This is the second part of a series of articles on how GenAI is revolutionizing Rev-Ops functions in enterprise technology businesses. In the first part, we saw some broad applications of Gen AI in Rev Ops. In this article, we will explore how GenAI use cases are being developed and deployed in the first of the  four core Rev Ops pillars - business strategy and planning. A little bit about myself : I am a GTM veteran & Sr. Director of RevOps at Cisco. Business Planning - what is it, how is it done, and what is changing? Business Planning is the primary cross-functional performance in the corporate ballroom. Lead actors from Product, Engineering, Finance and Sales participate in this song-and-dance routine with Rev Ops called upon to choreograph the arrangement as tightly as possible. The perennial challenge in these situations is that everybody performs to their own sheets of music with not-so pleasant outcomes for the company or stakeholders. A single source of truth for business planning has historically been the holy grail of corp-strategy and execution if ever there was one. Enter language models with context awareness and generative capabilities. Generative AI enhances data-driven decision-making in business planning by providing advanced tools and capabilities that improve the efficiency, accuracy, and depth of insights. Here are some ways it contributes to this process: Democratising Data Accessibility and Usability: Generative AI makes data more understandable and accessible to people across different functions in an organization. Finally, we can move past domain specific jargons and inconsistent data structures by giving average user the ability to query and understand data in natural language. By combining this with intuitive interfaces and user-friendly data presentations, it democratizes data access and encourages a more inclusive approach to decision-making.


Real-Time Insights and Analysis: Generative AI can process vast amounts of structured and unstructured data quickly, providing executives with real-time, actionable insights. This capability allows businesses to identify trends and patterns that were previously invisible, enabling more informed decision-making.


Dynamic Scenario Planning and Modeling: Generative AI models can drastically lower the reaction times of business to emerging trends and geo-political situations, by allowing businesses to quickly simulate various scenarios and plan contingencies. This helps in testing different strategies and predicting outcomes without the need for extensive real-world experimentation.


Automation and Efficiency: While there has been a lot of discussion on AI replacing analysts and other workers, the more pertinent use case of AI in this space is in augmenting human capability. The automation of repetitive and time-consuming tasks through generative AI allows teams to focus on higher-value activities. This not only boosts operational efficiency but also supports a more agile business model that can quickly adapt to market changes. In the following articles, we will continue these discussions across Marketing, Sales, and Customer Success. This is the second part of a series of articles on how GenAI is revolutionizing Rev-Ops functions in enterprise technology businesses. In the first part , we saw some broad applications of Gen AI in Rev Ops. In this article, we will explore how GenAI use cases are being developed and deployed in the first of the  four core Rev Ops pillars - business strategy and planning. In the first part In the first part A little bit about myself : I am a GTM veteran & Sr. Director of RevOps at Cisco. Business Planning - what is it, how is it done, and what is changing? Business Planning - what is it, how is it done, and what is changing? Business Planning is the primary cross-functional performance in the corporate ballroom. Lead actors from Product, Engineering, Finance and Sales participate in this song-and-dance routine with Rev Ops called upon to choreograph the arrangement as tightly as possible. The perennial challenge in these situations is that everybody performs to their own sheets of music with not-so pleasant outcomes for the company or stakeholders. A single source of truth for business planning has historically been the holy grail of corp-strategy and execution if ever there was one. Enter language models with context awareness and generative capabilities. not-so pleasant outcomes for the company or stakeholders. not-so pleasant outcomes for the company or stakeholders. Generative AI enhances data-driven decision-making in business planning by providing advanced tools and capabilities that improve the efficiency, accuracy, and depth of insights. Here are some ways it contributes to this process: Democratising Data Accessibility and Usability: Generative AI makes data more understandable and accessible to people across different functions in an organization. Finally, we can move past domain specific jargons and inconsistent data structures by giving average user the ability to query and understand data in natural language. By combining this with intuitive interfaces and user-friendly data presentations, it democratizes data access and encourages a more inclusive approach to decision-making. Real-Time Insights and Analysis: Generative AI can process vast amounts of structured and unstructured data quickly, providing executives with real-time, actionable insights. This capability allows businesses to identify trends and patterns that were previously invisible, enabling more informed decision-making. Dynamic Scenario Planning and Modeling: Generative AI models can drastically lower the reaction times of business to emerging trends and geo-political situations, by allowing businesses to quickly simulate various scenarios and plan contingencies. This helps in testing different strategies and predicting outcomes without the need for extensive real-world experimentation. Automation and Efficiency: While there has been a lot of discussion on AI replacing analysts and other workers, the more pertinent use case of AI in this space is in augmenting human capability. The automation of repetitive and time-consuming tasks through generative AI allows teams to focus on higher-value activities. This not only boosts operational efficiency but also supports a more agile business model that can quickly adapt to market changes. Democratising Data Accessibility and Usability: Generative AI makes data more understandable and accessible to people across different functions in an organization. Finally, we can move past domain specific jargons and inconsistent data structures by giving average user the ability to query and understand data in natural language. By combining this with intuitive interfaces and user-friendly data presentations, it democratizes data access and encourages a more inclusive approach to decision-making. Democratising Data Accessibility and Usability: Generative AI makes data more understandable and accessible to people across different functions in an organization. Finally, we can move past domain specific jargons and inconsistent data structures by giving average user the ability to query and understand data in natural language . By combining this with intuitive interfaces and user-friendly data presentations, it democratizes data access and encourages a more inclusive approach to decision-making. average user the ability to query and understand data in natural language average user the ability to query and understand data in natural language Real-Time Insights and Analysis: Generative AI can process vast amounts of structured and unstructured data quickly, providing executives with real-time, actionable insights. This capability allows businesses to identify trends and patterns that were previously invisible, enabling more informed decision-making. Real-Time Insights and Analysis: Generative AI can process vast amounts of structured and unstructured data quickly, providing executives with real-time, actionable insights . This capability allows businesses to identify trends and patterns that were previously invisible, enabling more informed decision-making. real-time, actionable insights real-time, actionable insights Dynamic Scenario Planning and Modeling: Generative AI models can drastically lower the reaction times of business to emerging trends and geo-political situations, by allowing businesses to quickly simulate various scenarios and plan contingencies. This helps in testing different strategies and predicting outcomes without the need for extensive real-world experimentation. Dynamic Scenario Planning and Modeling : Generative AI models can drastically lower the reaction times of business to emerging trends and geo-political situations, by allowing businesses to quickly simulate various scenarios and plan contingencies. This helps in testing different strategies and predicting outcomes without the need for extensive real-world experimentation. Dynamic Scenario Planning and Modeling Dynamic Scenario Planning and Modeling Automation and Efficiency: While there has been a lot of discussion on AI replacing analysts and other workers, the more pertinent use case of AI in this space is in augmenting human capability. The automation of repetitive and time-consuming tasks through generative AI allows teams to focus on higher-value activities. This not only boosts operational efficiency but also supports a more agile business model that can quickly adapt to market changes. Automation and Efficiency: While there has been a lot of discussion on AI replacing analysts and other workers, the more pertinent use case of AI in this space is in augmenting human capability. The automation of repetitive and time-consuming tasks through generative AI allows teams to focus on higher-value activities. This not only boosts operational efficiency but also supports a more agile business model that can quickly adapt to market changes. In the following articles, we will continue these discussions across Marketing, Sales, and Customer Success.

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Reimagining Rev-Ops with Gen AI: Always-On Strategy and Planning

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