Essentially, in the wake of mass AI adoption, the race for scarce AI talent will accelerate. Putting the right amount of resources into retraining will most likely differentiate leaders from the rest in this race. Here are the steps companies should consider when preparing for AI transformation in terms of workforce adaptation.
- Plan for the long term but set short-term objectives
This will help you understand how specialized your AI needs are. Given that AI adoption is a continuous process, your AI talent demands will vary over time. By having a clear implementation roadmap, you can estimate how many workers you need to be retrained and how many you’ll need to attract from outside.
- Evaluate your workforce’s skillsets
Basically, you need to find out who can be upskilled or retrained among your employees. Undeniably, some projects require building sophisticated AI models from the ground up, which calls for hiring competent AI and data engineers. For other projects, helping your business users learn how to utilize pre-built AI tools can be sufficient.
- Work out a change management strategy
Given that AI is likely to be the most impactful technology this generation of workers will ever experience, their reluctance to change is not surprising. This is why it’s important to involve business leaders and opinion makers as early as possible.
The reasons for AI integration should be transparently stated and discussed with exactly those people who will be the most affected. Leaders should highlight career development opportunities and provide a clear vision for assisting displaced employees if that’s the case. Moreover, AI usually implies more collaboration between different departments. By encouraging teamwork early on, it’s possible to maximize the cross-functional chemistry of different departments and facilitate their AI onboarding.
- Involve end users into AI development
Companies need to do their best to create intuitive and user-friendly tools while also demonstrating the reasoning logic behind AI decisions. This is especially important for such sensitive industries as healthcare and banking, where practitioners need to rest assured that machine learning challenges are resolved in the process. Instilling confidence comes down not only to employees’ emotional reassurance but to technically proving that tools they are going to use can be trusted.
- Educate beyond upskilling
Let’s take healthcare as an example. As life expectancy is increasing, healthcare has to deal with more and more patients having complex chronic problems that require a proactive approach. With a growing number of AI applications proving helpful in this regard, we will need more practitioners being knowledgeable about using them. Basically, by 2030 an average clinician will need to be well-versed not only in their standard medical areas but also in AI and machine learning. This calls for a complete rethinking of healthcare education, where areas like medicine and IT intersect. Creating centers of excellence or comprehensive training programs focused on AI-centric education should become a new standard in the next decade.