AI’s Influence on the Revenue Optimization Processes: Model N CMO Rehmann Rayani Explains with Examples

In 2024, business leaders are already optimizing their revenue operations (RevOps) using cutting-edge AI and analytical tools. For most industries, AI adoption is a critical process to lay the foundations for new-age automated revenue optimization workflows. According to Model N’s recent report, AI and analytics are ushering in a new era of revenue optimization for high-tech industries, such as Life Sciences. These new technologies empower users with advanced data-driven technologies gathered from AI-powered solutions to maximize revenue.

Discussing the outcomes of the 6th annual State of Revenue Report, I invited Rehmann Rayani, Chief Strategy & Chief Marketing Officer, to our AiThority.com’s Tech Bytes interview platform. In this interview, Rehmann mentioned how AI impacts revenue optimization across industries, and how his organization unlocks the data gap for the high-tech industries.

Here’s the full interview with Model N’s CMO, Rehmann Rayani.

Hi Rehmann, welcome to the AiThority TechBytes Interview Series. Please tell us about your recent announcement revealing the role of AI and Analytics in the Life Sciences industry.

Data is critical to business operations.

According to Model N’s 2024 State of Revenue Report, 85% of companies use data to inform at least some of their decisions, but only 44% leverage data for all of their decision-making. This stat indicates that leaders may be skeptical of their data’s accuracy or lack analysis capabilities. The medtech industry is the most data-driven, with 93% of organizations making many or all decisions based on data, compared to 81% in pharma. 

The survey also revealed that 68% of companies use or plan to use advanced analytics to enable revenue optimization and compliance teams, while 59% will leverage AI. Questions about data accuracy continue to plague executives, likely hindering full data analytics utilization. Nearly half (47%) of C-suite leaders don’t fully trust the data their companies use for revenue management decisions.

AI is impacting revenue generation across industries in all regions. Could you please highlight the benefits of using AI in supply chain management?

Using AI in supply chain and revenue management enables companies to respond to market trends proactively. AI analyzes leading data, including real-time sales, supply chain movement, material availability, channel and regional demand, market trends, and industry benchmarks, to more accurately predict demand. This insight informs pricing, marketing, production, and sales strategies. Life sciences companies using data analysis can improve operational efficiency and cut costs — a primary focus for 2024, according to our research — by reducing manual processes and enhancing data accuracy.

RPA, Generative AI, and Digital Twins are intertwined with each other in the supply chain optimization processes. What key areas should business leaders focus on in 2024-2025 to maximize revenue generation?

RPA is an excellent place for companies to start optimizing their supply chain processes because it offers the lowest barrier to entry. The technology automates time-consuming tasks like data entry and invoice processing and reduces errors. These streamlined workflows enhance productivity and decrease the resources required for revenue and supply chain management. RPA also creates more accurate and timely invoicing and rebate payments, ensuring the right prices are applied and payments comply with regulations.

According to Model N’s 2024 State of Revenue report, 40% of life sciences companies plan to use RPA.

As it matures, GenAI will further enhance strategic planning by modeling scenarios to generate demand forecasts, anticipate the impact of disruptions, and identify optimal supply chain configurations, inventory levels, and logistics strategies, among many other applications.

Digital twin technology can increase operational efficiency by tracking real-time performance and continuously optimizing operations.

AI for compliance management – please tell us more about the prospects of using advanced analytics to remain compliant in the highly regulated life sciences industry.

The vast majority (95%) of executives we surveyed are preparing their revenue management programs for upcoming regulatory changes in 2024. Two-thirds of pharma companies are turning to technology to help manage compliance, highlighting the growing role of advanced analytics in managing data.

AI automates data retrieval, collecting it in a centralized repository for increased visibility. Life sciences companies can use advanced analytics for tighter supply chain monitoring, faster adverse event reporting, and earlier identification of improper payments to remain compliant in a highly regulated environment.

Take Medicaid, for example.

Analysts spend hundreds of hours navigating portals, downloading documents, and re-uploading them to Medicaid processing systems. Automated solutions can accelerate this process while also eliminating manual entry errors, resulting in more accurate and timely rebate payments compliant with federal regulations.

What kind of skills in AI and data science should life sciences companies focus on to manage the modern needs of their supply chain workflows?

Data engineering, statistical analysis and modeling, and data visualization skills will be critical for the future workforce. Data engineers will knock down data silos and enhance data quality, allowing organizations to conduct the necessary monitoring and analysis to inform business strategies.

Data analysts help life sciences companies manage complex supply chains by forecasting demand, optimizing inventory levels, identifying market trends, and revealing business opportunities. Data visualization is increasingly important as more executives leverage data in strategic planning. These skills turn massive datasets into understandable takeaways to inform decisions.

Thank you, Rehmann! That was fun and we hope to see you back on AiThority.com’s Tech Bytes Interview again…

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