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Middle Managers Embrace AI Amid Complex Adoption Journey

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  Published in Science and Technology on Friday, November 7th 2025 at 0:35 GMT by Fortune
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Middle Managers Embrace AI, but the Journey Is Far From Straightforward

Fortune’s November 7, 2025 coverage of AI adoption among middle managers, featuring insights from LinkedIn expert Feon Ang, paints a nuanced picture of how professionals who sit between senior leadership and frontline staff are navigating the rapidly evolving technology landscape. According to the article, a 2025 LinkedIn Talent Solutions survey revealed that 68 % of middle managers now report using some form of AI in their day‑to‑day tasks, a sharp rise from the 45 % figure recorded in early 2024. The piece attributes this jump to a combination of platform‑driven tooling, increased corporate mandates for data‑driven decision‑making, and a generational shift toward digital fluency.

The Drivers of Adoption

Feon Ang, who has spent over a decade analyzing workforce trends on LinkedIn, notes that the primary driver for middle managers is operational efficiency. “AI chatbots, automated reporting, and predictive analytics give middle managers a real‑time pulse on key metrics without sifting through spreadsheets,” Ang explains. The article cites examples from the retail, healthcare, and manufacturing sectors where AI‑powered dashboards have cut the time spent on performance reviews by up to 30 %.

Another factor, Ang points out, is the pressure to justify budget allocations. In many organizations, middle managers are now expected to demonstrate ROI for departmental spend. AI tools that can forecast demand, optimize staffing levels, and model cost scenarios provide a defensible basis for budget discussions.

Skill Gaps and Training Needs

While adoption is growing, the Fortune piece emphasizes that proficiency remains uneven. Only 32 % of middle managers rate themselves as “advanced” in using AI, according to LinkedIn’s internal skill assessments. This skill gap is most pronounced in smaller enterprises where access to specialized training is limited. Ang stresses that continuous learning is critical: “AI is not a plug‑and‑play tool. Managers need to understand data science fundamentals, ethical considerations, and the limits of algorithmic recommendations.”

The article links to LinkedIn’s “AI for Middle Managers” learning path (https://www.linkedin.com/learning/ai-for-middle-managers) which offers a curated series of courses on data literacy, AI ethics, and implementation best practices. An excerpt from the LinkedIn blog on this page highlights a case study from a mid‑size logistics firm that trained its 12 middle managers in predictive routing algorithms, resulting in a 15 % reduction in fuel costs. The blog further underlines the importance of cross‑functional collaboration to ensure AI solutions are aligned with both strategic objectives and operational realities.

Ethical and Governance Concerns

A recurring theme in the article is the ethical dimension of AI adoption. Ang notes that bias in training data can inadvertently reinforce existing inequities in promotion and resource allocation. A linked Fortune analysis (https://fortune.com/2025/10/02/ai-bias-ethics-in-management) explores how a manufacturing company that implemented an AI‑driven talent recommendation engine experienced a 12 % increase in hiring bias toward senior men, prompting a company‑wide audit and the subsequent rollout of an audit framework to detect and mitigate bias.

The article points to the growing role of AI governance committees in large firms. These committees, composed of data scientists, ethicists, and human‑resources leaders, set policies for data usage, algorithmic transparency, and compliance with emerging regulations such as the EU’s AI Act.

The Human Side: Managerial Mindset and Change

Beyond technical tools, the Fortune piece delves into the psychological adjustments middle managers must make. Ang shares that managers who approach AI as a partner rather than a replacement report higher job satisfaction. A linked interview with a middle manager from a consumer electronics company, published on LinkedIn Pulse (https://www.linkedin.com/pulse/how-ai-enhanced-my-managing-skills-feon-ang), recounts how integrating an AI‑based sentiment analysis tool into weekly team meetings helped the manager quickly identify morale dips and adjust workloads, leading to a 20 % improvement in employee engagement scores.

The article also includes a survey from the Society for Human Resource Management (SHRM) showing that 47 % of HR leaders plan to allocate up to 25 % of their budgets to AI training programs over the next two years. The SHRM link (https://www.shrm.org/resourcesandtools/hr-topics/technology/pages/ai-training-priorities.aspx) expands on the need for soft skills such as critical thinking and ethical judgment, suggesting that these will become core competencies for managers who wish to leverage AI effectively.

Future Outlook

Looking ahead, Feon Ang predicts that AI adoption among middle managers will become mainstream by 2027, with hyper‑automation and natural language interfaces taking center stage. The Fortune article cites a Gartner forecast that by 2026, AI will be embedded in at least 70 % of mid‑level managerial workflows in Fortune 500 companies. However, Ang cautions that the pace will vary across industries: while tech and finance are already integrating AI at scale, traditional sectors like construction and agriculture may lag due to data silos and infrastructural limitations.

The piece ends with a call for a balanced approach: managers should harness AI to amplify human decision‑making rather than supplant it. By combining AI’s data‑driven insights with human empathy and ethical oversight, middle managers can not only improve operational efficiency but also cultivate a more inclusive, transparent workplace culture.