GenAI is being hailed as the catalyst in redefining how industries operate. Within its algorithmic embrace lies a promise: to unravel the intricacies of process automation, optimize the ebb and flow of workflows, and elevate service excellence to an unprecedented zenith. In this blog, we delve into the heart of genAI’s capabilities and its very essence, exploring use cases as varied as the industries they transform to unveil the tangible ways genAI is sculpting the future of businesses across sectors and crafting the path toward a future where innovation meets operation.
How GenAI is Transforming the Landscape for IT Companies
Over the past year, generative AI has undergone notable advancements, drawing considerable interest from the public, particularly content creators. Machines are now emulating the creative processes of human thought, synthesizing custom-tailored content with substantial implications for both organizations and consumers. The technology landscape has seen the emergence of various tools, sparking enthusiasm within the tech community. Chatbots take center stage as the most pertinent application of genAI, acknowledged by 83 percent of organizations.
The four key areas where genAI will become a critical component include:
Business process optimization: Generative AI strategically optimizes key facets, including customer service, supply chain, risk management, finance, and procurement. By introducing automation, expediting text generation, improving productivity, and refining processes, it creates a seamless and enhanced customer experience
Enhanced customer engagement: Generative AI enhances customer engagement and satisfaction through personalization, real-time insights, intelligent customer service, predictive analytics, continuous improvement, and optimized customer journeys, fostering improved loyalty.
Improved sales outcome: In sales, organizations can leverage genAI for personalized marketing, pricing optimization, demand forecasting, improved customer experience, enhanced sales support, and data-driven decision-making to attract customers, foster lifelong content-driven conversations, and enhance conversion rates.
Reduced costs: Businesses can realize cost savings and elevate overall financial performance by implementing generative AI to automate processes, optimize resources, implement predictive maintenance, streamline the supply chain, mitigate risks, and improve decision-making
Unlocking GenAI’s Potential Across Healthcare, Retail, and Insurance
The primary enterprise value derived from generative AI technologies is fostering innovation tailored to each company’s industry. This innovation may manifest through introducing new products, services, or transformative operational methodologies, such as process improvement with GenAI.
With AI innovation increasing rapidly and generating numerous use cases for genAI across diverse industries, here are three sectors where genAI is making headwinds.
Generative AI for Healthcare
The application of generative AI, particularly large language models (LLMs), holds transformative potential for healthcare. Its capacity to analyze unstructured datasets presents a breakthrough for healthcare operations abundant in such data types as clinical notes, diagnostic images, medical charts, and recordings. Whether used independently or integrated with large, structured datasets like insurance claims, these unstructured datasets can revolutionize healthcare processes.
Similar to clinician documentation, numerous applications for gen AI in healthcare are emerging, including:
Streamlined Drug Discovery: The projected growth rate for global AI in the drug discovery market is 53.3% annually, with an expected value of USD 24.7 million by 2029. Generative AI techniques can significantly speed up drug discovery by identifying potential candidates and assessing their effectiveness through in silico testing, utilizing computer simulations before advancing to clinical trials in animals and humans.
Optimized Clinical Trials: Conventional drug trials are lengthy and expensive. Large Language Models (LLMs) prove valuable in identifying appropriate patient populations, optimizing trial design, predicting outcomes, and expediting recruitment. This enhances the efficiency and success rates of clinical research. Additionally, LLMs can accelerate the generation of concise reports from Contract Research Organizations (CROs) for Research and Development (R&D) and Global Medical Affairs, facilitating swift submission for regulatory review and approval.
Improved Clinical Diagnosis: Generative AI models, trained on diverse medical data, detect early signs of various health conditions, including skin and lung cancer, Alzheimer’s, diabetic retinopathy, and more, offering early diagnosis and predictive insights. Moreover, AI models can uncover biomarkers that result in specific disorders and accordingly forecast disease progression.
Automating Admin Tasks: Studies indicate a concerning 62% burnout rate among US physicians, leading to increased risks of patient-endangering incidents, alcohol abuse, and suicidal thoughts. Generative AI in healthcare offers relief by automating administrative tasks and cutting associated costs (15% – 30% of healthcare spending). Integrated into systems like Microsoft’s Nuance integrated genAI tech GPT-4 into its clinical transcription software, it streamlines data extraction, transcribes patient consultations, and generates structured health reports, providing efficient support to physicians.
