AI-Enhanced RevOps’ Key Role in Digital Transformation

It’s no secret: artificial intelligence (AI) has gone mainstream. Much of the attention has focused on the rapidly growing number of prompt-activated large language AI models that help with everything from corporate brainstorming to travel planning to (gasp) schoolwork authorship. But AI—sometimes referred to as “business intelligence”, or BI— and automation isn’t new to the business world. For nearly a decade, AI and BI have played an increasingly important role in a range of data-intensive industries ranging from financial and business services to manufacturing.

Global AI market size June 2023

While the power of these technologies grows almost exponentially with each new release, they’re still reliant on good information to offer meaningful, actionable utility and fuel predictably beneficial results. In nearly every instance, revenue operations solutions like Riva are essential catalysts for the successful use of AI and automation to advance digital transformation.

The Growing Role of Automation and AI

Over the past decade, enterprises have increasingly turned to AI to automate a range of tasks traditionally carried out by humans. This trend has been particularly pronounced in customer engagement, where AI has proven enormously useful for its ability to process vast volumes of revenue and communications data for use in fueling true customer 360 to build customer relationships and grow customer lifetime value.

“Just like with other data types and methods, data quality will be an ever-present concern for AI processes and the data that feeds them.” – Emanuel Younanzadeh, Data Quality is Also an AI Problem, Forbes, 13 November 2022 

When carefully implemented and informed by accurate, complete, high-quality, relevant, and qualified data, AI’s impacts have been profound. At its best, AI has proven its value as a tool to accelerate product and service development, anticipate customer needs, and even enhance customer experiences. A few key areas where AI’s capabilities have successfully and effectively leveraged high-quality data include:

  • Process automation
    Automation of administrative tasks—like transferring email data to CRM, reconciling billing errors, or extracting provisions from contract documents—has resulted in dramatic productivity increases, error reduction, reduced costs, data trust, CRM adoption, and revenue growth.
  • Cognitive insight
    One of AI’s primary advantages is its ability to process vast volumes of data and recognize business-critical patterns. When fueled with high-quality data, AI’s pattern recognition capabilities can predict customer behavior, identify fraud, automate ad targeting, and identify product and process defects and safety issues.
  • Cognitive engagement
    AI has shepherded in a new era of customer service automation. Today, systems that use natural language and predictive technologies are often the first step used across many enterprises to increase collaboration, make product recommendations, help employees troubleshoot technology issues, diagnose and triage healthcare, and even increase personalization in customer engagements.

How to AI-Optimize Your Data Quality

When you make the commitment to embrace AI’s positive potential, recognizing its dependence on high-quality data is the most important first step. But if the commitment and tools necessary to produce high-quality data are not in place from the start, AI is more likely to harm your enterprise than to advance its objectives.

“Low-quality data are too often accepted as unavoidable, requiring large-scale system redesign, years of data collection, and significant labor and capital allocation to correct. For these reasons, many AI use cases are shelved for years while companies wait for the problems to resolve themselves, leaving significant value on the table.” – Lapo Mori et al., Clearing Data Quality Roadblocks: Unlocking AI in Manufacturing, McKinsey Digital, 20 January 2023 

Fortunately, revenue data operations solutions like Riva can have an immediate and profound impact on efforts to improve data quality at every interaction phase—and support successful AI initiatives. With revenue data operations integration, enterprise data quality improves through:

  • Unified revenue and communications data
    Revenue data operations are designed to eliminate siloing that separates revenue and communications data. This unification expands the universe of data available to your AI technologies to ensure its conclusions are based on current, comprehensive, relevant, and accurate customer histories.
  • Sophisticated data access and use governance
    Once data is unified—ideally using AI-enhanced automation—revenue operations technologies can be employed to govern data flows. This process supports a cycle of continuously improved data that supports accurate, meaningful AI-driven analysis and forecasting.
  • Improved data observability
    Revenue operations solutions increase customer data visibility at every point in its lifespan. This elevated level of observability ensures that errors, omissions, and duplications are identified and resolved before the data is processed by automated AI technologies. 
  • Improved “single source of truth” adoption
    When customer-facing teams understand the value of quality, trustworthy data, they embrace their essential roles as data stewards. This fuels a virtuous cycle that sustains data quality—and enhances AI technology performance.  
  • Reduced data security and compliance concerns
    Proven revenue operations solutions are designed to enforce data use compliance and security. This minimizes the risk that automated AI technology might access and distribute private or non-compliant data to unauthorized team members—or utilize it in analysis and forecasting.

