Current State

There are many applications of automation in the talent acquisition (TA) space, but one of the areas most ripe for improvement with automation is onboarding. The TA process is often hampered by long delays which has numerous consequences for the new employee experience, especially important during the time of the Great Resignation. Organizations looking to differentiate must find ways to speed up the process and find ways to let onboarding teams be more strategic in their duties. 

Complications

Increasing the speed of moving people through the onboarding phase is vital, but it is also worth looking at the benefits of improving integrations. One of the most common challenges when dealing with TA technology is the lack of quality data and analytics, which plays a big part in why organizations seek to upgrade their tech solutions. However, simply having AI or ML capabilities is not the easy fix some might expect. Although those technologies can improve speed and remove mundane tasks, it needs to be paired with a thoughtful analytics strategy that shows the further-down-the-line benefits of onboarding automation. 

Consequences

One of the easiest ways to determine if having automated TA processes is correlated to overall success is to look at those organizations with AI and ML capabilities in their TA technology and see how they performed against those without. 

Organizations that have automated processes are much more likely to see an increase in engagement, retention, customer satisfaction, and retention. This is likely because they can improve more long-term goals by giving over time-consuming tasks to automation and focusing on the more strategic aspects of onboarding such as early coaching and mentoring, linking onboarding to learning, and cultural communication. 

Critical Question

To improve your organization’s ability to expand onboarding through the use of AI and ML-backed talent acquisition technology, you need to determine what people and processes you have in place to help talent acquisition professionals make use of the automation technology they are given. Organizations should ask themselves those questions, along with the following: 

What data sources does your organization have access to in your current system, and what data sources are you missing? 

What should your onboarding process look like in the next few years and what is automated technology’s role in getting you there? 

What aspects of the onboarding process can benefit from AI, ML and RPA capabilities?

Brandon Hall Group POV

Automating Onboarding for Better Speed and Also Better Strategic Onboarding 

Automate continuous onboarding with check-ins based on any variety of date milestones (1 day, 30 days, 90 days, anniversary) or triggers, and deliver documents, assignments, and assessments at those predetermined intervals. This will allow your organization to handle necessary regulatory paperwork and also to be more strategic by taking the employee sentiment pulse, gathering employee referrals (a great thing to do in the first three months), understand what the company could be doing better, or do periodic skills tests. 

Automate All TA Processes for Better Data Integration 

For too long, onboarding has been seen as a standalone process and that has carried over into how onboarding technology is used. The biggest challenge organizations face when it comes to using TA technology is getting the right data and analytics, and that challenge is directly related to the second most common obstacle — lack of integration with other systems. 

Much of the current use of AI and ML-backed technology is in the early parts of the TA processes — sourcing and screening. This leaves post-hire TA processes sadly in need of more automation because the onboarding processes are just as ripe for improvement through the use of more modern systems. Document management, data collection and governance, and integration with other HCM systems are all areas where AI and ML can make a significant impact. Organizations would be wise to expand the use of AL and ML technologies into more post-hire processes. 

Determine Which Processes Should and Which Shouldn’t Require Human Interaction 

59% of organizations say they are not ready or only somewhat ready to have AI- and ML-driven technology replace human interactions and decision-making. Certainly, using advanced TA technology can improve the candidate experience, but only if the technology is used for that purpose. Significant decisions, meaning those that can affect people directly such as a hiring decision, should only be made by people. 

Other actions, such as eliminating redundant information, or automatically sending needed paperwork to candidates and filing data, can and should be automated to make the candidate experience more efficient and pleasant. 


About

Brandon Hall Group Strategy Briefs answer the critical questions learning, talent, HR and business leaders must address to manage their human capital. To tackle these critical questions in more detail, we built tools, frameworks, research summaries and business builders based on up-to-date research and case studies for you to implement best and next Human Capital Management (HCM) practices. To gain access to these valuable resources, contact success@brandonhall.com.

Leading minds in HCM choose Brandon Hall Group to help them build future-proof employee-development plans for the new era. For more than 28 years, we have empowered, recognized and certified excellence in organizations around the world, influencing the development of over 10,000,000 associates and executives.

Cliff Stevenson

Cliff Stevenson is Principal Analyst, Workforce Management Practice, for Brandon Hall Group. He came to Brandon Hall Group in 2015 from the Institute for Corporate Productivity (i4cp) where he was a senior analyst since 2012. Cliff's experience as human capital research analyst has focused on data and analytics, performance management, recruitment, acquisition, retention, and attrition.