Human skills and best practices in the age of AI

It took TikTok nine months and Instagram more than two years to reach the 100 million user milestone. ChatGPT needed just two months. According to a UBS report provided to Reuters, the OpenAI chatbot is the fastest-growing consumer application in history.

But what does this really mean for the future of work? Can artificial intelligence replace human intelligence?

Tim Serewicz and Randall Thornberry Vasquez from the Linux Foundation offer a more measured response to the artificial intelligence (AI) hype: “Hold onto your jobs people, everything’s fine,” Serewicz said.

AI uses computer science and robust data analytics to teach computers to mimic human decision-making and tasks, such as interpreting images or translating written language.

While AI is exciting and buzzy, it’s not entirely new. The technological concepts powering AI have been around for more than 60 years. AI is created and powered by people. And while it can be powerful, it best serves as a replacement for repetitive, simple tasks.

Serewicz pointed out that while AI is a great tool, not all efficiency is useful. “We're still not finite machines. We're still organic,” he said. “Because it's a flawed system, we should not think it's perfect. People should be central to this process.”

As industries and leaders look to adopt AI into their tech stacks, centering humans will be an important part of making sure new tools are effective, sustainable, and fair. It’s also up to people to help fill the gaps and provide context that AI is missing with human skills and knowledge.

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1. Strategic decision-making

When deciding whether to use cutting-edge technologies, you may need to weigh factors like speed, security, and practicality against each other. Being able to think strategically about these decisions is critical.

Serewicz and Thornberry Vasquez recommend a few considerations when deciding whether to implement an AI tool. Before deciding whether to rely on external tools (or develop your own), first ensure that security isn't compromised when choosing a model to work with or use. Once that's established, assess if your team has the expertise to create your own tools.

Courses on strategic decision-making:

• The University of Maryland, College Park: Making Evidence-Based Strategic Decisions

• edX: Generative AI for Business Leaders

2. Creativity

While AI can excel at processing vast amounts of information, human creativity enables people to think beyond existing patterns. New technology will require new approaches to problem-solving and implementation. Creative thinkers will help lead the way in finding connections between domains and maximizing the transformative potential of AI.

Courses on creativity:

• Imperial College London: Creative Thinking: Techniques and Tools for Success

• The University of Queensland: Design Thinking and Creativity for Innovation

3. Experimentation

Serewicz and Thornberry Vasquez recommend observing how AI functions before rushing into implementation. The iterative processes of experimentation, which includes hypothesizing, testing and refining, is a vital part of this process. Serewicz recommends testing a commonly misunderstood part of your business to see what AI thinks is the appropriate answer based on its training. This can help with risk reduction, innovation, and make your processes more adaptable to changes.

Courses on experimental thinking:

• Boston University: Driving Digital Innovation through Experimentation

4. Ethics

How might certain customers or employees be biased against AI implementation? How transparent do you have to be about your use of AI with your audience? How can you build trust between organizations, customers, and AI applications?

Learning data ethics can help leaders and organizations implement the ethical guardrails that consumers want.

According to the Cisco 2022 Consumer Privacy Survey (PDF, 4.2 MB), three-quarters of consumers would feel more comfortable with AI applications if organizations instituted an AI ethics management system or explained how the algorithm works.

Courses on data ethics:

• The University of Edinburgh: Data Ethics, AI and Responsible Innovation

• Tokyo Institute of Technology: Science, Engineering, AI & Data Ethics

• The University of Michigan: Data Science Ethics

5. Domain-knowledge

General purpose AI tools are missing the specific backgrounds of questions they’re asked. “All ChatGPT was trained to do is calculate the likelihood of the next word. It has no deeper context beyond the parameters in which it is trained,” Thornberry Vasquez said.

These tools do not have vital information, such as why you’re asking that specific question or what would be most useful to learn. Human expertise in a domain is needed to add that nuance and discern what is and is not a helpful AI response.

6. Communication

Effective communication enables non-technical professionals to articulate their requirements and expectations from AI systems, ensuring that AI technologies align with specific business needs. Moreover, strong communication skills are crucial in explaining AI-generated insights and recommendations to stakeholders, building trust, and facilitating informed decision-making.

Courses on communication:

• University of British Columbia: Business Communications

• Rochester Institute of Technology: Public Speaking

7. Emotional intelligence

Emotional intelligence (EI) helps in developing empathy, understanding human emotions, and responding with sensitivity, making AI-human interactions more positive and meaningful.

A Capgemini report shows that 76% of executives believe AI will increase the demand for emotional intelligence skills (PDF, 2.9 MB), as employees will have more people-facing roles. As AI technologies become more prevalent in various aspects of life, EI enables individuals to maintain a human touch, especially in roles like customer service, healthcare, and education, where emotional connections are paramount.

8. Collaboration

Non-technical professionals are just as important to a team using AI as tech-experts. Collaborative efforts between AI specialists and domain experts lead to better AI system design and implementation, meeting specific needs and addressing real-world challenges.

Courses on teamwork:

• Rochester Institute of Technology: Teamwork & Collaboration

• IBM: Soft Skills: Collaborate Effectively

9. Research skills and methods

Research methods empower professionals to gather and analyze relevant data, ensuring that AI initiatives are evidence-based and align with organizational goals. Serewicz and Thornberry Vasquez brainstormed a few examples of important questions to ask about AI: How was this algorithm made? Who owns it and what data was it trained on? Where will your information go once it is used as an input?

Effectively evaluating AI models and tools will require understanding how to find and interpret detailed information about algorithms and AI research.

10. Leadership

AI can seem daunting, risky, and destabilizing for many people who don’t understand how it works or will affect their career. Strong leaders will be able to guide their employees through digital transformations with confidence and while maintaining psychological safety. At the same time, an effective leader can foster an environment of innovation and collaboration that can shape the ethical and responsible use of AI in an organization.

Courses on leadership:

• Babson College: AI for Leaders

• State Bank of India: Leadership in the Digital Era

Last updated: September 2023