Artificial Intelligence: Task Slayer and Innovation Enabler

By Natalie N. Shah

Let’s clear the air: AI isn’t here to take your job—it’s here to take your tasks. The future of work is unfolding rapidly, and it’s far from the alarming predictions that have caused anxiety for many employees and leaders. Instead of erasing work, AI is transforming it—evolving the way jobs get done (Poba-Nzaou et al., 2021; Masriadi et al., 2023; ). Tasks such as scheduling, data entry, routine decision-making, and even crafting emails or generating policies are ideal candidates for AI automation (Hernández-Orallo, 2017; Filippi et al., 2023; Faishal et al., 2023; Boavida & Candeias, 2021).

Research indicates that automation technologies, including AI, are projected to impact 45% of tasks globally (McKinsey Global Institute, 2017). This automation frees employees to focus on higher-value responsibilities like innovation, and creates new levels of performance that require creativity, strategic thinking, and emotional intelligence—areas where AI still struggles to compete (Ramaswamy, 2018; Barbieri et al., 2019; Au-Yong-Oliveira et al., 2019). However, just because a task can be automated doesn’t mean it should be.

HR professionals must approach AI from multiple angles. In this technological shift, it is crucial to evaluate the return on investment (ROI) of these decisions to ensure that AI integration leads to measurable improvements in efficiency and productivity (Walton et al., 2024a; Cazzaniga et al., 2024). But, how does one effectively evaluate the ROI of AI in tasks when both AI technology and the scope of tasks it can augment are constantly evolving?

Research is heading toward understanding the AI-ability of tasks. AI-ability, in simple terms, is the capacity of tasks within an organization to be enhanced, automated, or supported by artificial intelligence (AI) (Kromann et al., 2011; Walton et al., 2024b). This concept goes beyond just identifying which tasks can be automated—it provides a structured framework to assess how AI can influence performance outcomes across various roles. Rooted in Outcome-Driven Innovation (ODI) methods and informed by data-driven insights on task interdependence, importance, and AI potential, AI-ability has the possibility to help organizations determine where AI can deliver the most value.

For example, consider the AI potential, importance, and interdependence involved in drafting a new company policy. The AI potential is high, as there are numerous AI tools available to assist with creating the initial draft of a policy. However, if the task is deemed highly important—impacting legal, regulatory, or operational outcomes—and highly interdependent with other policies or departments, full automation would be inappropriate. In such cases, AI could be used to handle the drafting and formatting of the document, allowing employees to concentrate on critical decisions, customizing the content, and refining the legal language. This approach ensures AI is used effectively without compromising the quality and oversight required for high-impact, interdependent tasks.

This focus on ROI and innovation through AI-ability allows organizations to unlock creativity and empower employees to engage in high-value, sophisticated work. This shift drives innovation and maximizes human potential, proving that investing in AI elevates the workforce rather than replaces it. As Tom Kelly insightfully remarked, ‘The ultimate freedom for creative groups is the freedom to experiment with new ideas. Some skeptics insist that innovation is expensive. In the long run, innovation is cheap. Mediocrity is expensive—and autonomy can be the antidote.

Action Steps for HR: Making AI Your Best Asset

AI shouldn’t be applied simply because it exists. Instead, its use must be carefully aligned with both the capabilities of the AI system and the significance of the task at hand. As you move forward with implementing AI solutions where they best fit, there are additional actions you can take to increase your likelihood of success.

Upskill and Reskill Your Workforce
- Action: Identify areas where AI cannot yet dominate—like critical thinking, problem-solving, and empathy. Create training programs to develop these skills. Consider bringing in experts to conduct workshops on data interpretation, ethical decision-making, and leadership in an AI-driven world.
- Why: AI thrives on processing data, but it lacks emotional intelligence and creativity. By developing skills that AI can’t replicate, you empower employees to work alongside AI, not in its shadow.
- Pro Tip: Encourage continuous learning with flexible online courses and hands-on AI tools. This helps your employees transition smoothly into augmented roles, equipped with the skills they need to thrive in this evolving landscape.
Redefine Job Roles for the Future
- Action: Update job descriptions to reflect AI integration. Emphasize how AI tools will augment current roles, allowing employees to focus on leadership, creativity, and client relations. Create hybrid roles that blend AI expertise with industry knowledge, like “AI-Augmented Financial Analyst” or “AI-Empowered HR Specialist.”
- Why: As jobs evolve with AI integration, HR plays a key role in designing hybrid roles that utilize AI’s strengths while preserving the human touch where it’s needed most.
- Pro Tip: Involve your employees in the redesign process. Hold open forums to discuss which tasks they believe AI can assist with, and encourage them to define new aspects of their evolving roles.
Create a Culture that Welcomes AI
- Action: Promote a mindset shift where AI is seen as a partner, not a replacement. Foster collaboration between employees and AI tools. For example, emphasize how AI can support decision-making by processing complex data, allowing workers to focus on interpersonal communication and creativity.
- Why: Studies show that employee resistance to AI can stem from fear of the unknown. By fostering transparency and a collaborative environment, you can transform AI from a source of anxiety to a powerful asset for teams.
- Pro Tip: Gamify the AI adoption process. Create challenges or friendly competitions to see how employees can use AI tools to optimize workflows. Celebrate success stories that highlight how AI empowers teams.
Measure Success—Track AI’s Impact on Productivity and Satisfaction
- Action: Establish key performance indicators (KPIs) that track the positive effects of AI. These should include not only productivity metrics but also employee engagement and job satisfaction. Monitor areas like the time saved by AI tools, and the quality of work life improvements.
- Why: Tracking both the tangible and intangible benefits of AI allows organizations to demonstrate its full value. Highlighting increased productivity alongside reduced burnout will make the case for further AI integration.
- Pro Tip: Collect employee feedback regularly to identify pain points or areas where AI might need tweaking. This ensures that AI integration is constantly improving and aligned with human needs.

