Artificial intelligence (AI) has big implications for the workforce of tomorrow. While some employees may fear the technology will replace them, it can help them expand their careers in some cases. Optimized training and development is an increasingly popular application, especially as roles shift amid broader AI adoption.

Savvy businesses can use AI to upskill their existing workforce to meet such changing demands, but doing so requires careful planning. Here are some crucial steps to follow.

1. Identify Relevant Use Cases

Tech advancement will drive job growth in over half of all companies, but these positions require different skills than the ones that are going away. The first step in using AI for training and development is to recognize where it’s most valuable. Ideal use cases will vary between brands, but they’re important to determine because many AI applications miss the mark.

Failure to align AI investments with actual needs or goals will hold back positive ROIs, which is part of why up to 80% of AI projects fail. Organizations can start by looking at common use cases in training and development and comparing these to their own needs. Here are three areas where businesses can typically benefit from AI adoption.

Recognizing Latent Talent

One option is to use AI to identify workers who may be ideal candidates for new roles. While 59% of millennial staff look for growth opportunities in their jobs, where and how to grow isn’t always evident. Similarly, enterprises can easily overlook people with optimal soft skills for an upcoming position. AI can detect what humans may miss and recommend employees for specific career development opportunities.

Personalizing Course Materials

Once someone begins their new training, AI can analyze their performance to adapt the coursework to their needs. Personalization like this often leads to improved outcomes, and AI is adept at tailoring content to specific audiences.

Streamlining Administrative Tasks

AI has less glamorous but still effective applications in staff development, too. HR departments can use it to automate the scheduling and documentation involved in enrolling workers in courses or transitioning them to new positions. By automating these repetitive tasks, firms free their employees to focus on other, more pressing tasks without sacrificing upskilling opportunities.

2. Compare Available AI Tools

Once an organization knows where to use AI in training and development, it can search for a platform to meet those goals. There are two primary choices here: choosing an off-the-shelf AI tool or building a proprietary solution.

The market for AI training tools has significantly expanded in recent years, so businesses can often meet their needs with ready-made options. These will be easier and cheaper to implement, too, which is ideal for the 63% of decision-makers who say they lack AI and machine learning skills.

Those with enough AI experience or particularly niche goals for the technology in their upskilling may benefit more from building their own solution. While this approach will entail additional costs and complexity, success could result in an AI system that better meets corporate needs.

3. Practice Good Data Governance

Regardless of which direction an enterprise takes, the AI model will require extensive data to work effectively. Consequently, leaders should consider their data governance to promote better outcomes and minimize risks.

Ensuring a model has enough relevant information to be accurate is critical. It may be necessary to supply it with synthetic details that don’t reflect real-world information but perform the same in machine-learning contexts. Because it’s AI-generated itself, synthetic data is also easier to acquire than real-world alternatives.

Data governance is also a matter of cybersecurity and privacy. Organizations must remove as much personally identifiable information (PII) as possible to prevent privacy breaches and continuously monitor their AI databases to catch potential breaches. In some areas, they may need to comply with legal regulations, too, so it’s important to review any applicable laws.

4. Watch for Bias and Errors

As companies train and deploy their AI solution, they should monitor it closely. Bias in training information can cause AI to amplify human prejudices, which is particularly damaging in applications involving workers. Similarly, machine learning models can hallucinate or produce other errors, limiting their ROI if businesses aren’t careful.

Regularly reviewing the model and its decision-making processes during training can help teams catch bias and other errors early. They can then reprogram it as necessary to avoid such mistakes in practice.

Review and adjustment will likely be necessary after deployment, too. Consequently, brands should develop a formal schedule and process for analyzing the system’s performance and tweaking it if necessary.

5. Start Small and Respond to Feedback

Given this need for regular review and adaptation, businesses should take a conservative approach to their rollout of AI in training and development. Fine-tuning a generative AI model costs between $5 million and $6 million in upfront expenses alone on average. Consequently, achieving a positive ROI will be difficult if the project initially has too big a scope.

Firms should start by applying AI to a single process under their revamped training program. Beyond that, they should test it with a small group of employees before they’re ready to expand it to all career development workflows. That way, they can optimize their approach before investing too much in the technology.

Teams should gather as much feedback as possible during the initial rollout. Understanding user experiences is key to making the AI system as practical and effective as possible in the future.

AI Can Supercharge Employee Training but Requires Care

AI has significant potential in ongoing staff development. However, capitalizing on this promise can be more challenging than it seems at first. Businesses must take care to avoid common mistakes and ensure a successful implementation.

These five steps will help any company revamp its training workflows with AI. As more follow such strategies, this technology will reshape the future of career development.