By Jonathan H. Westover, PhD
Abstract: This article provides guidance to organizations on how to successfully adopt artificial intelligence (AI) technologies in a way that streamlines operations while empowering employees. The author argues that companies should start by conducting an "AI readiness assessment" to identify high-potential but practical use cases for automation or augmentation. Small pilot programs with clear goals and metrics allow proving value before expanding uses. As AI capabilities grow, companies must proactively reskill workers and establish new "AI-first" roles to maintain competitive advantage. The article highlights examples of successful AI integration in healthcare and finance that involved employees in expansion plans. It concludes that leadership must cultivate an adaptive, learning culture where human and artificial talents collaborate to take on bigger challenges. A thoughtful, people-focused approach to AI from the start helps organizations optimize workflows while refocusing employees on more engaging work.
As an experienced management consultant and academic researcher, I've seen firsthand how emerging technologies like artificial intelligence are beginning to reshape entire industries. While transitioning to AI-powered workflows can seem daunting at first, I've found that approaching it as an opportunity to streamline operations and refocus employees on more engaging, meaningful work tends to yield the best results for organizations.
Today we will explore how to adopt AI-assisted strategies, along with recommendations for leveraging available AI tools to make your own work - and your company's work - run more efficiently.
Understanding What AI Can (and Can't) Do For Your Organization
To start, it's important to have realistic expectations of what AI is capable of today. While some stakeholders may envision systems with human-level cognition, many currently available AI tools have narrow applications (Gain et al., 2022). Assessing your specific organizational needs and pain points is key. Ask yourself: Which routine administrative or analytical tasks could be automated or augmented? Where could AI help surface hidden patterns or insights in data? Getting granular about practical use cases will help identify the most impactful ways AI can lighten employees' workloads without replacing human judgment or decision making (Salter et al., 2020).
It's also wise to acknowledge the limitations of current AI. For tasks requiring deep domain expertise, nuanced communication skills or complex problem-solving, human workers will likely remain indispensable. However, when applied judiciously to standardized, repetitive processes, AI can act as a force multiplier - allowing employees to focus on more engaging work that leverages their uniquely human strengths. By first analyzing your operations through this pragmatic lens, you set the stage for a successful, employee-empowering AI integration.
Conducting an "AI Readiness Assessment"
To determine the best initial AI applications for your organization, I recommend conducting an "AI readiness assessment." This involves surveying employees across departments about their daily routines, recurring tasks and biggest time sinks. You may be surprised by some universal pain points AI could effectively address, like data entry, routine form processing or file sorting. Simultaneously, assess your technology infrastructure and gather insights from relevant industry peers about AI tools showing promise for your sector (Agrawal et al., 2018).
With findings in hand, prioritize two to three high-potential use cases for a pilot program. Strong candidates exhibit well-defined parameters, access to appropriate training data and measurable key performance indicators (KPIs) for assessment. Ensure legal and IT teams greenlight any data privacy and security procedures involved. With cross-functional buy-in and clear success metrics, you set the stage for pilots that prove AI's value at your organization in a controlled, low-risk manner.
Piloting AI Applications and Evaluating Impact
When pilots launch, maintain close communication with end users about their experiences. Capturing qualitative feedback on interface design, effectiveness and needed improvements will be as valuable as tracking the KPIs. I also advise designating "employee advocates" from each functional area involved to champion adoption and help colleagues through initial learning curves. Their support alleviates transition issues that could otherwise color perceptions of the new technology negatively from the outset (Orlikowski and Gash, 1994).
Halfway through and at the end, formally assess each pilot against its goals using data as well as employee and advocate input. Highlighting time and cost savings, productivity increases or other gains will justify expanding successful use cases. If a pilot underperforms, discontinue it promptly and regroup on alternative applications. Maintaining a test-and-learn mindset helps maximize ROI. Don't hesitate to involve third-party analytics or implementation partners for added rigor where helpful.
Expanding AI's Role Tactfully
With pilots proving value, your organization gains validation to thoughtfully extend AI into new domains. But scaling prudently is paramount to sustaining buy-in. Two industries where AI expansion has gone well include healthcare and finance:
Healthcare: A large hospital network piloted AI-based diagnostic tools in radiology departments, showing 5-10% faster, more accurate diagnoses (Harnett et al., 2021). Recognizing this early success, leadership launched division-wide training to introduce similar AI applications across other specialties like dermatology and cardiology over 18 months. Dedicated support staff helped clinicians confidently transition certain routine tasks to AI, freeing up precious clinical hours for more complex cases.
