(This article was originally published in Nice Machine AI).

In 2023, the Automation Revolution kicked off in full gear.

And those wanting to remain employable better pay attention.

Fortunately, as small and big companies adopt AI, the need for talent with the right mix of technical “hard” skills and/or 20th-century “soft” skills is growing.

First, let’s dive into the hard stuff —

Technical AI (“Hard”) Skills

While math and computer science form the foundation, applied skills in programming and platforms open the most doors.

Python

According to BairesDevBlog, Python is considered a top programming language, on par with JavaScript or C++, and it’s one of the most used languages by businesses and enterprises. Python’s easy syntax, vast libraries, and flexibility for statistical modeling and machine learning make it a must-have technical skill in a data-driven world.

Python-based tools continue to dominate the machine learning frameworks, according to Kaggle’s 2021 State of Data Science and Machine Learning Survey.

SQL

Extracting insights from data starts by accessing and manipulating databases. Structured Query Language (SQL) provides this capability and remains essential for AI teams.

SQL helps companies manage and analyze their data, and those who can manage these databases remain in high demand across industries in the 2023 Most In-Demand Skills List by Linkedin. With so much untapped data in storage, SQL expertise is a prerequisite for mining core business value.

R, RStudio

While used less than Python, mastering R and RStudio is vital for many data scientists. R’s packages dedicated to statistics and visualization make it ideal for exploratory analysis.

According to the July 2023 TIOBE Index (July 2023), its significance within the data science and statistical analysis communities remains evident.

MATLAB, Octave

For engineers and scientists building algorithms, according to CodersLegacy, MATLAB is very popular for prototyping machine learning and deep learning. This programming language integrates numerical computing with visualization and a library of ready-made functions.

Octave, a free open-source language, is a viable alternative. Familiarity with either provides a versatile platform for designing and testing models before deployment.

Julia

Miss Julia is used in the data science and mathematical computation world, and it made the top 20 for the first time in the August 2023 TIOBE Index.

Julia is faster than Python, more suitable for writing large systems than R, and less expensive than Matlab.

The speed, scalability, and free, open-source nature make Julia an attractive alternative to the other three languages. However, she requires more programming skills to master.

TensorFlow, PyTorch, Scikit

According to Google Trends, TensorFlow, PyTorch, and Scikit are highly popular ML frameworks.

These three frameworks are oriented towards mathematics and statistical modeling (machine learning) instead of neural network training (deep learning), according to BMC, an autonomous digital enterprise company.

Cloud Platforms

Managing massive datasets and computations requires specialized infrastructure.

The major cloud platforms each offer AI-focused services and APIs. According to Statista, in Augist 2023, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform account for over 65% of global cloud computing.

Familiarity with these environments allows large-scale deployment and contributes to full-stack software capabilities.

Now let’s dive into the soft stuff–

AI Adjacent (“Soft”) Skills

Building algorithms is just the start. Domain knowledge and communication enable real business-value impact.

Communication Skills

With AI systems touching more business functions, communication and emotional intelligence are, perhaps, more crucial than ever (unless we want to mirror our machine counterparts and slowly become them because “it’s all about the data.”)

Explaining analytical findings, coordinating projects, aligning stakeholders, resolving conflicts, and making persuasive presentations call for articulate writing and speaking skills:

• Writing — Conveying technical concepts clearly in documents, reports, emails, and other written forms is essential for productivity. Strong writing distills complex details into digestible intelligence for audiences.

One AI tool — whether you’re hard-caked into Google, Microsoft, or Apple — that’ll effortlessly level up your grammar skills is Grammarly.

• Public Speaking — Presenting insights in convincing yet accessible ways expands data science’s influence. Effective public speaking builds trust, interest, and human connection.

Consider taking Dale Carnegie Courses or Find Your Voice workshops to supercharge these vital leadership skills and center your culture around humans — not machines ;-)

• Data Storytelling — Using data visualization, dashboards, and compelling narratives, effective storytelling makes statistics relatable. This helps teams extract insights for decisions with a human — emotive — touch.
• Listening — Truly hearing colleagues’ needs and questions allows AI talent to provide relevant solutions. Active listening strengthens collaboration.

