Whether in healthcare, finance, logistics, or retail, AI's ability to analyze vast amounts of data, offer insights, and automate critical processes is revolutionizing business operations. However, as businesses increasingly adopt AI, several key challenges remain. Among the most pressing concerns are security, scalability, and transparency, three essential pillars that companies must address for AI to deliver sustainable and reliable outcomes.
Satya Kaliki, CTO - Infra.Market, emphasized that AI should primarily serve as a tool that assists human decision-making, helping individuals make more informed and efficient decisions. In his view, every organization should start by integrating AI into their decision-making processes with a clear understanding that while AI provides valuable insights and recommendations, the final decision rests with human operators. He added that the adoption of AI should focus on augmenting human capabilities rather than replacing them entirely. By adopting AI as a collaborative assistant, businesses can maintain control while benefiting from the technology's ability to process large volumes of data and generate insights faster than any human could.
Every decision-maker should start using AI as an assistant or an agent to support their decisions by providing data. Satya Kaliki
Giving an example, Shivani Karia Jhaveri, Co-Founder and COO of Blox, shared that their firm provides clients with a platform where both direct-to-consumer (D2C) and broker-led business-to-business-to-consumer (B2B2C) sales models can coexist. “It incorporates AI and data analytics to help clients explore properties, check prices, and connect with brokers without the hurdles often associated with traditional broking. This shift is particularly important in a country like India, where the process of purchasing a home can often be opaque and intimidating.”
The hybrid model of D2C and B2B2C channels empowers both individual buyers and business entities. Shivani Karia Jhaveri
AI systems built with reinforcement learning or transfer learning techniques are designed to learn from their mistakes. AI learns rapidly from the feedback it receives, which is a significant advantage over human decision-making processes, where mistakes may often be repeated, concurred Ankur Prabhakar, Partner, Deloitte India.
Transparency in AI is key to its successful integration. People need to understand how AI works and how decisions are made. Ankur Prabhakar
AI security is not just about protecting organizational data; it is about ensuring that AI algorithms cannot be manipulated or used for malicious purposes. Abhijeet Kumar recommended implementing multi-layered security protocols, such as encryption, access controls, and regular vulnerability assessments, to safeguard AI systems from both internal and external threats.
Organizations should have a clear governance structure to protect their data and ensure that AI operates within ethical and legal boundaries. Abhijeet Kumar
Manoj Dhanotiya, Founder and CEO of MicroMitti, urged organizations to adopt a proactive stance when it comes to security. As AI systems become more integral to business operations, the potential risks associated with data breaches, algorithm manipulation, and cyberattacks only increase. Therefore, investing in AI-specific security measures and ensuring that security protocols evolve alongside AI technology is essential.
Scalability doesn't just refer to the volume of data AI systems can process. It also involves ensuring that AI models are adaptable to organizational needs. Manoj Dhanotiya
Building a Strong Governance Framework
As more data and organizational context are fed into AI systems, they become more attuned to specific business needs and challenges. With each iteration, AI can provide increasingly personalized insights, making it an invaluable tool for businesses looking to optimize their decision-making processes. Abhijeet Kumar, Co-Founding Partner at ah! Ventures Fund, highlighted the importance of a strong governance framework to ensure that AI deployments remain secure and ethical. He emphasized the need for businesses to clearly differentiate between public data and private organizational data when training AI systems. A lack of understanding in this area can lead to the unintended exposure of sensitive business data.
By establishing a comprehensive governance framework, businesses can ensure that AI is used responsibly and securely. Such a structure should outline specific AI use cases, ensure compliance with data protection regulations, and provide clear guidelines for ethical AI usage. Satya Kaliki advocated educating employees on these guidelines is essential to foster a culture of responsibility and vigilance when dealing with AI systems.
Ankur Prabhakar also addressed the security challenges that arise with AI adoption. With the increasing complexity of cyber threats, organizations must prioritize security when integrating AI into their operations. Ankur stressed the need for robust guardrails to protect AI systems from potential exploitation. “Having a robust set of guardrails around your AI operations is crucial,” Ankur said. “Without strong security measures and controls in place, AI systems can be vulnerable to exploitation, leading to catastrophic consequences.”
Shivani Karia Jhaveri, elaborating in the context of the real estate broking industry, stated that historically, buying a home in India could be a complex and overwhelming process. But with a digital-first approach, clients can now experience a transparent property-buying journey. Real-time data, AI-driven analytics, and intuitive user interfaces eliminate the guesswork and uncertainties commonly associated with real estate transactions. Brokers operating within this structured framework are incentivized to act ethically, ensuring a higher standard of service that benefits both clients and the broader industry.
