Empathetic AI is the Key to a Successful AI Risk Management Framework

To help companies remain competitive amidst changing markets, the Solutions Review editors are exploring how an empathy-first approach to AI risk management can transform a company’s ability to adopt and utilize AI technology successfully.

Implementing artificial intelligence (AI) into your company is as much about integrating the technology itself as managing the potential ripple effects it could have on the business. As the National Institute of Standards and Technology (NIST) explains, as many benefits as AI can provide—economic growth, improved productivity, boosted agility, etc.—it can also “pose risks that can negatively impact individuals, groups, organizations, communities, society, the environment, and the planet.” That’s where the value of an AI Risk Management Framework comes into play.

If these frameworks aim “to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems,” as the NIST says, empathy must be an essential part of any risk management strategy. With that in mind, this article will examine the crucial role AI risk management plays in today’s evolving world, specifically focusing on how valuable an empathetic AI (EAI) policy is to an AI risk management framework.

Addressing the Empathy Gap in Current AI Risk Frameworks

If you didn’t already know, the most widely adopted and recognized AI risk framework is the NIST AI Risk Management Framework (AI RMF), released in January 2023. However, much has changed in the years since, as few as they are. According to a report McKinsey & Company released in 2025, “78 percent of respondents say their organizations use AI in at least one business function, up from 72 percent in early 2024 and 55 percent a year earlier.” That’s a significant increase since the NIST released their AI RMF, and the landscape has changed.

While the NIST’s AI RMF remains the standard, and rightfully so, public perception of what it means to have a risk management strategy for AI adoption seems to lack the proper focus on empathy. Most AI risk management frameworks being deployed treat risks as quantifiable variables that can be addressed through technical controls and governance processes. That approach makes sense, since companies require a methodology that can be replicated and deployed as easily as possible. However, it can also create what you might call an “empathy gap,” resulting in AI systems failing to account for the emotional, contextual, and relational dimensions of human decision-making.

Consider the case of AI-powered customer service systems that function correctly but cause brand damage by failing to deliver the correct tone during customer interactions. While these systems could technically pass a traditional risk assessment, they fail in practice, harming consumers, users, and the company. There have been studies done on AI’s ability (or lack thereof) to utilize empathy in various settings, including medical care, for example, and most of the findings demonstrate that, despite AI’s growing capabilities, it cannot replicate the experienced empathy humans use on a daily basis.

Consequently, empathy must be a top priority in developing or deploying an AI risk management framework. With an EAI mindset, we believe companies can transform how they create and use AI technologies to maximize business potential and support their human workers. It’s like the NIST’s framework says: “AI risks–and benefits–can emerge from the interplay of technical aspects combined with societal factors related to how a system is used, its interactions with other AI systems, who operates it, and the social context in which it is deployed.”

The Business Case for Empathetic AI Risk Management

Unlike traditional AI metrics that focus on speed or accuracy, empathetic AI focuses on sticky, differentiated value propositions that are inherently difficult for competitors to replicate because they require deep integration of emotional intelligence, cultural sensitivity, and contextual awareness across entire product ecosystems. To get specific, the business case for empathetic AI in risk management rests on the premise that traditional risk frameworks catastrophically underestimate human-centric failure modes by treating users as rational actors rather than complex emotional beings.

An EAI-centric risk management strategy recognizes that the most disruptive AI failures often emerge not from technical malfunctions but from misaligned human-AI interactions where systems fail to understand user emotional states, cultural contexts, or unstated needs. By shifting to an empathy-first approach, companies can move their risk assessment from purely probabilistic models toward dynamic, relationship-aware frameworks that can predict and even prevent the social and reputational damages that emerge when AI systems inadvertently cross a line.

A study from 2021 explains, “AI lacks a helping intention towards another person as the basis of its attentional selection, because it does not have the appropriate motivational and inferential structure.” That lack does not mean AI is incapable of being helpful or acting empathetically. However, it does necessitate that humans adopt an empathy-first mindset when designing AI or giving it directions. Failing to do so can result in empathy failures that generate negative publicity that affects market capitalization, far exceeding the technical infrastructure investments.

EAI risk management can help your brand avoid that negativity by providing early warning systems that the technology and its users identify by continuously monitoring emotional sentiment, cultural alignment, and relationship quality metrics that traditional risk systems ignore entirely.

These AI risk management frameworks take time and investment, requiring companies to collect extensive training data about human emotional states, cultural norms, and psychological vulnerabilities—information that presents massive privacy and security risks. Yet, even with the complexity, an EAI risk management strategy is still worth exploring, especially since it means getting in “on the ground floor” for an emerging methodology already sending ripples throughout the enterprise technology marketplace.