5 Change Management Lessons When “Hiring” An AI Recruiter

How enterprises should approach the “hiring” for their own recruitment efforts

Artificial intelligence’s promise to bring revolutionary change often sparks an unwanted reaction: fear.
 

Some of this aversion to super-intelligent machines can be chalked up to pop culture fascination and speculative science fiction — movies like Terminator and 2001: A Space Odyssey certainly didn’t portray the tech as being kind to its human counterparts. But as AI advances — and the technology proves it can accomplish increasingly complicated tasks — the discomfort persists, especially among professionals whose jobs absolutely require a human touch. In a recent report on AI in the enterprise, 78 percent of executives believe that AI will fuel ways of working and augment existing workers jobs — meaning like it or not, professionals need to be ready to do their jobs differently.
 
More than a third of respondents said AI is nascent and unproven — a sizable group to convince if companies are going to get serious about bringing the technology aboard.
 
Making the case for AI is especially important among HR and talent acquisition professionals. These positions very much depend on the “human” element of human resources, so any technology that claims it can function similarly can be met with skepticism. This doesn’t have to be the case, however. Getting hiring teams and recruiters on board with AI can be accomplished by rolling out a change management plan that makes it clear what the new technology does (and doesn’t) do, and has a sharp focus toward measuring and communicating the benefits it brings. Is it an investment in new technology, new process, or replacement for people — or all of the above?
 
This perception of value and degree of change when hiring an AI recruiter also varies significantly depending on the stakeholder — while HR and business leadership may view it as a technique to improve the performance metrics like staffing levels and time to hire, the field recruiter and the hiring manager are hoping for more streamlining of the day-to-day processes. The change management approach must account for such differences in expectations.
 
So, how should enterprises approach the “hiring” of AI for their own recruitment efforts? Drawing on the past successes of AI in recruiting, across various industry segments, the following are five best practices to help manage change while accelerating AI’s time-to-value:

A common mistake among adopters is the assumption that AI will solve all recruiting problems, rather than a targeted set of pain points. To counter this misperception, it’s important to define recruiting performance metrics with clear baselines and target improvements in advance. Be sure to align stakeholders and pertinent members of the team on the identified use-cases of AI technology and the projected performance improvements before the technology arrives.
 
For example, a technology-based mailing services provider identified a lag in filling distribution warehouse roles as a major recruiting challenge. Leaders then determined a target improvement to demonstrate ROI after implementation. Without an ROI metric, it is difficult to deem the pilot successful and encourage the adoption of AI in other locations and roles.

The buying process is often relegated to and led by the chief human resources officer (CHRO) or the vice president of talent acquisition. This siloed information structure excludes the broader organization and results in a lack of alignment on expectations. Worse, it can lead to downright failure. A recent poll shows 49 percent of enterprises that deployed AI experienced challenges getting stakeholder buy-in.
 
Avoid siloed information and prepare all levels for adoption by mapping out the current and future state of the recruiting workflow. Consult with key roles and levels during decision making and adoption planning. For example, departmental leadership is often involved in defining job titles and roles within their particular teams. Because an AI system will have an impact on how candidates are vetted for these roles going forward, it is important they are made aware of the new systems, and how they might benefit from them. Involve these stakeholders during the purchasing cycle with demos, agreement on the business case and joint selection of the pilot scope.

A full AI rollout requires a detailed plan of activities, timelines, risks and dependencies. It’s important that these plans are managed by parties that are all on the same page.
 
Proper planning requires an assigned leader or “owner” of the transformation. This role requires a leader with a broad perspective on current practices and enough influence to lead change across the enterprise. Through communication tactics, like training, feedback collection and self-service information tools (FAQ pages, demo videos), leadership teams can ease into the new technology and the changes it brings.
 
Once AI arrives, it’s critical that measurement and optimization are not neglected. Transformation “owners” must apply a laser focus on the business case achievement and the ongoing optimization of the technology — e.g., promoting AI engagement channels and adjusting screening criteria.

While it’s critical to have leadership onboard with the change, a top-down rollout has the potential to make on-the-ground users fearful and uncertain. To avoid skepticism from lower levels, the project team should identify visionary and influential team members across various job categories, locations, and business units. These “champions” will help build excitement about the change, answer questions and provide helpful, ground-level feedback to the project team.
 
The “champions” form a resilient pilot group that won’t easily give up on the new technology, which is key to AI’s long-term success. Involvement in planning and rollout activities makes the group early adopters before the solution even goes live. As a result, the “champions” are familiar with the technology and become AI’s biggest advocates during broader implementation across the enterprise.

Pilot programs are necessary for smooth adoptions, but project teams often struggle to scale them. The challenge is finding the balance between an abrupt, full roll-out and a pilot that’s too small to determine impact.
 
A successful pilot program has a healthy mix of recruiting challenges and job titles, supported by a small team that can quickly implement and provide feedback. Success hinges on the metrics as well as the timeline, which necessitates the use of incentives (across roles and with the vendor) to ensure that goals are achieved. The feedback team must periodically monitor progress and measure it against the target success criteria.
 
It’s also critical to ensure that project teams communicate positive results throughout the pilot period. This is necessary in reassuring pilot participants that their work is meaningful and impacts the end goal of AI adoption across the enterprise. Then, after the pilot period has ended, these learnings can be incorporated into the full adoption plan.
 
An enterprise-wide adoption of AI recruiting technology cannot be accomplished without careful consideration of change management. But with these five points in mind before, during and even after implementation, recruiters of tomorrow will have the full power of machine learning on their side.

Bennett Sung is Head of Marketing at AllyO. Connect Bennett Sung Follow @bennettsung