AI in Workforce Development: Navigating Limitations & Recommendations for Service Providers
In the past few years, Artificial Intelligence (AI) has gone from a futuristic concept to a household term as it plays a larger and larger role in our daily lives. Nearly 50% of businesses currently use AI to manage big data, and 83% of companies see AI as a top priority for the future. Due to its flexibility of application and ability to scale to any size, AI has the power to revolutionize numerous industries, including workforce development.
As the CEO of a company on the front lines of software innovation, I’ve seen how AI-driven workforce development software can radically transform organizations, but I’ve also seen its limitations. It’s important to understand that AI is not a technological cure-all, and it’s not perfect. In the right setting, AI can provide more efficient and personalized training programs and support workers in their career progression. However, AI needs a huge, diverse data set to be useful — otherwise, it may produce inaccurate or biased information or recommendations. In this article, I’ll explore some of the more powerful applications of AI in workforce development as well as suggestions for navigating its limitations.
Predictive Analytics Can Help Avoid Future Labor Shortages
One of the significant advantages of using AI in workforce development is predictive analytics. Predictive analytics uses historical data to predict future outcomes. In workforce development, predictive analytics can identify which workers are most likely to need training, what type of training they need, and when they will need it. Moreover, AI algorithms can help identify which workers are most at risk of being displaced by automation and provide targeted training programs to help them transition to new job roles. This can help workforce development agencies allocate their resources more efficiently and ensure that workers receive the training they need when they need it. It also lessens the likelihood of future skilled labor shortages like the one we’re currently experiencing.
AI Automation
AI can automate certain repetitive or tedious aspects of workforce development. For instance, AI-powered chatbots can answer frequently asked questions and provide information about training opportunities. Chatbots and virtual assistants can also deliver real-time feedback and support as participants move through their program, helping them better understand the material and improve their learning outcomes. This can be particularly useful for individuals who may not have easy access to traditional forms of support, such as in-person training or mentorship. Using AI in this way can free up workforce development staff to focus on tasks that benefit from a human touch, such as developing new training programs or working one-on-one with clients.
Reskilling/Upskilling Through Customized Training Programs
AI can also help in reskilling and upskilling workers by identifying the skills gap and providing targeted training programs that align with the individual’s abilities, strengths, and weaknesses, helping them improve and progress in their careers. Based on this data, algorithms can recommend targeted training programs to help workers acquire the skills they need to progress. This can help workers stay updated with the latest technologies, improve their job prospects, and increase their earning potential.
Avoiding the Pitfalls of Artificial Intelligence
In all of these applications, a lack of high-quality data can severely limit AI’s effectiveness. To provide recommendations that accurately reflect the diversity of your clients, you need a large, longitudinal sample of representative data — something that most organizations don’t have access to or are still in the process of gathering. Additionally, if an AI algorithm is trained on historical data reflecting systemic bias, it may make biased recommendations for training and job opportunities. This can perpetuate bias and discrimination and result in certain groups of workers being left behind.
Complementing AI with a Rules-Based Engine
To combat the limitations of AI, I recommend workforce development agencies adopt software that uses a combination of rules-based workflows and AI algorithms while they continue to grow their dataset. A rules-based engine responds to triggers or conditions that are predefined in the system, so it can be built to reflect specific community needs and demographics. For example, if a client user indicates on an intake form that they are a veteran and require childcare services, a rules-based engine can be configured to recommend local services and resources specific to their needs. AI alone may not have access to enough local data to accurately process the request and make the same recommendation.
Harnessing the Power of AI
Overall, AI has the potential to revolutionize workforce development by providing more efficient and personalized training programs and supporting workers in their career progression. However, understanding its limitations and best-practice applications is critical to ensuring equitable outcomes for clients. By leveraging the potential of AI with other tools, we can create a more efficient, effective, and equitable workforce development system.
