By 2024, Applied AI will be one of the most impactful factors for regulated industries. Boosting efficiency by augmenting human roles could lead to significant changes in today’s workflows. This evolution will integrate AI into everyday operations, and those who are slow to move will be going against unfair odds with competitors that do.
The challenge lies in transitioning AI from a disruptive side project to a productive tool, with the confidence regulated industries require where errors can have serious consequences. At the same time, regulatory landscapes are also evolving. The recent White House executive order attempted to create governance guidelines but created less clarity for AI adoption. The National Institute of Standards and Technology (NIST) has set guidelines since January 2023, which need updating after this executive order, giving leadership a clear set of measures to implement as they adopt AI in 2024.
According to MIT Sloan Management, 90% of organizations have developed or plan to develop AI for a competitive advantage. By 2026, the U.S. AI market is poised to reach nearly $300 billion, and it is already beginning to transform various sectors.
The primary risk for leaders in Applied AI is not compliance but also whether they can outpace competitors in bringing efficient AI solutions to the market and achieve significant results.
Manufacturing
Industries are currently facing a significant challenge: the loss of deep expertise as experienced professionals retire and the rate of new talent turnover increases. This expertise is particularly vital in AI for what’s known as the ‘logic layer’ – the ability to interpret complex results through a comprehensive understanding of interconnected systems, situational context, and accumulated experience.
By 2024, we anticipate the emergence of AI-enabled knowledge systems specifically designed to capture and preserve this organizational knowledge. These systems will extend into the logic layer, offering a more robust foundation for situational management. They will be adept at retaining and adapting to logical shifts over time. This advancement will benefit organizations by enhancing their intellectual property and deepening their understanding, but it will also facilitate quicker onboarding and ramp-up of new employees. These systems represent a strategic investment in maintaining continuity of expertise and fostering a more agile and knowledgeable workforce.
Healthcare
In the healthcare sector, significant performance improvements are already evident through using AI-enabled agents for tasks like record labeling, billing validation, and workflow assistance. By 2024, healthcare providers and insurers will significantly invest in these technologies to enhance accuracy and alleviate practitioner burnout. Early adopters have begun realizing the benefits in 2023, and we expect a swift adoption of these use cases as they present a win-win scenario with each system implementation.
Additionally, we foresee a surge in deploying AI-enabled, single-function agents tailored to specific use cases. The AI agents seamlessly integrate with existing systems and workflows, covering various applications from emergency medicine to drug supply chain management. They reduce repetitive tasks, operating under a ‘human-over-the-loop’ approach. While the agents assist in speeding up processes and expanding confidence in decision-making, the ultimate decisions remain in human hands.
Energy
Scope 3 emissions continue to be a hot topic in the energy sector, alongside the development of knowledge systems, as previously mentioned. Scope 3 emissions, which encompass indirect emissions not produced by the company itself but related to its value chain, require a comprehensive understanding and management approach. This includes emissions from upstream and downstream activities, such as sourcing raw materials, transportation, and the use of sold products.
In 2024 and beyond, we can expect an increased focus on accurately tracking and reducing these emissions. Companies will likely invest in advanced AI and data analytics systems to better understand their entire supply chain’s carbon footprint. These systems will help identify the key areas contributing to high emissions and develop strategies to mitigate them. This could involve optimizing logistics, sourcing from suppliers with lower carbon footprints, or investing in more sustainable production methods.
Addressing AI Apprehensions
Despite the growth of AI and many expected positive impacts of the technology, there are some understandable concerns to be wary of. There has been a rise in companies either exaggerating their AI capabilities or ‘AI-washing’ their products, labeling them AI-driven without substantial AI functionality. Both practices add to market confusion, set unrealistic expectations, and dilute the value of genuine AI solutions.
The rapid growth of AI demands robust guardrails, and ensuring that AI models and agents operate within defined and ethical boundaries is paramount. In addition, transparency in how these systems function and make decisions is essential to maintain trust and ensure they align with our values.
One way to address these concerns is through meaningful AI education. Businesses and professionals must understand how to leverage modern AI tools and technologies effectively. With this foundational knowledge, industries can avoid missing out on the transformative potential of AI or misapplying it.
Looking Forward
2024 holds great promise for Applied AI as it integrates into various sectors, transforming manufacturing, healthcare, energy, and beyond. However, as we move forward in this AI-driven future, addressing concerns such as misleading marketing, complex interactions, the need for guardrails and transparency, and the importance of meaningful AI education is crucial.
By proactively addressing these concerns, we can harness the full potential of Applied AI and ensure that it aligns with our values, creating a more innovative future in 2024 and beyond. We’ve seen significant progress in 2023, and in 2024, it’s up to all of us to harness them to shift and augment mission-critical workloads in regulated industries.
About the Author
Nick King is the CEO and Founder of Data Kinetic and has worked on enterprise technology platforms for the last 20 years. In his role at Data Kinetic, he has focused on driving the applied use of AI/ML with enterprise companies. Before Data Kinetic, Nick worked across multiple startups and open-source projects, from Snowplow to DataRobot. He has also held roles at Microsoft, Google, VMware, and Cisco, focusing on various enterprise-applied technologies and platforms.
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