However, while tools like GPT4, Claude 2, and DALL-E 3 are making their way into enterprise workflows, many users don’t feel equipped to use them. A study conducted by Salesforce revealed that 62% of workers think they don’t have the skills to effectively and safely use the technology.
With more and more organizations incorporating AI into their operations, there are some essential skills that employees need to have if they want to thrive, from soft skills like communication and problem-solving to technical skills like programming and prompt engineering.
10 Skills For An AI-Dominated Workplace:
Problem-solving is one of the most important soft skills employees need to exercise in AI-dominated environments. With AI, employees can use their capabilities to identify problems and inefficiencies in the workplace and propose automated solutions to fix these issues and optimize processes.
At the same time, problem-solving is also critical for employees to respond quickly in scenarios where AI doesn’t work as intended.
Another vital skill to have is communication. Any employees who want to advocate for more AI adoption or the automation of particular processes/workflows need to have the ability to communicate the technical and non-technical benefits of this technology to other employees and stakeholders.
Having the ability to communicate the value of AI to other employees and stakeholders can help to achieve greater support for automation initiatives. It’s also critical for helping to respond to and address the concerns of those concerned about adoption.
8. Data Prep
Insights are only as good as the data they’re based on, and machine learning-driven solutions often require input data to be structured in a certain format. This means that you need to know how to prepare data for analysis by AI models if you want to generate high-quality insights.
Knowing how to select a data sample (taken from one or multiple sources), clean, format, and sort the data, as well as remove errors and blank values, will help you to get better results from adoption.
7. Fact Checking
If you want to use generative AI to inform decision-making or create content, then you need to be adept at fact-checking its output. Chatbots like ChatGPT tend to hallucinate and make up details, so it’s essential to double-check that any factual information provided by LLMs is legitimate before acting on it.
In addition, you’ll also want to consider the underlying bias or prejudice that LLMs may have on specific topics. Generally, aim to verify all claims, request citations where possible, and cross-check with a reputable third-party source.
Mathematical principles have created the foundation that modern AI solutions rely on. As such, knowing mathematical concepts like linear algebra, probability, and calculus helps you understand how technologies like machine learning, deep learning, and natural language processing work.
It’s important to note that you don’t need to be a mathematics expert to get results from AI, but learning about these concepts can help you find out what’s going on under the hood.
Creativity is another indispensable soft skill for using AI effectively. AI is a relatively new technology, and while use cases are being increasingly well-defined, new ways to use AI are being discovered daily. Being open-minded and experimenting with AI can help you discover new solutions to problems you haven’t considered before.
Exercising your creativity and being willing to play around with AI models helps you find new ways to use the technology and prepares you to keep up as these solutions evolve.
4. Model Evaluation
To measure how an AI model or project is performing, you need to know how to evaluate it. Learning to evaluate models with quantitative metrics like F1 Score and RMSE or via qualitative testing, alongside optimizing hyper parameters and fine-tuning the results, will allow you to improve an AI model over time.
Determining where your model is performing well and what could be improved with further fine-tuning provides the opportunity to work toward continuous improvement.
3. Data visualization
Knowing how to create data visualizations is essential for translating insights from datasets into a format that’s easy for other stakeholders to understand. After all, dashboards, graphs, and charts make it much easier to identify patterns and anomalies in your training data.
There’s only so much information that the human brain can process at once, and breaking datasets down into accessible displays ensures that non-technical users can easily understand what’s happening.
Knowing how to program gives an employee the skills they need to start building custom AI-driven platforms or products. It’s also an excellent way for employees to increase their overall employability.
1. Prompt Engineering
With generative AI all the rage at the moment, brushing up on prompt engineering is critical for getting the most out of tools like GPT4 and Claude 2. Prompt engineering is the practice of creating inputs for generative AI tools that will produce optimal outputs.
This comes down to knowing how to write prompts in a format that an LLM can understand. The better your initial query, the more detailed and accurate the output will be.
While all the above skills are integral to thriving in an AI-dominated workplace, don’t think you need to be a computer scientist or experienced programmer to get value from AI.
Tools like ChatGPT have shown that all you need to begin is a little open-mindedness and a willingness to experiment with the technology at your disposal to work more efficiently.