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In the coming years there won’t be many opportunities for organizations, small and large alike to avoid Data Analytics & Artificial intelligence (AI).
AI is getting a lot of attention and is showcasing potential for usage across a wide variety of industries including banking, non-banking finance, healthcare, life sciences, insurance, logistics and retail.
As we look at the current adoption rates, analytics and AI is making existing businesses smarter by unlocking power of data that organization collects. Collective advancements in machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP) have also made it easier than ever before to embed AI algorithms into organization’s existing software applications.
This is primarily true for a few ‘early adopter’ organizations, that have deployed these new technologies or are in different stages of deployment.
Such organizations are expected to pull away from the pack and will probably be AI’s eventual winners. Rest of the organizations would fear falling behind in the competitive landscape and would need to rush to leverage these technologies.
Organizations would need to evaluate their in-house capabilities to design and deploy new tools and specific technologies that are at the heart of data analytics in general and AI in specific.
Here is a short list of such areas:
Apart from management buy-in commitment/ investment for such initiatives in identified business areas; capability gaps, technology fitment and its application become key areas to be deliberated.
Few of the organizations we are interacting with are undertaking due diligence exercise to assess new business demands, study current state, prioritize technology application areas, determine changes needed, seek recommendations to choose best-fit technology option and plan phased implementation. Unlike in the case of earlier technology adoptions, for AI waiting would carry higher risks.
Category: Data Analytics, RPA & AI