AI is considered the most disruptive technology, according to Gartner’s 2019 CIO Survey (it includes over 3,000 CIOs from 89 countries). So yes, this is big reason why there has been a major increase in adoption and implementation.
Yet, there is a bottleneck that could easily slow the progress – that is, finding the right talent. The fact is that there are few data scientists and AI experts available.
“In our recent State of Software Engineer report, we found that demand for data engineers has increased by 38% and demand growth for machine learning engineers has increased by 27% in the last year,” said Mehul Patel, who is the CEO of Hired. “Based on data from our career marketplace, we believe the difficulty of recruiting for tech talent with specialized skills in machine learning and AI will continue to become increasingly competitive. Machine learning engineers are commanding an average salary of 153K in the SF Bay Area, which is nearly 20K above the global tech worker’s average salary.”
Actually, this is why one approach is to acquire companies that have strong teams! This appears to be the case with McDonald’s, which recently paid $300 million for Dynamic Yield. It’s an AI company that helps personalize customer experiences.
But of course, this option has its issues as well. Let’s face it, acquisitions can be difficult to integrate, especially when the target has a workforce with highly specialized skillsets.
So, what are other approaches to consider? Well, here’s a look at some ideas:
Automation: With the growth in AI, there has also been the emergence of innovative automation tools, whether from startups or even the mega tech operators. For example, this week Microsoft introduced a new set of systems to streamline the process.
“The biggest and most impactful way that organizations can leverage their current team for data science is to implement a data science automation platform,” said Dr. Ryohei Fujimaki, who is the founder and CEO of dotData. “Data science automation significantly simplifies tasks that formerly could only be completed by data scientists, and enables existing resources — such as business analysts, BI engineers and data engineers — to execute data science projects through a simple GUI operation. Automation of the full data science process, from raw business data through data and feature engineering through machine learning, is enabling enterprises to build effective data science teams with minimal costs, using their current talent.”
Now, this does not mean that a platform is a panacea, as there still needs to be qualified data scientists. But then again, there will be far more efficiency and scale with AI projects.
“If organizations have data scientists already, an automation platform frees up highly skilled resources from many of the manual and time-consuming efforts involved, and allows them to focus on more complex and strategic analysis,” said Ryohei. “This empowers data scientists to achieve higher productivity and drive greater business impact than ever before.”
Reskilling: If you currently have employees who are business analysts or have experience with data engineering, then they could be good candidates to train for AI tasks. This would include focusing on skills like Python and TensorFlow, which is a deep learning framework.
“From a training and learning perspective, there are an abundance of online resources via Coursera, Udacity, open.ai and deeplearning.ai that can help companies develop their employees’ AI/ML skills,” said Mehul. “Additionally, it will be valuable for a company to acquire someone with existing experience in AI to be a leader and mentor for developing employees. The interesting thing about AI/data science is that you don’t need to be an experienced software engineer to do it. The field is so exciting because of the diversity of talent and backgrounds spanning science, engineering, and economics.”
But the training should not just be for a small group of people. It should be company-wide. “Without a data-driven culture and mindset, data science and AI cannot be truly implemented,” said Ryohei. “It is important for enterprise leaders and business teams to understand how to best work with the data science team to meet the organization’s key business objectives. While the business stakeholders do not need to be data experts, they need to know ‘How to use’ AI and ‘How it changes their businesses.’”
The views of the author of this article do not necessarily represent the views of Gradifi. We make no claims, promises or guarantees about the accuracy, completeness, or adequacy of the information contained here. Readers should consult their own attorneys or other tax or financial advisors to understand the tax, financial and legal consequences of any strategies mentioned in this article.