Why This Matters: AI is Your New Best Teammate

HR leaders have a vital role in shaping this future by creating a workforce that’s ready to collaborate with AI. The result? Teams that are more productive, engaged, and ready for the challenges of tomorrow.

By investing in skills, fostering collaboration, and tracking success, HR leaders can transform AI into the ultimate tool for growth and innovation within their organizations. AI-ability provides a practical way to assess how roles and tasks can be enhanced by AI, ensuring the right balance between AI capabilities and human input. As organizations continue to integrate AI, understanding AI-ability will be crucial for identifying which tasks are best suited for automation and which require human oversight.

Future research into AI-ability will help develop metrics and frameworks to evaluate how AI can not only automate tasks but also enhance overall team performance. By enabling workers to focus on innovation, creativity, and problem-solving, AI becomes more than just a tool—it becomes a driver of team success.

Natalie Shah, D.B.A(C) is an author, instructor, and researcher at the Center for Innovation Management & Business Analytics, Florida Institute of Technology. She is pursuing a doctorate specializing in Organizational Behavior, Strategic Human Capital Management, and Innovation Management. Previously as a Resident Director for Student Life, she managed 13 staff members, overseeing community development and programming for 2000+ students, focusing on health, wellness, diversity, and inclusion. With an MBA and B.S. in Biomedical Engineering, she leads the Graduate Teacher Orientation Program and service-learning initiatives. She also serves as U.S. Alternate Delegate on the ISO Technical Committee for Blockchain and Digital Ledger Technologies.

For further reading:

Au-Yong-Oliveira, M., Canastro, D., Oliveira, J., Tomás, J., Amorim, S., & Moreira, F. (2019). The role of AI and automation on the future of jobs and the opportunity to change society. In New Knowledge in Information Systems and Technologies: Volume 3 (pp. 348-357). Springer International Publishing.

Barbieri, L., Mussida, C., Piva, M., & Vivarelli, M. (2019). Testing the employment impact of automation, robots and AI: a survey and some methodological issues.

Boavida, N., & Candeias, M. (2021). Recent automation trends in Portugal: implications on industrial productivity and employment in automotive sector. Societies, 11(3), 101.

Cazzaniga, M., Jaumotte, M. F., Li, L., Melina, M. G., Panton, A. J., Pizzinelli, C., … & Tavares, M. M. M. (2024). Gen-AI: Artificial intelligence and the future of work. International Monetary Fund.

Faishal, M., Mathew, S., Neikha, K., Pusa, K., & Zhimomi, T. (2023). The future of work: AI, automation, and the changing dynamics of developed economies. World Journal of Advanced Research and Reviews, 18(3), 620-629.

Filippi, E., Banno, M., & Trento, S. (2023). Automation technologies and their impact on employment: A review, synthesis and future research agenda. Technological Forecasting and Social Change, 191, 122448.

Hernández-Orallo, J. (2017). Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement. Artificial Intelligence Review, 48, 397-447.

Kromann, L., Skaksen, J. R., & Sørensen, A. (2011). Automation, labor productivity and employment–a cross country comparison. CEBR, Copenhagen Business School.

Masriadi, D., Ekaningrum, N. E., Hidayat, M. S., & Yuliaty, F. (2023). Exploring the future of work: Impact of automation and artificial intelligence on employment. Endless International Journal of Future Studies, 6(1), 125-136.

Ramaswamy, K. V. (2018). Technological change, automation and employment: A short review of theory and evidence. International Review of Business and Economics, 2(2), 1.

Poba-Nzaou, P., Galani, M., Uwizeyemungu, S., & Ceric, A. (2021). The impacts of artificial intelligence (AI) on jobs: an industry perspective. Strategic HR Review, 20(2), 60-65.

Walton, A. L., Demirjian, E., Shah, N. N., Sandall, D. L., Eskridge, T. C., & Henderson, J. (2024a). DOT&E and National Defense Strategy: Virtual teams as a proxy for evaluating return on investment of human-machine teams. In-Press.

Walton, A.L., Sandall, D.L., & Henderson, J. (2024b). The development of AI-ability: Insights from subject matter experts [Interview].