Is your sales team spending too much time with tasks that don’t move deals forward? If they are, read on!
In this article, we explore how to leverage Artificial Intelligence Large Language Models (AI LLM), Machine Learning (ML) and Robotic Process Automation (RPA) tools to automate selected sales motions introduced in our previous article on supercharging B2B Sales Automation.
Finding Prospects
Sales reps spend hours researching companies and decision makers to reach out to. They surf Linkedin for company and people names, scan press-releases for buying signals, and enter data into your CRM. These tasks are laborious, repetitive and do not move the ball down the field.
RPA can be used to crawl online resources such as industry directories and company websites, and ingest the data into your CRM. Rather than spend hours searching for data online and entering it into your CRM – or worse, not entering it – sales reps just need to check the quality and relevance of the information. That’s far less unproductive effort on their part.
ML models can then score and prioritize leads using historical conversion patterns. The result is a dynamic ranked list of companies that are more likely to engage. Because the process is automated, the list can be refreshed frequently. This ensures that your sales reps always have access to a fresh list of prospects to reach out to, with far less unproductive effort on their part.
Reaching Out and Nurturing
To develop relationships with prospects, sales reps spend time writing personalized emails, re-engaging old leads and following up on the right timeline. The manual effort increases with the number of leads, which causes them to lose momentum or let opportunities fall through the cracks.
Using the information collected in your CRM, AI LLM can draft context-rich messages. Rather than starting from a blank email, sales rep starts from a high-quality first draft. They still need to review the content, check facts and adjust the tone, but that requires far less unproductive effort.
Reactivating dormant leads
Dormant leads sit forgotten in your CRM. Sales reps ideally review these regularly, identify the ones that are worth re-engaging and decide on how to best reach out to them.
Automated, rule-base processes or ML models can work behind the scenes within your CRM to identify and flag dormant leads. When rules flag that a dormant lead is worth reaching out to, it triggers a process to do so. Your LLM takes in relevant context, such as previous interactions, and drafts a message for the sales rep to work with.
Powerful tools, but not silver bullets
Automation tools can dramatically improve sales productivity, but they’re not silver bullets. Each tool has strengths, but also limitations that need to be understood. Success depends on applying them in the right context and designing processes around their constraints.
LLM are fantastic at producing content, like drafting emails, summarizing conversations, or creating proposal outlines. But they are woeful with factual accuracy. Their output must be fact checked for accuracy.
ML models are powerful for identifying patterns across large datasets, like helping forecast deal likelihood, prioritize leads, or flag unusual churn risks. However, ML models need high-quality, well-labeled data, ideally lots of it. It’s not well-suited for rapidly changing environments where yesterday’s patterns no longer hold. It also requires care in interpretation: a model’s output should inform decisions, not replace judgment.
RPA excels at automating structured, repeatable tasks. It’s ideal for copying data, triggering workflows, or formatting routine reports. But it’s brittle in the face of change. If a webpage layout shifts slightly or a form field is renamed, the bot can break. RPA needs clearly defined rules and stable systems. Otherwise, it becomes a burden to maintain.
Getting the most out of automation means knowing not just what these tools can do, but what they shouldn’t be asked to do. Done well, they remove repetitive work and boost consistency. Done poorly, they add risk, complexity, or noise.
Start with simple, well-scoped use cases, apply human oversight where needed, and iterate based on real-world feedback.
That’s how automation scales — not with shortcuts, but with discipline.
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Authors – Nicolas Mialaret, Partner and Maurits Van Roozandal, Consultant
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