AI for HR: Shop for Outcomes, Not for Features

1. Define Your Goals and Needs

Don’t get bogged down by treating symptoms – delve deeper to uncover the root causes. Instead of aiming for a generic “improve efficiency” target, set specific, measurable, achievable, relevant, and time-bound (SMART) goals. For example, you might aim to:

• Reduce time-to-hire by 30% in 6 months;
• Increase employee retention rates by 15% in a year;
• Improve candidate satisfaction scores by 20% within 4 months of new system implementation.

These HR goals should seamlessly integrate with your broader organizational objectives, which will increase internal buy-in and project success of your new technology implementation.
 

 

2. Building a Diverse Team

Evaluating and delivering AI projects will be more effective with diverse teams that understand the interdependent disciplines that AI technologies incorporate, such as HR, data ethics, system and data engineering, and model development. Align on shared goals by establishing common objectives across disciplines.

Why is this crucial? Each team member brings a unique perspective, ensuring decisions are not just technologically sound, but also practical, ethical, and aligned with your organizational priorities. 
 

3. Assessing the Benefits and Risks of AI Deployment

Like any powerful tool or new strategy, AI comes with its own set of advantages and drawbacks. 

Benefits:

• Enhanced Efficiency: AI can automate repetitive tasks that bog down your HR and TA team, from job description writing to resume screening. This frees up valuable time to focus on more strategic initiatives.
• Improved Decision-Making: By leveraging data-driven insights, AI empowers HR professionals to make informed decisions regarding recruitment strategies, performance management, and employee retention initiatives.

Risks:

• Potential for Bias: AI algorithms are only as good as the data they’re trained on. Biased data can perpetuate discrimination in hiring or promotions, impacting diversity and fairness within your organization.
• Data Privacy Concerns: Mishandling sensitive employee data can lead to breaches, violate regulations, and erode employee trust. With new AI and data regulations being introduced all the time, the fines for irregularities are only bound to increase.

4. Conduct a data assessment before starting your procurement process

Data is currently the basis of the majority of AI-powered solutions. Before you start your procurement process, ensure that the following things are in order: 

• Availability of Relevant Data: Different AI solutions require different types of data. Make sure you have access to the necessary data (e.g., job descriptions, employee performance reviews, etc.) and that you can share it with the vendor securely.
• Data Governance Mechanisms: From the outset, establish clear procedures for data governance. This ensures responsible data collection, storage, and usage.
• Data Cleaning and Debiasing: You need to address any flaws or biases within your data before engaging a vendor, because what you train the algorithm on shouldn’t be based on a flawed (biased) premise.

This data assessment is a crucial step. Building your AI solution on a foundation of weak data is a recipe for potential project failure.

5. Focus on the Challenge, Not the Shiny Object

The world of AI can be alluring, with the latest trends and buzzwords. But avoid getting distracted by the bells and whistles or a “solution in search of a problem”. 

Instead, ensure that the technology solution you are purchasing truly addresses your challenge. The vendor’s solution should align with your organization’s specific goals and future needs, and can solve them effectively.

6. Understand the Technology

It’s essential to demystify the technology behind the curtain. Avoid getting locked into vendor-specific solutions that become black boxes. Instead, seek vendors who prioritize transparency and explainability.

Ensure that the AI’s algorithms are understandable, enabling you and your team to assess their capabilities and limitations. Collaborate with your vendor to understand the technical intricacies and how they align with your organizational needs. Consider the scalability and adaptability of the technology to meet your evolving needs and future-proof your project.

 

7. Navigating the Ethical and Technical AI Landscape

It’s crucial to address technical and ethical limitations of AI deployment during your vendor evaluation:

• Ensure data quality, system integration, scalability, and reliable accuracy; clarify maintenance and updates responsibilities.
• Mitigate bias, ensure decision transparency, comply with privacy laws, and secure employee consent for data use.
• Conduct trials to assess real-world performance, measure outcomes, and address unintended consequences.

8. Your Vendor Checklist

A strong AI vendor is more than just a technology provider; they are your partner in innovation. Here is your checklist for what your AI vendor should help you with: 

• A clear and firm governance framework.
• A written commitment to ethical AI development and usage.
• Perform regular audits
• Incorporate stakeholder feedback for continuous improvement
• Demonstrate adherence to data privacy laws (e.g., GDPR, CCPA) and provide certifications or audit reports.
• Help test and refine the AI system to reduce bias and promote fairness in decision-making.
• Provide robust mechanisms for data encryption, anonymization, and breach prevention.
• Ensure AI models are retrained and updated to maintain relevance and accuracy over time.

Conclusion

Embarking on an AI technology journey may seem daunting, but with careful planning and the right vendor, you can transform your HR operations. By following these best practices, you’ll be well-equipped to select an AI partner who aligns with your strategic goals and drives sustainable value.