Artificial intelligence continues to make an impact on recruiting. But even the most innovative TA teams find themselves overwhelmed with options, and unable to find budget to support their AI-driven recruiting initiatives. In this post, we’ll explore how you can build a rock-solid business case for artificial intelligence in recruiting.
1) Identify Your Biggest Opportunity
Pursuing AI without a specific problem or opportunity in mind is a recipe for wasted budget and unhelpful vendor partnerships.
Ben Eubanks, Principal Analyst at Lighthouse Research & Advisory hit the nail on the head in our recently published AI Buying Guide:
“If a vendor comes to you with ‘AI’ as their leading part of the conversation, it’s time to rethink the relationship. This is about solving real problems with how we hire, and AI should not be the focus of the discussion. Instead, it should first look at the ‘what,’ such as results, impacts and outcomes. Then the conversation can naturally delve into AI and other ‘how’ elements of how the system works on a technical level.”
Most recruiting opportunities that can be solved for with AI fall into one of the following four buckets:
- Hiring Process Effectiveness: The efficiency of the hiring process of finding the right candidate for a position. It considers the number of steps in the process, the communication to the candidate and the ability of that process to keep the right candidates in and disposition candidates that are not a good fit. An ineffective hiring process leads to low quality of hire, lengthy fill times, an inability to deal with high applicant volume, and an inability to hire a more diverse range of candidates.
- Sourcing and Attraction: The generation of interest amongst potential candidates and encouraging them to apply. This considers segmenting candidates, identifying optimal potential candidates, contacting passive candidates, employer branding, job boards, and other pre-funnel activities. Ineffective sourcing and attraction leads to low applicant volume, lengthy fill times, and an inability to fill niche, low volume positions.
- Candidate Experience: The way candidates receive and interact with the recruiting process. This is often influenced by an organization’s responsiveness, communication, and feedback, and how the candidate perceives their time and effort spent. Poor candidate experience leads to poor feedback from candidates and high rates of candidate dropout.
- Recruiting Productivity: How effective members of a recruiting team are in performing the tasks required to fill vacant roles with high quality talent. These include the number of candidate screenings performed per day and the number of interviews scheduled per day. Recruiter productivity may also be measured in activities like aligning hiring managers with better interview practices and providing more contact to candidates who are high potential. Low recruiting productivity leads to high recruiter turnover, poor feedback from hiring managers, and long delays when moving candidates from one process step to the next.
Once you’ve established the problem you’re trying to solve, it’s time to pull the data that shows why it should be solved, and the potential impact from solving it. Many of the more robust AI-driven solutions will solve several of these challenges.
2) Gather Your Metrics
Recruiting metrics are the most important tool to quantify opportunities and successes.
While some metrics – like time to fill – are universally tracked, others may be harder to come by. For many recruiting teams, gathering metrics is the hardest part of the case-building process.
The following are a list of metrics you should consider gathering as you prep your business case. Keep in mind that some will be more difficult than others, so you should focus on those metrics that align with the problem you’re trying to solve:
- Time to Fill
- Candidate Conversion Rates (in each process step)
- Hiring Manager Satisfaction
- New Hire Productivity
- New Hire Performance/Promotability
- Average Turnover
- Candidate Experience
- New Hire Diversity
- Cost to Hire
- Hiring Manager/Recruiter Productivity
- Cost of Vacancy
For example, a lengthy time to fill will support your case that a) your hiring process is ineffective; b) your sourcing is ineffective; or c) you have low recruiting productivity (or any combination of the above.
In Step 4, we’ll show you how you can create some of these metrics from others, and what sorts of metrics are the most powerful when it comes to building your business case.
3) Create Urgency With Industry Comparisons
Once you have your own metrics, it’s time to find your competitors’ metrics.
For example, here are the latest times to fill by industry from DHI Hiring Indicators:
|Industry||Time to Fill (Days)|
|Retail & Wholesale||27|
|Warehouse & Transportation||33|
|Leisure & Hospitality||22|
Other metrics, like turnover, can be harder to pin down. You can usually find these in industry reports, like those from ContactBabel (in the case of call center hiring) and SHRM. SHRM reports also provide insight into highly granular benchmarks, like requisitions filled per recruiter.
4) Translate Conventional Metrics into ROI Metrics
Many traditional recruiting metrics – like time to fill – can be translated into ROI metrics that increase the visibility of your recruiting initiatives.
For example, when you reduce time to fill, you also:
- Decrease the time hiring managers spend evaluating candidates. If hiring managers’ time could be spent on projects which have direct dollar values attached, such as performing work for clients, ROI can take the form of billable hours. More generally, for roles without this component, you can show ROI as cost savings in the form of a managers’ average hourly wage.
- Decrease time recruiting spends on each requisition. Reducing recruiting time means fewer recruiting resources are necessary to fill all roles across the organization.
- Decrease cost of vacancy. For roles whose work can be directly tied to revenue, every day a requisition stays vacant is a day of missed revenue generation. For example, assuming the average sales person generates $1000 in sales per day, reducing time to fill by 15 days increases the revenue-generating potential of the organization by $15,000.
Put a different way, this process converts soft dollar metrics to hard dollar metrics. Soft dollar metrics, like candidate experience, appeal to principles and values. Hard dollar metrics appeal to fiscal sensibility. They represent your case in the universal language of business: dollars.
Both soft and hard dollar metrics are important components of a business case. That said, soft dollar metrics tend to only appeal to specific audiences. Presenting an improvement in candidate experience might win over your CHRO, but it is less likely to win over your CFO. Presenting an improvement in quota attainment for revenue-generating roles will win over both.
Putting It All Together
A complete business case has three components: Industry Comparisons, Potential Improvements in Soft Dollar Metrics, and Potential Improvements in Hard Dollar Metrics.
Industry comparisons generate urgency. They juxtapose your efforts with those of your talent competitors. While comparing your current state to industry benchmarks is not enough on its own to win budget, it opens the door for the buying conversation.
Soft dollar metrics appeal to principles and values. Candidate experience is a great example of this. It is difficult to directly tie improvements in candidate experience to increases in revenue. But if you believe your organization is the type that treats its candidates with respect, erasing the mismatch between your organization’s values and its current recruiting practices is an ROI in its own right. Keeping your audience – and their values – in mind when presenting soft dollar metrics is critical.
Hard dollar metrics appeal to logic and fiscal sensibility. Unlike soft dollar metrics, hard dollar metrics are accessible to everyone in the business. They are the strongest of the three components listed here.
Taken together, you have the makings of a robust business case that shows the value of an AI-driven solution on a number of different levels.