Filling The AI Talent Gap
Businesses of the 21st century are facing a severe lack of AI talent. Tapping into the AI talent pool is difficult and requires a specific skill set separate from the traditional, technically-minded programmer or developer. While the fundamental skills of AI professionals revolve around a technical and analytical background, they also need to offer organizations very distinct traits on the business side:
- The ability to cross business boundaries and limitations.
- The insight to strategically understand how AI will augment business performance today and in the future.
- The ability to realistically understand and convey AI-insights to programmers and developers in a way that implements and builds better business solutions.
But don't let the needle-in-the-hay-stack talent description make you panic. The reality is AI technology is showing up in many forms. The majority of today’s AI and machine-learning developments are being released in an open-source framework, packaged in a way that mid-level computer science engineers can quickly learn and adopt into existing operations. There are also many vendors and third-party solutions that can address one of the many applications of AI for your business.
The challenge for AI comes down to implementing the technology across the enterprise, driving new research and disrupting the market with new techniques. The unchartered, yet-to-be-explored territory of AI is what makes the hunt for talent a challenge.
How exactly do you address the AI talent gap?
- Train executives well.
Integrating AI technologies into business will always fall flat without the support of senior, executive leadership. Start first by identifying executives willing to adopt a new mindset; those willing to intimately learn how to implement AI as part of the business’ strategic direction. When executives begin understanding the realistic possibilities of AI—based on the data available and the needs of the company—the true potential of the technology can begin to be unlocked.
- Understand the complexities of AI exploration and when it's time to look externally.
The most complex, and arguably most exciting, part of AI is the opportunity it offers into revolutionary developments. Yet, that is also where the challenge of AI lies. To be disruptors, to build and to break into the new avenues, leaders cannot rely solely on traditional machine-learning algorithms and pre-packaged solutions.
To build cutting-edge, AI technology, new talent will be needed. The talent you target must be equipped to both research and build an AI program that can scale. But as you recruit for AI talent, know that this is the stage where you'll need to compete against the Silicon Valley technology giants, if you hope to have any chance at finding and employing AI researchers.
- It's not always about chasing new talent.
To develop the research and framework of an AI practice, you'll certainly need to hire one or two data scientists that bring along their experience in implementing machine-learning techniques. But take pause before on-boarding too many data scientists; your own technical talent is likely your best pick for the job. With the right training materials, conferences, courses and resources, building the AI talent and techniques among your current developers is easier than you might think
Many of your software developers and engineers will be eager to learn and tackle new challenges, as they work alongside the guided support of data scientists. This approach gives you the chance to implement and scale technology across the enterprise, while also giving your employees new experiences in their career development journey.
- Understand when external resources are needed.
There are times when a solution is so complex—one that requires significant data wrangling, data mining, research, or software engineering—that AI implementations at scale cannot be done completely in-house. Executive support, developer training and new talent is important, but there are times when an outside organization is needed to get the job over the finish line. That's where startups, third-party consultants and software organizations will be integral to embedding AI into businesses over the next five years. View the outside vendor as a partner and added resource to your current operations. That relationship will help build solutions that scale, support the volume of work and help propel and embed AI into all parts of the business.
The keys to making AI work
The process of integrating AI is complex and will take time to get right. But if AI is the path you plan to travel, don't waiver against the fundamental aspects of an effective AI operation:
- Ensure executives are operating with a realistic, pragmatic point of view. Executives must demonstrate relentless initiative. These leaders are hard to find but they are essential to success.
- Trust and evaluate your current workforce talent and know when it is easier to invest in building and developing AI technologies internally, rather than chasing and competing for ‘hot’ talent in the external market.
- Recognize when there are too many priorities and understand the third-party and out-of-the-box solutions available to you.