Organizations are discovering how artificial intelligence and machine learning can transform their business. AI’s contribution to global GDP is expected to grow from $2 trillion in 2019 to $15 trillion in 2030 according to PwC. Every organization needs professionals to digest data and translate it into action, but the labor market is woefully unprepared to meet the exponential growth in demand. How do we start the AI revolution without any revolutionaries?

Sometimes the answer lies within. Upskilling your team in machine learning and AI can help you organically build a future-ready workforce with the necessary skills to face upcoming challenges. Reskilling doesn’t just happen. Leaders have to commit to training their people and fostering a culture of learning that never ends. Let’s look at a few ways of putting upskilling into practice.

Where Are You on Your AI Journey?

Before we begin, we have to determine what role AI plays in your organization. Ask yourself a few questions:

The answers to these questions will serve as the foundation of your upskilling plans. If you haven’t jumped into the world of AI and machine learning, you may need to make a few key hires or bring on a strategic partner to help you get the effort off the ground. This initial step can put knowledgeable people in place to support your upskilling effort.

Ground Learning in the Practical

Next, you need to decide how you are going to implement this training initiative. Traditional classroom learning and bootcamps certainly work for some, but others find the strict scheduling too rigid to fit the demands of a busy workplace. Virtual and on-demand learning can hold a lot of appeal, especially self-paced programs geared towards the busy professional. It also helps when the team studies together, allowing them to lean on one another and learn collaboratively.

Perhaps most importantly, learning needs to be tied to the practical. Your workers should be able to immediately put theory into practice, working on AI and data science projects within the organization. Grounding knowledge in their day to day, known as experiential learning, has huge benefits. Students who learned this way were found to have up to 90% higher retention versus traditional learning.

Embrace Continuous Learning

The culture of your organization may need a fundamental shift. Upskilling workers in AI isn’t a one-and-done activity. This is a journey in learning that never stops. Organizations must prioritize learning if they want to keep pace with the relentless demands of this ever-changing technology landscape. This means carving out time for employees to not only learn AI but continually sharpen their skills based on the latest advancements. Without that commitment, business needs will always trump training, and this essential activity will forever be neglected. Executives have to support the training effort for it to be successful.

Don’t Forget About Compensation

We need to incentivize AI learning to get your team fully engaged in the process. You should establish training milestones for the team to reach and reward them with financial bonuses each time those milestones are met. Further incentives can be applied when the business meets tangible goals around its AI projects. This helps your team have ownership in the project’s success.

Don’t stop there. Once you’ve reskilled your workforce, you need to reevaluate their compensation and benefits to ensure they are competitive with the market. There is nothing worse than putting considerable time and effort into training your people only to have your competitor snatch them up because you failed to pay them what they were worth.

The World Economic Forum expects AI to displace 75 million jobs while creating 133 million new jobs in the process. The labor market can’t absorb this seismic shift. Employers must be proactive in training their people with the skills necessary to succeed.

When done correctly, reskilling employees, through continuous learning programs, can be a fantastic tool for future-proofing your workforce. It also aids in retaining and attracting new talent. Upskilling shouldn’t be limited to data professionals. Software engineers, business analysts and business leaders are just a few roles who will need to embrace this change through learning

Reskilling takes planning, investment and follow through. Management must recognize the value of upskilling its workers and put their weight behind this effort. Dollars and time have to be allocated to keep training from becoming another casualty of the endless demands of the business. It takes work and sacrifice, but upskilling can help you close the gap in your need for talented AI and machine learning professionals.