From automation to augmentation: Essential skills for building AI-ready teams

The Turing test has long been considered as the ultimate benchmark for whether Artificial Intelligence (AI) could match human-level intelligence.

Developed in the 1950s by Alan Turing, the Turing test involves a text-based conversation with a human interrogator, who has to determine if the other party is a human or machine.

In a March 2025 study conducted by cognitive scientists Cameron Jones and Benjamin Bergen, four of the latest Large Language Models (LLMs) were subjected to the test, including ELIZA, GPT 4.0, LLaMa-3.1, and GPT-4.5.

Participants in the study were required to converse for five minutes in conventional messaging interface, and then determine if the other party was a human or AI chatbot.

The results were overwhelmingly positive for the AI chatbots, with participants judging GPT-4.5 as human 73% of the time, while LLaMa-3.1 to be human 56% of the time.

The other two models fared less well, convincing participants only 23% and 21% of the time, respectively. The GPT-4.5 model was even judged to be human significantly more than often than actual humans.

In recent weeks, a new AI agent called Manus, developed by Chinese startup Butterfly Effect, further bridges the gap between conception and execution.

Although popularized as an indicator of machine intelligence, the validity of the Turing Test faces contention.

In fact, researchers and scholars frequently raise objections, arguing that it measures behavior rather than true thinking, which allows machines to pass the test without genuine intelligence.

Critics also believe that the test’s narrow focus on imitation makes it insufficient to assess true intelligence.

Researchers have noted that passing the Turing test does not mean AI-based technologies have human-level intelligence.

LLMs are an advanced form of pattern recognition that is able to predict what a correct answer might be based on large training data sets.

In my new book, Artificial Intelligence For Business, I delve into the exponential evolution of AI-based technologies and its deepening influence on many industries, from healthcare to financial services.

Rather than fearing the potential for AI to replace human capabilities, it becomes increasingly evident that these two forms of intelligence will become intertwined with one another.

In essence, the present technological advancement trajectory cannot be separated from this mutual relationship between human ingenuity and what we can do through AI.

How AI can augment humans, not just automate them

AI is not just replacing humans but augmenting them.

According to the World Economic Forum’s 2025 Future of Jobs report, it is expected that 33% of tasks will be performed through human-machine collaboration by 2030.

This shift signifies a growing trend towards augmentation, where AI and humans work together to achieve better outcomes.

The integration of AI into various industries is not merely about automating tasks but enhancing human capabilities and productivity.

This collaborative approach allows humans to focus on more complex and creative aspects of their work, while AI handles repetitive and data-intensive tasks.

The shift from automation to augmentation is evident in the way industries, and specifically HR departments, are adapting their workforce strategies.

While the WEF’s Future of Jobs report found that 82% of the reduction in human-only task performance will come from automation, 19% will come from increased collaboration between people and machines.

Many industries, especially healthcare, government, and education, are leaning towards augmentation-focused workforce strategies over full automation.

This strategic focus on augmentation highlights the importance of human involvement in critical areas, ensuring that technology serves as a tool to enhance human potential rather than replace it entirely.

As AI application expands exponentially across industries, building highly skilled teams with the right combination of capabilities has become a strategic priority for organizations to thrive in the digital business landscape.

It takes a combination of technical expertise, strategic leadership and a culture of innovation to build an AI-ready organization that embraces the potential of technology solutions to enhance and augment human capabilities.

Five skills needed to build AI-ready teams

1. Technical proficiency

Core technical roles are essential to develop, deploy and maintain AI-based technology solutions within the business environment.

These include data scientists and analysts, machine learning engineers, user interface designers, and software developers with expertise to ensure that the AI solutions are scalable and integrated effectively with the existing infrastructure.

To ensure AI application is user-centric and relevant for the business contexts, continuous cross-functional collaboration between the technical and business teams is essential.

2. Analytics and problem solving

Although technical skills are crucial, the ability to analyze complex problems and develop innovative solutions that meet the business needs are equally important.

Upskilling in this area could target teams with deep business expertise to develop capabilities in data analytics and interpretation, as well as model evaluation and optimization.

AI application typically involves massive data sets, and teams with the ability to utilize analytics tools to uncover patterns, trends and insights from the data in the context of the business will be essential.

Models that have been developed will need to be evaluated for performance and accuracy through testing and evaluation of the outputs.

Business teams with analytics capabilities will be able to optimise the models by adjusting algorithms and parameters to improve performance.

3. Prompt engineering

The ability to communicate effectively with AI-based technologies will become as crucial in the business worlds as human-to-human communication.

Prompt engineering, non-technical instructions provided to AI systems, is a critical skill for all levels in an organization.

By formulating requests in a way that leverages the full potential of the AI systems, teams will be able to extract the desired output.

Although AI-based technologies are becoming increasing adapt in natural language processing (NLP), communicating requests with well-crafted prompts can directly impact the quality of the outputs generated.

Effective prompt engineering is a non-technical skill that focuses on translating business frameworks, workflows and strategy into language structures that LLMs can understand and act upon.

4. Continuous learning and adaptability

Given the rapid pace of advancements in AI, it is essential for team members to stay updated with the latest trends and developments.

This commitment to ongoing learning ensures that the team can effectively incorporate new technologies and methodologies into their work, maintaining a competitive edge in the industry.

Embracing adaptability will ensure teams are able to quickly respond to changing industry demands and leverage the most advanced solutions available.

By fostering a culture of continuous learning and adaptability, organizations can ensure that their teams are well-equipped to navigate the dynamic landscape of AI and drive innovation.

5. Ethical considerations

For long-term success, building AI solutions that are fair, transparent, safe and responsible is critical.

AI-ready teams need to focus on recognizing and mitigating biases in AI models to ensure equitable outcomes for all users.

For example, prioritizing the protection of customer data and complying with key regulations such as GDPR is essential for maintaining data privacy.

Additionally, ensuring that data models developed can be easily explained to stakeholders will foster transparency and trust.

AI-ready teams must include in-depth capabilities in AI ethics and safety, ensuring sufficient human oversight and governance frameworks are established.

Building an AI-ready organization requires a conscious approach to upskilling teams with technical expertise, human-centric capabilities and commitment to ethical development.

By assembling well-rounded AI-focused teams, organizations will be able to drive competitive advantage and thrive in the digital future.