RevOps and AI: The Cobbler’s Children Need Shoes Too

Revenue operations (RevOps) teams are at the heart of driving growth and efficiency. Despite being champions of AI-enabled tools to make that happen, they often end up like the cobbler’s children with no shoes and come last in adopting the latest tools themselves. At the same time, most revenue teams in B2B organizations are leveraging AI, and many plan to make more use of AI in their marketing going forward. In reality, the total may even be higher, given that AI is invisibly embedded in many tools and platforms without it even being realized. The explosion in the adoption of “visible” AI has come since the launch of ChatGPT 3.5 in late 2022, and that tends to center primarily around content generation use cases. (The clue’s in the name: “Generative Pretrained Transformer.”)

There’s more to AI than generative, though. At Forrester, we see five categories of AI capabilities that are applied and combined to solve a broad range of go-to-market challenges spanning automation, perceptive, predictive, and prescriptive, along with generative. While we hear a lot about the application of these AI capabilities in sales and marketing, what are the use cases from the perspective of RevOps professionals? With its focus on data, process, efficiency, and more, how can RevOps leverage these technologies to improve its performance and deliver even more value across the revenue ecosystem?

Harness AI and drive RevOps value delivery

There are plenty of options for RevOps teams to leverage AI:

• Analytics. A key responsibility for many RevOps teams, analytics represents fertile territory for the adoption of AI. A skillful analyst will rapidly identify patterns, build segments, identify personas, and generate more actionable insights. Similarly, AI models make possible advanced predictive analytics that highlights likely future outcomes, patterns, and risks, supporting planning and decision-making. AI also makes sophisticated visualization a snap, including charts, customer journey maps, and interactive dashboards.
• Workflow automation. Here, AI offers to streamline and automate routine and recurring tasks that save RevOps teams time and free them up for more value-added activities. This might include assessing and routing incoming requests, perhaps even creating an initial requirements document and responding with requests for further information to complete the request. AI will also automatically generate reports on in-flight and completed tasks to monitor throughput, highlight bottlenecks and suggest improvements to streamline operations.
• Data governance. Aspects of machine learning and algorithmic processing have long been employed by data quality tools. The recent advances in AI mean these tools are now even smarter at identifying and fixing a range of data anomalies. AI also has a role in unifying audience identities, combining profiles and associated signals that are crucial to understanding who is in the market and then taking appropriate action. This cuts two ways, though, as data quality is a primary factor limiting GenAI adoption.
• Revenue process enhancements. Go-to-market teams are always looking for ways to improve core revenue processes, and they can lean on AI capabilities across the revenue technology stack to help. AI can analyze vast amounts of customer data, including demographics, behavior, and interaction history, to generate opportunity propensity scores that guide sales and marketing efforts to the most promising prospects. When looking at signal processing, AI can monitor customer interactions and sales data, identify patterns, and alert revenue teams when a customer is likely to churn or present expansion opportunities. New AI capabilities also help organizations identify and complete buying groups.
• Campaign optimization. RevOps teams can use AI to unlock more success with campaigns. AI can manage multivariate testing, looking at the performance of different campaign elements, channels, and timing combinations and provide insights on the best-performing combinations. For more accurate target audiences, RevOps teams can use AI’s ability to combine market intelligence and customer data to organize them into segments with similar attributes and high propensity to buy. AI can also continuously monitor campaign performance and provide real-time analytics and insights, helping marketers track KPIs and make data-driven decisions.

Lead the AI adoption charge

In addition to driving the adoption of AI across the revenue ecosystem, RevOps must embrace the technology for its purposes. Don’t be cobblers’ children and go barefoot while everybody else sports the best shoes! In approaching any AI endeavor, be it within RevOps or the broader revenue ecosystem, the first step is to ensure that the goal of the initiative is clear and focused on meeting business objectives. Experimentation is fine as long as there is a hypothesis by which to judge the outcome in a way that is meaningful to the organization. Then, assess existing technology, AI adoption, processes, data, and skills to determine what is needed to reach the goal. Finally, add the intended developments to your roadmap and share this widely so stakeholders have visibility over planned AI deliverables. Don’t forget to consider privacy and security compliance at all times. Whatever happens, keep experimenting!

The original story is here.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/Shaiith