Due to the popularity of unreliable data sets, it is difficult for many organizations today to gain business value from artificial intelligence (AI). Low-quality data is one of the most important barriers to widespread adoption of AI, especially in enterprise environments. This is an essential part of humans in circular (HITL) services during data preparation.
AI models, especially complex models such as neural networks, are often seen as “black boxes” because their decision-making processes are not easily explained by humans. Throughout the model lifecycle, from model training to model fine-tuning, HITL ensures that data is properly curated, enhanced, annotated, refined and continuously monitored to maintain its relevance and accuracy.
HITL can also introduce vital field expertise in various industries such as liquidity, finance and healthcare. For example, in the case of large language models (LLM) being trained to implement new medical data sets, only trained physicians will be able to provide the necessary expertise to verify and verify data accuracy.
It is ethical for AI models, and there is an increasing need to adopt safe, secure and environmentally friendly AI methods. Strong AI ethics can include avoiding bias, ensuring privacy of users and their data, and mitigating environmental risks. Combining AI technology with human expertise is crucial to ensuring ethical decision-making, maintaining accountability and mitigating potential risks.
India’s AI talent pool
Due to its large, highly skilled talent pool, strong technical infrastructure and cost-effective operational environment, India provides unparalleled investment and acquisition opportunities for AI. The world’s second largest AI talent base, including more than 420,000 professionals, combines professional knowledge with affordability and provides competitive advantages for the domestic and outsourcing markets.
Also read: Human intelligence must guide artificial intelligence
India is leading the way in AI skills penetration and ranks highest among the top countries in AI recruitment rates, making it a hot spot for companies seeking to effectively expand AI operations.
India – From outsourcing hub to AI hub?
India’s reputation in IT services and software development positioned it as a natural choice for AI outsourcing. Major global companies are increasingly conducting AI development, data annotation and model training to Indian companies. Domestic demand further expands this potential. Microsoft has expanded $3 billion in investment in AI infrastructure in India, Telus International’s acquisition of games and Lionsbridge, UK International’s investment in Imerit, and investments in Krutrim such as Krutrim, like Indus Indus By Tech Mahindra Project by Tech Mahindra, Bharatgpt, Bharatgpt, Bharatgpt, Bharatgpt, Bharatgpt, Bharatgpt have lowered confidence in AI in India’s global investors. Whether meeting domestic demand in areas such as healthcare, agriculture and fintech, or serving as a global provider of AI project outsourcing, India offers a unique convergence of talent, innovation and scalability, making it an ideal destination for AI to focus on investment and acquisitions.
Hitl: Human, the edge of the tip?
A new business model has evolved in which players who provide high-quality data sets, model training and tuning services combine cutting-edge software and HITL expertise to provide AI data solutions for AI model builders and adopters with results-based software.
When choosing an outsourcing partner to train their AI models, businesses will use complex software as a key decision-making factor. Some key aspects will include the software’s ability to easily integrate with the enterprise’s data and technology stack, the ability to ingest and output multimodal data, integrated domain-specific tools, and modular workflow design.
Also read: Global AI and the Future of Work
As AI software companies continue to lead allegations of cutting-edge technology powered by companies around the world, demands for AI-specific data solutions are growing. AI data solution players that combine human expertise, cutting-edge software and domain expertise provide a strategic entry point for the AI ​​ecosystem. These companies are an important driver in the AI ​​value chain, providing solutions for high-quality data, model training and tuning solutions for customized AI models needed by enterprises operating in the intelligence/application layer. We expect the global TAM (total addressable market) of the AI ​​data solution space to reach USD 23 billion (CAGR 37%) by 2028.
AI has the ability to change the way we work, but we still need human intervention to get optimized output. This is a seemingly obvious idea, also known as the Moravik paradox. Human-led AI data solutions are the backbone of successful AI models. From data collection, planning and augmentation to ethical supervision and ongoing monitoring, human expertise ensures that AI systems are accurate, equitable and aligned with social values. As AI becomes deeply rooted in everyday life, collaboration between humans and machines will only become stronger and stronger, human data solutions play a key role in shaping the future of AI.
We believe that the demand for Indian talent and technology in the global AI competition will continue to drive investment in Indian companies and talents.
The author is Director and Head of Investment Banking at Avendus Capital.