Given its rapid rise and adoption as a customer data technology enabler, the use of AI to leverage the power of data has quickly transitioned from aspirational to essential. But improving data quality can’t happen without a commitment by individual contributors—supported by a dedicated, enterprise-wide commitment. Fortunately, implementing a proven revenue operations solution supports that commitment by unifying and governing the accurate, consistent, relevant data your AI tools need to help, not harm, enterprise customer engagement, and revenue growth goals.

How Revenue Operations Drives Digital Transformation 

AI and automation can play a vital role in digital transformation—but only when enterprises invest in the revenue operations technologies necessary to tap the potential of the data they gather from customers. This is particularly true in complex industries like financial services, business services, and manufacturing, where customer-facing teams need trustworthy data to effectively nurture customer relationships. To overcome these challenges, grow customer lifetime value, and drive revenue growth, companies need to focus on the following emerging trends as they employ AI and automation in their pursuit of digital transformation:

  • Data-driven decision making
    Revenue operations-enabled digital transformation allows enterprises to analyze historical trends, customer behavior patterns, and market dynamics to make strategic decisions—and to allocate resources effectively, identify growth opportunities, and optimize revenue generation strategies.

“We want to make sure that all the different tools we’re using are flowing data into Salesforce, and that we’re able to make decisions off of that data.” – Mike Fazio, Head of Marketing & Rev Ops at LSQ
  • Real-time data integration
    Digital transformation-minded enterprises must invest in revenue operations solutions that enable seamless access to evolving data from various sources in real time. This ensures up-to-date insights, facilitates timely decision-making, and enables organizations to respond quickly to market changes.
  • Personalized customer experiences
    Customer expectations are shifting towards personalized experiences. By implementing revenue operations solutions that unify and govern customer data and leveraging customer analytics, organizations can create personalized marketing campaigns, offer customized products and services, and improve customer satisfaction and loyalty.

“If you have access to customer data, and you’re tracking it right, you can make decisions that are customer-centric.” – David Goldstein, GCA Strategy & Insights at Johnson & Johnson
  • Data privacy and security
    Through customer data unification and governance, revenue operations-driven data transformation allows enterprises to ensure data compliance and security—allowing ongoing monitoring of MNPI and evolving data privacy regulations such as GDPR and CCPA.

“Implementing data governance best practices ensures the quality, integrity, and security of data throughout its lifecycle.” – Azret Deljanin, VP of Infrastructure and Security at Yieldstreet  
  • Collaboration and integration
    Revenue operations support digital transformation by eliminating data silos, ultimately supporting collaboration between various teams and departments within an organization.

“Data is only valuable if you can access it.” – David Goldstein, GCA Strategy & Insights at Johnson & Johnson
  • Continuous data quality improvement
    Ensuring the accuracy, reliability, and consistency of data is crucial for effective revenue operations and successful digital transformation. Continuous data quality improvement—and maintenance of CRM as the “single source of truth”—minimizes errors, enhances data integrity, and enables organizations to improve customer engagement through the uniform use of accurate and reliable data.

Summing up

As more and more industries embrace the trend toward digital transformation, it’s clear that AI will play an increasingly essential role in leveraging the enormous potential of customer data. By incorporating AI’s ability to automate data unification, processing, and analysis, revenue operations solutions like Riva will serve as catalysts to improve data quality, boost CRM adoption as a single source of truth, and enhance both customer engagements and enterprise revenue growth.

To learn how Riva can enhance the experiences of both your customer-facing teams and the customers they serve—while accelerating your path to digital transformation—we encourage you to contact us.

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