Artificial Intelligence (AI) and Machine Learning (ML) has been fueling the digital transformation wave in the country and revamping the technological landscape. Now, organizations are increasingly considering these types of investments to create differentiated services for its customers, thereby driving competitive advantage.
A recent survey by Deloitte covering over 300 senior executives across organizations in India indicates that 63% of them believe AI/ML is important to company success and 86% organizations expect to increase their investments in AI/ML by an average of 29% in this fiscal year.
In recent years, this space has witnessed unprecedented interest with numerous AI/ML based startups disrupting traditional ways of solving problems or doing business. Lately, this ecosystem has also experienced a gripping investor enthusiasm and a boom in funding.
How do you succeed in the AI/ML age?
While advanced technologies like Deep Learning, Natural Language Processing, Computer Vision form the core of quite a few AI startups, it is important to note that the path to success for AI/ML driven startups involves more than just technology.
These organizations need to truly imbibe AI at a strategic, cultural and operational level in order to scale and drive sustainable value. For organizations to achieve this deep AI-business integration and ensure value realization, they must put together three key pieces of the AI puzzle – AI strategy, risk management, and AI-enabled workforce.
As a startup scales up, having a well-thought AI strategy is of paramount importance. Key strategic choices such as – centralization vs decentralization, build vs buy, the right platform, capex management, industrialization of AI – all need to be deliberated and debated. Putting in place a roadmap for this journey goes a long way towards minimizing the risk of business disruption and easing change anxiety.
Then comes the growth journey of AI and its share of risks. These risks are compounded by the very nature of AI – its potential for automated decision making (autonomy) and our limited ability to follow and troubleshoot AI logic (explainability). Considering these nuances, organizations must anticipate AI-related risks and build comprehensive frameworks to pre-emptively mitigate them. The implications of being reactive could range from reputational damage and revenue losses to severe regulatory and litigious consequences
Arguably the most obvious and yet enormously stubborn problem for startups to solve is ensuring availability of AI talent. The advancements in AI technologies and use cases have far outpaced the availability of an AI-ready workforce. Organizations need to focus across stages of talent management, including attracting, engaging, managing, retaining, and upskilling the workforce to grow and remain relevant.
Source: yourstory.com
