After the headline making success of Artificial Intelligence systems, AI is making headway in the field of business applications. In a recent survey carried out by the market research specialist Vanson Bourne on behalf of Teradata, 80% of the respondents, senior decision makers from around the globe, testified that their respective enterprises are investing strongly in the development and rollout of AI systems.
With this level of commitment, early signs of ROI potential are beginning to show. As Forbes reports:
“Amazon has achieved impressive results from its $775 million acquisition of Kiva, a robotics company that automates picking and packing according to the McKinsey study. “Click to ship” cycle time, which ranged from 60 to 75 minutes with humans, fell to 15 minutes with Kiva, while inventory capacity increased by 50%. Operating costs fell an estimated 20%, giving a return of close to 40% on the original investment.
Netflix has also achieved impressive results from the algorithm it uses to personalize recommendations to its 100 million subscribers worldwide. Netflix found that customers, on average, give up 90 seconds after searching for a movie. By improving search results, Netflix projects that they have avoided canceled subscriptions that would reduce its revenue by $1B annually.”
But there is still a significant roadblock in the way of a more broad adoption of AI in regular business practices: The lack of talent! According to some estimates, only around 10,000 specialists exist around the globe, who are specialized enough to be able to contribute to R&D efforts in this field. This necessarily leads to fierce competition on the side of enterprises for this limited pool of talent. Most organizations are trying to counteract the shortage of skilled employees with competitive salaries. This has resulted in the average salary in this field being $185,986, even when 54% of the specialists have only a Bachelor’s degree, according to Paysa.) ! Some are advocating for a salary cap – just like in top sports – to counteract the rise of wages.
But even with this level of commitment, the respondents of the Teradata survey report that only 28% of enterprises have enough AI talent in-house to build and deploy solutions. The majority of them have to rely on external vendors for the rollout of AI solutions. However, even dealing with vendors requires qualified personnel on the organization’s side, and not just at the C level. Therefore the problem still very much remains. When taking a deeper look at the problem, a rather curious finding can be made: Amidst this huge talent shortage, 11.7% of computer science graduates are unemployed in the UK according to reports. How can this possibly be? The crux of the problem seems to lay in the lack of very specific skills. A survey shows that even data scientists lack the necessary skills in AI.
However strange it may sound, this represents an opportunity!
It is highly likely that enterprises already have a pool of talent inside their organizations, and just need to provide the opportunity for employees with backgrounds in computer science, business analytics, or statistics to move into the area of AI via specialized training programmes.
And indeed, it seems to be a viable option! Forbes reports that 63 percent of the companies surveyed are seeking to provide formal or on-the-job training. “One big plus of developing analytics skills among current employees,” says the report, “is that they already know the business.” Based on these considerations, it is likely that tapping into the pools of inside talent is the way in which enterprises can realize the promises of increased ROI with the help of AI.
But how to hire Artificial Intelligence talent?
There is a skills shortage, yet many computer science graduates are unemployed, since they lack the desired and necessary AI competencies. Retraining talent is the key, similar to DS, it should be in AI. It is the mainstreaming of data science and the specific challenges of acquiring and benefiting from this still-scarce talent pool that is the focus of the MIT Sloan Management Review survey. Four in ten (43%) companies report their lack of appropriate analytical skills as a key challenge, but only one in five organizations has changed its approach to attracting and retaining analytics talent.
As a result of the scarcity of data scientists, 63 percent of the companies surveyed are providing formal or on-the-job training in-house similar to the courses Frankfurt School offers. “One big plus of developing analytics skills among current employees,” says the report, “is that they already know the business.” These companies are also doing more to train existing managers to become more analytical (49%) and train their new data scientists to better understand their business (34%). Still, half of the survey respondents cited turning analytical insights into business actions as one of their top analytics challenges.