Up to 89% of respondents emphatically asserted that their institution is not prepared for AI-related changes
Public sources,Yakov and Partners analysis
According to an annual survey by Cengage, about 95% of administrators, faculty, and trustees in higher education anticipate that generative artificial intelligence will change their institutions within the next five years. However, experts describe the professional community's stance toward AI as somewhat resistant, dubbing it a "bunker mentality." When questioned about their institution's readiness for AI-related changes, a resounding "no" was expressed by 64% to 89% of respondents.¹
As companies and professions increasingly integrate AI into their operations, failing to leverage AI capabilities and being unprepared now appears almost inconceivable. Engaging with AI is emerging as a vital professional skill across various domains ranging fr om law to medicine. Professionals must grasp how AI functions and comprehend its limitations. Yet, current statistics highlight a gap in understanding: for instance, only around 30% of residents in the US reportedly have a strong grasp of AI applications.
The rapid advancement of AI may lead to a decrease in the number of highly specialized experts, yet their significance will only grow. Since the 18th century, specialization has increased alongside the rising complexity of professions and the amount of knowledge required. However, AI is reshaping this landscape, rendering it unnecessary to retain deep knowledge as in the past. This phenomenon mirrors similar trends observed in other fields, wh ere skills once considered commonplace have become professionalized and are now retained by only a sel ect few due to technological transformations.
Take a good look at the picture², and answer six questions:
- Is the steamboat traveling upstream or downstream?
- What season is depicted in the scene?
- Is the river deep at this location?
- How far is the wharf fr om the steamboat?
- Is the wharf located on the right or left bank of the river?
- What time of day is portrayed in the picture?
Answers
1. The wooden triangles supporting the buoys are always angled against the current, indicating that the steamboat is traveling upstream.
2. The image depicts a flock of birds flying in a V formation, with one side shorter than the other, characteristic of cranes. Cranes typically fly in flocks during spring and fall migrations. The thicker growth of trees on the side facing south indicates the southern direction. Cranes typically fly south during fall migrations, which means that the picture represents fall.
3. The river at this location appears shallow, as evidenced by a sailor on the steamboat's bow using a pole to measure the depth of the fairway.
4. The steamboat is clearly docking at the wharf: a group of passengers have taken their belongings and are getting ready to get off.
5. We have determined which way the river flows in Question 1. To find the right and left bank of the river, we should stand facing downstream. We know that the steamboat is docking at the wharf. We can see that the passengers have prepared to get off fr om the closer side. This means the nearest wharf is on the right bank of the river.
6. There are lanterns on the buoys; they are put up in the evening and taken down early in the morning. We can see shepherds driving the flock to the village. Having compared these two signs, we may conclude that the picture shows the end of the day.
The focus will be on broadening one's horizons, which can be achieved, for example, by pursuing two or more majors simultaneously. Therefore, university programs should incorporate courses fr om related fields. Currently, even in the USA, wh ere students decide on their major relatively late, only 20% of graduates pursue a double major despite the potential financial benefits.
The set of essential soft skills is thus acquiring new significance and undergoing transformation. The ability to articulate tasks in a "redundantly" understandable manner and oversee their outcomes is becoming increasingly important. This ability was highly valued in the early 20th century when managing low-skilled workers. However, as various tasks are increasingly delegated to artificial intelligence, this skill is regaining relevance. Moreover, given the new realities, management skills will need to be cultivated during the educational phase rather than solely in the workplace, as is often the case today. According to experts, nowadays only 10% of professionals possess adequate management skills.
The ability to articulate tasks in a "redundantly" understandable manner and oversee their outcomes is becoming increasingly important
Considering the profound transformation that AI promises for the global economy, akin to how creativity shaped the Renaissance, the reimagining of education with AI could herald the onset of a new Renaissance era. Recalling Isaac Newton's famous words, we hope that the graduates of the years to come will stand on the shoulders of giants that not only elevate them to new heights but also generously impart their knowledge in response to the right queries.
