AI is a Journey, Not a Product: How Kuehne+Nagel Leverages AI in the Employee Experience

If you’ve engaged in a conversation about AI technology lately, you might notice an abundance of clichés: You get out what you put in. You reap what you sow. It’s a journey.

AI-powered technology often is described in these terms for good reason. Rather than functioning as plug-and-play products, these tools require ongoing human attention and adjustment to achieve the type of high-impact results that attract organizations to the technology in the first place. Think of the regular maintenance a car needs for optimal performance – AI systems need tinkering and tuning as well.

For Sandra Aasma, Global HR Systems Expert at Kuehne+Nagel, implementing an AI-driven approach to internal employee mobility has definitely lived up to the “it’s a journey” adage. During our virtual event, AI & The Evolved Recruiter, Aasma spoke with Phenom’s John Deal, Director of Product Management, about how Kuehne+Nagel’s approach to a robust internal talent program is evolving with AI technology.

Get the highlights right here, including Aasma’s insights on program objectives, crucial steps in their journey, and key success metrics.

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AI-Assisted Talent Mobility: Objectives and Vision

Kuehne+Nagel, a logistics company with 82,000 employees worldwide, launched an AI-driven internal talent mobility platform with the goal of creating better career pathways and learning content for employees.

“We want to show employees that they own their career,” Aasma says, a vision supported by AI’s potential to empower Kuehne+Nagel employees to self-direct their career rather than wait for their line manager to show them the next step.

As a member of both the HR and technology teams, Aasma oversees digital recruiting. By integrating AI into their HR technology systems, the company hoped to improve the employee experience around internal mobility in two major ways.

The first was by delivering meaningful career content to employees in a more streamlined fashion. Second, they wanted to transform their internal careers page, which previously functioned as a standard job search page.

Today, it's an AI-driven internal mobility platform capable of providing personalized job recommendations and education to help employees understand how their capabilities and skills can elevate their career path at the company.

Key Steps for a More Effective AI-Powered Internal Mobility Program

AI systems depend on context, human interaction, and learning. This is the reason implementing AI isn’t a “flip-the-switch” prospect but an ongoing journey. Aasma shared that two key elements she and her team found at the outset are crucial to helping the system continuously improve performance.

1. The employee profile is the key.

AI depends on data inputs to “learn” trends and preferences. In the context of training the system to provide relevant career path information and best-fit job recommendations, building a body of employee data is crucial. Kuehne+Nagel is currently educating employees on inputting data on skills, preferences, job titles, and previous work history to improve AI-assisted job matching.

2. Feedback is crucial to training AI.

In the early stages of the launch, initial employee feedback suggested that job recommendations often did not match their aspirations and goals.

The challenge, Aasma notes, is that employees’ aspirations and goals don’t always match the information included in their profile. The lesson here is that it isn’t enough to simply feed the system data – manual feedback is needed to train the system on how to provide recommendations better tailored to employees’ career goals. For example, employees need to engage with the system and “tell” it when a job isn’t a good match to improve future results.

The Role of Internal Recruiters in Leveraging AI Technology

To help recruiters use the system effectively, Aasma’s team has undertaken both systems-related and process-related initiatives in tandem with the AI integration.

On the systems side, they’ve plugged data from employee profiles as well as core HR systems into the CRM to enable better decision-making for recruiters. With growing bodies of data, the system can generate better job matches.

On the process side, they’ve launched an internal sourcing concept focused on encouraging recruiters to search the internal market for candidates first. This goes both ways: Her team is working on promoting awareness among employees to apply to jobs through the internal mobility platform.

Educating hiring managers on this concept is an important step in the journey, Aasma says. The challenge lies in helping managers understand that it’s beneficial for both the employee and the organization to propel top performers up the internal career ladder. (Which, of course, means coming to terms with letting a star employee move on from the team.)

Measuring Success: Metrics to Guide the AI Journey

Aasma is tracking the following benchmarks and metrics to help assess whether the program’s chief goal – to increase the internal hire rate – is being met:

• The number of internal hires versus external hires
• Internal application numbers
  

Another aspect she monitors closely is the quality and relevance of content the system is generating. Her team is tracking employee engagement with the platform and gathering feedback in an effort to constantly make content more relevant and engaging.

As for where Kuehne+Nagel stands in the journey toward optimizing AI for internal talent mobility? Aasma is excited and optimistic.

Not only is her team making progress toward providing an efficient, engaging experience, they’re looking ahead at additional benefits, including helping to reduce bias in recruiter decisions.

“We’re currently doing things that four years ago we were just dreaming about,” she says, remarking on the speed at which new AI-driven opportunities are unfolding.

Let the journey continue!

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