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Many organisations today struggle to derive business value from artificial intelligence (AI) due to the prevalence of unreliable data sets. Low-quality data is among the most significant barriers to widespread AI adoption, particularly in an enterprise setting. This is where human-in-the-loop (HITL) services become indispensable in the data preparation process.
AI models, particularly complex ones like neural networks, are often seen as ‘black boxes’ because their decision-making processes are not easily interpretable by humans. Across the model life cycle, from model training to model fine-tuning, HITL could ensure that the data is properly curated, augmented, annotated, refined, and continuously monitored to maintain its relevance and accuracy.
HITL can also bring in crucial domain expertise across various industries such as mobility, finance and healthcare. For instance, in a case where an Large Language Model (LLM) is being trained to implement a new medical dataset, only trained medical practitioners will be able to provide the necessary expertise to authenticate and validate the data accuracy.
For AI models to be ethical, there is an increasing need to take a safe, secure, and environmentally friendly approach to AI. A strong AI code of ethics can include avoiding bias, ensuring the privacy of users and their data, and mitigating environmental risks. Integrating AI technologies with human expertise is crucial to ensure ethical decision-making, maintain accountability and mitigate potential risks.
India’s AI Talent Pool
India presents an unparalleled opportunity for investments and acquisitions in AI due to its vast, highly skilled talent pool, robust technological infrastructure, and cost-effective operational environment. With the second-largest AI talent base globally, comprising over 420,000 professionals, India combines expertise with affordability, offering a competitive edge for domestic and outsourced markets.
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India leads globally in AI skill penetration and ranks among the top nations for AI hiring rates, making it a hotspot for companies seeking to scale AI operations efficiently.
India – From an Outsourcing Hub to AI Hub?
India’s established reputation in IT services and software development positions it as the natural choice for AI outsourcing. Major global companies are increasingly turning to Indian firms for AI development, data annotation, and model training. Domestic demand further amplifies this potential. Investments such as Microsoft’s $3 billion expansion in AI infrastructure in India, TELUS International’s acquisition of Playment and Lionsbridge, British International Investments in iMerit along with investments towards home-grown LLMs like Krutrim, Project Indus by Tech Mahindra, BharatGPT underscore the confidence global investors have in India’s AI talent. Whether addressing domestic needs in sectors like healthcare, agriculture, and fintech or serving as the outsourced vendor for global AI projects, India offers a unique convergence of talent, innovation, and scalability, making it an ideal destination for AI-focused investments and acquisitions.
HITL: Humans-In-The-Loop, the Cutting Edge?
A new type of business model has evolved where players that offer high-quality data sets, model training and tuning services combine cutting-edge software and HITL expertise to deliver outcome-based software-delivered AI data solutions for AI model builders and adopters.
Enterprises use sophisticated software as a key decision-making factor when choosing outsourcing partners to train their AI models. Some key aspects would include the software’s ability to easily integrate with the enterprise’s data and tech stack, the ability to ingest and output multimodal data, integrated domain-specific tools and a modular workflow design.
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As AI software companies continue to lead the charge in developing cutting-edge technologies driven by global enterprise adoption of AI, there is a growing requirement for domain-specific AI data solutions. AI data solutions players that combine human expertise, cutting-edge software and domain expertise offer a strategic entry point into the AI ecosystem. These companies are essential enablers in the AI value chain, providing solutions with high-quality data, model training and tuning solutions for custom-built AI models required by businesses operating in the Intelligence/Application layer. We expect the global TAM (Total Addressable Market) of the AI data solutions space to reach $23 billion by 2028 (CAGR of 37 percent).
AI has the power to change the way we work completely, but we still need human intervention to get an optimised output. This is a seemingly obvious idea, also known as Moravec’s Paradox. Human-led AI data solutions are the backbone of successful AI models. From data collection, curation and augmentation to ethical oversight and continuous monitoring, human expertise ensures that AI systems are accurate, fair, and aligned with societal values. As AI becomes more ingrained in daily life, the collaboration between humans and machines will only grow stronger, with human data solutions playing a pivotal role in shaping the future of AI for the better.
We believe the demand for Indian talent and technology in the global AI race will continue to drive investments in Indian companies and talent.
The writer is managing director and head, enterprise technology & services investment banking at Avendus Capital.