Meet Your Work Twin: How Digital Twins Are Reshaping Workforce Strategy

The age-old question asked by managers around the world: How do you know if a strategy you have designed will result in the desired employee behaviour before implementing it? Companies have yet to find an answer to this question in the ever-changing workplace. To solve this problem, companies have been adapting their HR systems on an iterative basis to keep up with the new generations of employees. One notable way is using digital twins.

Current Trends and HR Tool Limitations

Organizations today rely on a range of HR tools to manage hiring, training, and career development. These tools have streamlined processes but also come with significant limitations. For hiring and recruitment, Applicant Tracking Systems (ATS) help streamline the recruitment process but overlook hidden workers. The process utilizes historical data rather than evaluate a candidate's capacity for growth or adaptability, leading organizations to emphasize efficiency over accuracy and make incomplete hiring decisions.

When companies train their employees, Learning Management Systems (LMS) struggle to engage employees meaningfully because they are not tailored to individual learning styles or job competencies. Real-time feedback is also uncommon, making it difficult to measure the progression of training. To manage career development, HR information systems (HRIS) often provide siloed views of an employee's history and potential, which limits HR teams in making proactive talent decisions. While these tools have improved scale and efficiency, they fall short in personalization, integration, and accuracy, highlighting the need for a dynamic solution that digital twins can fulfill.

Benefits of Digital Twins

Digital twins can be deployed by simulating future scenarios related to changes in a company's strategy and incentive systems. Companies are realizing three core benefits through their use:

• Accelerating Decision-Making: Simulations allow companies to analyze the effectiveness of various incentive systems (financial or non-financial) to find the sweet spot for the amount of incentive that will result in peak employee motivation and performance. Instead of reacting by adjusting a strategy post-implementation, companies can take a proactive approach to see what will work and what won't.
• Improving Employee Experience: After the COVID-19 pandemic, companies place greater emphasis on employee well-being. Digital twins help make data-driven decisions to improve work-life balance and address a lack of motivation. Running hypothetical scenarios before releasing changes shows employees that the company carefully considered the change, enabling the development of incentive systems that employees value.
• Enhancing Performance Evaluation: Using digital twins to test incentive plans provides insight on whether incentives are highly correlated with better performance and lead to desired traits and behaviours. Simulations can surface the pitfalls of a specific change in strategy, enabling companies to adapt and revise changes before implementation in the real world.

Challenges of Digital Twins

The integration of simulating hypothetical scenarios introduces significant challenges regarding privacy, legalities, and ethics.

The continuous collection of employee data poses privacy and security concerns due to the sensitivity of digitally mirrored information. Without data protective measures in place, employee data may be vulnerable to breaches and identity theft through ransomware attacks. The large data collection in cloud databases may lead to cybersecurity attacks, particularly when gathered without explicit consent.

Legally, under Canadian privacy law (PIPEDA), businesses are required to establish a tracking program to monitor consent mechanisms and anonymization of data. This poses a challenge for digital twins as the nature requires linkages to identifiable employees in the workplace.

Ethically, tracking behavioural data such as communication styles and work routines may cause employees to feel they are under constant surveillance. This oversight reduces trust in employers and may undermine the ethical practice of autonomy. Furthermore, reliance on historical data, such as past performance ratings and promotions, may inherently lead to bias, requiring organizations to perform scheduled audits for fairness.

Implementation Requirements

When it comes to the implementation of digital twin technology, there are four components needed: a physical system (the employee), a virtual system (the virtual representation), systems data, and a communication interface for data exchange.

The purpose of the digital twin will inform what types of data need to be collected. Some AI twins are intended to simulate behaviours to provide information about circumstances, identifying goals such as monitoring workloads, predicting desire to leave, and suggesting needed training. Employee calendars can be collected when considering workload, while surveys and performance evaluations can be used to evaluate retention status and tailor training programs. Organizations can build these systems in-house or outsource to platforms like ServiceNow and Delve.Ai.

Case Study: Digital Twins in Action (Zensar)

Zensar, a technology solutions company, created a digital twins platform to help businesses with strategic workforce planning. The platform uses real-time HR data, including skills, availability, and performance results, to optimize resource allocation. This data simulates situations through "Forward Simulation" to predict performance based on current conditions, and "Reverse Simulation" to estimate required steps to achieve a goal.

Zensar's clients have reported several benefits from these simulations:

• Streamlined Resource Allocation: Replaced tedious manual processes with data-driven recommendations, reducing delays in project completion.
• Enhanced Experiences: Employees enjoy being allocated to meaningful projects that align with their skills and career aspirations, while clients appreciate receiving optimal service.
• Proactive Staffing: Clients can foresee upcoming skills gaps and resource needs rather than scrambling when performance is nonoptimal.
• Data Efficiency: The platform allows the HR team to run multiple simulations simultaneously, making data easier to analyze.

Overall, the key client-reported advantages are operational speed, cost efficiency, and strategic flexibility.

Conclusion and Future Outlook

Companies that want to capitalize on the vast capabilities of data-driven information have a possibility to address the rapidly-changing workplace using digital twins. Companies who recognize that digital twins have immense potential will be able to design better employee strategies that align with the company's objectives.