One of the key elements for success of enterprise AI initiatives is to work on the right AI use cases that can enable them to create long-term value for themselves. My previous article on AI use case landscape in BFSI: Guide to start your AI journey highlighted AI opportunities in the BFSI space through a unique periodic table representation. The breadth of AI use cases that BFSI enterprises can implement to start their AI journey are detailed out in the article, enabling them to understand the “art of possible”. However, before enterprises start their journey it is important that they prioritise the right use cases to accelerate their AI journey and this article talks just about that.
A comprehensive framework encompassing all the critical parameters is necessary before making the business case for working on an AI use case. Enterprises will have to evaluate the use cases thoroughly based on the respective organization’s priorities, vision, roadmap, culture and capabilities. Our recent report describes a simple, yet powerful, use case prioritization matrix based on a combination of business impact and ease of implementation considering existing capabilities and limitations of the enterprise. The below framework, prioritizes the use case categories and provides a directional view for enterprises who want to start or scale their AI journey.
For details on each of the use case categories download our full report here
Factors considered for use case prioritization: Business Impact and Ease of Implementation
Business Impact=f (Customer experience, Employee experience, Operational efficiency, Reduced business risk)
Ease of Implementation=f (Technology complexity, Data complexity, Ecosystem presence, Regulatory risk)
The four quadrants in the use case prioritization framework represent use cases that are both in pilot / POC stage and those implemented in an enterprise. Specific AI technology used in each of the use cases, (from ML algorithms to computer vision, NLP, deep learning and others) were identified while scoring these use cases. The complexity of the data (structured, unstructured, etc.) required for implementation of the use cases have also been taken into account while assigning the impact and ease of implementation score.
Enterprises can start with “Quick wins” – use cases that have high business impact and but are also easy to implement, or “Big bets” – use cases having high business impact but also higher implementation effort, or retailers can look to implement “Incremental” use cases – uses cases that are easily implementable but may not have a huge impact on business.
Image Credits: TechInsight, Pymnts, Emerj
The pandemic has affected MSMEs the most, and technology can help them get back on their feet. The critical factors that MSMEs consider while implementing AI uses cases are cost effectiveness, ease of implementation, high business impact and low business risk.
Priority use cases for MSMEs – chatbot, digital KYC and personalized product recommendation.
Our full report on Indian BFSI – Unlocking the Transformation Potential of AI provides further details of these use cases, AI in retail ecosystem and bold plays for retailers to traverse the AI maturity curve. Watch out this space for more interesting articles on AI
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