According to IBM’s Global AI Adoption Index, organizations are increasingly looking to employ artificial intelligence (AI) for improved efficiency and decision making. However, to successfully integrate the technology across business functions, corporations need to ensure that today’s workforce is equipped with the requisite AI skills. This is only possible through concerted corporate training efforts that focus on making employees well-versed in the technology’s capabilities as well as its limitations.

Let’s take a look at some of the steps you can take to help corporate AI training programs deliver immediate value.

Step 1: Assess Internal AI Training Needs

Before deploying a corporate AI training program, organizations need to take stock of the different business functions that stand to benefit from AI and how the technology will impact the growth of their business.

Today, many business functions can be made more efficient through AI implementation. For example, human resources (HR) professionals can leverage AI to identify strong candidates and screen resumes, while marketing teams can employ AI to identify new leads and to improve search engine optimization (SEO). Similarly, customer success teams can better and promptly engage with clients through AI-enabled chatbots.

Instead of implementing AI training for all departments, business leaders need to identify which function is most critical to their business growth and how much of an immediate value AI technology can add to their bottom line.

This allows businesses to prioritize training needs by departments and business functions, while mapping out existing skills gaps and opportunities within their organizations.

Step 2: Identify Types of AI Training

Once the skills assessment and specific internal talent needs are mapped out, corporations need to proceed to prioritize which levels of employees within their organization can benefit from AI training.

Not all employees within an organization will require the same type of AI training. For example, the executive team is the one that makes critical decisions around AI implementation, so their training might be focused on building an AI strategy. Therefore, their training needs to equip them with knowledge around how to ascertain AI’s impact on everything from employees’ roles and barriers to adoption to talent development and how AI can help deliver cutting-edge solutions. They also need to learn how to define key performance indicators (KPIs) and benchmarks for AI implementation success. The training for this level also needs to help them visualize their company’s impact from an industry perspective.

For instance, the vision of the chief executive officer of a health care company might be drastically different from that of an insurance business. While the former may want to implement AI to automate patient visits or conduct virtual visits, the latter might seek AI’s assistance to minimize business risks and grow their client base. In both instances, the executive team needs to familiarize themselves with the path forward and challenges to anticipate as they institute an AI and data-driven company culture.

Similarly, mid-level managers can benefit from training that is focused on analytics, data and AI features that go into making groundbreaking products and product decisions. Since these managers play a pivotal role in communicating the executive vision to their teams and vice versa, they need to understand how AI and data work together to bolster operational efficiencies.

There are various AI tools available today that can help automate the building of reports and in forecasting or in making predictions based on past actions and performance. A sales manager, for example, often works with a lot of data to identify leads and with the help of AI can efficiently generate reports to better convey their work results and even challenges to senior leadership. This is just one use case of data-focused training for mid-level managers.

Technical teams and engineers, on the other hand, should be focused on core AI skills based on mathematics and coding. Since AI is an ever-evolving field, this group needs to be continually reskilled and upskilled.

Step 3: Employ Training Methodologies that Enforce a Continuous Learning Mindset

Once the types of training programs and the different levels of the AI programs are identified, organizations need to identify how to best deploy the training and in what format. Today, leaders have an option to choose from a number of methodologies — from hiring in-house senior AI training instructors to working with consultants or embracing online training options.

Online video training platforms can be used to engage employees across all levels. Similarly, organizations can deploy mixed models that offer both in-person training coupled with online courses that employees can partake in on their own time. A third and effective way to train employees is by sponsoring their participation in accredited university programs.

Regardless of which avenue the organizations pursue for the training, regular and timely assessments of employees are important to guide growth and alignment to goals. As a result, to ensure continued knowledge retention in AI, employees should be provided opportunities to exercise their skills on various projects.

Conclusion

AI is one of the core drivers of digital transformation, so building an AI-ready workforce is critical. The combination of the COVID-19 pandemic and The Great Resignation have caused companies to take stock of the demand for upskilling. 76% of organizations have already made AI and ML a more significant priority than other information technology (IT) solutions in 2021.

Nonetheless, business leaders must recognize that everyone in a company must be on the same page about the vision and the implementation goals of AI. Only then will they be equipped to take advantage of the technology and work synergistically with it.