HR departments have struggled to transition from being transactional and compliance-focused to being a true strategic partner to the business.
In an attempt to be a strategic partner, there has been a significant increase in technology investment within HR since 2022. Although 80% of businesses use some type of HR software, 36% of HR professionals say the technology isn’t adequate and either doesn’t meet basic requirements or doesn’t include the functionality needed to streamline processes, enhance decision-making, and improve overall efficiency the way artificial intelligence (AI), machine learning (ML), and automation can.
Why use AI in HR?
AI has the potential to drive HR's investment transformation, providing a unique opportunity for HR leaders to shift from being transactional to strategic partners with the business. AI has the potential to transform HR in five key areas:
Reporting and analytics. Analyze large volumes of people data and identify patterns, trends, or insights to support real-time decision-making.
Solving problems. Analyze and solve complex people challenges through programmed algorithms or learned scenarios.
Automation. Free up resources to perform repetitive and transactional HR tasks with fewer errors, allowing more focus on strategic HR programs.
Predictive planning. Accelerate forecasting and scenario planning by analyzing past, current, and predicted trends to allow HR to be more proactive and prescriptive in meeting future business needs.
Experience. Deliver a customized experience based on individual user data, preferences, or behaviors — resulting in personalized recognition, content, and actionable recommendations.
What are the primary types of AI used by HR?
The AI landscape is complex. There are many types, variations, and use cases where AI can be implemented. Three stand out for HR use.
Automation has been used by HR departments for several years to create efficiency when performing HR transactions. One of the first use cases was streamlining candidate screening.
Today, automation has taken on a much more complex role and is used across the HR life cycle to support management, tracking, and execution of various workflows, including the following:
Generative AI is a tool that mimics human interaction. An example of this is a virtual assistant or chatbot. Based on information loaded and learned it can support basic employee inquiries, such as employee guidelines for a leave of absence, or how many vacation days an employee has available.
Generative AI can also be used in a similar manner for candidates. It can be used to create job descriptions and help with other HR tasks, such as:
Candidate screening and interviewing
Employee feedback and analysis
Drafting inclusive language to remove bias
Predictive analytics
Personalized communication and assignment of tasks
ML has become a critical application when helping organizations assess, analyze, and predict various people scenarios in organization. The use of ML algorithms provides HR and businesses the ability to evaluate large data sets and information, providing insights to understand the overall workforce.
These algorithms help organizations understand their level of engagement, sentiment, job satisfaction, performance, talent, skill-based gaps, and more. This information allows organizations to proactively manage and mitigate risks to achieve the business outcomes.
Challenges with AI
The use of AI in HR or other areas of the business hasn’t come without debate or question. How much can we empower an application without creating organizational risk?
Although the tools we use can be powerful, they’re only as good as the information they receive or learn, and computers lack the capability of being empathetic, thoughtful, or meaningful. They simply disseminate information based on their programming. Below are three key risks that have surfaced when considering the use of AI.
Bias. A key reason organizations leverage AI tools is to reduce bias in content and communications. However, there have been instances in which AI has missed the mark and potentially perpetuated bias rather than remove it.
Data. Organizations rely on these tools and applications for accuracy and elimination of human error. However, there are examples of data not being interpreted in the right way, resulting in incorrect information or analysis.
Privacy. AI doesn’t comprehend data access and privacy; it requires instructions for proper use and protocols of data management. There are examples where personal or company data has been released or accessed through the wrong channels.
Steps to successfully implement AI
Follow these six steps for a smoother AI implementation process.
Create a strategic plan
Creating an AI strategy is critical when making the decision to leverage these types of technology applications. Whether this strategy is a stand-alone or embedded within your overall Human Resource Information Systems (HRIS) or organization’s digital strategy, a strategic AI plan should clearly define your:
Purpose
Goals
Objectives
Key performance indicators (KPIs)
Use cases
User and stakeholder expectations
Establish governance policies
Establish governance policies and procedures to ensure the proper use of AI tools. This includes data management, privacy, and ethical considerations.
A well-defined governance plan allows your organization to establish clear lines of accountability for the use of AI tools, including identifying the roles and responsibilities and maintaining compliance with legal and ethical standards.
Develop policies and procedures
Develop organizational policies and rules for generative AI systems. An SHRM report indicated 78% of HR functions don’t have organizational policies or rules in place for Generative AI systems. By developing strong policies and procedures, organizations can ascertain they are used responsibly and ethically, while increasing their benefits and reducing risks to their organization and employees.
Identify impacted people and processes
Identify the people and processes that will be impacted by the implementation of AI tools. Identify the roles and responsibilities of stakeholders and ensure they have the necessary skills and training to effectively use AI tools.
Select and implement tools
Carefully select and implement AI tools that align with your organization’s strategic goals and governance policies. This includes evaluating vendors, assessing data quality and security, testing the tools, and evaluating costs and scalability. AI is growing and changing rapidly, so selecting tools that can scale with your organization is an important aspect of your selection and implementation process.
Manage the transition
Implementing a change management process is crucial for transitioning to AI tools. The change management process involves setting expectations, communicating benefits, addressing concerns, and providing training and support to your organization’s stakeholders.
By doing this, organizations can reduce resistance and fear of the tools. They can also introduce governance policies, procedures, and training programs to increase their return on investment and adoption rates across their AI investments.
