How Machine Learning Met Economics to Change the Game for Cloud Businesses

As machine learning continues to evolve, its intersection with economics and revenue optimization is starting to reshape enterprise environments, particularly in the cloud industry. One professional who has spent the last several years at this intersection is Pavan Mullapudi, an applied science leader who has led efforts to build a scalable, machine-learning revenue-optimisation platform and frameworks that are now widely accepted by professionals who are actively seeking enterprise economic growth.

At a time when enterprises are under increasing pressure to grow revenue efficiently, Mullapudi and his team focused on a question that many organisations are still grappling with: Can machine learning not only predict customer behaviour but also influence enterprises to add economic value?

The effort culminated in a machine learning-powered optimisation platform designed to parse massive volumes of usage telemetry and recommend prioritised growth actions for sales, marketing and finance teams. The outcome: cloud sellers and analysts get a ranked list of revenue opportunities embedded directly into their workflow, with contextual explanations tied to measurable business levers.

The machine-learning revenue-optimisation program he led impacted multibillion-dollar annual recurring revenue. Deal-level conversion rates for enterprise opportunities rose by ≈ 15 per cent, while customer behaviour predictions improved by 50 per cent over prior baselines. The system now analyses terabyte-scale telemetry each week, surfaces ranked actions to more than 10,000 sales professionals and provides transparent reports to ≈ 30,000 business users across sales and marketing.

A key challenge in implementing such systems, Mullapudi notes, is trust. Early pilots showed that without visibility into how recommendations were generated, business teams were reluctant to use them. 

The solution was a layer of explainability that converts each recommendation into concrete levers—revenue potential, rightsizing opportunity value, workload-migration probability—unlocking rapid stakeholder adoption. The usability led to widespread adoption amongst professionals, and conversion rates for cloud contracts increased by around 15%. The program was credited with influencing billions of dollars in annual recurring revenue.

Another component that he and his team worked on was a large-language-model pipeline that extracts latent intent from unstructured customer data (tickets, architecture docs, solution briefs) and recommends actionable insights. 

These advances didn't come without technical considerations. The models had to scale across terabytes of data with minimal latency. Mullapudi and his team built hardened MLOps pipelines that enabled automated monitoring, weekly model refreshes and streaming inference, ensuring that insights were delivered quickly enough to influence active deal cycles.

Apart from formal engagements, Mullapudi's work has been shared through publications such as All Multidisciplinary Journal (An Adaptive Explainability Framework for Machine Learning Predictions of Deals in Cloud Computing) and IJIRMPS (Dealing with Label Noise in Machine Learning Predictive Models in Financial Revenue Management: A Clustering-Based Approach), where he explored machine learning's role in financial revenue management. 

Further, when asked about the insights in the field, he tells us that Machine-learning initiatives in cloud economics succeed only when three pillars align: grounding in business understanding, MLOps maturity, and organizational trust. 

First, he tells us that the explainability of machine learning models in terms of economic drivers matters as much as raw predictive lift—models that ignore economic drivers tend to overfit historical usage patterns and misguide sales strategy. Second, the future belongs to streaming feature stores and retrieval-augmented LLM pipelines that marry structured telemetry with the rich context hidden in unstructured data such as customer communication, support tickets, and contracts. Third, explainability must move from post-hoc visuals to actionable narratives that tie every recommendation to a margin curve or migration-probability delta; only then do non-technical stakeholders adopt AI at scale.

Looking at the current trends, he tells us that contracts will become dynamically priced in near-real-time, demanding sub-second inference loops, and causal simulators—digital twins of cloud businesses—will replace static dashboards, letting leaders test scenarios before they commit capital. His advice to emerging teams is to invest early in causal architecture and human-centred design; the models will evolve, but trust gained through transparency and economic relevance compounds faster than any algorithmic breakthrough.

As more organisations adopt AI in pricing, sales, and customer retention, the integration of economic reasoning into machine learning systems may no longer be optional—it may be the deciding factor in accelerating the delivery of real-world business value and decisions.