Introduction. Natural obstacles to the widespread adoption of AI in the workplace
Today, it's undeniable that AI is making a profound and positive impact on modern society across multiple domains, ranging fr om medicine and physics to marketing and entertainment. By 2028, the global economic impact of AI is projected to reach USD 17–26 trillion per year.³ In Russia, the economic potential of AI is anticipated to range from RUB 22 trillion to RUB 36 trillion.⁴ With such a promising outlook, most sectors of the economy exhibit considerable interest in the use of AI. However, education appears to be somewhat lagging behind this trend. Some experts characterize the academic community's stance on AI as "bunker mentality."⁵ The education system has shown only limited readiness for the rapid advancement of generative AI, which enables the automation of tasks that were previously inconceivable without human intervention.
This situation is partly attributed to the significant shortage of AI talent: up to 99% of employers struggle to find such experts,⁶ and by 2030, the global shortage of IT professionals will reach 85.2 million.⁷ However, it's also evident that educational institutions frequently find themselves playing catch-up, reacting to market demand rather than driving it.
Four main blocks of challenges emerge in the process of mastering any profession amidst the widespread adoption of AI:
- Since AI is quite effective in handling a wide range of routine tasks, it's essential for every worker to grasp this technology and comprehend its limitations. This means that educational programs need to incorporate sessions on the use of AI.
- Proficiency in working with generative AI necessitates strong communication skills, commonly referred to as soft skills.⁸
- As knowledge accessibility increases, individuals may no longer require deep expertise in the specific field they pursue, especially given the frequent obsolescence of knowledge by the time of graduation.⁹ As a result, workers will increasingly need to have a much broader knowledge base.
- The human mind's key advantage over AI lies in its capacity to formulate original, atypical queries, highlighting the importance of right-brain skills, which are often underdeveloped in left-brain professions.
In this article, we will delve into these four blocks of challenges and explore potential strategies to address them.
Part 1. Learning our way around AI
It's quite astonishing to think about, but just four decades ago, the ability to operate computers was largely confined to engineers and software developers. It wasn't long ago that proficiency in software like Microsoft Excel or Word was a noteworthy addition to one's resume. Today, however, computer literacy has become the norm, thanks in part to simpler interfaces, enhanced user experiences, and widespread educational initiatives spanning all age groups, from preschoolers to retirees.
A similar trend is observed in the realm of AI today. For a long time, its use was limited to individuals proficient in programming languages like Python, as well as companies with extensive computing resources. Commands were traditionally issued in programming languages to analyze data, with interpretation often carried out by individuals who did not comprehend the intricacies of information processing. Until recently, AI was primarily utilized within large corporations boasting hundreds or even thousands of analysts. Many of them served as intermediaries between managerial staff and algorithms. Between 2018 and 2023, there was a notable 41% increase in the number of students pursuing four-year degrees in computer science and information technology, while the number of humanities majors experienced a significant decline.¹⁰ However, this trend was unsustainable and is now changing before our eyes, as evidenced by frequent media reports about tech giants laying off thousands of IT professionals.¹¹ Moreover, according to LinkedIn's Future of Work Report, up to 96% of programming and computer science skills, including Jenkins, Docker Products, AngularJS, TypeScript, and Git, will undergo substantial transformations due to generative AI.¹²
According to LinkedIn's Future of Work Report, up to 96% of programming and computer science skills will be significantly reshaped due to generative AI
As natural language AI interfaces became commonplace, with the cost of accessing the technology dropping to zero thanks to "national champions" like OpenAI, GigaChat, and YandexGPT, the barrier of entry for using AI decreased to a minimum level. Today, it's common for individuals who can write basic queries or generate simple content like pictures and music using AI to not be considered advanced AI users. While the number of these users is increasing rapidly, only about 33% of people believe they are actively utilizing AI.¹³
Consequently, the entry requirements for any profession are becoming much more demanding. Simply aspiring to be a journalist, artist, researcher, or programmer is no longer sufficient, as artificial intelligence is poised to outperform humans in these roles, often delivering faster and better results. This means individuals must continuously build upon their expertise in their respective fields while leveraging AI to address the tasks at hand.
At the same time, it's evident that AI will substantially enhance productivity across various sectors, even beyond data analysis and business tasks. Let's take medicine, for instance. A specialized service based on a neural network can record and transcribe patient conversations, reducing the time spent on documentation from the usual two hours to just 20 minutes a day.¹⁴ This not only spares doctors from routine tasks but also allows for more meaningful interactions with patients. Additionally, more sophisticated applications of AI, such as interpreting MRI scans and results, are being implemented, as demonstrated in Moscow.¹⁵
Medicine is not the only industry wh ere the cost of error is high and AI finds active use. In law, for instance, neural networks can identify relevant case studies and generate initial drafts of opinions or contracts, allowing lawyers to focus on finalizing them.¹⁶
In a nutshell, AI offers avenues for productivity enhancement in multiple domains: 47% of US executives anticipate that employing generative AI will boost productivity, with 92% acknowledging the growing significance of soft skills. However, achieving such outcomes will require serious effort. This includes systematically fostering AI skills while also providing basic insights into AI functionality, limitations, and associated risks. To achieve this, integrating AI training into the early stages of higher education (or even high school) becomes imperative. At these educational levels, most students will undoubtedly have some previous exposure to AI tools. Even today, in the US, 27% of respondents report using AI a few times daily, while an additional 28% interact with it about once a day or several times a week.¹⁷
AI presents an opportunity for enhanced productivity across various sectors, but realizing these benefits requires developing AI skills and effectively communicating its limitations and risks
These skills can be grouped into three categories based on their level of importance, ranging fr om essential to desirable. At the basic level, the primary objective is to use AI for information retrieval. Users need to understand the underlying principles of this technology, such as why AI is commonly likened to a "stochastic parrot."
They should have practical experience with existing AI tools and be aware of their strengths and limitations. Specifically, it's crucial to understand the concept of AI hallucinations, which even the most advanced models can exhibit. This knowledge is indispensable across all professions, serving to minimize AI-related risks and save time. Interestingly, despite 86% of individuals encountering AI hallucinations, 72% still consider artificial intelligence a reliable source of information even though 75% have been misled at least once due to such hallucinations.¹⁸
Individuals in all professions must comprehend the strengths and limitations of AI tools to save time and minimize associated risks
At the intermediate level, the emphasis should be made on content creation, including texts, images, music, and more. Individuals need to be proficient in using AI to generate preliminary versions of creative works while being aware of the limitations involved. It's essential to understand that content produced by AI will serve as a draft that requires further refinement. Here, all the risks and complexities studied at the basic level become even more pronounced. For instance, if a person requests AI to summarize multiple reports or datasets without reviewing them, there's always a risk of making decisions based on erroneous data resulting from AI hallucinations. Hence, the skill of crafting queries that AI can comprehend (known as prompt engineering) becomes particularly crucial. The significance of this skill is poised to grow, with already 22% of individuals regularly using generative AI in their work.¹⁹
At the advanced level, the primary objective is to leverage AI effectively to develop tools that automate human tasks. For instance, a program can be designed to search for information on a search engine and input it into a table, thus streamlining repetitive tasks. Another example involves using AI to write macros in VBA for Microsoft Excel without prior knowledge of the language. However, this will require a deep understanding of the macro's intended functionality. To fully utilize such AI capabilities, individuals must possess a solid understanding of the programming language in which they intend to write code. This understanding is essential for verifying the correctness of the code and identifying potential errors. Currently, this approach is primarily adopted by developers and is gaining popularity. According to GitHub, 92% of developers in the US already use GitHub Copilot for coding.²⁰ It's conceivable that non-professionals may also begin utilizing AI for this purpose in the future.
It should be noted, however, that mastering the skills outlined above is facilitated by having at least a basic understanding of the mathematics and logic behind AI models. Individuals pursuing STEM majors typically study these courses as core subjects, while related fields like economics or management are often offered as supplementary ones.
Only 10-20% of people are capable of breaking the habits that lim it their professional development
Public sources, Yakov and Partners analysis.
However, individuals who only studied math at secondary school will need appropriate courses and knowledge infrastructure. This is also true for those who will be learning math later in life. In many countries like the USA, there are very few people with even basic math skills from high school. According to the National Science and Technology Council, only one in five college-bound US high school students is prepared for college-level courses in STEM.²¹
AI tools promise significant productivity gains across a wide range of sectors, but their downside is potential job displacement. Several studies suggest that AI is already used in 40% of professions globally, with advanced economies witnessing even higher rates of around 60%.²² These structural shifts in the labor market will inevitably impact the education sector, altering demand for different majors and affecting the number of graduates in relevant fields. While private schools can adapt independently to market changes, regulation may be necessary for enrollment and graduation from public institutions.
Several studies suggest that AI is already used in 40% of professions globally, with advanced economies witnessing even higher rates of around 60%
It's important to highlight that developing the knowledge and skills necessary for using AI should extend beyond the education system (universities and other institutions). To efficiently train a broad spectrum of potential users, this process should also involve employers. Companies should actively promote AI usage among their staff, emphasizing it as an opportunity to enhance their skills and performance rather than a threat. For this purpose, managers must develop a cohesive AI strategy, communicate it effectively to their teams, and establish infrastructure for training staff and assessing their competencies.
Part 2. Learning to set goals for AI and make sure it achieves them
It is common knowledge that one of the foremost challenges in one's career is to be able to progress from being a subordinate to being a leader, if only owing to the fact that a mere 10% of human beings are born with the talent to lead.²³ Among the many difficulties a leader may face, delegation of powers is in a league of its own. For employees, changing the way they think can be a tall order. Instead of trying to do things themselves, an executive should be able to tell an employee what to do in a way they can understand, and then follow up on the implementation.²⁴ AI makes it possible to handle ever more complex tasks. It is, therefore, imperative that one knows how to give task instructions from day one. AI is already equipped to solve math problems, write code in any of the major programming languages, compose fiction and non-fiction texts, paint pictures, and do much more. But it cannot set itself goals – at least not yet, so a skilled human is needed to do that.
These are the three steps a person has to complete to get the desired outcome with AI:
- Visualize, in as much detail as possible, what it is you want to accomplish.
- Articulate the target output in a coherent text, splitting it into multiple conceptual units, which will be the tasks given to AI.
- Assess your write-up and, wh ere necessary, adjust the wording of the tasks articulated at step two as many times as needed.
Many people find this type of work to be much harder than doing the actual job themselves. A young leader will often find it easier to do the employee's job for them than to explain how to correct the faults later.²⁵ For their part, employees often complain about their superiors failing to accurately articulate a task or give feedback.²⁶
Young leaders often find it easier to do the job themselves rather than suffer it done by their subordinates and explain how to fix the faults later. But that is exactly what they have to do when dealing with AI
At present, very few academic curricula cover management through task setting, and undergraduate and graduate degree programs are particularly deficient in that area. In most cases, even the MBA programs of the world's top 10 universities teach this skill set as an elective course. At best, competencies of this nature are taught by specialist training courses for administrators, typically attended by seasoned professionals. In the end, management skills are almost always gained fr om corporate training, or sometimes from personal experience alone.
Most companies offer corporate training programs. In the UK, for instance, only 30% of employees polled said they had not taken any training in the past five years.²⁷ However, 75% of executives find the training quality to be less than satisfactory, and only 12% use the new knowledge in their work.²⁸
One of the tools that can be employed to set goals effectively for AI is the SMART²⁹ (Specific, Measurable, Achievable, Relevant, Time-based) goals method. Many executives know what it is, and yet choose to ignore its potential benefits. Meanwhile, beyond solid assistance with individual work and people management, the SMART method could be of service in AI goal setting. But SMART is only one of many powerful goal-setting techniques.
AI management appears similar to people management on the face of it, but methodologically, these two management areas are not entirely identical. For one thing, a person has to have a clearly articulated task. Misunderstanding of instructions by employees is a managerial nightmare. As for AI, it is necessary, wh ere possible, to complement the setting of a goal by giving AI the directions on how to get there. Working with AI could be compared to the management of an unqualified but diligent team that will follow all instructions to a T. When people act in this manner, it is called an "Italian strike" or work-to-rule. With AI, however, similar performance is viewed as a positive.
Management practices such as giving excessively detailed task instructions and tightly controlling performance take the science of management back to the end of the 19th century, when the ideas of Frederick Taylor, the father of scientific management, were in vogue. Taylor postulated precise measurement of performance indicators and sweeping enforcement of standardization as a way to streamline work on the factory floor.
12% of employees use what they learn fr om corporate training in hands-on work
Public sources, Yakov and Partners analysis
Taylorism and Fordism, which postdated it (Henry Ford also stressed the importance of standardization), were eventually dismissed as unprogressive, although Taylorism had garnered serious attention in some countries, including the Soviet Union.³⁰ Scientific management methods may yet see a resurgence in management practice in some new incarnation.
To know how to articulate a task in detail is a good skill to have, but one day it may backfire on the manager. A successful employee may ascend to management some day, and then he or she will get to manage people as well as algorithms. The new manager will have to draw a sharp distinction between tasks that should be delegated to people, and those more suitable for algorithms. If this manager continues, by force of habit, to "spoon-feed" tasks and micromanage performance of rank-and-file employees, the manager runs the risk of completely destroying their motivation.
Already many employees complain that their superiors are excessively meticulous in their articulation of tasks, leaving the employee no room for creativity.³¹ The general rule of thumb in people management nowadays is to create an environment that enables people to demonstrate their best ability. Time and time again it has been attested that, by engaging employees' creative potential on the ground, companies stand to dramatically enhance their competitive edge. This holds for all industries, including such traditional sectors as car manufacturing. Toyota is a case in point. After the Second World War, this company was able to significantly outstrip its competitors by tapping employees' potential on the ground. "People are underrated," tweeted Elon Musk after trying to replace humans with robots on the Tesla assembly line.³² It's not going to be easy, but managers-to-be will have to retrain and change their routines if they want to manage people effectively. Only 10% to 20% of people are capable of making lasting changes to their career-limiting habits.³³ In this light, the significance of HR management teams rises to a whole new level: now they have to proactively prepare employees for managerial roles.
The general rule of thumb in people management nowadays is to create an environment that enables people to demonstrate their best ability
Among other aspects, it is part of effective AI management to be able to streamline task performance, transforming it into a continuous process. This is particularly relevant for a company working to develop its in-house AI tool. Every time a query delivers a successful outcome, the experience must be preserved. The ideal course of action would be to get the hang of AI-assisted development enough to systematically automate the performance of your tasks, while you yourself would focus on the types of work that require intellectual effort. Since fewer than 1% of the world's population know how to code,³⁴ this state of affairs is anything but easy to achieve. It would not suffice to include programming as such in the course curricula – basic task division logic has to be covered, too.
It is important to note that the aforesaid competencies associated with management, goal-setting and task performance automation do not supersede, but rather complement the set of skills obtained by future professionals. For instance, designers are supposed to be able to develop solutions without the use of AI in their area of expertise, lawyers are expected to be well versed in law, and marketers should know how to analyze human behavior. A qualified professional will be able to appraise AI outputs, but if required, do the job without the use of AI. This will naturally increase academic pressure on students, for they must devote full attention to the study of their field, as well as learn to work with AI and master people management skills.
Part 3. Wider not deeper: how to hone the approach to specialization
No matter how much information even the most diligent of students absorb, it won't last a lifetime. Firstly, new knowledge keeps coming up, and secondly, the average person tends to go through three to seven careers in their lifetime.³⁵ In consequence of that, the concept of lifelong learning is gaining popularity, and increasing numbers of people pursue second degrees or learn supplementary skill sets – a lawyer, for example, may be willing to learn a programming language.
There were no search engines and no Internet in Albert Einstein's time, and yet he said:
"Never memorize something that you can look up" and "The only thing you ought to remember is how to get to the library"
With search engines, the entire wealth of knowledge accumulated throughout human history can be easily accessed anytime from any part of the world. Now that the current generation of generative models has made it even easier, you can get the search results returned by the search engine and query their interpretation in ordinary human language. This naturally drives new demand for cross-functional expertise that is capable of creating new value at the intersection of multiple disciplines.
Several studies have found that graduates with at least two unrelated majors earn considerably larger incomes compared to those with a single or two majors from related fields under their belt. A 2008 study illuminated the fact that returns of the former exceeded those of the latter by at least 7%, up to a maximum of 50%.³⁶ A 2021 study by the US National Bureau of Economic Research also came up with some interesting findings. It looked at how the alumni were doing who had majored in arts, humanities or social sciences, but had also completed a second major in business, engineering or science. It transpired that people with similar double degrees were 56% better protected from the vicissitudes brought about by dramatic changes in the job market.³⁷
And this is not about multiskilled alumni being more talented than their single-major peers.
Expert surveys have found that few universities or colleges encourage, let alone require, students to broaden their outlook.
This practice has failed to take root even in colleges that traditionally boast strong financial study options, despite finance being an area wh ere knowledge diversification can make a great difference. It is essential that students are at least strongly encouraged, if not required, to seek supplementary knowledge. But there is no need for universities or colleges to set up new chairs or departments to that end – it would be sufficient to work in partnership with other educational institutions.
The point to bear in mind is that it is never too late to broaden one's horizons. Regardless of their age, a person can always benefit fr om a second or further degree, or any course in an entirely new field. At the same time, surveys indicate that – in the UK, for instance – only 6% of workers retrain for a new job.³⁸
It is never too late to broaden one's horizons, but you have to be willing to do so
Additional learning needs to be self-driven. Meanwhile, more often than not, the employer will lock employees into a rigidly confined area of expertise. The leaders of some companies go so far as to completely dismiss the value of higher education. They believe a basic course, such as in programming, provides sufficient qualifications to start work in the job of one's choosing.³⁹
On the other hand, even in the era of AI it would be wrong to belittle the importance of industry-specific training. A historian would be hard put to articulate a search query in terms of physics, while a developer might find it difficult to query AI about some aspect of law. But even if the query was worded perfectly, the answer will need to be analyzed using common sense, which would be simply impossible to do without understanding of the broader professional context.
Part 4. It takes a good question to get a good answer
As previously remarked, success with AI largely hinges on the correct formulation of queries and tasks, coupled with output quality assurance. When these prerequisites are in place, AI will write texts, compose music, generate images, make videos, and create other outputs all-but-indistinguishable from creations of a human mind. In fact, AI has a fairly accurate sense of which of its outputs are appreciated more, and is capable of reproducing similar items. But there is one thing AI is incapable of, at least for the time being: it cannot formulate a request to perform an action, especially an unusual one.
Formulation of irregular queries is by definition off limits for the algorithms. It's the realm of creativity. Many scholars name the abilities necessary for generating new, previously non-existent ideas or collating knowledge from unrelated science fields among the right-brain abilities. Nobel Prize winning neurophysiologist Roger Sperry cited imagination, holistic thinking, intuition, creativity, rhythm, body language, and emotive visualization among them. Capabilities such as linear, analytical thinking and reasoning, customarily attributed to the left hemisphere of the brain, assist us with tasks like answering questions. They also come into play when there is a logical or mathematical problem to solve. Some researchers question the validity of dividing human beings rigidly into left-brained and right-brained thinkers, contending that any person is perfectly able to develop both sides of the brain.⁴⁰
Formulation of irregular queries is by definition off limits to the algorithms. It's the realm of creativity
Nowadays most students studying engineering majors are predominantly taught left-brain skills. This corresponds to the study of such subjects as software development, data mining, mathematical analysis, and so on. There is very little in these academic curricula that could help students develop their creative potential. Such skills are viewed as the domain of liberal arts students.
But this is not to say that traditional subjects should not be part of tech professionals' training. Many businesses' stories attest that tech expertise is not to be foregone. There is no dearth of businesses wh ere the development teams make ample use of AI-based tools such as GitHub Copilot. As should be expected, every team sees a significant rise in productivity following their implementation. But if the AI tool were to be taken away fr om the developers after a certain time, their productivity would plummet below the level they had achieved before the AI tool became available.
A similar state of affairs can be found in other areas as well. When AI is put to work in tech support (to add more chat or call center features), the least competent employees will show the fastest improvement in productivity.⁴¹ These examples testify that traditional subjects and skills are today as crucial as they ever were. But at the same time, the surveys referenced in the previous chapter prove beyond doubt that when students voluntarily take up supplementary subjects, they are laying a rock-solid foundation for their future career. Whether it is technical expertise or, say, narration or acting skills that they gain, they secure a serious competitive advantage going forward.
In business and in the tech realm alike, people have long since realized that creative skills make a big difference. Many of the major, successful companies have offered smartly structured executive training programs for years, wh ere a great deal of attention is devoted to the fostering of such abilities as emotional intelligence and storytelling. It is rightfully acknowledged that skills of this nature are a must-have for middle- and top-level managers, whereas the training given to young professionals usually focuses on technical competencies. Areas akin to consulting are an exception: in this business, even new graduates have to deal with poorly structured tasks spanning many different industries right fr om the start. Members of a similar profession had better possess creative skills from day one.
However, this learning strategy presents certain challenges. First of all, right-brain skills are learned better and faster at a young age. And second of all, even a young professional is likely to need management and goal-setting skills at some point. It would, therefore, make sense to either supplement higher education curricula, starting as early as the undergraduate level, with individual subjects conducive to learning right-brain skills, or embed such academic features in the existing subjects that would help use and refine the skills in question. And schools don't even have to hire new faculty for that. Contrarily, this offers them an excellent opportunity for mutually beneficial partnership: liberal arts students get to explore what AI can and cannot do while future techies get the hang of unconventional thinking.
It would be advisable to introduce subjects promoting right-brain skills already at the undergraduate level. Alternatively, existing subjects could be augmented with new features designed to use and refine such skills
The ideas set forth above are not new to higher education. During the Middle Ages, certain celebrated European universities that their modern counterparts sometimes look up to offered a fairly versatile education. Eminent thinkers from the classical era and the Renaissance, such as, respectively, Aristotle and Leonardo, as well as their learned peers in the early modern age, notably Benjamin Franklin and Mikhail Lomonosov, were experts in several fields at once. While the depth of their knowledge was no match for that of modern-day academics, they owed their outstanding achievements to their breadth of mind.
Conclusion. The new Renaissance?
Artificial intelligence has the potential to dramatically transform most industries, either partially or completely automating routine tasks and thereby significantly boosting labor productivity. Even today, AI streamlines access to vast pools of human knowledge across all domains.
Yet, to fully harness its capabilities, proper training is imperative. Educational programs across various majors need revision to ensure graduates possess a wide array of required skills. This applies not only to technical fields that largely contribute to shaping AI's development but also to sectors wh ere AI adoption is prevalent among users.
We have outlined four key areas requiring transformation:
- Professionals across various fields, fr om lawyers to doctors, must grasp the key fundamentals of AI and related limitations alongside acquiring their core hard skills.
- Soft skills, which are fundamental for success in the workplace from day one, should be supplemented with management competencies, enabling employees to set clear tasks for AI and monitor outcomes.
- Given AI's integration into various professions, there may be a need to upd ate the timing of specialization stages in education and encourage the acquisition of knowledge in multiple, loosely related fields to broaden one's horizons.
- It's essential to expand the skill se t that professionals must possess. Presently, education primarily focuses on providing answers rather than fostering the ability to articulate questions. Formulating questions and setting tasks, which is associated with right-brain thinking, should be cultivated alongside creativity, a trait traditionally emphasized in the arts. This could be the most crucial aspect of transformation in the current landscape.
Just as creativity shaped the Renaissance, the AI-driven transformation of education could mark the onset of a new Renaissance era. Recalling Isaac Newton's famous words, we hope that the graduates of the years to come will stand on the shoulders of giants that not only elevate them to new heights but also generously impart their knowledge in response to the right queries.
Sources
1 https://campustechnology.com/Articles/2024/02/22/Survey-Suggests-Higher-Ed-Institutions-Are-Not-Ready-for-Generative-AI.aspx
2 Chkanikov I. N. Leisure Time. Pictures with Fun Tasks. Moscow, 1947.
3 https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
4 https://yakov.partners/publications/ai-future/
5 https://www.youtube.com/watch?v=cIp8LpdYvWc&ab_channel=%D0%91%D0%B0%D0%BD%D0%BA%D0%A0%D0%BE%D1%81%D1%81%D0%B8%D0%B8
6 https://yakov.partners/upload/iblock/c5e/c8t1wrkdne5y9a4nqlicderalwny7xh4/20231218_AI_future.pdf
7 https://www.redaktcms.com/blog/the-global-it-talent-shortage-how-to-prevent-this-growing-crisis-from-affecting-your-business#:~:text=The%20impacts%20of%20IT%20talent%20shortage%20on%20businesses&text=By%202030%2C%20the%20global%20tech,positions%20compared%20to%20other%20roles
8 https://www.fastcompany.com/91012874/new-study-finds-ai-makes-employers-value-soft-skills-more
9 https://www.forbes.com/sites/joemckendrick/2023/10/14/half-of-all-skills-will-be-outdated-within-two-years-study-suggests/
10 https://nscresearchcenter.org/current-term-enrollment-estimates/
11 https://www.engadget.com/big-tech-layoffs-183005386.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAMSLtIgKIy1HuLdSwAkj5bSJNAlttvP3AGDp2euBT--_cJkkXJAmnMA0B77shrNM00-L1I9ADRGEIHksnrdtNmhZqmE_tfT1MYWDwo4exy7oQc14VE61GjayNK_8DLnb8Md8XzV0Kpz9CJRIyjTioWTacKIKmkSlqcKBZG-EgZQ6
12 https://economicgraph.linkedin.com/content/dam/me/economicgraph/en-us/PDF/future-of-work-report-ai-august-2023.pdf
13 https://business.adobe.com/uk/blog/perspectives/15-stats-about-artificial-intelligence
14 https://hbr.org/2023/11/how-to-capitalize-on-generative-ai
15 https://www.comnews.ru/content/231589/2024-02-15/2024-w07/1007/analiz-medicinskikh-snimkov-pomoschyu-ii-stal-dostupen-polisu-oms
16 https://www.americanbar.org/news/abanews/publications/youraba/2017/september-2017/7-ways-artificial-intelligence-can-benefit-your-law-firm/
17 https://www.pewresearch.org/science/2023/02/15/public-awareness-of-artificial-intelligence-in-everyday-activities/
18 https://www.tidio.com/blog/ai-hallucinations/
19 https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year
20 https://www.zdnet.com/article/github-developer-survey-finds-92-of-programmers-using-ai-tools/
21 https://apnews.com/article/math-scores-china-security-b60b740c480270d552d750c15ed287b6
22 https://www.imf.org/en/Blogs/Articles/2024/01/14/ai-will-transform-the-global-economy-lets-make-sure-it-benefits-humanity#:~:text=In%20advanced%20economies%2C%20about%2060,from%20AI%20integration%2C%20enhancing%20productivity
23 https://www.gallup.com/workplace/231593/why-great-managers-rare.aspx
24 https://medium.com/@khilversumcornell/the-art-of-delegation-a-crucial-skill-for-new-managers-24c164588894
25 https://hbr.org/2012/07/why-arent-you-delegating
26 https://www.fastcompany.com/90384884/how-to-handle-a-boss-who-gives-you-vague-feedback
27 https://www.aihr.com/blog/learning-and-development-statistics/
28 https://hbr.org/2019/10/wh ere-companies-go-wrong-with-learning-and-development?registration=success
29 https://hbr.org/2017/01/3-popular-goal-setting-techniques-managers-should-avoid
30 https://en.wikipedia.org/wiki/Scientific_managementhttps://www.td.org/insights/overcoming-career-limiting-habits
31 https://hbr.org/2023/09/the-anxious-micromanager
32 https://finance.yahoo.com/news/elon-musk-says-humans-underrated-211908979.html?guccounter=1&guce_referrer
33 https://www.td.org/insights/overcoming-career-limiting-habits
34 https://cmr.berkeley.edu/2016/10/computer-literacy/
35 https://study.uq.edu.au/stories/how-many-career-changes-lifetime#:~:text=Research%20shows%20most%20
36 https://www.sciencedirect.com/science/article/abs/pii/S0272775707000659?via%3Dihub
37 https://www.nber.org/system/files/working_papers/w32095/w32095.pdf
38 https://neweconomics.org/2021/10/uk-faces-a-skills-shortage-with-only-6-of-workers-upskilling-for-a-new-job-pre-pandemic
39 https://techcrunch.com/2011/04/10/peter-thiel-were-in-a-bubble-and-its-not-the-internet-its-higher-education/?guccounter=1
40 https://www.healthline.com/health/left-brain-vs-right-brain#left-brain-vs-right-brain-myth
41 https://campustechnology.com/Articles/2024/02/22/Survey-Suggests-Higher-Ed-Institutions-Are-Not-Ready-for-Generative-AI.aspx