Building analytics capabilities doesn't equate to AI

The reason why the introduction of AI poses some unique questions is because, for the first time, the cost of human intelligence is heading for a free fall. AI has disrupted not only jobs which require physical faculties like driving cars but also industries like software and equity research which completely rely on human intelligence. Traditional business models have historically relied on better processes, procurement, machines and better employees with higher intelligence to grow and become more profitable. As AI gains popularity, and companies and their executives fall for AI’s ever-growing capabilities, it is essential to pinpoint that many often mistakenly equate any form of analytics system with having an AI infrastructure in place, even if they have not collected any relevant data.

It's important to note that while analytics play a crucial role in generating insights from data, it does not necessarily equate to having an AI infrastructure in place. Many organisations have invested heavily in data collection and analysis but have yet to delve into true AI applications. Although AI-driven applications can take the form of something as straightforward as a chatbot, true AI applications encompass a wide range of tasks, from machine learning in healthcare to robotics, natural language understanding and much more. However, as to what constitutes an AI application is often misunderstood. Some believe that only sophisticated systems capable of tasks like diagnosing diseases or autonomously driving cars qualify as AI, while more common applications, such as chatbots, are somehow less worthy of the AI label. It's crucial to understand that building a smart chatbot for customer assistance is just as much an example of AI as using AI models for pattern identification in MRI results.

As far as AI adoption is concerned, it's our belief that business leaders might be harbouring overly optimistic expectations at this stage. As AI becomes increasingly integrated into various industries, there's a looming potential for disappointment in terms of short-term Return on Investment (ROI). Although artificial intelligence (AI) has the potential to greatly improve business operations and decision-making, its benefits often manifest gradually which makes AI adoption a methodical, long-term investment rather than a quick fix. The potential ROI associated with AI adoption may also vary depending on the specific applications. This calls for the need to explore AI technologies and examine relevant use cases that can serve as enlightening experiences for organisations/businesses seeking guidance on embarking on their AI journey.

For the first time in the history of mankind, we have no idea what the future will look like in a decade. Historically, the life of an individual had a greater sense of stability or certainty when it came to skills required in the job market and the kind of jobs that will be around. Now with the advent of LLMs and its capabilities of even writing software programs, and with newer companies like OpenAI suddenly changing the way we look at human intelligence, we are indeed heading into unchartered waters. Only time will reveal which strategies prove effective in such circumstances. The only thing which is certain is that corporations around the world will have to incorporate AI into their core strategy, albeit not with very high expectations, lest it leads to an existential crisis.

(Pawan Prabhat and Paramdeep Singh are co-founders, Shorthills AI)