By Kapil Makhija, Head-Technology Cloud, Oracle India
Artificial Intelligence (AI) and Machine Learning (ML) have become the technology anchors for many organizations looking to ensure business continuity and growth in the new decade. Per Gartner, by 2025, 10% of enterprises that accept AI/ML engineering best practices will garner a significant three times more value from their AI/ML efforts, vis-a-vis 90% of enterprises that don’t.
AI/ML have been pillars of strength for many businesses and have directly resulted in innovation and better outcomes. A significant example of their applicability is visible today, in how marketers are personalizing offers to customers – be it shopping offers or financial investments or loan offers and more. Further this has helped companies measure their customer satisfaction levels using advanced ML algorithms. Businesses are pushing the boundaries of AI/ML led innovation to stay relevant and be successful leaders.
As these technologies become mainstream, enterprises are acknowledging the extensive advantages of cloud infrastructure and applications with built-in ML and AI algorithms. They are allowing businesses to truly venture on their digital transformation journeys, one of the key attributes being by letting their resources spend lesser time on mundane, operational tasks, and instead dedicate themselves to more mission critical, business driven and customer centric ventures. Businesses that efficiently use ML for IT workloads will ultimately lower IT infrastructure costs and India is gearing to be one of the leaders driving this change. Per a report by Accenture, leveraging AI could potentially result in adding a significant USD 957 billion to the country’s economy in 2035, provided the right investments are made towards new age technologies.
With extensive resources like acquiring the status of having the third-largest startup ecosystem in the world, to building an evolving, new age education environment and with the abundance of talent, India is best poised to take full advantage of AI’s true potential and capabilities. Important to note though that there exist limitations while using AI and the primary one is implementation. This can range from lack of experts in data science to inability to extract real time performance of the platform. This leads to organizations’ reluctance in truly depending on AI and this contributes towards inconsistencies and lack of results. To resolve this it is imperative to start building the requisite skills in AI and ML.
While an army of data science PhDs may seem like a utopian situation given most companies won’t have enough resources for it; a more practical solution would be to build “ML Ops” teams which are similar to DevOps teams in application development, but smaller and focused. Along with data scientists, such teams could also have developers as well as IT engineers, all focusing on deploying, maintaining, and constantly improving AI/ML models for business growth.
Once inconsistencies like skill gap and the initial reluctance are left behind, AI/ML will be crowned kings of the technological world leading to a significant revolution. In fact, according to Forrester, one in five organizations will double down on AI and ML embedded in their systems and operational practices. As organizations accept new and emerging technologies for transformation, improving organization growth trajectories will continue to be the ultimate goal with a key requirement to get the maximum value out of their investment. For this, AI/ML use cases, though still under experimentation, have shown extensive potential with key solutions like precision in data pattern detection, quick detection of problems and bettering cyber security and more. As the world becomes more unpredictable, harnessing AI/ML is the best bet to navigate through the onslaught of any eventuality and have better chances at not just sustaining but growing the organization and achieving all digital transformation goals.
