Despite all the fears about how AI would replace human beings, it is very clear that in many knowledge-intense tasks, AI would augment human work. Humans and AI would complement as well as compete with each other.
In such a situation, both humans and AI require both cooperative and competitive skills. They need to know the advantages of the other in specific tasks and also understand when and how they can work together. Organisations must ensure that humans and AI work together and think about how human skills can work in tandem with technological capabilities.
Humans cooperative skills.
AI technology need not necessarily replace humans in human-centric work but it can help transform human work. Humans need to work with AI technologies and get the best out of it.
This requires data-driven analytical skills as well as an understanding of the capabilities and limitations of the machines. they should also understand the ethical considerations of AI-based decision-making.
- Data-centric skills: Humans must become skilled to understand the results generated by AI. They should understand and distinguish relevant data and have the capability to validate results through hypothesis testing.
- AI literacy: Humans must get to know how AI algorithms work, how they can complement human decision making and at the same time, continuously audit the program for its accuracy and suitability.
- Communication: Humans need to understand the human needs and objectives of AI programs and at the same time interpret the results. Humans should understand that AI programs need inputs in a certain format to build on their strengths. By using the inputs properly, humans can teach AI to produce the desired results.
Human competitive skills.
Humans also need to sharpen certain human-centred abilities that cannot be replicated by AI programs. These include:
- Emotional Intelligence: The ability to recognise your emotions and reflect on them in the context of interacting with AI programs is important while interacting with the emotional impacts of AI-generated results. The results given by AI programs need to be personalised using our understanding of the emotional requirements of the users.
- Holistic thinking: This is the ability to understand the big picture in the context of the decisions taken and how the AI results fit into the overall context of the problem. This is necessary to arrive at an informed and comprehensive solution.
- Creativity & unique thinking: This is the ability to think creatively about AI-recommended solutions in novel and innovative ways. AI recommends solutions based on massive consumer data and identifying patterns and it is for the end users to be creative and craft a solution that is suitable for their audience.
- Ethical thinking: Humans must have the capability to understand the ethical implications and responsibilities associated with using the solution recommended by AI programs. Humans need to work along with AI systems to address the potentially biased information that the systems may be trained on.
AI’s competitive and cooperative skills.
AI systems are rapidly evolving and expanding their abilities but they still need to improve their cooperative skills to be widely used by organisations. The lack of explainability remains a big challenge in high-risk decision making and this may contradict the legal requirements and bring up compliance issues
AIs’ cooperative skills:
- NLP(Natural language processing): This refers to the ability to understand, analyse and process human language. AI systems are capable of interacting with humans as they make it easy for people to ask questions and naturally express them. However, these systems are not sensitive to the real situation which is best done by human supervision.
- Explainability: The results of AI programs may seem mysterious at times and this is an ongoing challenge that requires building an explainability framework. This is particularly important in critical areas where the results have a very high impact.
- Adaptability: AI systems are quick to learn from previous interactions though they still are lacking in developing personalised responses based on human responses. These tools are being used extensively to direct day-to-day activities and the systems must be capable of working collaboratively with the individual user to enhance their productivity.
- Context awareness: AI systems must be capable of understanding the context in which the interaction takes place and respond quickly. They should gain the ability to offer solutions that are more relevant to user needs.
AI competitive skills.
- Analytical capacities: AI systems are becoming more capable of performing complex calculations, processing huge amounts of data quickly and identifying patterns within data.
- Generativity: Using large amounts of data, GenAI is transforming the creation of images, text and even videos that are on par with what humans can generate. These systems can automate content generation, improve quality and offer personalised content.
- Performance at scale: AI systems today are capable of handling large real-time transactions and supporting large applications efficiently. It can help create a structured consistent operational framework at a huge scale.
Working with and against the machines.
The challenge for organisations is to design and build systems that balance the cooperative and competitive skills of humans and AI systems.
- AI systems can generate large insights of data but translating this into a competitive edge requires human strategic thinking and creativity. To achieve this companies should democratise access to data at all levels to make workflows efficient and serve the end customer.
- Skills updation of existing employees is a primary requirement. Companies should look outside the organisations and hire experts to rapidly scale up their capabilities while the existing employees upskill themselves.
- Today technical work can be done from anywhere and hence geography has become irrelevant to finding the required skills anywhere in the world. Enabling remote work strategies would help companies capture the skill sets required and win the race against the technology as well as competition.
Focusing on how humans and AI can work in tandem and use their strengths would help companies benefit from both their strengths as well as retain control of the result by enabling human supervision taking into consideration the ethical and legal requirements. this relationship would help maintain their relevance and help them work as a team to achieve the overall goals of the organisation.
What Will Working with AI Require?
by Mohammad Hossein Jarrahi, Kelly Monahan, and Paul Leonardi
HBR 2023/06
R2G 