Feature
Earlier this year in Gartner’s Top Trends in Data and Analytics report, leaders responsible for strategy and innovation identified three imperatives driving the top trends in data and analytics for 2022. One of the imperatives, Augment People and Decisions, addressed the ongoing skills and literacy shortfalls that are impacting brands’ ability to achieve their strategic data-driven business goals. Let’s look at the ways that skilled literacy shortfalls are impacting AI development and adoption.
A 2019 study by SnapLogic titled The AI Skills Gap revealed that 93% of US and UK businesses consider AI to be a business priority and have projects that are planned or already in production, but 51% indicated that they don’t have the right mix of skilled AI talent available to achieve their goals. As such, a lack of skilled talent was cited as the No. 1 barrier to moving forward with their AI initiatives.
What Skills Are Needed by AI Professionals?
The adoption of AI across industries is rapidly accelerating, and according to research from MarketsandMarkets, the overall AI market is expected to be worth $407 billion by 2027. Artificial intelligence is ubiquitous in our world today, and we interact with it often without even realizing it. AI is used by our smartphones, our digital assistants, our cars, the search engines we regularly use, the chatbots we interact with and more. In a study by Pegasystems, What Consumers Really Think About AI, 6,000 consumers were asked the question, “Have you ever interacted with artificial intelligence technology?”
- 34% said yes.
- 34% said no.
- 32% were not sure.
Although only 34% thought that they had interacted with AI, in reality, 84% actually had, but they just didn’t know it. This was discovered when they asked those polled about the kinds of technology they used regularly, many of which, unbeknownst to them, have an AI component. Artificial intelligence is intertwined with all of our lives, but for it to continue to evolve and progress, skilled literacy shortfalls must be addressed.
Zoe Hillenmeyer, chief commercial officer at Peak, a cloud AI platform provider, told CMSWire that as someone who did not come from a data or computer science background, she would postulate that there isn’t a formal list of skills professionals need to have to work in AI — and her technical colleagues agree. “A willingness to learn and a love of solving problems are the most important attributes for any person jumping into this field. The belief that they need to possess certain ‘top skills’ holds far too many people back from exploring it as a possibility."
Soft skills such as communication, collaboration and analytical thinking are helpful, as is a general proficiency in programming. As Hillenmeyer says, while there may be no prerequisite “top skills” for an AI professional, there are skills that are very useful such as:
- Knowledge of advanced mathematics and algorithms
- Problem-solving skills
- Industry knowledge
- Management and leadership skills
- Machine learning
- A strong focus on ethics
- Data visualization and analytics
- Governance
- Security
A basic understanding of AI frameworks such as Scikit-learn, Theano, Apache Mahout, TensorFlow, PyTorch, Microsoft CNTK and Caffe provides those interested in an AI profession with an advantage as well. Additionally, some prospective employers expect AI job candidates to have at least a bachelor’s degree in math and basic computer technology, with a preference given to those with a master’s degree in artificial intelligence. Other, more complex skills such as data engineering, neural network architecture and other areas of AI require more of an investment to acquire. By conducting a needs assessment, brands are able to better understand the skills that are required for their particular application.
Hillenmeyer said that for nontechnical teams, they most frequently encounter AI in one of a few ways: either seeking a solution to a known business problem or challenge (e.g. how do I personalize my customer experiences?) or in testing or adopting an AI-driven solution. “For those trying to evaluate if AI could help solve a problem, there are many resources — but also, it can be quite easy to be overwhelmed. This is why we believe that all AI implementations should be reviewed by an expert in the space — to ensure you are adopting it effectively and efficiently.”
Need for Renewed Emphasis on AI Education
In order for AI to remain a positive, ethical technology in our lives, there needs to be a renewed emphasis on AI education for those who will make key decisions about AI application development, deployment and policy. A continued focus on Explainable AI, Ethical AI and Adaptive AI will require brands to invest in upskilling, reskilling, and continuing education for employees.
Hillenmeyer believes that a degree of data literacy takes away the mystery that often surrounds AI. “AI isn’t a black box to these teams but a useful tool — they understand its limits as well as how to use it to its full potential.” Surbhi Rathore, CEO and co-founder of Symbl.ai, a conversational intelligence (CI) platform provider, told CMSWire that data literacy is really about an organization’s ability to communicate internally with data and make clear data-driven decisions, and that this greatly affects a brand’s ability to effectively use the data it has collected. “Conversational intelligence, as an example, is an area of AI where there is a huge opportunity to improve customer experience, but where understanding the meaning of the data is essential to unlocking its value,” said Rathore.
“Conversations with customers during either a live or recorded conversation on video, chat or phone, for example, include massive amounts of unstructured data waiting to be consumed and acted upon.” Rathore explained the problem is that the people who are consuming this data typically do not have the analytical training or expertise to know how to use it. “These are typically people on the front lines of customer support, revenue intelligence or even the company's own business intelligence group. Personalization, customer empathy and outcomes all suffer if patterns cannot be recognized in the conversation data and piped back into the CX strategy.”
