AI Shows Value and Gains Traction in HR

Seventeen percent of organizations use AI-based solutions in their HR function and another 30% will do so 2022, according to the Gartner 2019 Artificial Intelligence Survey. HR leaders cite cost savings, more accurate data-based decision making and improved employee experience as the top reasons to deploy AI. 

Two-thirds of early AI adopters have one to three active AI projects, with plans to double the number of projects by the end of 2022. Many use cases already show proven results, with improvements reported as significant by: 

• 62% of those that have deployed AI to improve data-based decision making
• 57% of those wanting to improve employee experience
• 56% of those trying to further automate repetitive or manual tasks
• 51% of those hoping to capture cost savings

“The levels of improvement being reported by early-adopter organizations are remarkable, especially given that most use cases in HR are currently supported by ‘narrow’ AI solutions,” said Seyda Berger-Böcker, Director Analyst, Gartner.

AI-based solutions can drive faster, easy-to-use HR services and help HR functions develop new personalization strategies to engage the technology-enabled workforce and improve employee performance. Organizations that embrace a more personalized and consumer-centric approach to employee experience increase the average employee’s performance by 17%.

Every HR process is an opportunity for AI. However, most organizations are focusing their AI efforts in three areas: HR operations (40% of organizations), talent acquisition (38% and employee engagement monitoring (38%).

As AI continues to improve within the HR space, investments will increase. The Gartner survey found that 47% of HR leaders will increase their investments and 51% will maintain their investments.

HR’s top challenges in adopting AI

More than one-third of the HR leaders Gartner surveyed report the same three key challenges when deploying AI:

1. Funding AI initiatives. Quantifying the benefits of AI is a major challenge for HR leaders. Some benefits — employee productivity or time saved — are easily measured. Others, like the impact on the employee or candidate experience, are more difficult. Address AI funding challenges and risk aversion proactively: Build awareness and develop clearly defined use cases that link to business priorities and HR-related challenges. Make the case for AI investments by prioritizing AI projects that help your function address critical challenges, such as improving data-based decision making, accelerating the employee experience or driving process efficiency.
2. Security and privacy concerns. AI collects and analyzes huge amounts of structured and unstructured data, creating ethical concerns about its use and trustworthiness. As AI-based solutions move deeper into HR processes, be able to justify how AI algorithms arrive at their decisions — making sure the inputs to AI aren’t biased — and how data is used to avoid brand and reputation risk. Establish robust and transparent data collection practices and ensure diversity in terms of data selection and management. 
3. The complexity of integrating AI into existing in-house infrastructures. This is a top challenge, because current AI solutions for HR are often specialized, narrow and singularly focused. Evaluate customizable and commercial off-the-shelf AI applications before deciding to build solutions from scratch. Keep the option of replacing customized solutions with off-the-shelf components once they become available.

AI success depends on collaboration

Of the surveyed organizations that report successful AI implementation, 41% highlight a close collaboration between IT and HR as one of the top three reasons for their success. Use these trusted relationships to evaluate the technology capabilities of potential vendors and possible implementation difficulties and complexities, and to prioritize AI use cases based on feasibility.

Having data of sufficient volume and quality is critical for successful AI implementations (and cited as key by 35% of survey respondents). Quality data is the foundational component of AI-based solutions and a prerequisite for ensuring the accuracy of AI algorithms.

A well-articulated strategy is also key to success (cited by 32%), and does three important things:

• Establishes clear links between AI, business priorities and HR-related challenges
• Provides a robust plan for improving data collection practices
• Outlines which use cases will benefit most from this new technology to build trust in AI over time

Ultimately, successful AI implementation requires a shared ambition and vision for what this technology means to HR and the organization.

AI solutions are evolving and becoming better every day, and companies are continuing to explore possible AI deployments across a large swatch of HR activities, including workforce planning (52% of organizations), learning and development (51% of organizations), skills management (48% of organizations), and performance management (44% of organizations).