The AI and Talent Strategy in 2021 | Part 2

Faculty Insights Analytics

This article is the second in a three-part series that builds on the key findings of interviews with symposium participants and other professionals, focusing on AI and talent strategy for 2020 and beyond. Part I discusses the need for experiential learning and successful analytics and AI talent strategies. Part II focuses on the interplay of humanics and technology. Part III offers humanics solutions for the human crisis of COVID-19.

Part II: AI and Talent Strategy in 2020—Key Findings


Northeastern’s president Joseph Aoun explains that “…we will need data literacy to manage the flow of data and technological literacy to know how machines work, but we need human literacy—the humanities, communication, and design—to function as human beings.” This philosophy is helping to set graduates apart and fosters their ability to identify cases where AI can be applied, and where Machine Learning is a good solution.  

“Skillful communicators, ones that can lead cross-domain communication, are in high demand,” says Armen R. Kherlopian, Co-Founding Partner of the BAJ Accelerator and Co-Founder of Voxface. Creativity, agility, and innovative capacity are sought-after skills, for example, when start-ups are created. Or in telemedicine, “when treating patients remotely, communication skills become even more important.” This need is only further accelerating in times of COVID-19. Kherlopian suggests “that we may look at a learning system like a Rosetta Stone to learn humanics skills, to essentially learn a new language. It requires years of practice and experience to become a master poet or journalist. A Rosetta Stone-like device can help contribute to achieving humanics excellence.”

“We ask a lot of our data science and AI teams: Be all science, but also tell a story.  That is a lot to handle,” adds the VP of Data Science at a major pharmaceutical company. “Leadership must focus on the fundamentals, the science, and have the baseline of humanics. We do understand that this also comes with experience.”

That suggestion of a baseline in humanics aligns well with the Northeastern and the College of Professional Studies philosophy. Our focus is on experiential learning and, next to data and technology, humanics is at the core of success in our digital transformation programs like Analytics and Artificial Intelligence.  


All symposium interviewees agreed that creativity and critical thinking are the most difficult subjects to teach, especially for data analysts and scientists. Participants also agreed that the quality of applicants is generally getting better, but that is not good enough. The other literacies that require attention are good coding, coding hygiene, and documentation. 

Artificial Intelligence and Machine Learning (AI/ML) professionals who possess these skills are in high demand. “Even pre-COVID-19, more organizations were adopting AI/ML, accompanied by regulatory efforts in the EU and Singapore,” says Anand Rao, PhD, Global Artificial Intelligence Lead at PriceWaterhouseCoopers. In the U.S., a good indicator of the same trend is the rise of the Chief Data Officer (CDO) in the federal market. Iverson explains that “…this is a recent phenomenon—the CDO gained widespread adoption in the federal market, almost as quickly as in industry. The need for data leadership could also be considered an organizational indicator that reveals something about maturing data and data analytics capabilities. Organizations that invest in data leadership and foundational capabilities have a competitive advantage when it comes to AI. If you haven’t seriously invested in data management, for example, you can’t expect to achieve sustainable results of any kind from your AI strategy.”

In the time of COVID-19, companies that will rely on rapid digital transformation as a potential solution to disruptions in their workflow will further increase. But if graduates “…are trained on core needs only, we are missing a good understanding about scientific methods, hypothesis, theory, and framework,” says the VP of Data Science at a major pharmaceutical company. That will force a steep learning curve. Kherlopian imagines AI/ML professionals should possess skills like computer vision, natural language processing, and cloud computing required to translate technology into real-world applications. While the promise of technology is certainly there, we know based on previous introductions of AI/ML platforms that they can result in turbulent experiences. Therefore, professionals need to be able to operate and sustain systems, maintain machine learning over time, and detect and mitigate issues as soon as possible. 

While we are living with COVID-19, society is also experiencing an increased reliance on tools and technology. We gather more data and practice tracking, sensing, and employing AI/ML with the goal to live safely and healthily while the virus persists and the vaccines are rolled out to the public. But that drives us inevitably to questions of privacy, ethics, and social good; for example, in instances where facial detection and recognition and thermal imaging technology are used to help police to enforce social distancing guidelines. Society needs to have both in place, Anand Rao points out. “[We need both] privacy and the tools. What needs to be in place to contain the virus, and what are the safeguards to guarantee that information will be erased?”