Where Is Your Talent-Acquisition Department On the AI Maturity Scale?

Key takeaways:

• An AI maturity framework has multiple dimensions: capability, posture, and investment
• AI maturity in talent acquisition spans a range from haphazard use to embedded to transformational
• It is possible to move up the maturity levels by removing roadblocks to improving your business and your hiring through AI

How mature is your talent-acquisition department when it comes to artificial intelligence?

That’s a good question to ask as you develop plans to keep progressing on your use of AI in human resources.

Below, we take a look at different dimensions of AI maturity. Then we take a look at the different levels of AI maturity. Finally, we provide some suggestions for moving up the AI maturity scale.

What is AI maturity?

AI maturity is defined as a measure of “the degree to which organizations have mastered AI-related capabilities in the right combination to achieve high performance.” (Accenture)

There are different dimensions involved in examining the maturity of an organization, as follows.

Capability

This is a measure of “Can we do it?” It includes factors of the level of AI literacy; governance; and integration ability (are there connected data and systems?). AI literacy is the strongest “unlock” when it comes to maturity. In other words, the more people in an organization are knowledgeable about AI, the more likely they are to progress up the maturity scale.

Posture

This could be called “Will we do it?” It includes such factors as whether there is a strategy; whether there is an “owner” or ownership plan; and the company’s risk orientation — is the organization interested in experimenting, or does it tend to wait for others to adopt new technologies and ideas first?

Investment

A good way to sum this up: “Are we backing it?” It includes the amount of resources, such as time or money; the availability of people; and the scope. Certainly, a specific budget for AI is a big signal of backing.

Where Organizations Are Now With AI Maturity 

Here are some recent findings from SmartRecruiters’ research of companies in the U.S., EMEA, U.K, and APAC.

Exploration dominates:

most organizations are still in the “exploring” state.

Function-level differences:

Talent acquisition as well as compensation & benefits are further ahead in AI maturity. Learning & development and payroll are behind. 

Mid-market momentum:

Large enterprises are moving a little more slowly than medium-size organizations. 

Clarity accelerates:

Governance and EU/UK regulatory guardrails drive adoption. Sometimes security and compliance kick companies into gear faster than anything else.

Coalitions win:

Cross-functional ownership is a maturity accelerator. More siloed organizations are at an AI-maturity disadvantage. 

Two AI mindsets:

There appear to be two general categories of people and organizations as they adopt AI. There are automation chasers who are cutting costs, and there are decision-support builders whose goal is more about better insights and decision-making.

AI maturity levels explained: what level is your department?

There are different methods of measuring AI maturity. Often, it’s done on a four- or five-level scale. For example, a Forbes article suggests five stages learning, experimenting, standardizing, innovating, and leading. Similar, Gartner uses a five-level maturity scale: awareness, active, operational, systemic, and transformational.

Let’s take a look at how an AI assessment maturity framework might play out in a talent-acquisition department.

Level 1 – least mature 

These are organizations that are not using artificial intelligence. It doesn’t play a role in the company’s talent strategy or hiring. There is no chatbot, generative AI use, and essentially no automation. Messages are personalized and sent manually. Resumes are screened one by one, by hand. Decision-making is not guided by AI insights, either because of silos, poor data, poor integration, or other challenges.

Level 2 – some usage

People are dabbling in artificial intelligence. For example, a recruiter might use ChatGPT to draft a message to a job candidate or language for the career site. Some have used AI in other ways, such as to build job descriptions. There is some limited technology use and/or automation, such as a chatbot or interview scheduling.

Level 3 – common usage

More than just random or uneven usage of AI, these organizations may have an AI strategy or they are on an AI journey. AI is used regularly, such as the use of labor-market data that can match people to jobs based on their skills.

Level 4 – embedded

AI may not be used in all of the hiring process, but it is embedded into significant parts of it. AI is used for key parts of the employee lifecycle, such as sourcing, screening, or matching. Artificial intelligence might provide insights into which recruiters or managers have scheduled too many interviews one day and thus may not be available. It might help the company improve diversity when writing job descriptions or choosing advertising venues. The organization has embraced AI and is using it to improve quality, speed, and cost per hire.

Level 5 – transformational impact 

At these organizations, AI is a critical part of the hiring process. The talent-acquisition department thinks “AI first”: how can automation and AI helps us improve a process and free us up for less manual work? These companies use an end-to-end operating system where the workflow is supercharged with AI. An AI-driven, natural language, conversational chat, not an outdated chatbot relying on keywords, improves hiring speed and the candidate experience. AI is used to screen and match people to jobs, with humans making the decisions. An AI companion helps automate and improve parts of hiring. 

Gartner says that at this level, “organizations are using AI technologies to transform their business model and create new revenue streams. They are recognized as leaders in their industry and are driving innovation and disruption. The focus is on creating new business models, products, and services, and building a culture of innovation and experimentation to drive continued growth and success.”

Moving up the AI maturity level 

Let’s look at those levels of maturity and how they interact with the dimensions of AI maturity mentioned at the outset. Here are some questions to ask yourself as you assess AI maturity as a talent-acquisition organization.

• Do we have silos that are making it hard to improve? Are people from different functions working together or at odds?
• If data integration is a roadblock in our company, how can we improve it?
• Are our AI adoption challenges affecting our ability to be in compliance? For example, is our approach to hiring drawing on an unnecessarily limited pool of job candidates?
• Do we have the resources we need? How can we better make the case for a higher level of AI maturity?
• Are we just focused on automation and costs, and not what insights we can glean that will make us better at decision-making?
• How might AI improve each aspect of our hiring process?
• When we begin thinking about improving a process such as onboarding or sourcing, do we begin with an AI-first mindset?

More on AI capability

SmartRecruiters delivers an AI-powered hiring platform that automates and optimizes the entire talent acquisition process. We have been working with more than 4,000 companies, including Amazon, Visa, and McDonald’s, and are learning something new every day about how to improve AI maturity. Let us know if you would like to talk more about our customers and about AI maturity and adoption.