The Purpose and Future of Work

AI can assist human resources departments by automating and speeding up tasks that require collecting, analysing, or processing information. This can include employee records data management and analysis, payroll, recruitment, benefits administration, employee onboarding, and more. (Investopedia, 2026)

AI is now embedded across all these stages. According Tambe et al. (2019), AI in HRM primarily operates through three mechanisms: augmenting human decision making with predictive analytics and personalising employee experience at scale. The result is an HR function that is faster, most consistent, and increasingly strategic rather than administrative.

Key Applications of AI in HRM

AI powered recruitment & applicant screening
- Natural Language Processing (NLP) tools scan thousands of resumes in seconds, ranking candidates based on role relevant skills and past experience.
- Chatbot-driving initial interviews (e.g., HireVue, Paradox’s “Olivia”) conduct first round screening, dramatically reducing time to hire.
Predictive Workforce Analytics
- Machine Learning models analyse historical workforce data to predict turnover risk, identifying which employees are likely to resign within 90 days.
- HR teams can then proactively intervene – offering training opportunities, schedule adjustments or recognition programmes.
Automated Scheduling & Workforce Planning
- Ai Scheduling tools use sales forecasting data, foot traffic patterns and employee availability to generate optimal shift rosters.
Performance Management & Sentient Analysis
- AI tools analyse employee survey responses, internal communications and performance data to generate sentiment scores and wellbeing indicators.
- Platforms such as Microsoft Viva Insights provide managers with aggregated, anonymised data on team engagement and collaboration patterns.
Learning & Development Personalisation
- AI driven Learning management systems (LMS) recommend tailored training modules based on an individual’s role, skill gaps and career goals.
- Supermarket example: Tesco’s internal “Tesco Academy” platform leverages AI to recommend training pathways to department managers and team leads based on performance data.

Resume screening dashboard (with candidate scores/rankings)

AI in Retail

Supermarkets are uniquely demanding workplaces. A large format store might employ 300-500 staff across dozens of departments, operating seven days a week across early morning and late-night shifts. Demand fluctuates hourly based on promotions, weather, pay cycles and seasons. This complexity makes workforce management one of the most operationally challenging functions in retail.
AI was first applied in supermarkets to solve supply chain inventory problems and those foundations have informed how AI is now being used for workforce and HR challenges. The supermarket sector is, in many ways, a leading test bed for applied AI in people management, given the scale, data availability and operational urgency involved.
According to Chung et al. (2020), retail is among the most active industries in deploying AI workforce tools, driven by the combination of thin margins, high labour costs and high employee turnover rates that can exceed 60%-80% annually in some markets.

Created by Tengis Amartuvshin

Pros of AI in Supermarkets

1. Efficiency within the recruitment process (Vrontis et al., 2020)

2. Efficiency increased through an automated scheduling system.

3. Productivity levels can increase as repetitive and time consuming tasks can be handled through an automated system (Vrontis et al., 2020).

4. Analysis of foot traffic can be streamlined. Managers whose performance is usually tracked with foot traffic and results can hold bias in analysing this data, the implementation of AI during this analysis process can help to remove this bias.

5. Learning Management Systems can be tailored to individual employees learning styles, job titles and career goals.

Cons of AI in Supermarkets

1. Decisions are made through data analysis and an algorithm (Tambe et al., 2019).

2. Reduction in human presence in stores.

3. Requires policy development within most organisations as the existing policies are designed with human presence in mind rather than an automated data-analytical tool (Mukhopadhyay et al., 2026).

Created by Kristin Ford

Challenges and Ethical Considerations

The implementation of AI within the supermarket industry needs to take into account several ethical considerations and initial challenges. The creation of a fair system needs to be considered firstly as an AI system built on an unfair or biased system will continue to foster discrimination and biases (Mukhopadhyay et al., 2026). Decisions within an AI based system are produced through a data-based algorithm, which can be viewed as removing the ‘human’ aspect of human resource management systems , and may raise issues with employees (Tambe et al., 2019).

The complexity of the HRM system can be a challenge for the implementation of an AI system within an organisation as HRM consists of many aspects which are continually evolving (Tambe et al., 2019). The implementation of an AI system within the supermarket industry and within HRM processes will require a review of policy and the re-development of some key policy ( Mukhopadhyay et al., 2026). Challenges can also occur if the initial data set provided is too small as this will limit the ability for the system to be applied across the business as the supermarket industry and HRM are complex in themselves (Tambe et al., 2019).

