Artificial intelligence (AI) has the potential to revolutionize industries and bring about significant economic growth and positive societal change. As a result, many companies have been quick to adopt AI-powered technologies, including generative AI (gen AI), in recent years. However, this rapid adoption has been met with a sense of unease and skepticism. According to a study by McKinsey, 91 percent of respondents expressed doubts about their organizations’ readiness to implement and scale AI technology safely and responsibly.
Building trust is crucial for organizations looking to fully leverage the benefits of AI. Without trust in the outputs of AI systems, customers and employees are unlikely to embrace these technologies. Recognizing the importance of explainability, 40 percent of respondents in the McKinsey survey identified it as a key risk in adopting gen AI. Yet, only 17 percent stated that they were actively working to address this issue.
To address the need for enhanced AI explainability (XAI), organizations are turning to tools and practices designed to provide insight into how AI systems operate and generate results. By shedding light on the inner workings of complex AI algorithms, XAI can increase trust and engagement among users. This transparency is crucial as organizations move from initial AI deployments to widespread adoption across the enterprise.
Investing in XAI can yield significant returns for organizations. By enhancing transparency and understanding of AI models, XAI can help mitigate operational risks, ensure regulatory compliance, support continuous improvement, boost stakeholder confidence, and drive user adoption. XAI is not just a compliance requirement; it is a strategic enabler that can enhance the value of AI technologies across the organization.
To successfully implement XAI, organizations should build cross-functional teams, define clear objectives, develop an action plan, select appropriate tools, and continuously monitor and iterate on their explainability efforts. By integrating explainability into the design, development, and governance of AI systems, organizations can foster trust, ensure compliance with regulations, and drive adoption and innovation.
As AI becomes increasingly integral to decision-making processes, transparency and understanding will be essential for organizations to build trust and realize the full potential of AI technologies. By prioritizing explainability, governance, information security, and human-centricity, organizations can create a foundation for responsible AI adoption that benefits both users and the business. Trust, supported by these pillars, will enable AI systems to deliver tangible value while upholding human autonomy and dignity.