Finance: An investment bank piloted AI robo-advisors for fundamental equity analysis against a control group of analysts. Finding robo-output comparable in accuracy, leadership invited senior analysts to suggest portfolio management subtasks next ripe for AI handling, like quantitative screening and daily market scanning. Advisors now assist more high-net worth clients directly while AI handles greater volumes of lower-touch cases.
In both examples, gradual, consensus-based expansion nurtured ongoing comfort with - rather than fear of - how AI augments critical work. As your pilots prove wins, similarly involving teams in defining AI's evolving role fosters long-term, organization-wide integration success.
Reskilling and Roles of the Future
A primary objective of thoughtfully introducing AI should be empowering your workforce, not displacing it. Specific roles may change, but new, higher-value duties will emerge in step with evolving technology. I advise proactively reskilling employees via internal training programs and external certifications as applications expand.
For example, when one logistics firm's AI routing software cut delivery fleet needs, leadership retrained now-excess truckers as fleet managers overseeing routing optimizations. Trained data analysts also evaluate and improve the AI based on edge case insights only humans provide. Overall, workforce headcount remained stable while roles gained new purpose aligning better with strategic goals (Moore et al., 2021).
Additionally, consider designating AI "career paths" with clear progressions and specialized skill requirements. Titles like Data Scientist, AI Ethicist and AI Program Manager signal growth opportunities for interested employees and attract new tech-savvy talent. With reskilling investments, your organization maintains competitive advantage through an AI-fluent workforce as skillsets transform all around it.
Leading the AI-Powered "New Normal"
By piloting judiciously, evaluating impactfully and expanding AI strategically as outlined, your organization builds internal capabilities and confidence for managing emerging technologies long-term. Leadership must now guide cultural adaptation to a new operational paradigm and help employees thrive within it.
Continue fostering understanding of AI's abilities and limitations through open forums, new hire orientation and refresher briefings led by advocate teams. Elevate stories of AI streamlining work via company intranet or newsletters to energize adoption across functions. Also critically assess progress against human capital goals like career development, retention, engagement and well-being through pulse surveys.
A supportive, learning-oriented culture where employees partner productively with AI tools, not compete against them, will define your organization's sustainable competitive edge in rapidly changing times. By leading the transition thoughtfully from the start, you establish an "AI-first" mindset empowering all to tackle bigger challenges together.
Conclusion
AI promises great potential to simplify tasks at work and optimize organizational operations if approached judiciously as an employee-empowering technology. Conducting thorough AI readiness assessments and piloting high-potential use cases first establishes proof of value. Gradually expanding applications responsibly by involving end users nurtures long-term integration success. Equally critical are proactive reskilling and establishing roles for an AI-transformed future. With full cultural buy-in cultivated from the outset, leadership guides adaptation to a new paradigm leveraging all available human and artificial talents. For companies pursuing AI in this pragmatic, people-first spirit, the technology acts as a force multiplier rather than a disruptive force, allowing employees to focus on what they do best.
References
Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review, 96(6), 88-95. https://hbr.org/product/prediction-machines-the-simple-economics-of-artificial-intelligence/118167-PDF-ENG
Gain, J., Satzger, G., & Thummer, E. (2022). Narrow or general? The conceptualization of artificial intelligence by the general public and information systems researchers. Information Systems Frontiers, 24(2), 579–598. https://doi.org/10.1007/s10796-021-10154-1
Harnett, B. M., Lee, C., Rajpurkar, P., Roy, A. G., Jayapandian, C. P., Park, H., ... & Lungren, M. P. (2021). Diagnosis of COVID-19 pneumonia on chest radiographs with deep learning. Radiology, 29(9), 200905. https://doi.org/10.1148/radiol.2020200905
Moore, J. F., Reinhart, E. J., Park, Y. T., & Broberg, O. (2021). Transitioning workforces and jobs: The impact of artificial intelligence. Journal of Economic Perspectives, 35(3), 205-220. https://doi.org/10.1257/jep.35.3.205
Orlikowski, W. J., & Gash, D. C. (1994). Technological frames: Making sense of information technology in organizations. ACM Transactions on Information Systems (TOIS), 12(2), 174-207. https://doi.org/10.1145/196734.196745
Salter, S., Antonopoulos, N., & Young, T. (2020). Providing ethical guidance in the development of autonomous systems: The limitations of top-down policy principles versus situational judgement. AI & SOCIETY, 35(1), 45–57. https://doi.org/10.1007/s00146-019-00829-9
Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Chair/Professor, Organizational Leadership (UVU); OD Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.
Suggested Citation: Westover, J. H. (2024). Making Work Work Better: How to Leverage AI to Simplify Tasks and Optimize Your Operations. Human Capital Leadership Review, 11(1). doi.org/10.70175/hclreview.2020.11.1.7