Perhaps the best way to do this is to unplug from the grid for a bit?!

Business Strategy

Understanding how AI can transform business models in a digital world unlocks tremendous company value. Strategic thinking about operations, products, markets, and competitors provides context for where AI should target and how to increase business productivity, among many other benefits.

Leadership & Management

And as headcounts grow, AI groups need effective managers. However, leadership skills apply beyond the management track. Influencing colleagues, guiding decisions, representing teams externally, and resolving conflicts require tactical leadership (soft skills) abilities.

Consider enrolling in Harvard Business School Online to level up your skills.

Creativity

As Steve Jobs and Walt Disney helped illustrate, designing genuinely innovative solutions requires creativity. While technical skills enable creation — yes — imagination and ingenuity inspire it!

Ethics

With algorithmic systems profoundly impacting society, ethics knowledge is essential. Understanding biases, transparency, and responsible design helps technologists anticipate risks.

Adaptability

AI changes rapidly. Flexibility to learn new techniques and tools to adjust approaches prevents obsolescence. Adaptability will be vital to sustaining careers.

Problem-Solving for Both Hard and Soft-Skill Talent

Applying analytical and critical thinking to tackle complex problems with rigorous solutions is central to a data scientist’s and a senior manager’s value-add.

Individuals who develop technical and AI-adjacent abilities will, indeed, be in demand.

Education Paths

How can today’s students and working professionals build these capabilities? A range of educational options exist:

Traditional Degrees

Computer science, data science, statistics, applied math, and electrical engineering degrees establish core competencies. Leading programs also teach Python, SQL, modeling techniques, data visualization, and ethics.

Elite programs like MIT, Stanford, and Carnegie Mellon should provide invaluable AI preparation and network value. However, many less prominent universities also offer quality curricula.

Coding Bootcamps & Certificates

For focused-skills upgrading, coding boot camps and online certificates in data science and machine learning provide viable alternatives to multi-year degrees.

Leading boot camps such as Metis, General Assembly, and Springboard report, over 80% of graduates are placed in roles within six months.

MOOCs

Massive Open Online Courses (MOOCs) make AI education accessible to all. Platforms like Coursera and Udacity offer affordable courses from top universities in Python, TensorFlow, machine learning, and more.

While MOOCs lack in-person instruction and coaching, motivated self-learners can build skills with guided projects.

AI Master’s Programs

A new generation of graduate programs focuses exclusively on applied AI. They teach productization and teamwork using real-world problems.

Examples include CMU’s MS in AI, MIT’s MEng in AI, and Stanford’s MS in AI.

Internal “Reskilling” Development

Some companies offer technical training programs or rotations to build AI skills internally (reskilling). Real projects and mentors provide targeted learning within given business contexts.

Lifelong Learning

Given AI’s rapid evolution, learning can’t stop after formal education.

Lifelong habits of upskilling through online courses, conferences, and experiments prepare technologists and business leaders for continuous change.

Organizations must also support ongoing workforce education as AI matures. Training budgets, digital platforms, apprenticeships, and hiring for potential can help develop talent ahead of exact needs.

The Outlook for AI-Powered Careers

The accelerating pace of AI adoption leaves no doubt: We better prepare or be left behind. While some programming languages and frameworks will come and go, foundational soft skills will remain evergreen.

Blending technical depth with business savvy and communication abilities promises the brightest career opportunities. For those who want to focus solely on soft skills, reading a book (or two!) on machine learning is prudent. (We’re in the Automation Revolution, after all. Plus, your newfound knowledge will help build a deeper connection with your tech team!)

Whether pursuing traditional education, alternative credentials, or in-house development — the quest to integrate AI expertise with practical human experience should remain our collective north star.

Now, and always, let’s make sure our future is human-driven!

This article was originally published in Nice Machine AI (September 2023).