Author(s): Sophia Banton Originally published on Towards AI. Cover image created with Google ImageFX. Illustrates the shift from overwhelmed validation work to empowered AI collaboration. “Check this for accuracy.” “Verify that output.” “Review for ethical issues.” “Can you just look this over real quick? The AI wrote it.” This wasn’t in the original job description. This is the new reality in workplaces adopting generative AI. Across industries, professionals find themselves increasingly burdened with an unexpected responsibility: ensuring AI systems don’t make critical mistakes — a duty far from what their original job descriptions entailed. The “New Reality” of AI in Workplaces Picture this: A marketing director who previously created inspiring campaigns now spends her days fixing AI-generated content. She checks if it sounds right, matches the brand, and avoids legal risks. This wasn’t what she expected from AI. Picture this: A senior scientist in biopharma, formerly focused on breakthrough discoveries, now spends hours double-checking AI predictions and literature summaries. Instead of driving innovation, she’s stuck ensuring AI gets it right. Both scenarios reveal the hidden cost of AI: the loss of human potential. When Human Expertise Becomes ‘Validation’ Work As a builder of AI solutions, I see firsthand the limitations of AI and the need for the “human-in-the-loop”. My goal as an IT professional isn’t to dictate or impede how my colleagues work but to empower and support them. But when human expertise is spent validating AI outputs instead of driving innovation, the cognitive and operational strain becomes clear. Repetitive validation tasks drain job satisfaction, leading to cognitive fatigue and burnout. As professionals prioritize AI validation over their core responsibilities, the cost to both individuals and organizations becomes apparent. Why Human Oversight is Essential AI systems require human validation because GenAI systems have limits: they often produce misinformation, lack contextual understanding, and struggle with handling complex or nuanced tasks. While complete automation may be possible in some industries, heavily regulated and life-critical environments like biopharma cannot afford to remove experts from the validation process. Consequently, a new role has emerged — the human validator, requiring both domain expertise and AI literacy. This responsibility is often added on top of their existing workload, further contributing to the strain. These experts must understand AI capabilities, identify mistakes, and address ethical concerns. But is validation the best use of their expertise, especially given the risk of burnout? While AI’s current shortcomings make human oversight essential, it creates a double-edged sword. The Double-Edged Sword of AI Validation On one hand, human validation makes AI more trustworthy, safer, and more effective: Medical Affairs: Teams confirm that AI-generated responses are accurate, compliant, and prioritize patient safety. Content Creation: Content teams refine AI writing for clarity and brand voice. Compliance and Quality Control: Legal and operational teams ensure AI outputs meet industry regulations and adhere to safety standards. Regulatory Affairs: Teams validate AI-generated submissions for regulatory compliance and accuracy. On the other hand, human validation is costly, with significant practical and ethical concerns: Demanding Workloads: Validation tasks require extensive time and expertise. Exploitation Risks: Companies outsource validation to underpaid workers globally. Mental Strain: Continuous validation leads to burnout and reduced morale. Finding Balance: Prevention and Workforce Development At first glance, solutions like validation dashboards or specialized tools — such as workflows to simplify tasks or systems to flag errors — seem promising. However, these approaches often mask the issue by shifting validation work to other teams or adding extra steps to business processes. More tech isn’t always the solution. Organizations should address root causes and view human expertise as central to AI success. Validation can shift from a burden to an opportunity by investing in prevention and workforce development. Prevention Over Band-Aids Set clear limits on daily tasks. Build breaks into schedules and vary tasks to reduce monotony and fatigue. Give workers freedom to handle validation tasks their way. Separate creative work from validation tasks. Workforce Development Train teams in AI literacy and critical thinking. Create clear career paths while protecting core expertise. Provide non-invasive mental health support. Keeping Humanity in the Loop Responsible AI isn’t just about protecting companies’ reputations or the public — it’s about protecting the workforce and safeguarding human potential. As Eliyahu Goldratt wisely said, “Human potential is the only unlimited resource we have.” By addressing these challenges thoughtfully, we can shift validation from a burden to a source of empowerment, ensuring AI becomes a tool that elevates human expertise and “prompts” our creativity. This article was written by a human. Let’s keep the “human” in human resource. About the Author Sophia Banton is an Associate Director and AI Solution Lead in biopharma, specializing in Responsible AI governance, workplace AI adoption, and strategic integration across IT and business functions. With a background in bioinformatics, public health, and data science, she brings an interdisciplinary lens to AI implementation — balancing technical execution, ethical design, and business alignment in highly regulated environments. Her writing explores the real-world impact of AI beyond theory, helping organizations adopt AI responsibly and sustainably. Connect with her on LinkedIn or explore more AI insights on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI