Will there be a coming shortage of AI savvy professionals in the next few years?
Most recent reports and studies from various global sources say “but”. The demand predictions vary in total as well as the type of AI skills that will be in most demand and will be the highest paying AI jobs and career tracks.
There is also much discussion regarding job displacement due to AI. It is generally forecasted that AI will replace the need for a significant percentage of today’s current workforce. But this is not just attributable to AI, any major innovative technology has shown to have a disruptive impact in the workforce currently performing repetitive, routine tasks in their jobs that now can be automated.
Hopefully, the new jobs created by AI will be more than those that are displaced by its continued application and utilization. And those displaced by AI-driven automation will be responsibly re-skilled to perform work made necessary to maintain this new AI-centric automation.
Another aspect that is not frequently discussed is that we will be entering a time of new AI-driven “products” replacing older technologies and the business functions they automate today.
Added to the fact that designing, engineering, and maintaining an AI-driven solution will create a lot of new AI workforce opportunities, the demand for institutional experts (i.e., professionals, long-tenured senior management, and “knowledge” workers) with deep domain knowledge of their industry, function and institution will not be less - but more.
This is because for an AI to be “engineered” to replace a suite of business automation software applications today, or to build an AI supported decision-making tool, industry trends, data management and governance, and business functional knowledge must be present to “reimagine” how they do business today with an innovative technology like AI.
A successful Enterprise AI team must be multi-disciplinary, and multi-competency to apply their respective areas of expertise and knowledge to assist their new AI colleagues with an understanding of how to engineer the new AI product to satisfy the business objectives and requirements for whatever use case is being developed. We see an “integrated” team model as a critical success factor as any organization begins its Enterprise AI Journey.
So, will the newly AI skilled resources that become available by the tens of thousands after massive re-skilling investments, outnumber the business and domain experts, and skilled knowledge workers required? Both groups of resources are necessary to “reimagine” business today as an integrated team with innovative AI capabilities as a key design focus.
In the short-term, it will not be possible to hire or engage AI resources who do not have industry knowledge or a business or domain understanding of the data they are trying to model into a representable data model input to an AI/ML engine that has a high probability of meeting use case objectives and requirements. A probable conclusion: Today’s data scientist and machine learning gurus are not necessarily, for example, financial services or healthcare domain experts.
This creates a risk factor to manage for any AI product to be engineered. It is difficult to predict how to mitigate and manage this risk, but here are some considerations for the leaders who are pioneering the execution of significant Enterprise AI programs:
1. Ensure that you have adequately planned for the type and number of AI resources that will need to be added to your organization to meet resourcing needs to achieve use case business objectives and requirements
2. When today’s business and decision-support systems are facing their “sunsetting”, and replacement systems will be AI-driven, then there is an opportunity to acquire a third-party AI product, or you may choose to build your own. Regardless of your build vs. buy decision, make sure to retain the “knowledge” inside your organization to help your AI vendor, or your internal AI team learn your business operations from your most experienced workers who possess an often-undocumented organizational asset – the years of business and institutional experience that resides in their heads (aka “tribal knowledge”)
3. A third and possibly less acknowledged AI need from the business is the experience, knowledge and culture established by an organization’s most senior long-term executives and management. Understand, capture, and represent in the machine learning model their principles of business to ensure that their experience is not lost when they retire, pass away or for whatever reason are no longer available to provide their thoughts and insights on a professional and personal level when the institution faces difficult and complex business situations and decisions.
There are additional considerations to be added to this initial list. Let’s discuss them.
One of the principal takeaways from this article is not just to gear up to hire or engage AI specialists but to ensure the continued retention of and support from the workforce you have today. This is a critical success criterion.
Who else but your current workforce at all levels of seniority is going to assist the AI data scientists, AI engineers, AI programmers and other AI skilled resources to learn and understand your current business? Who is going to test the delivered AI product’s responses, insights, information and recommendations for accuracy and reliability if not those who possess the accurate information, relevant experience, and institutional knowledge to fully confirm or determine the AI’s information is not correct?
Your collective project/program charter should state you all work as “one” to reimagine your institution’s future state conceptualizing an AI-driven organization to meet its strategic objectives, ensure its mission is being met, and supporting its: Long-term sustainability, relevance in its chosen markets leveraging its competitive advantages, while providing an ever-growing level of insight, knowledge, intelligence and recommendations to advise management decision-making every step of the way.
In summary, the experience, and many forms of collective institutional process knowledge (aka your organization’s “secret sauce”) are needed from your current senior management, middle management, experts, and knowledge workers. Capturing and properly modeling this experience is crucial to the success of any new AI product, solution, or application that every organization that will need to adopt AI into its daily operations will be facing in a truly short period of time.
We see a model of collaboration to successfully bring both sets of human capital together in a creative environment to deliver AI-driven products and solutions to meet competitive challenges, achieve operational excellence and exceed the expectations of consumers and business partners while being in solid compliance with regulatory and governmental agencies for highly regulated industries.
The bottom-line: No one has any better “crystal ball” to predict the future - but remember to think strategically yet execute your Enterprise AI program pragmatically.
If you would like to discuss our perspectives, our responses to the above questions for your organization, and our approach to Enterprise AI, please contact us using the form below or at [email protected].