For AI to help us, first we must help AI: Why AI needs humans
To overcome this challenge, the language surrounding
AI integration
should emphasise this human-centric approach. Specifically, AI should be portrayed as a tool that empowers employees. It should be seen to augment human capabilities, requiring human guidance and input, rather than as a replacement for the workforce.
AI providers should proactively engage with customers to explore potential applications, initiating discussions, identifying client pain points, and demonstrating how AI solutions can effectively address these challenges. In India, this could mean contextualising solutions for multilingual data sets, region-specific user behaviours, or local compliance needs like those under the
Digital Personal Data Protection Act (DPDPA).
Human direction: Driving efficiency and service innovation
The classic IT adage, 'garbage in, garbage out,' is particularly pertinent for AI. If the data used to train or operate a model is poor, the outcomes will be similarly lacking. Data scientists and engineers are crucial in ensuring data acquisition, cleansing and lifecycle management for AI applications. Their responsibilities include evaluating data quality, identifying appropriate datasets, and reviewing data governance practices. Should the data be substandard, AI experts may suggest improvements in data cleaning, structuring, or governance before proceeding.
Even in production environments, human oversight is crucial for directing and controlling AI applications. For example, in process optimisation, AI and machine learning can reveal insights that humans can leverage to enhance efficiency. This scenario is a reversal from the training phase: AI now provides the data, while humans process this information by interpreting and acting on it to refine processes, innovate services, and enhance decision-making. We are fast approaching a world where humans and AI agents collaborate seamlessly to boost efficiency, but with humans still very much in the driving seat.
In Indian enterprises – especially in sectors like logistics, e-commerce, and smart manufacturing – this collaborative model is enabling faster decision cycles, improved customer experiences, and data-driven innovation. We are fast approaching a world where humans and AI agents collaborate seamlessly to boost efficiency, but with humans still very much in the driving seat.
Human responsibility: Maintaining and monitoring AI
Many AI projects struggle to advance beyond the proof-of-concept stage, often due to insufficient maintenance resources. This underlines the need for continuous human involvement, not only to prevent model decay but also to guarantee the long-term success, adoption and scalability of AI initiatives. Change management is also a crucial part of its success.
For instance, human oversight is indispensable to ensuring that AI models maintain their accuracy and relevance over time. This is especially vital in complex, evolving environments where data inputs and conditions can change.
Similarly, human experts play a crucial role in monitoring the performance of AI models by identifying and addressing emerging issues that could hinder functionality or accuracy.
Human context: Addressing India's AI challenges
In India, regulatory clarity is still emerging. However, government-led initiatives like
IndiaAI
, and academic collaborations between IITs and the industry, are laying the foundation for scalable and ethical AI. Experts in law, ethics, and policy will play a crucial role in aligning innovation with local regulatory frameworks, such as the DPDPA, ensuring a safe and inclusive growth.
Human-centric AI
AI holds great promise, but its models require care. Only with human oversight can this technology fulfil its potential to deliver the efficiencies promised. Furthermore, when humans maintain control, AI becomes a powerful tool that not only enhances their capabilities but also optimises workflows and fosters greater innovation.
As Indian enterprises accelerate their digital transformation journeys, placing humans at the centre of AI strategies will ensure not just business impact – but ethical, scalable and context-aware innovation.
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