Editorial
Generative AI (GAI) has significantly influenced and impacted human resources and learning and development functions. We have already seen the use of AI-based technologies in coaching, service delivery, content curation, screening and interviewing with significant improvements in quality and efficiency.
Many HR and L&D professionals are tentative when considering AI technologies for their organization. The challenges related to privacy, intellectual property and lack of understanding on how and where best to use these technologies act as barriers to adoption.
This article proposes five guidelines for HR and Learning and Development professionals to follow to drive responsible AI adoption.
5 Guidelines for Responsible AI Adoption
Guideline 1: Be Clear on the Objective of AI
When adopting AI solutions, it is critical to understand what the organization hopes to achieve through the adoption of AI. Mass-scale adoption of “AI for AI’s sake” is irresponsible and can have many negative consequences. For impactful adoption, the organization needs to identify what the goal and purpose of what this specific AI technology needs to achieve and the metrics that will highlight successful adoption.
The following questions can act as a guiding frame for HR and L&D to get started:
- What is different when AI is adopted?
- What benefits will we gain from this adoption?
- What limitations will we face?
These questions can form the basis for building a more robust business case for adopting AI within a specific process, practice or experience.
Related Article: AI Is Transforming Corporate Learning. Here's How to Evolve Alongside
Guideline 2: Find Partners and Vendors That Share Your Values
Given the explosion of vendors in the AI market, finding a partner with the same shared values can be daunting. Many vendors use AI as a marketing or sales technique, but can’t clearly articulate their stance on ethical issues within AI. As the field evolves, we will face ethical dilemmas, and it is essential to have a transparent partner that admits what they don't know and can align with you on confidentiality, data privacy, protection and bias concerns as part of the adoption.
Below are some critical questions to ask potential partners:
- What safeguards are built in to assess the effectiveness of the AI?
- What is your view on privacy and intellectual property protection?
- What data do you use to train your AI models?
- Are these sources credible and used with permission?
- What data security assurance can the vendor offer?
Keeping these questions in mind can help you choose trusted partners that align with your AI goals.
Guideline 3: Pace Your AI Adoption and Start Within an Environment That Can Be Controlled
To responsibly drive AI adoption, it is important to first experiment with low-risk opportunities that can easily be monitored and controlled. An iterative approach is valuable for improving quality and allowing for safe experimentation.
- Critical questions to ask include the following:
- Do we know all the sources from which the AI is drawing data?
- Are the models providing us with the expected outcomes?
- Do we know how the AI learns and what additional sources it needs to ingest to offer quality answers?
- Can we identify where the AI is hallucinating, or not giving accurate answers?
Creating a feedback loop to collect insights and lessons learned from initial AI deployments to inform future adoption strategies is also crucial. Once AI reaches maturity, you can optimize its use and utilize a snowball effect to scale impact by adopting more use cases over time.
Guideline 4: Be Transparent With Your Employees Regarding the Use of AI
As your adoption of AI increases internally, be transparent with your employees on why, how and where AI implementations occur. This transparency builds trust in the adoption process and allows the opportunity to manage the change with employees. Given the current narratives about AI replacing jobs, it is important to help employees understand why you are adopting AI — most often, it is not about replacing jobs but rather about complimenting human effort and opening up new opportunities.
Provide ongoing training and educational resources to employees to enhance their understanding of AI technologies and their implications. Establish channels for employees to voice concerns and provide feedback on AI implementations, fostering a culture of openness and collaboration.
Related Article: Reduce Uncertainty to Drive AI Adoption
Guideline 5: Implement a Guiding AI Committee to Oversee Governance
The last guideline relates to establishing an AI risk committee to oversee the responsible adoption of AI across the organization. The goal of this committee is not to act as a barrier to adoption but rather to allow for knowledge sharing of AI adoption between functions, bring together different perspectives from representatives such as HR, IT, Risk and Operations, and ensure consistency in how AI adoption is approached.
A first priority of this committee should be to develop guidelines and procedures for evaluating and approving AI initiatives to ensure alignment with organizational objectives and ethical standards.
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Wrapping up
GAI is opening up new opportunities for HR and L&D professionals. To ensure responsible adoption, HR and L&D professionals need to follow the guidelines highlighted in this article as a frame for responsibly benefitting from AI in the future. By doing so, HR and L&D will be well-positioned to capitalize on the opportunities to be leveraged by AI adoption.
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