Though Artificial Intelligence (AI) has been around for a long time, it is only in the past decade that we have seen its explosive adoption across industries, functions, and geographies. Algorithmic advancements, lower investment costs, and the advent of big data have together made it easier for enterprises of all sizes and industries to invest in building AI use cases.
Having said that, AI adoption levels vary among industries. Customer-sensitive and technology-intensive verticals such as BFSI and technology & communications experience far higher AI adoption than others. This is in part because different industries have different goals with respect to AI.
Whatever be the goal, one thing is clear: enterprises are increasingly deploying AI to achieve different business objectives. Everest Group categorizes the use of AI to drive business impact into four broad categories, depending on the kind of value it delivers:
- AI for efficiency
- AI for effectiveness
- AI for experience
- AI for evolution
AI mature verticals such as BFSI and technology and communication are leveraging AI for not just a better stakeholder experience but also for their overall evolution as well.
In this report, we analyze 230 large enterprise-class AI adoption use cases, identify 38 unique use cases across different industries, and map them on our AI for business impact framework to recognize how enterprises are leveraging AI and the impact they are deriving from it and to understand the driving factors for AI adoption.
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