Artificial intelligence holds the promise of significant economic gains and positive social change worldwide. In 2024, the adoption of AI-powered software and platforms has surged, but so has the trepidation. McKinsey research shows that 91 percent of respondents doubt their organizations' preparedness to implement and scale AI safely and responsibly. This doubt is understandable given the novel risks posed by generative AI, such as hallucinations and inaccurate outputs.

Why Trust is the Foundation for AI Adoption

To capture the full potential value of AI, organizations must build trust. Trust is essential as it leads to the adoption of AI-powered products and services. If customers or employees lack trust in AI systems, they won't use them. Understanding how AI-powered software works and how its outputs are created increases trust. In a McKinsey survey, 40 percent of respondents identified explainability as a key risk in adopting generative AI, yet only 17 percent were working to mitigate it.

Enhanced AI Explainability (XAI): The Key to Building Trust

XAI is an emerging approach to building AI systems that helps organizations understand their inner workings and monitor output objectivity and accuracy. By shedding light on black-box AI algorithms, XAI increases trust and engagement among AI tool users. This is crucial as AI initiatives move from early use case deployments to enterprise-wide adoption.

Why Invest in XAI: Getting ROI

In an uncertain AI landscape, organizations must consider the benefits and costs of enhancing AI explainability. Leading AI labs like Anthropic are betting on XAI as a path to differentiation. Enterprises also need to meet stakeholder and regulatory expectations. Demand for XAI is rising, with global AI regulations imposing transparency requirements. Organizations need methods to provide visibility into AI model building and testing. XAI is a set of tools and practices to help humans understand AI model predictions and content. It requires investments in tools, people, and processes.

Operational-Risk Mitigation through XAI

XAI enables early identification and mitigation of potential issues in AI models, reducing operational failures and reputational damage. For example, financial services companies use AI in fraud detection but often struggle to understand their systems. Explainability helps them fine-tune systems and introduce human oversight.

Regulatory Compliance and Safety with XAI

XAI ensures AI systems operate within frameworks, minimizing compliance risks and protecting brand integrity. In human resources, explainability ensures fair hiring decisions. It helps organizations understand why models make certain decisions and avoid bias.

Continuous Improvement with XAI

XAI supports the ongoing refinement of AI systems by providing insights into their functioning. It helps developers debug and improve systems to align with user and business expectations. Online retailers use explainability to improve recommendation engines.

Stakeholder Confidence in AI through XAI

XAI shifts the focus from AI model technical functioning to users, fostering a human-centric approach. In healthcare, it helps doctors understand AI systems, driving confidence and adoption.

User Adoption Boosted by XAI

XAI helps organizations monitor model output-user expectations alignment, increasing adoption, satisfaction, and top-line growth through innovation and change management.

XAI as a Human-Centered Approach to AI

Organizations need to understand diverse stakeholder needs and align explainability efforts. Stakeholders include executive decision makers, AI governance leaders, affected users, business users, regulators/auditors, and developers. Different stakeholders require different types and formats of explanations. AI explainability is like a bridge between engineers and end users, with AI-savvy humanists in the middle.

How XAI Works and Available Techniques

The XAI community creates new explainability techniques. They can be grouped based on stakeholders' intents and goals along two dimensions: when the explanation is produced (before or after training) and the scope (global or local). Post-hoc methods analyze trained models, while ante-hoc methods refer to intrinsically explainable models like decision trees. Global explanations help understand model decisions across all cases, while local explanations focus on specific decisions.

How to Start with XAI

Organizations should build cross-functional XAI teams comprising data scientists, AI engineers, domain experts, compliance leaders, regulatory experts, and user experience designers. They should establish a mindset of builders, engage early in the idea shaping process, define clear objectives, develop an action plan, measure metrics and benchmarks, select or build appropriate tools, and monitor and iterate.As enterprises rely more on AI-driven decision making, transparency and understanding become crucial. Trust is the key to responsible AI adoption, supported by pillars like explainability, governance, information security, and human-centricity. These pillars will enable AI and its users to interact harmoniously and deliver value while respecting human autonomy.