The buzz around AI and the possibilities it offers has grown tremendously over the last few years. From the rare use of AI to write hit musical numbers or tell fortunes to practical and life-changing applications across sectors such as healthcare and agriculture, AI applications range from the mundane to the advanced.
This exponential rise in the possibilities, nay, the reality offered by AI applications has been powered by several factors: the rise of big data exemplified by the omnipresence of social media, internet-connected machines and devices, bigger and stronger compute powered by public cloud providers and the evolution of sophisticated algorithms that can crunch massive amounts of data and offer transformational insights. Enterprises have taken to this journey in their individual ways.
While digital native startups globally have unleashed the power of AI to reach their customers online and give them a great experience thus winning market/wallet share, traditional enterprises, barring a few exceptions, have been a tad shy of using AI to enhance their front office operations and largely experimenting with it to augment middle and back offices, boosting the bottom-line (profitability).
The touchpoints and channels a business uses to interact with its customers has increased in number along with the quality of the interactions. This has resulted in a business being able to observe and capture the customer’s online behavior and transactions they make.
Digital native startups have been first out of the gate to identify more improved ways of gathering customer data and using AI to hyper-personalize their customers’ experience. They have been quick to adapt AI powered customer insights, crunching data across buying or consumption frequency, brand preference, location, channel of choice, product feedbacks, social network linkups … the list is endless. More robust data capture mechanisms help create a customer 360-degree view.
The central driving business models of new-age companies such as food delivery businesses, cab aggregators, edtech and fintechs pivots on AI across various components of their business from customer recommendations to promotional/personalized offers, seamless payment operation or ways to maximize customer experience.
Given the large current market share, traditional businesses have been increasingly focusing on using AI to streamline operations in the middle and back office by tracking their processes better and gathering more operational parameters. Their immediate problem is to use AI to serve their existing customers in better and profitable way.
More traditional and regulated businesses such as financial services have largely focused on leveraging AI to handle and manage their risk applications such as early warning systems, underwriting processes and fraud detection. Similarly, large manufacturing companies such as automotive, consumer goods and pharma industries have started using AI for predicting machine failures, inventory management, supply-chain and logistics insights.
The adoption of AI across industries has been inorganic, with each sector and each enterprise prioritizing the use of AI in areas driven by their immediate business needs, whether it be to improve company’s top-lines (at digital native companies) or optimize costs (at traditional enterprises).
These examples display that there is no one path to instilling AI into the fiber of an organization. While digital natives start using AI with customer experience as primary motive and move towards operational efficiencies as they progress, traditional enterprises are focusing on operational excellence to begin with and moving towards improving customer experience over a period of time.