How HR Supports AI in AI Transitions

Human Resource (HR) has a vital importance for improving artificial intelligence (AI) service in retail supermarkets. This is because AI has impact on staff as well as customers, and HR is involved in improving AI from that angle.

1.HR Supports in AI for Retail Supermarkets:

Recruitment & Training- HR uses AI to filter candidates, skill matching, and automation of recruitment procedures.
Offers customized training and fast onboarding for staff members.
Facilitates continuous learning by offering real time coaching services.

Outcome- Well equipped and adequately trained staff offer high quality customer service

2. Workforce Management & Employee Assistance:

AI forecasts peak hours and schedules the workforce efficiently.
Minimize staff burden by automating simple and repetitive procedures.
Assists employees in utilizing AI applications effectively

Outcome- Fast paced and efficient service delivery, shortened queue times, and increased employee satisfaction

3. Utilizing AI to Boost Customer Satisfaction:

AI examines customer information (preferences and shopping behaviour).
Uses finding to enhance customer experience.
Improves transactions by enabling self-checkouts and minimizing fraud.

Outcome- Streamlined, tailored, and secure shopping experience.

4. The Strategic Significance of HR in AI Implementation:

Develops an AI-enabled workforce via training and development activities.
Monitors change and facilitate employee’s adjustment to new technologies.
Guarantees appropriate usage of AI technologies and alignment with corporate objectives

Conclusion: AI proves efficient when staff members are adequately trained.

Created by Riddhi Dholariya

Focus on Customer Service & Human Capital

In retail supermarkets, HR supported AI enhances customer experience by improving human capital helping recruit, train, and support employees so they can deliver faster, more personalized, and efficient customer service.

Development of Human Capital (Employees)

AI assists the HR department to recruit talented employees who understand their customer
It gives rapid training and learning opportunities
It lowers the burden of repetitive processes

Outcomes: Talented, competent, and happy employees

2. Enhanced Customer Service via Employees

Employees can respond to queries and identify products more efficiently using AI.
Proper staffing leads to sufficient employee availability during peak times.
This allows employees to concentrate on customers rather than administrative tasks

Outcome: Increased efficiency in customer services

3. Personalized Customer Services

AI enables insight into customer preferences and shopping behaviours.
Employees make product recommendations based on the collected information.
it leads to more personalized interaction between the customer and the employee.

Outcome: More content customers.

4. Fast and Secure Transactions

AI facilitates self- checkouts and eliminates queues.
Increase accuracy in invoicing
Prevents frauds.

Outcome: Easy and safe transactions

How AI Promotes Operational Efficiency

The use of AI in retail supermarkets leads to improve operational efficiency through its ability to automate human resources processes, which include scheduling and recruitment screening, and performance evaluation.
The system operates through three main functions, which enable organizations to make data-based decisions while reducing human mistakes and optimizing their employee distribution.
The organization achieves faster operational processes which result in reduced expenses and increased work output through this solution while HR staff members can concentrate on their essential strategic responsibilities.

Created by Dilpreet Kaur

Factors for Success in these Transitions

Successful organizations require three specific elements, which include complete digital infrastructure and skilled workforce and backing from executives to achieve their AI- based HR transformation goals.
Organizations need to provide ongoing employee development opportunities which should include both training and reskilling programs to support workers through the technology transition process.
Three essential factors which include effective change management and ethical AI adoption and strategic alignment between HR functions and organizational objective, need to be established for organizations to accomplish successful and long-lasting organizational transformation.

References

Bullhorn (2018), “2018 UK recruitment trends report: the industry’s outlook for 2018”, available at: http://pages.bullhorn.com/rs/131-YQK-568/images/2018%20Trends%20Report_UK.pdf

Chung, T. S., Wedel, M., & Rust, R. T. (2020). Adaptive personalisation using social networks. Journal of the Academy of Marketing Science, 44(1), 66–87. https://doi.org/10.1007/s11747-015-0441-x

Dastin, J. (2018, October 10). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G

Gartner. (2022). HR technology benchmarking survey: Employee experience and digital HR adoption. Gartner Research.

Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ko, R., & Sanghvi, S. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute. https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages

Mukhopadhyay, S., Senanayake, S., & Prasad, P. (2026). Innovative Technologies in Intelligent Systems and Industrial Applications.
https://doi.org/10.1007/978-3-032-10898-2

Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910

Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. The International Journal of Human Resource Management, 33(6), 1237–1266. https://doi.org/10.1080/09585192.2020.1871398

Created by:
Tengis Amartuvshin
Kristin Ford

Riddhi Dholariya
Dilpreet Kaur

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