Overcome hidden challenges in AI adoption for your industry with effective strategies and frameworks.
AI is the largest commercial opportunity our economy has seen for decades. A recent PWC study found AI is set to boost global GDP by 14% by 2030. While there are seemingly unlimited business opportunities to leverage AI, there are just as many pitfalls to avoid. Deloitte reported that 79% of executives expect generative AI to drive “substantial organizational transformation” at their organizations in less than three years. Yet only a quarter of these leaders believed their organizations are “highly” or “very highly” prepared for governance and risk issues associated with AI.
Some of the most common barriers to AI adoption include a lack of technology infrastructure, proper frameworks and concerns about data quality, privacy, and security. AI presents immense opportunities across various industries; however, each sector faces its own unique adoption challenges.
Increasing Manufacturing Output with an Automation Foundation
The manufacturing industry frequently grapples with urgent challenges, ranging from resource constraints and shifting economic dynamics to supply chain disruptions. AI stands to benefit manufacturing across these challenges. For example, generative AI supports proactive maintenance to mitigate machine failures and provides accurate demand forecasts for enhanced operational efficiency. A Deloitte survey on AI adoption in manufacturing found that 93% of companies believe AI will be a pivotal technology to drive growth and innovation in the sector, yet another survey of industry professionals noted that only 10% had found significant financial gains from AI over recent years.
Some of the industry’s underlying stressors, like outdated technology infrastructure hinder successful AI adoption. Many manufacturing centers still rely on manual, paper-based processes, which don’t provide an infrastructure that AI can learn on top of. Instead, manufacturers should modernize their digital infrastructure with a foundation of process management and automation to serve as a base to gather and process data that powers and derives value from AI agents. As a result, manufacturers can improve supplier operations, transform equipment inspections and increase efficiency, safety, and compliance at production facilities through automation and AI.
Improving Healthcare Patient Data Privacy and Security with the Proper Tools
In the healthcare industry, security and privacy protocols are crucial for safeguarding sensitive patient information and enabling healthcare professionals to deliver high-quality care. When considering the adoption of new technologies like AI, healthcare providers must prioritize these concerns above all else. Recent high-profile breaches have stoked fears of data security and raised concerns about integrating AI.
Between 2009 and 2023, 5,887 healthcare data breaches of 500 or more records were reported. Those breaches have resulted in the exposure of 500M+ healthcare records, 1.5x the population of the U.S. What’s more, the rate of healthcare security breaches is increasing. In 2018, breaches of 500+ records were being reported on average 1 per day but almost doubled to an average 1.99 per day in 2023.
Cybersecurity threats are among the largest barriers to successful AI adoption within the healthcare industry. The sector needs to prioritize its digital security and address privacy concerns by implementing stringent frameworks and optimizing processes through automation. By streamlining processes and automating workflows, healthcare companies can minimize human error, enforce consistent protocols, add auditability and governance across all movement of data and processes, and create a foundation to build in AI effectively and securely.
Public Sector’s Embrace of AI Requires a Proper Framework
The government sector often faces more distinctive challenges to modernization than private industries, given factors like longer procurement cycles and often older software and technology vendors. This year, the White House issued an order for every federal agency to implement a Chief AI Officer to oversee proper AI implementation, indicating a nationwide focus on adopting AI with data security and adequate safeguards in mind.
But as new Chief AI Officers join the ranks and government agencies across federal, state, and local levels seek the benefits of AI, they will first need to create clear frameworks and governance structures to drive successful adoption from employees, who report the lack of these systems as the top hindrance to AI adoption in the government.
These elements are needed to ensure accountability, transparency, and ethical use of technology in addition to establishing clear guidelines to mitigate risks associated with bias, privacy violations, and unintended consequences, fostering public trust in AI applications.
Establishing an Automation Foundation and Thorough Framework is Key to AI Adoption
When implemented correctly, the benefits of AI are almost limitless, but to do so, there are some fundamentals it needs first, regardless of the industry. Chief among these needs is a solid foundation of automation to build AI from and a thorough framework to guide its use.
One of the first steps to a solid automation foundation is to streamline the core processes and workflows that help run day-to-day operations. The best way to do this is by automating them with an end-to-end process automation platform, which will enhance efficiency, reduce errors, enhance security and governance, and facilitate data-driven decision-making. Upon this foundation of automated business processes, organizations and industries can easily build AI applications that can amplify these benefits. Furthermore, process automation allows industries and organizations to derive true value from AI agents as it collects, analyzes, and feeds the clean, structured data AI agents need to run on and learn from.
Once the foundation has been built and AI can be implemented, automation and AI can work together to fuel faster time-to-value for organizations across industries. Because friction is removed from the work being done, organizations can create momentum to propel their people, work, and business forward.
The journey to successful AI adoption presents unique barriers and opportunities for any industry. Factors include ensuring adequate technology infrastructure and frameworks and effective data quality, governance, privacy, and security approaches. As investments in AI accelerate, addressing these considerations across manufacturing, healthcare, and the public sectors is increasingly important to support AI adoption safely and effectively for the betterment of industries and the society they serve.
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