{"id":18819,"date":"2026-03-03T06:19:11","date_gmt":"2026-03-03T06:19:11","guid":{"rendered":"https:\/\/multiqos.com\/blogs\/?p=18819"},"modified":"2026-03-03T06:19:11","modified_gmt":"2026-03-03T06:19:11","slug":"generative-ai-for-enterprise","status":"publish","type":"post","link":"https:\/\/multiqos.com\/blogs\/generative-ai-for-enterprise\/","title":{"rendered":"6 Ways to Use Generative AI for Enterprise for Maximum ROI"},"content":{"rendered":"

Generative AI has been pivotal for enterprises. Especially, the kind of automation that generative AI has been offering has transformed most enterprises’ functions. However, a key question still remains- \u201cDoes generative AI increase revenue?\u201d This question became a talking point following <\/span>MIT\u2019s report<\/span><\/a> that 95% of AI pilots failed. What this report has done to CEOs and CFOs is create an environment of scepticism around gen AI revenue use cases.\u00a0<\/span><\/p>\n

This article clears all the scepticism around some of the best gen AI revenue use cases for enterprises. It also covers a phased approach to implement and a decision matrix to choose the right use case.\u00a0<\/span><\/p>\n

Top Revenue-Generating Use Cases of Generative AI for Enterprises\u00a0<\/b><\/h2>\n

Revenue generation needs a multi-faceted approach. Organizations need to boost conversions and, at the same time, improve cost savings to maximize revenues. And generative AI for enterprises has been at the forefront of technologies, helping businesses with higher cost savings. <\/span>A Gartner survey<\/span><\/a> shows 81% of organizations citing cost savings as a major outcome of implementing generative AI technologies<\/a>.<\/span><\/p>\n

However, it differs from organization to organization based on the type of use cases their implementations align with. For example, the same report shows some organizations showing 81% of cost savings due to higher productivity gains. However, there are others as well, with 54% of cost-saving generation due to lower productivity gains. This is why understanding the use cases becomes important.\u00a0<\/span><\/p>\n

\"Top<\/p>\n

1. Hyper-Personalized “Next Best Experience” (NBE) Orchestration<\/b><\/h3>\n

Hyper-personalized Next Best Experience (NBE) orchestration is currently the most significant use case of generative AI for enterprises. It is helping companies move from reactive support to proactive value creation. Hyper-personalized NBE is an AI-driven, real-time customer engagement strategy. NBE engines synthesize real-time data to determine the precise moment and method to engage a customer, effectively creating a “digital twin” of customer interactions.<\/span><\/p>\n

Unlike static segmentation, this approach uses dynamic data (transaction history, weather, social interactions) to trigger engagement. For instance, Starbucks utilized its<\/span>\u00a0“Deep Brew” AI<\/span><\/a> to align offers with inventory and weather, generating 4 million additional visits in early 2024.<\/span><\/p>\n

How to Plan for the Implementation of NBEs for Your Enterprise?<\/b><\/p>\n

Applying NBEs requires an understanding of how they work. NBEs need a unified infrastructure, a trigger mechanism, and automation configuration.\u00a0<\/span><\/p>\n