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Enterprise AI Strategy: Pioneering Opportunities at the Executive Level

The enterprise AI hiring spree started years ago. The creation and development of AI systems require machine learning engineers, data scientists, and specialized researchers. These professionals are increasingly in demand and well-compensated.
Candice Wu

According to the World Economic Forum’s Future of Jobs Report 2020, AI is expected to replace “85 million jobs worldwide by 2025.” While those figures may stop you in your tracks, the report also goes on to say that it will create 97 million new jobs in that same timeframe. While many have bemoaned AI’s introduction into the workplace as a potential job killer, the facts seemingly don’t align in their favor. Statistically, the current American labor market has begun to favor those seeking employment.

Over the next decade, there will likely be a constrained labor supply, and as a result, skilled workers seeking higher wages will be in a better negotiating position given the limited talent pool. A spiking demand and labor scarcity forced many employers to consider nontraditional candidates who inspired high potential and trained them if they lacked direct experience. Of course, this bodes well for those seeking to enter the new AI generated functions.

AI will provide a new frontier of tech related positions as workers transfer out of jobs that will be quickly eradicated or slowly phased out due to the recent advancements in automated and AI technologies. A recent McKinsey study even suggests some 8.6 million occupational shifts already took place between 2019 and 2022 — 50 percent more than in the previous three-year period. Without question, many people left their previous roles and landed better-paying jobs in more specialized occupations.

A brief examination of available AI positions demonstrates the competitiveness of recruiting topflight talent with AI salaries outpacing the rest of the tech market at a significant rate:

AI engineers are in high demand, and their salaries are rising rapidly. According to a report by ZipRecruiter, the median salary for an AI engineer is $140,000, which already represents a 20% increase from last year.

Data engineers are also in high demand, and their salaries are rising nearly as fast. According to a report by Indeed, the median salary for a data engineer is $120,000, a 15% increase from last year.

At Apple, the median software engineer makes $287,000 a year, including salary, bonus and stock awards according to, a tech salary tracking site. If we believe the next Apple is going to be built atop a substrate of AI technology, it’s a safe assumption that the demand for these positions will only continue to rise, perhaps to the level of an Apple engineer? At present this demand is driven by a scarcity of talent.

In a Growing Fintech Market, Current AI Applications Only Scratch the Surface

According to the World Economic Forum and the Cambridge Center for Alternative Finance, 85% of Financial service providers use Artificial Intelligence in some form, and as of 2020, AI’s implementation across the fintech ecosystem has been applied as follows: 58% use AI for fraud prevention, 41% finance processes and analysis, 33% cyber-security, 33% personalization of products and services, 31% customer care, and 25% asset maintenance.

Breakthroughs in generative artificial intelligence have the potential to bring about sweeping changes to the global economy, according to Goldman Sachs Research. As tools using advances in natural language processing work their way into businesses and society, they could drive a 7% (or nearly $7 trillion) increase in global GDP and lift productivity growth by 1.5 percentage points over a 10-year period.

However, according to estimates, the market for AI in fintech will reach $31.71 billion in 2027, growing at a rate of 28.6% year-over-year. Already, AI is being applied in a number of different ways. The Cambridge Centre for Alternative Finance reached a consensus that 90% of fintech companies already use AI in base forms.

In the world of Fintech, AI has been enhancing customer experience by advancing software  and targeting consumer needs.

  • Upstart makes more informed lending decisions. Upstart analyzes a wide range of data about borrowers, including their education, employment history, and credit history, to assess their risk of default, offering loans to borrowers who may not be approved for loans by traditional lenders.
  • Robinhood improves customer support. Robinhood’s AI-powered chatbot can answer customer questions about their accounts, investments, and trading. The chatbot can also create personalized investment recommendations for customers.
  • Persado develops personalized marketing campaigns for its clients. Persado AI algorithms analyze large amounts of data about customers’ financial needs and preferences to generate marketing messages that are more likely to resonate with them. The AI can generate personalized email messages tailored to each customer’s individual financial situation and goals.
  • PayPal detects fraud and improves the accuracy of its risk assessment models. PayPal’s AI analyzes large amounts of data about transactions and user behavior to identify patterns of fraudulent activity. It also uses AI to assess the risk of fraud associated with each transaction.

Enterprise AI Hiring in a Booming Healthcare Market

AI can also contribute to the rebuilding and redefinition of traditional sectors, creating new job opportunities. For example, in the healthcare sector, AI can enhance medical diagnosesexpedite drug discovery and support new modalities of care like telemedicine. This paves the way for professional profiles like “health data analysts,” who will interpret data collected by AI systems in order to provide more precise and personalized diagnoses. Ultimately, this practice will evolve to generate one-off medications or treatment plans that are fully personalized, enhancing the field of precision medicine exponentially.

According to a study from MIT, 75% of healthcare facilities that utilized AI reported improved capacity to manage illnesses, and 80% of facilities said the AI aided in reducing employee fatigue. AI in healthcare is a promising strategy for the future of medical delivery – providing patient data analyses at an exponential rate and tracking changes in patients receiving medical attention while reducing the physical and psychological strain on a clinical workforce spread thin and overworked since the pandemic began more than three years ago.

Using healthcare as a model, let’s take a comprehensive look at the ways in which AI can enhance and fundamentally alter healthcare:

1. Automated health record analysis: Automated health record analysis is already one of the most common applications of AI in healthcare. Using algorithms to analyze significant quantities of medical data helps an array of healthcare professionals discern patterns and identify trends that can be used to provide the appropriate treatment and improve patient care. This type of AI-based analysis is being used by a number of different organizations, including the Mayo Clinic, IBM, and Google. This means individual patients receive better care bolstered by large data sets of anonymized patient charts.

2. Predictive analytics for population health management: Healthcare organizations are also using predictive analytics to manage the health of large populations. This involves using machine learning algorithms to predict future health risks, analyze patients’ symptoms, and evaluate needs based on data from a variety of sources including electronic medical records, claims data, and patient surveys. AI in telehealth is also being used to assist physicians in providing care. A number of companies are working on AI-powered telehealth solutions, including Teladoc, which offers a platform that uses machine learning to provide real-time insights to doctors during in-person consultations. Equity is one of the hidden benefits here, because so much of our medical knowledge base is the result of studies and care provided to people of northern european descent. Broadening the base of knowledge means better outcomes for the rest of us.

3. Remote Patient monitoring and engagement: RPM (remote patient monitoring) has proven to be one of most popular approaches for managing chronic diseases. RPM is a way to gather and transmit patient health data to medical specialists outside of a doctor’s office or a clinical environment via linked technology, often in the form of wearable devices. Just this week Oura released a new AI product that analyzes stress. This type of technological advancement enables both health professionals and patients to take a proactive role in treating and managing chronic diseases too, as well as other conditions which can result in improved health outcomes and reduced healthcare costs. Monitoring devices and other wearables (particularly in diabetes treatment) are being used to collect patient information and other vital signs like heart rates, sleep patterns, and physical activity levels. This data is transmitted to a secure server where it can be accessed by primary care physicians, specialists, and other healthcare providers. Anomalies can be flagged automatically and interventions administered much faster.

4. Medical Training: One example of how AI is being used for medical training is by providing virtual reality (VR) simulations. These simulations can provide a realistic and immersive experience for healthcare professionals (HCPs). One company that is working on this technology is Medical Realities, which offers a VR platform that allows HCPs to experience different medical procedures.

In addition, another example of how AI is being used for medical training is by providing online courses that are tailored to the individual learner. This type of training can help learners focus on the areas where they need the most improvement. Coursera is one company that offers such courses. Their AI in Healthcare Specialization includes four courses that cover a variety of topics, including machine learning, natural language processing, and predictive analytics. This not only improves comprehension for different learning types (auditory, visual, etc.), but the more frequently a user learns with AI then the more efficiently the AI learns how to instruct them.

5. Assist in delivering healthcare to patients via telemedicine services: AI is also being used to assist in delivering healthcare to patients via telemedicine services. These are some of the top telemedicine firms utilizing AI today:

  • Babylon Health uses AI to provide personalized healthcare advice and recommendations.
  • Ada Health uses AI to help users diagnose their symptoms and connect with the right doctor.
  • K Health uses AI to help users diagnose their symptoms and develop customized treatment plans.
  • Lemonaid Health uses AI to fill out medications for common conditions that require a prescription.
  • TytoCare is a telemedicine company that uses AI-powered devices to help users perform physical exams at home.

Despite the advent of AI, McKinsey recently released data, emphasizing that the largest future job gains are expected to be in healthcare, an industry that already exists in a state of imbalance, with 1.9 million unfilled openings as of April 2023. Consequently, the use of AI will merely function as a tool for greater job growth in a booming healthcare sector desperately in need of qualified candidates. In fact, it’s estimated that there could be demand for 3.5 million more jobs for health aides, health technicians, and wellness workers with cutting edge softwares and technologies paving the way for exponential job growth.

While these figures and opportunities in healthcare represent a growing job market, all of the companies explored here – healthcare, Fintech, or otherwise – will require high level executives with a working knowledge of AI as well as AI models and structures within their given industry. Although we are currently at the forefront of Enterprise AI, there will need to be a pathway carved out at the executive level for those who are hoping to take on the responsibilities of managing AI goals and the ethical usage of this burgeoning technology at the enterprise level.

In a booming AI field still in the early stages of development, the new opportunities don’t just exist at tech companies. Rather, they may be applied to AI in sectors like fintech and healthcare, as discussed above. All of these AI related positions will require tactical operators who will function in engineering roles as well as possessing a unique set of forward thinking, problem-solving skills. At the executive level, these positions will require verbally adroit and highly skilled executives who can sell an AI vision to a board of directors, carving out a new upwardly mobile career path specific to the needs of AI as its usage becomes even more commonplace and essential to a company’s growth.

Enterprise AI Strategy