Since generative Artificial Intelligence (AI) models entered the mainstream in 2022, the AI bandwagon has continued to gain momentum, with no signs of slowing. The scale of investment flowing into AI-led initiatives has created a strong sense of urgency, and, in many boardrooms, a fear of missing out. For many executives, it has become almost essential to include AI in corporate narratives, strategy presentations, and investor statements, regardless of their own understanding of the matter or whether the organization has developed a practical roadmap for its adoption.
The irony is that incomplete, inaccurate, and sometimes hallucinated outputs produced by AI systems are often accepted as gospel truth, while the more measured and pragmatic views of technology leaders are overlooked. The excitement around AI is understandable, but an impatient endeavour will only lead to stalled pilots, which is visible across the industry.
A common misconception among business executives is that AI is the solution to every technology problem and that it can operate autonomously from day one. AI is undoubtedly a compelling and transformative technology, but it still requires experienced professionals to design, deploy, supervise, and manage it on a sustained basis. Given the speed of evolution, a framework developed today may no longer be relevant tomorrow or become legacy in no time; hence, continuous oversight.
Stringent Guardrails Needed
Like any other enterprise technology initiative, AI also requires a robust security framework. In fact, because of its probabilistic nature, wide-ranging access to information, and ability to generate or act on content, AI demands even more stringent guardrails. These safeguards are essential to prevent data leakage, inaccurate decision-making, regulatory breaches, operational disruption, and unintended consequences within enterprise systems.
With DPDPA 2023 around the corner, the data protection and privacy aspect of AI is not yet fully understood. There are exceptions, though, like USD 900 million Meta’s investment in Cred, which, according to claims, is primarily to get hold of millions of crème de la crème Indian users’ expense patterns, before DPDPA makes it difficult to analyse them for targeting marketing without consent.
Rising Costs
Cost is another factor that is often underestimated. Conversational AI, generative AI, and autonomous agent-based models can involve significant expenditure, depending on their architecture, usage patterns, integration requirements, and the way they are configured. A carefully designed combination of deterministic systems and AI-led capabilities may offer a better total cost of ownership for enterprises. However, this approach requires active involvement from business teams with clear knowledge of business processes, an area where tech leaders usually find huge resistance owing to ignorance or lack of knowledge.
AI cannot be effectively integrated into a process unless the process itself is properly understood. Business teams must help define the workflow, exceptions, decision points, data dependencies, and expected outcomes. Only then can AI be woven into the process in a way that creates measurable value rather than merely adding another layer of technological complexity.
The Human Angle
More recently, several prominent technology experts have raised concerns about the protection of intellectual property while using AI models. These concerns are genuine in several use cases. In conversational AI interactions, users may unknowingly disclose proprietary information, confidential business knowledge, internal strategies, technical designs, legal positions, or operational insights.
Human behaviour can further intensify this risk. People often enjoy demonstrating the depth of their knowledge, particularly when interacting with a system that appears intelligent and responsive. In doing so, they may reveal far more than they intended. Such interactions can potentially expose valuable organisational knowledge to external AI platforms, especially where data retention, model training, and information-handling practices are not clearly understood.
This is where the analogy of the Trojan horse becomes relevant. AI may enter the enterprise as a powerful productivity tool, welcomed for its ability to improve efficiency, accelerate decision-making, and transform business processes. Yet, if adopted without adequate governance, it may also carry hidden risks relating to data, intellectual property, security, cost, accountability, and organisational dependence.
The real question, therefore, is not whether AI is an angel of transformation or a Trojan horse. It has the potential to become either.
The outcome will depend on how responsibly enterprises adopt it. Organisations that combine ambition with governance, innovation with security, and automation with human oversight are more likely to benefit from AI. Those that embrace it without understanding its limitations may eventually find themselves controlled by the very technology they expected to control.
AI should neither be feared nor worshipped. It should be understood, governed, and deployed with purpose.