Related Article: What's Next for Artificial Intelligence in Customer Experience?
Upskilling and Reskilling Are Crucial
A 2022 report by Pluralsight revealed that 91% of tech leaders believe that implementing new technologies in their businesses requires continual investments in talent and business processes. The report indicated that 77% of leaders said that upskilling is extremely effective at improving employee retention, and that skills gaps are the biggest risk to businesses. This is indicative that investing in upskilling is crucial for business success, especially in still-emerging fields such as artificial intelligence and machine learning.
The COVID-19 pandemic accelerated the need for learning initiatives across industries. A report from TalentLMS indicated that since the COVID crisis began, 42% of brands have increased their reskilling and upskilling efforts, and furthermore, 74% of employees who didn't receive any additional skill training said they would prefer to work for a business that provided it.
“This technology will drive growth across the board, so upskilling and reskilling are essential for both the growth of our economy as well as the sector more generally,” said Hillenmeyer. Reskilling refers to learning initiatives that enable employees to learn new skills that prepare them to move to a new job within a business.
Upskilling, on the other hand, improves an employee’s existing skill set and adds to their capabilities and knowledge. These types of learning initiatives provide growth opportunities within a company, help to improve company culture, increase retention and resilience, and create a more positive employee experience, which facilitates a better customer experience.
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“Employment in tech is expected to grow at twice the national rate of employment,” said Hillenmeyer. “Upskilling existing talent in the workforce is the only way we will be able to meet the labor demand. Upskilling and reskilling means businesses not only retain top talent, but they build a workforce that has both technical and functional expertise.” Hillenmeyer believes that this will in turn speed up innovation in and commercial utility of AI.
Emerging technologies such as AI and ML are forcing brands to take an even closer look at reskilling and upskilling. A 2018 McKinsey report (mirrored by a 2021 World Economic Forum report) revealed that 62% of executives said they will need to retrain or replace more than 25% of their employees between now and 2023 due to advancing automation and digitization. The report also indicated that 82% of executives at companies with more than $100 million in annual revenue view retraining and reskilling as at least half of the solution to their skills gap, with new hires accounting for the other half.
Peter Hirst, senior associate dean of executive education at the MIT Sloan School of Management, told CMSWire that businesses are reconsidering who they are, what they do and who their customers and competition could be. “These pivots and reinventions require large-scale upskilling, reskilling and career transformations — all of which are effective strategies to keep organizations up to speed and to combat an accelerating skills shortage that was problematic long before the pandemic." Continued learning initiatives enable brands to identify strengths and weaknesses, which allows them to fill any existing knowledge gaps.
Related Article: 4 Ways AI, Analytics and Machine Learning Are Improving Customer Service and Support
The Rise of the (Intelligent) Machines
Hillenmeyer said that while the lack of data scientists is well documented, what isn’t discussed enough is the skills needed by nontechnical teams, i.e., the eventual end users of AI. “There is a strong correlation between data literacy within a business and its ability to implement AI. Our research shows that businesses with high AI maturity typically have a workforce with high data literacy, who are comfortable with analytics and routinely make data-driven decisions (even at junior levels).”
One proposal to solve skilled literacy shortfalls lies in the use of AI which does not require an in-depth understanding of AI. Rathore has embraced this type of solution to solve the problem of data literacy. “CI has evolved to assist customer-facing teams by providing solutions that can be implemented with little to no expertise in conversation AI or machine learning,” said Rathore. “For instance, the use of trackers or other zero-shot detection tools, which both automatically recognize phrases and interpret their meaning, allow customer support teams to trigger automatic or manual workflows, identify coaching opportunities for employees, and identify customer intents and issues.”
IBM's solution is to combat AI skill gaps using AI itself. To begin, IBM's research indicated that brands should use analytics and AI to predict and infer the skills that are available throughout the business and transparently share that information with employees, which helps to encourage a culture of continual learning.
Additionally, IBM’s Talent & Transformation business is applying end-to-end AI capabilities for every aspect of the employee life cycle to help businesses foster talent, empower employees and transform the business for what it refers to as the era of AI and automation. These services help assist companies to close the skills gap that came with these new technologies and facilitate the ability for their employees to make the shift to partnering with intelligent machines.
Related Article: Real-Time AI: A Necessity for Great Customer Experience
Final Thoughts on the AI Skills Gap
Artificial Intelligence already plays a large role in most of our lives, and the ways that we interact with it continue to evolve as new AI applications are adopted. With a renewed focus on explainable AI, ethical AI and adaptive AI, brands are struggling with skilled literacy shortfalls that impact their ability to achieve their goals and complete their projects. By upskilling and reskilling employees and fostering a culture of continual learning, brands can add to their employees’ AI skill sets and get their AI projects back on track to success.
