.png)
.png)
In today's high-pressure sales environment, revenue leaders face mounting challenges. With 97% of executives expecting increased revenue mandates and 26% of potential revenue lost to operational breakdowns, the need for efficiency is clear. While many organizations focus on pipeline generation, it's deal velocity that determines whether quarters are won or lost. The mid-funnel—where deals are won or stalled—represents the biggest opportunity for transformation. This is where AI Sales Agents come in, working behind the scenes to automate expert tasks and accelerate deals.
An AI Sales Agent is fundamentally different from basic AI assistants or generative AI tools. While generative AI creates content when prompted, an AI Sales Agent operates autonomously to complete tasks without requiring prompts. It's the difference between a system that responds to input and one that acts with context and expertise.
True AI Sales Agents have a "brain"—a logic layer that allows them to operate autonomously within specific business domains. For example, for Vivun's AI Sales Agent, our proprietary model, Agent Intelligence, separates the "brain" (structured knowledge graphs derived from expert behavior) from the language model (the "mouth"). This architecture enables our agent to deliver output that is not just fluent, but fundamentally correct, relevant, and actionable.
Key capabilities include:
For Vivun's AI Sales Agent, our Accelerators are dynamic, context-aware assets triggered by real-time signals across sales systems and conversations. They're not generated ad hoc based on prompts; instead, they're proactively created by monitoring interactions across platforms, like email, calendar, and call intelligence.
The mid-funnel is where deals often stall, creating significant challenges:
These aren't isolated inefficiencies—they signal a broader structural challenge. At the core is time: sellers spend most of theirs on non-selling activities, while buyers find themselves stalled in the middle of the funnel, awaiting technical validation, internal alignment, or tailored guidance.
An AI Sales Agent addresses these challenges by:
For a sales team working 50 qualified opportunities per quarter, this translates to 12-21 full work weeks of reclaimed time—time that can be reinvested in building pipeline, advancing deals, and closing revenue.
Real-world use cases include automatic follow-ups, discovery summaries, solution documentation, stakeholder maps, and handoff documents. These Accelerators eliminate the administrative tax that slows deals down, allowing sellers to rebalance their workload toward customer-facing interactions.
With the explosion of interest in AI, the term "AI Agent" is being applied to everything from glorified chatbots to simple automation scripts. This has made it increasingly difficult for sales leaders to understand what's truly transformative versus what's just "agent-washing."
Start with the problem you're trying to solve. Is it ramp time? Discovery depth? Technical validation? Be clear on where the friction is, and target that zone.
Identify whether the solution is:
Can this tool take action without being prompted? If it can't generate work independently, it's not an agent—it's an assistant. True agents don't just automate existing tasks—they are virtual team members who partner with reps on important work at every stage of a deal.
Review outputs like solution docs, MEDDICC analyses, value cases, technical briefs, or buyer response drafts. Do they reflect the expertise of your top SEs or reps? The agent should understand the workflows, outcomes, and edge cases of everything required in successful B2B sales.
Determine how well the AI tool connects to your existing stack—CRM, call intelligence, enablement platforms. Seamless integration is essential for the agent to maintain context across all interactions.
Security remains a primary concern. Determine how your data is used, accessed, and protected with these questions:
Agents should deliver value immediately, not after weeks of configuration. Set a short evaluation period and watch for signs of time saved and deals accelerated.
Many organizations attempt to build agentic solutions themselves, but as Gartner notes, "It is possible to build AI agents from scratch...however, this requires a high level of expertise and is both challenging and time-consuming for most organizations."
When evaluating whether to build or buy, consider:
Successful integration requires thoughtful planning across several dimensions:
Align the AI Sales Agent with your existing tools:
Feed relevant data sources into the agent's memory:
This enables the agent to maintain context across all interactions and produce more relevant outputs.
Configure triggers for Accelerators at key deal stages:
Address the human side of implementation:
Take a phased approach:
To maximize ROI, establish clear metrics and continuous improvement processes.
Track these indicators to measure impact:
Integrate agent KPIs into existing business intelligence and revenue operations reports. This provides visibility into performance and helps identify areas for optimization.
Establish mechanisms for sellers to provide input for knowledge graph refinement. This helps the agent learn from real-world interactions and improve over time.
Regularly review and refine the agent's triggers and templates as products or processes evolve. This ensures the agent stays aligned with your business needs.
Create a sustainable framework for managing the agent:
Be aware of these challenges when implementing an AI Sales Agent:
Not all tools labeled as "agents" deserve the title. As Gartner warns, "Many vendors are contributing to the hype by engaging in 'agent washing,' rebranding existing products, such as AI assistants, RPA tools and chatbots, to capture buyers' attention without substantial agentic capabilities."
Distinguish true autonomy from mere chatbots or RPA by asking: Can it generate value on its own? Does it produce high-quality outputs that mirror human expertise?
Adding tools that create new sources of admin work defeats the purpose. If your sellers are spending more time prompting than selling, the ROI is backwards.
The true test is how seamlessly the AI tool integrates into the seller's workflow. Does it anticipate what's needed next? Does it cut steps, not add them?
Verify vendor data policies and technical safeguards. Enterprise-grade security is non-negotiable for AI tools that access sensitive customer and deal information.
Foster cross-team collaboration to keep the agent aligned with evolving workflows. Siloed implementations limit the agent's effectiveness and can create new bottlenecks.
Watch for these warning signs:
To protect your team, scrutinize vendor claims, avoid feature bloat disguised as innovation, and favor use-case-driven pilots before committing to expensive contracts.
Implementing an AI Sales Agent represents a strategic shift in how work gets done across the revenue engine. The mid-funnel is the ideal point of intervention—where buyers demand answers and reps need support. By selecting the right agent, integrating it thoughtfully, and measuring its impact, you can transform your sales execution and drive meaningful results. This isn't just about adding another tool; it's about giving your sellers the expertise they need, when they need it, to clear bottlenecks and restore momentum in your deals.
Want to see it in action? Take a tour today.
.png)
.png)
.png)
