This article is the second in a two-part series that builds on the key findings of interviews with symposium participants and other professionals, focusing on AI and talent strategy for 2023 and beyond. Part I discusses the need for experiential learning and successful analytics and AI talent strategies.
Artificial intelligence’s impact on human life is impossible to ignore as it affects almost every part of our daily routine. For example, the use of artificial intelligence (AI) as virtual assistants has increased as they are able to handle schedules and answer questions. Additionally, AI simplifies tasks and improves efficiency in various sectors.
One of the areas that AI has transformed is the hiring process, which has revolutionized how organizations seek and recruit talented individuals. By using AI algorithms, employers can efficiently identify suitable candidates for their businesses. AI’s ability to analyze large amounts of data provides valuable insights into a candidate’s suitability for the job, allowing for better-informed human decisions.
It is vital to find a balance between utilizing AI technology’s potential and addressing the impacts it can bring for the future. Specifically, it is important to acknowledge how the workforce will continue to change as AI becomes more integrated. While some job roles may be replaced by automation, it is critical to highlight the new opportunities and needs for AI-related skills. There will likely be an increased demand for soft skills, creativity, and emotional intelligence as AI cannot fully replicate these distinctly human traits.
Part II: AI and Talent Strategy—Key Findings
Human Crises and Humanics Solutions
As mentioned above, it’s crucial for professionals to acquire in-demand technical skills. In addition to these technical skills, however, there’s a significant need for individuals who can offer personalized experiences that only humans can provide. This human touch is necessary because, as Northeastern’s president Joseph Aoun explains, “… 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.”
The COVID-19 pandemic accelerated our reliance on tools and technology, modernized domains, and spiked new questions of ethics and privacy. Digital transformation has highlighted the need for professionals that are able to bring data, technology, and humanics skills together.
Kherlopian acknowledged the demand for these individuals as they would be able to “ develop better ways to enable auto-translation from legacy to modern programming languages to that of progress toward not just Artificial Intelligence, but rather Augmented Intelligence.”
That required combination of different literacies demands professionals with the right talent. Kherlopian suggests looking to learning systems like Rosetta Stone to learn humanics skills. “It requires years of practice and experience to become a master poet or journalist,” Kherlopian explains. “A Rosetta Stone-like device can help contribute to achieving humanics excellence.”
As a result of the pandemic, employers have identified a significant value in finding professionals that can blend analytics and artificial intelligence (AI) technologies into tools for data-driven decision making. “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. While it might be challenging to balance the two, incorporating science and humanics is an adaptable talent that professionals can foster. “Leadership must focus on the fundamentals, the science, and have the baseline of humanics. We do understand that this also comes with experience.”
The 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. When it comes to talent acquisition, participants also agreed that applicant quality is generally improving, but that is not good enough. The other literacies that require attention include:
- Good coding
- Coding hygiene
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.
Chad Iverson, PhD, Senior Manager of Applied Intelligence at Accenture explains that “…this is a recent phenomenon—the CDO gained widespread adoption in the federal market, almost as quickly as in industry.” Iverson continues to explain that the need for data leadership could reveal key information about an organization’s maturing data and data analytics capabilities. “Organizations that invest in data leadership and foundational capabilities have a competitive advantage when it comes to AI,” he says. “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.”
Companies may rely on rapid digital transformation as a potential solution to disruptions in their workflow. 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. In the future, hundreds of thousands of people will face a steep learning curve as a result.
Kherlopian believes that, to translate technology into real-world applications, AI/ML professionals should possess skills like:
- Computer vision
- Natural language processing
- Cloud computing
While the promise of technology is certainly there, AI/ML platforms 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.
Shortcomings of Artificial Intelligence
The pandemic was effective in exposing some of the deficiencies of AI, machine learning (ML), and deep learning. This technology has the potential to surpass humans—not only through speed, but also in terms of accuracy—by detecting patterns in historical data that humans are unable to identify.
However, these technologies depend on pertinent data in order to find these patterns, and they implicitly accept the notion that today’s circumstances are the same as the ones represented in the data. In other words, AI, ML, and deep learning implicitly assume that what has worked in the past will still work in the future.
We can’t feed our technology with data as we used to; there is a need for human intervention to help us create behavioral and structural models.
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Humans can learn lessons from specific circumstances and apply them to unique settings, draw conclusions based on ideas and concepts, and make predictions on potential future scenarios. This, together with our technology and data, can help to keep people safe, according to Anand Rao, PhD, Global Artificial Intelligence Lead at PriceWaterhouseCoopers.
The Positive Side of Artificial Intelligence
Rao offers additional examples of how AI and ML can be effective: “Calculating foot traffic in a building, minimizing contact, organizing a workforce to be self-contained, essentially supporting specific sectors to become resilient. How much extra demand should a consumer good company expect, how many fewer customers will airline companies have, how do we plan for recovery, and when opening up again, how do we help people to stay safe?”
Rao uses a healthcare example to illustrate the point: “Epidemiologists need to sit side-by-side with data scientists, coming up with something very fast and practical together with the healthcare specialist, and having the socioeconomic specialist review that from an ethical perspective.”
This is where experiential learning, humanics, data, and technology merge in the need to apply AI and ML as tools guided by humans who design them and use them. It is critical to leverage the data and technology; embed them in the comprehension of what the business, organizational, and/or educational questions are; anchor that understanding in the human ability and innovation to recognize opportunities and barriers; and provide actionable solutions.