Agentic AI refers to autonomous AI systems designed to pursue complex goals and workflows with limited human intervention. These systems exhibit goal-oriented behavior, plan, make decisions, use tools, and adapt to changing conditions to complete tasks, effectively acting like independent “agents”. Key capabilities include: autonomy, contextual reasoning, adaptable planning, and natural language (NL) understanding, allowing them to integrate with various tools and data to achieve objectives
Agentic AI can easily learn with minimal human input. Unlike traditional AI, which mainly analyzes data or provides recommendations, agentic AI takes action, functioning like a digital sales or marketing team member.
We are now experiencing a major shift in how sales and revenue ops functions will interact with CRM data. This will change how we interact with our customers, impact job roles, and accelerate productivity by allowing us to increase customer coverage. It will require careful job design thinking to ensure that we put our “humans in the loop” in the most important areas that customers truly value – this is what excites me about the possibilities of Agentic AI in Sales.
Removing highly routine work so sales professionals can connect more with customers, better understand their business, and deliver greater value may sound utopian—but it is within reach, as long as we plan and think carefully about design.
Earlier AI methods in sales would, for example, provide a list of qualified leads, analyze channel health to surface the leads with the most positive historical winning attributes, and often add in propensity to purchase signals, and then create a unique score from different data sources.
Even doing this lead scoring intelligence was not easy to do without quality data. But these earlier methods enabled CROs to learn the importance of data, and organizations started to put in data quality control metrics, which had more “teeth,” i.e., not receiving your commission, job performance data reviews, etc.
Although many organizations are still struggling with these crucial data behavior alignment needs, CEOs must ensure that they are selecting CROs who are data-quality centric, or they simply will not be able to take advantage of Agentic AI. The stakes are very high, and many CEOs will get this wrong.
What we are now going to experience is effective scoring, for example, which will be completely done by agents. Agents will engage leads, direct, send outreaches, schedule the meetings, complete the order, and even ship the products, with minimal human interaction.
Who will get this right?
Gartner Group is already predicting that only 30% of sales organizations will successfully implement AI agents by 2028 due to challenges with data quality and organizational readiness. I agree with their forecast, but we have also learned that one must get an understanding of what agentic AI can do for an organization, allocate budget for learning, and work with Agentic AI Design experts to get these strategies right. There is a speed imperative required, and watching on the sidelines is one fast way to remain irrelevant.
Agentic AI is also not a technology problem; it’s an integrated job design problem.
At SalesChoice, we have been testing diverse agentic AI methods to advance our SalesInsights and our MoodInsights SaaS innovations, and like many of our peers, we are learning as we go.
Below is a summary of Sales Use Cases profiled in the context of an Agentic AI Sales Action
#1 Sales Use Case – Dynamic Account Planning: (live account plans) automatically updates them with signals such as budget changes, leadership changes, or shifts in demand, and continually updates relationship mapping intelligence
Agentic AI Actions:
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Customer Risk Detection: AI monitors external signals such as financial news and regulatory changes to predict churn or disruption.
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Growth Identification: AI finds cross-sell and upsell opportunities based on customer behavior and market trends.
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Continuous Optimization: Account Plan structures are updated dynamically in real time.
#2 Sales Use Case: Proposal & RFP Management: AI can manage complex sales processes, such as responding to RFPs.
Agentic AI Actions:
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Scanning for new opportunities automatically.
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Drafting proposals using past successes, compliance templates, and pricing data.
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Coordinating internal workflows for review and submission
#3 Sales Use Case: Revenue Forecasting & Pipeline Coaching Optimization: Agentic AI goes beyond reporting to self-correct the pipeline
Agentic AI Actions:
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Monitors sales velocity, deal sizes, and conversion rates.
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Adjusts win probabilities dynamically.
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Reallocates marketing spend or resources automatically to high-priority deals.
#4 Sales Use Case – Competitive Intelligence: Agentic AI can continuously monitor competitors and make autonomous strategic adjustments
Agentic AI Actions:
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Tracking competitor activities: pricing, campaigns, and market moves.
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Running simulations to predict competitor responses.
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Launching strategies: defensive campaigns or recommending counter-strategies automatically.
#5 Sales Use Case Cross-Sell & Upsell Intelligence: AI identifies natural upsell points by analyzing customer lifecycles and market behavior.
Agentic AI Actions:
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Predicting Upsell: When a customer adopts one product, AI predicts which complementary solutions they’re most likely to need next and generates a proactive campaign or proposal.
#6 Sales Use Case – Customer Retention & Success Automation: Agentic AI acts as a virtual customer success manager, proactively engaging with customers.
Agentic AI Actions:
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Predictive Churn Detection: Detects signals like product usage and negative sentiment
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Automated retention actions – sends tailored outreach or offers to re-engage the customer
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Intelligent escalation: Escalated to a human when complex negotiation or relationship management is required.
