Introduction
The rapid rise of artificial intelligence has amplified many sectors, but it comes with a challenge: recent studies reveal that AI models trained with AI-generated data begin to produce increasingly degraded and nonsensical outputs over time. This phenomenon, known as “model collapse,” highlights that AI cannot thrive in isolation. For anyone working in AI, it is clear that the journey still requires significant human involvement at every step.
We saw the significance of curating data, training models, prompt engineering, fine-tuning, and reinforcement and making them all deeply human-led activities. What might look effortless from the outside is, in reality, the product of immense cognitive talent and careful orchestration—key to transforming raw data into intelligent decision-making tools
The Importance of Data Curation: How Human Talent Leads the Way
The foundation of AI lies in data curation. Data drives machine learning, and without high-quality datasets, an AI model is doomed to fail. Unlike AI-generated data, which can lack diversity and nuance, data curation by skilled humans ensures that datasets are clean, contextual, and rich in the diversity needed for models to learn effectively.
This cements the importance of focusing on domain-specific datasets for AI in customer service. From our experience, by employing iCXeed cognitive talent to curate this data, we ensured that the AI models that are built and trained are primed for real-world customer interactions—enabling more natural self-service interactions.
The Role of Human Talent in Training Models
Humans play the crucial roles of overseeing training processes, ensuring biases are minimised, and validating outcomes. Domain expertise does not just revolve on training models with generalised data — it also involves the use of specialised, industry-experienced knowledge to create models that understand specific domain nuances.
We recognized that AI developers need to bridge human insights with AI, ensuring that the solutions we deliver are truly representative of the customer service domain they are intended to impact. Our result? AI models that are robust, contextually aware, and genuinely helpful to customers they support.
Prompt Engineering: A New and In-Demand Skill
Prompt engineering has emerged as a key career field in AI development. It is how you give instruction to AI to receive user inputs, process understanding of the inputs to decide a course of action, and executing that course of action to deliver the desired output. These prompts quickly evolve to a complex list of instructions to guide AI throughout this entire process. This skill, which blends creativity with an understanding of AI’s underlying logic, is where human intelligence meets cutting-edge technology.
Further, a study by MIT Sloan found that:
Human-AI Collaboration: Prompt engineering still relies heavily on human expertise. About 75% of effective AI outputs depend on well-crafted prompts, highlighting the irreplaceable role of human cognitive talent in maximising AI’s potential
Impact of Specificity: Providing a well-defined, specific prompt can improve AI output quality by up to 60%. More explicit input often leads to more accurate and useful responses, reducing the need for repetitive corrections.
Iterative Process: Prompt engineering often involves an iterative approach, where 60% of prompts are refined multiple times to ensure better context retention and interaction quality during long AI conversations
The growing demand for talent in this field is foreseeable for many years to come. It is then necessary to leverage deep expertise to craft precise prompts for customer service scenarios, enabling AI to better serve both customers and businesses -—a process that requires human cognitive talent. iCXeed nurtures this cognitive talent, combining technical expertise in prompt engineering with human agility to craft exceptional customer service experiences tailored to meet specific client and customer needs.
Fine-Tuning and Reinforcement: Continuous Human Involvement
AI models require fine-tuning and reinforcement to adapt to changes and improve over time. This is similar to the ongoing training an employee might need to grow in their role. At iCXeed, we provide a managed service where we apply analytics to continually optimise AI interactions. Much like how a business coaches its employees, we monitor AI, provide guidance, and continually upgrade its capabilities.
Data engineers analyse the decision making of AI solutions and evaluate the base data set being leveraged. Based on our experience, the opportunities for improvement identified in this fine-tuning process are either opportunities for improved prompts or improved data. Once this is determined, they can then further refine output of the model to optimise performance of particular scenarios with either a more enhanced prompt or an improved data set of knowledge. This fine-tuning process is continuous.
It is an ongoing loop of supervision, coaching, and training—fueled by human insight. At iCXeed, our goal is to deliver AI solutions that evolve, staying responsive to changing business and consumer needs. This may seem automated, but the real application of AI is very dependent on human instruction and human provided knowledge. This is an iterative process that nurtures higher performance over time.
Final Thoughts
The core message is simple—cognitive talent is not just important; it is indispensable for effective AI design, deployment, and ongoing operations. Whether it is through data curation, prompt engineering, model training, or reinforcement, the human element remains a prominent factor in delivering high-quality AI solutions.
At iCXeed, we believe in leveraging AI to complement human abilities, rather than replace them. The goal should be to improve the overall experience of customers and employees. AI creates opportunity for cognitive human talent, and its impact should augment human support teams to create an environment where stronger human connection can flourish. It is a tool that can help drive added convenience and increase efficiency in resolving service requests from customers. It is creating new categories of employees to help build and maintain these solutions. It is this cognitive talent that makes it genuinely transformative.