Behind all the stories of ChatGPT’s myriad daily uses is something far more interesting. Yes, generative AI has gone mainstream and is enabling extraordinary productivity boosts and shortcuts to traditional processes, but beyond headline news, there are opportunities for explosive growth in key sectors that have a lasting impact on the economy and the way we live our lives.
With the development of more refined, faster-moving, and generally more emotionally intelligent systems, generative AI is poised to have a substantial impact in adaptive environments where decision-making is made in real time. Where humans are often tasked with exceptionally stressful, quick actions that can mean life or death, success or failure, AI will automate crucial processes, provide instant access to vital information that impacts decision-making, and streamline business-critical operations.
McKinsey’s recent report on “The economic potential of generative AI” outlines a sweeping list of 63 potential use cases, representing up to $4.4 trillion in value added to the global economy. AI offers a powerful tool to supplement that decision-making, tapping into a wealth of instantly available information to operate smarter, faster, and with fewer errors.
The Expanding Role of Generative AI in Complex Decision-Making
AI has been an integral part of business strategy for more than a decade, streamlining analysis, customer interactions, and logistics in both small and large ways, but it’s only recently that the technology has reached a level at which non-technical users can interact with its output. Generative AI offers a layer of operability that enables analysts, customer service staff, and healthcare providers to directly access the productivity-enhancing benefits of such systems. From doctors reclaiming valuable time previously spent transposing notes and organizing charts to financial analysts being able to assess risk in near real-time, end-users are seeing tangible, immediate benefits to generative AI systems.
A year after the launch of GPT-4, businesses are investing heavily in generative AI solutions to support rapid decision-making needs in high-stress environments. In a recent study assessing the productivity impact of generative AI in the workplace, Valoir estimated that 40% of daily tasks could be automated. Already, companies have seen as much as 20% of their manual tasks automated in the last two years. In high-demand, labor-intensive fields like finance, healthcare, and logistics, this could have a substantial, positive impact.
In the banking industry, generative AI is being expanded to support a range of efforts that previously required manual intervention. Fraud detection, for example, has been augmented with real-time alerts and responses. As financial crimes surge (rising 43% in 2023 alone) and losses spiral for many companies, financial institutions are investing heavily in GAI to identify and respond to fraud faster than ever. Beyond fraud management and personalized chatbot experiences, financial institutions are leveraging GAI to supplement risk assessment and decisioning, accelerating processes to support small businesses and make smarter, data-backed decisions. In such a process-heavy industry, heavily reliant on compliance with regulations, automated systems are ensuring greater adherence and faster response times.
In healthcare, generative AI is being used to both supplement the work of overburdened providers and to accelerate research and development. A recent study in Cureus outlines the emerging role of GAI in healthcare applications, describing how the technology has “the potential to transform healthcare, education, research, and clinical practice.” They identify ways in which GAI tools like ChatGPT will play a role in bridging language gaps in healthcare, both in practice and within research. It emphasizes the increasingly difficult role of managing knowledge as a medical expert, something that GAI is uniquely suited to supporting.
During the COVID-19 pandemic, we saw how fragile the global supply chain can be. Significant disruptions can take years to untangle, but generative AI offers a unique toolset to support faster decision-making and responses to such substantial shifts. From demand forecasting to process and inventory management and route optimization, GAI helps streamline crucial steps in the management of complex supply chains while providing a more robust means through which users can engage with the technology and its output.
Adapting Generative AI to High Stakes Real-Time Environments
While generative AI is poised to have a substantial impact on the way the global economy operates, streamlining countless tasks and supporting better-informed decision-making at scale, there are challenges to overcome.
AI models are still developing and are wholly reliant on the data that’s fed into them. There remains a need for clean, actionable data, and not all industries are yet able to support this. At the same time, the cost of launching new test pilots can be substantial. It’s vital that decision-makers in operations and IT alike work together to outline and test potential use cases in a way that helps show the benefits as soon as possible.
At the same time, generative AI is still developing. The highest profile real-time applications for AI remain transportation and military applications, where recent tests show how far we still need to go. Level 4 automation for self-driving cars remains a decade or more away, according to McKinsey, and the US Military, while working to integrate AI into electronic warfare operations, is equally invested in ensuring AI is only implemented within acceptable parameters or risk and system vulnerability.
The Next Phase of Generative AI and Real-time Decision-Making
From pending EU legislation that will codify acceptable risk levels to the massive carbon cost of training a new model and the always tenuous trust the general public has in automated systems, there are challenges ahead for the implementation of broad-scale generative AI. But the technology is here and will continue to develop in a way that is sure to have a substantial, long-term impact on how we make decisions, save lives, and engage with the economy. Real-time applications are increasingly being streamlined and improved with GAI, and the decade ahead will show just how far we can go.