#7 Sales Use Case – Employee Engagement: Collecting insights (moods) on how employees are feeling (Sentiment analytics)
Agentic AI Actions:
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Correlation Agents to Sales Performance (Win Rates, leadership styles, personality profiles, etc.)
Lessons Learned
Our company has been advancing Agentic AI methods in two areas: 1) Revenue Forecasting and Pipeline Coaching, and 2) Employee Moods and correlating to Sales Performance.
We have learned that it is critical to think carefully about the coaching cadence messages so they resonate and add value to sales professionals, especially when 80% of the knowledge is tacit vs codified in organizations. What may seem like a viable coaching message to a junior sales professional could very easily be offensive to a senior sales professional, so I do see that sales agents are going to have to be designed with different persona profiles, or we will overly generalize and add limited value.
We have also learned that designing the workflow logic experience must be carefully attuned to the risk profile of the job task and be designed from a customer interaction perspective to ensure customers will see the value in the new experience.
Organizations must think very hard about the “human in the loop” approval process for sensitive outreach on different customer profiles, as well as on proposals that have sensitive information. If organizations focus only on the automation possibility, they will not build optimal work processes that balance AI actions with human relationships, nor will they tap into the immense tacit knowledge that humans have.
How we are approaching Agentic AI for Build CRO Agentic AI Strategies:
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Phase 1 — Select Agentic Sales AI Use Cases. We usually engage our customers in a workshop to identify the most relevant AI sales use cases that can truly add value to the business. For each sales use case, we dig deep into classifying the type of agent that is needed as different agents perform different functions, i.e., a planning agent, an autonomy agent, a goal agent, a sensing agent, or a memory agent. We rigorously scrutinize the sales “jobs to be done” in relation to agent action to ensure AI capabilities are optimized to achieve specific sales outcomes rather than as isolated features. We also only work with the top Agentic AI experts in the industry, as we know how critical it is to work with experienced talent to augment our skills.
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Phase 2 – Prioritize the Agentic AI Use Cases, using our proven value and risk segmentation frameworks.
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Phase 3 – Conduct Agile Proof of Concepts (POCs) to build AI agents and validate value.
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Phase 4 – Value Realization: Move to production; prove the ROI/Value and communicate results, ensuring celebration and recognition are integrated to increase momentum and adoption success.
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Phase 5 – Sustaining Growth: Continue to add more Sales agents, building upon a proven track record of value. Encourage benchmarking and award recognitions, and help customers and suppliers learn from one another, strengthening a collaborative ecosystem and optimizing knowledge growth.
Lessons Learned
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Evaluating AI agent solutions on generic capabilities without considering specific desired agent characteristics can lead to a poor solution fit.
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Too many vendors are marketing “AI agents,” sometimes even rebranding existing AI assistants and chatbots, leading to market confusion.
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Effective AI agents for complex enterprise sales require orchestrating diverse AI capabilities, not just simple large language model (LLM)-driven automation.
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Many technology providers struggle to integrate multiple AI techniques into mature, comprehensive agent solutions.
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Greater autonomy of AI agents in sales brings new security threats and governance challenges beyond those of stand-alone models.
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Robust guardrails, data privacy, auditability, and continuous monitoring are essential for responsible deployment, but these measures are often immature or incomplete in current solutions.
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Deploying AI agents in sales necessitates specific operational readiness, including workflow documentation, strong governance, and specialized talent development within sales operations.
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Selling relies on the interplay of specialized components, integrated data access, and process efficiency.
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Implement strong security, governance, and trust mechanisms by establishing comprehensive guardrails and policies — including legal, ethical, and brand alignment — to prevent unauthorized actions. Ensure strong data privacy and identity management, deep auditability, and explainability to track agent actions and decisions and to continuously monitor agent performance and compliance.
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Document sales processes at the most granular level through the lens of role-based workflows, as agents thrive on clear detail. This step will help set the focus for where to automate and where to augment with AI Agents.
Conclusion:
LLM-based AI agents are sparking a pivotal transformation for sales, expanding beyond traditional automation and chatbots by adding more proactive and goal-centric decision support approaches.
This evolution will enable AI to be designed with tighter control methods to take an initiative, plan, and then execute a highly routine task either autonomously or with a human in the loop, with careful oversight. CROs must understand that not all AI agents are equal and their capabilities will be on a spectrum of value and risk. What they must ensure is that they get underway to master data quality and align accountability structures, as without clarity of data and the right “jobs to be done” by humans or agents, we will end up making so many mistakes.
This is a time for more integrative design thinking and ensuring customers are front and center as humans value human interactions – so careful, grounded thinking is needed by leaders everywhere to get Agentic AI right.
If you would like to learn more about Agentic AI in Sales, please contact us to learn more.
Source Inspirations: Forrester Agentic AI Reports, Gartner Group Agentic AI Reports
