Automation, Advanced Analytics, and the Transformation of HCM Administration

AI-driven automation is the wave of the future for global business, driven by the accelerating need to do more with less in order to remain competitive and economically viable. COVID-19 has only accelerated this trend, placing tremendous pressure on business leaders to identify new efficiencies and boost performance without the benefit of hiring more employees or spending on third party partnerships.

Taking Control of a Complex Future

Known for its reliance on manual, labor-intensive processes, HR has been undergoing a pronounced shift for more than a decade, with increasingly sophisticated HRIS toolsets playing a significant role, along with advancements in recruitment and applicant tracking, workforce planning, talent and performance management, and collaboration/knowledge-sharing tools. To one extent or another, all of these technologies are designed to automate and streamline workflows that were once entirely manual. However, data entry and report generation often remain resource intensive, particularly in environments where HR operations are dependent on multiple, poorly integrated systems that cannot seamlessly share information.

Our Members have consistently expressed their frustration with the preponderance of manual processes and workflows, and the challenges this poses to meaningful insights generation, trend analysis, and reporting. Disparate, misaligned technology investments, multiple data warehouses, and a lack of built-for-purpose toolsets to support interoperability have all figured prominently in our conversation over the past year. HCM leaders desperately want a single source of truth for all enterprise knowledge, employee records, documents, and performance data, but without robust automation engines, this is largely impossible. Bolt-on integration tools are prevalent, but our conversations indicate that these frequently require an inordinate amount of manual intervention to operate effectively, robbing them of much of their supposed value.

As such, there is a growing movement to adopt powerful automation, machine learning, and AI tools with a range of potential applications. By way of example, Members have expressed interest in the following use cases:

• Candidate Screening: This is one of the most obvious applications for automation in an HR setting. Automating this workflow according to a defined set of rules can completely eliminate manual touches from what is otherwise a low-complexity, highly resource intensive process. Further, AI-driven process automation ensures that only candidates who meet qualification, skills, and competency benchmarks will be forwarded to leaders for additional review.
• Data Normalization and Cleaning: Converting raw, unstructured data into meaningful insights on a range of key indicators, including business outcomes; not only does this improve the ability to measure and benchmark staff performance, but it boosts productivity by freeing staff from menial tasks.
• Informed Leadership: Empowering leaders with all the information they need, when they need it, to drive rapid decision-making and responsiveness; over time this makes the entire organization more dynamic, more intelligent, and more prepared to drive and respond to change.
• Workforce Capacity Analysis: Understanding what the current workforce skillset looks like, how that reflects business goals, and where the organization should devote resources to capacity building. And by incorporating labor management, employers in industries like healthcare, sales, or manufacturing can optimize team deployment based on expected customer demand or production timelines.
• Data Management and Sharing: Creating an integrated employee record that incorporates all payroll, performance, time-off, learning and development data – a true single source of truth. Not only does this make tracking the employee journey much easier, supporting effective trend analysis at the enterprise level, it can also completely eliminate the need to keep multiple documents and records housed in different systems or physical files. In turn, both employees and their managers have access to the same dynamically updated source of information and can access it on-demand.
• Cybersecurity: Advanced machine learning and AI tools are essential to keeping highly sensitive employee information secure, facilitating compliance with data privacy regulations.
• Knowledge Management: Providing employees with comprehensive access to all organizational knowledge, from operational protocols, to documents for collaboration, to individual benefits, healthcare, and rewards in an integrated self-service framework.

Conclusion

Investing in intelligent automation and analysis is the most effective, least expensive approach for achieving a complete view of the enterprise and generating on-demand insights into the performance and productivity of the larger workforce and individual contributors. Further, the digital age has flattened talent markets, and the impact of COVID-19 has forced more and more companies are forced to reconcile themselves to the fact that the future of work is remote. Talent shortages, intensifying competition, and tightening margins mean that organizations must find smarter, more efficient ways to select from massive candidate pools that don’t require additional investment in human capital; they must reduce the burden on HR staff by eliminating repeatable tasks, allowing them to apply their talents to higher value work; and they must be able to anticipate emerging needs, challenges, and opportunities in workforce planning.

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