Gen AI can reshape the healthcare industry like never before, surpassing the capabilities of previous technologies. As it matures, the convergence with emerging technologies like virtual and augmented reality holds the potential to redefine the very essence of healthcare delivery.
Generative AI for Retail
Accenture’s Technology Vision 2023 research reveals that the capabilities of foundation models highly inspire 96% of retail executives. Over 90% anticipate the importance of these models in their retail strategies over the next three to five years, with more than half already considering genAI experiments, especially in customer support and process automation.
GenAI has multiple use cases in the retail industry, including enhancing customer experience to optimize operations and minimize costs.
Conversational Search for Enhanced Product Discovery: While traditional search bars serve shoppers in finding desired products, integrating conversational commerce powered by genAI can expedite the search process, potentially boosting conversion rates and increasing average basket sizes for retailers.
Brands can conduct A/B tests using “chatGPT-like” product search bars to help customers efficiently locate specific products, such as searching for all ingredients in a recipe or assembling a complete outfit.
Crafting Tailored Experiences: Through genAI algorithms, consumer behavior and preference data can be analyzed to craft personalized customer experiences. This encompasses customized product recommendations, promotions, and content.
Strategic Inventory Optimization: Leveraging generative AI, businesses can enhance inventory management through accurate demand predictions and streamlined supply chain logistics. This proactive approach enables retailers to minimize costs by mitigating stockouts and eliminating excess inventory. Moreover, it facilitates agile responses to market fluctuations, ensuring a more resilient and efficient inventory ecosystem.
Fraud Prevention: Through generative AI algorithms, businesses can analyze transaction data to identify and prevent fraudulent activity, minimizing losses and safeguarding customer data for retailers.
By offering valuable insights, streamlining processes, and enhancing the customer experience. genAI empowers retailers to stay competitive, enhance operational efficiency, and deliver superior customer service.
Generative AI for Insurance
The global genAI market within the insurance sector is poised for significant growth, with projections indicating an increase from USD 346.3 million in 2022 to a substantial USD 5,543.1 million by 2032. For insurers aiming to maintain competitiveness, adopting GAI is swiftly transitioning from an optional consideration to an essential requirement.
Regarding generative AI in insurance, three notable models take the spotlight: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Autoregressive Models. Some key areas in which they can benefit the sector include:
Efficient Claims Processing: GenAI automates the processing of claims, extracting and validating data from claim documents. This accelerates the entire claims settlement process, reducing turnaround time and minimizing errors. Speedier and more precise claims settlements contribute to heightened customer satisfaction and enhanced operational efficiency for insurers.
Enhanced Risk Assessment and Underwriting: GenAI models excel in accurate and efficient risk assessment and underwriting processes. By analyzing historical data and employing pattern recognition, AI algorithms predict potential risks with increased precision. This optimization empowers insurers to make informed underwriting decisions, provide tailored coverage options, and mitigate the risk of adverse selection.
Ensuring Regulatory Compliance and Transparency: GAI, incorporating explainable AI (XAI) techniques, guarantees transparency and regulatory compliance. Insurers can comprehend the rationale behind AI-generated decisions, facilitating adherence to regulatory standards and fostering customer trust in AI-driven processes.
Customized Insurance Solutions: GAI allows insurers to create personalized policies tailored to individual customers. By analyzing vast datasets, AI algorithms generate customized coverage options, pricing, and terms, enhancing overall customer satisfaction. For instance, an auto insurer can utilize genAI to analyze a customer’s driving history, vehicle details, and personal characteristics, offering a personalized car insurance policy aligned with specific requirements.
Generative AI provides insurers with a promising path to derive additional value from their technology investments and deliver enhanced value to shareholders, customers, and society to expedite their journey toward modernization.
The transformative impact of generative AI reverberates across industries, ushering in a new era of innovation for ISVs and enterprises. From optimizing operational efficiency to redefining customer experiences, it emerges as a catalyst for progress. In retail, it propels conversational commerce, reimagining how customers discover and engage with products. In healthcare, GAI accelerates drug discovery and diagnosis and alleviates the burdens on medical professionals, offering promising solutions to combat burnout. For insurers, it becomes an indispensable tool for streamlining processes, enhancing risk assessment, and providing personalized offerings. As we navigate this landscape, it’s evident that GenAI is not merely a technological advancement; it’s a strategic imperative for those aiming to stay competitive and innovative amidst turbulent waters.
Capgemini Research Institute, Generative AI Executive Survey, April 2023.