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3 skillsets that are "safe" with AI

(Image credit: Image Credit: PHOTOCREO Michal Bednarek / Shutterstock)

With digital calculators (opens in new tab) that can tell you the likelihood that your job will be automated in the future, it’s no wonder that people are questioning if a robot will replace them! However, there are some skills that machines just can’t emulate - like emotions, interpersonal communication, collaboration, creativity, and flexibility. What’s important to realize with the rise of Artificial Intelligence (AI) and automation is that even though they will inevitably cost some jobs, it will also create many others (opens in new tab). Take the ATM industry (opens in new tab), for example. Instead of ATMs putting bank tellers out of work, US bank-teller employment actually increased over the three decades between 1980 and 2010. The reason behind the growth was the added productivity gains presented by automation allowing bank-tellers to focus on communication centric aspects of the role. 

AI and machine learning are rising forms of innovative technology that can be used to automate mundane tasks within an organization and keep businesses productive. But in the future, they ultimately won’t replace many of the skills that humans have today. In a nutshell, organizations need to be able to anticipate change, readjust their strategies and come up with creative ideas to motivate staff and market products - just to name a few. This article will take a deep dive into the following three skills: communication, creativity and flexibility, that are “safe” with AI and can work in tandem with the technology to improve business productivity, keep customers happy, and brainstorm new ideas to problem solve with clients, better market products and work better as a team to reach common goals and impact business. 


While machines may be mastering communication between each other via telemetry, they still can’t communicate with internal and external stakeholders within the organization. The concept of telemetry involves remote machines and sensors collecting and sending data to a central point for analysis. Machine to machine (M2M) communication takes it to the next level by incorporating modern-networking technology like wireless sensors, the Internet and personal computers to better monitor infrastructure. Concepts like machine learning provide computers with the ability to learn without being explicitly programmed and is ideal for automating mundane decisions within an organization that don't require the expertise of an employee. Machine learning gathers new data and continuously makes business applications smarter. 

While there is no doubt that machines are starting to communicate better with one another and deliver actionable insights to users at the point of work, within their daily business processes, they can’t speak to humans, and only communicate with each other or the data that’s being fed into them. Machines lack the context needed for people internal and external to an organization to communicate emotions and interpersonal situations on a daily basis. Business decisions are often nuanced with a lot of different aspects that come into play that aren't black and white. Human context can make someone interpret an action or help with decision making in ways that AI just can't allow for. 

One example of this lies within customer service. If a customer has an issue with a product, it’s important to have a talented customer service team to meet their needs. The social, communication and empathy skills that humans have are critical to making an organization successful, especially if it’s one that interacts with people on a daily basis. It’s simple -- machines have no social skills!


Creativity is often defined as the use of the imagination or original ideas, and common terms associated with being creative include, cleverness, vision, inventiveness, and so on. When saying any of those words, machines don’t typically come to mind. And, while there are some that may argue that machines are becoming more creative than humans, there will always be limitations to this creativity (opens in new tab), and humans are an integral part in programming these machines. A big part of competitive advantage in the technology industry is being unique to competitors and standing out with innovative ideas, ways to market products and the ability to reach different customer bases. There is a need for creative professionals across every industry including healthcare, finance and retail, and this simply can’t be automated. 

Machines can handle mundane tasks while creativity is an exercise of thought. Even as technology advances to allow machines to perform human tasks with greater efficiency, creative job skills will need to be needed as machines are unable to truly understand the essence of different tasks and are not able to always fill in the gaps in our lack of understanding for how things work. 


One of the biggest human strengths is our ability to be flexible problem solvers. With machines, human intervention is still needed to problem solve in situations where the current rules don’t exist. The healthcare field is a good example here as there are instances where there isn’t a predetermined cure for a disease for a machine to pull from, and doctors need to think creatively about solutions for unique cases and rare diseases. Doctors are still struggling with their trust of AI (opens in new tab) and are hesitant to put their trust into self-thinking machines. While machines can play a big role in prevention, diagnosis, treatment plans, and drug creation, it’s most effective for doctors and AI to collaborate together. AI in healthcare is idyllic when it comes to improving wait time at clinics and reducing human error, but there’s a reason why they say medicine is an art and not a science. Doctors have better judgment and flexibility when it comes to a risky operation or a unique diagnosis.

Technology companies and leaders in major vertical industries like healthcare, finance and retail must push their employees to increase their conceptual understanding and problem-solving skills and let machines take on automated tasks like approving applications and scheduling. There needs to be flexibility within an organization in terms of strategic planning, brainstorming, and collaborating. Machines can’t replace a group of people in a room with different skillsets and experience that are problem solving. You simply won't be able to solve that problem when "rules" are involved. With mundane tasks automated, employees can be challenged to spend more time finding new approaches to problems and setting goals for team members. 

Looking ahead

Humans and machines will be successful if they work together and balance out each other’s strengths and weaknesses. Machines can make an organization extremely productive and ultimately create more jobs as the business continues to grow. Humans are still needed to interact with customers on a daily basis, come up with creative ways to market their product and communicate with team members. Being flexible is key as the technology industry is constantly changing and evolving, and humans are able to recognize the holes in the market and anticipate the need for change. Visionaries within organizations should look towards a future where automation stimulates job growth, and focus on designing a system that allows humans and computers to collaborate together if they want to be as competitive and successful as possible. 

Roman Stanek, CEO and Founder, GoodData (opens in new tab)
Image Credit: PHOTOCREO Michal Bednarek / Shutterstock

Roman Stanek is the CEO and Founder of GoodData. Roman is a passionate entrepreneur and industry thought leader with over 20 years of high-tech experience. His latest venture, GoodData, was founded in 2007 with the mission to disrupt the business intelligence space and monetize big data. Prior to GoodData, Roman was Founder and CEO of NetBeans, the leading Java development environment (acquired by Sun Microsystems in 1999) and Systinet, a leading SOA governance platform (acquired by Mercury Interactive, later Hewlett Packard, in 2006).