Case Study: Accelerating Sales and Deal Desk Operations with Agentic AI
Industry: Enterprise Sales / Revenue Operations
Company Profile: Publicly traded cloud communications platform
Primary Use Case: CRM Automation, Deal Desk Enablement
The Challenge
A publicly traded enterprise communications platform processing millions of customer interactions across a global sales organization was facing a data quality crisis hiding in plain sight.
As in most enterprise companies, each week, their Sales reps were expected to manually enter every customer conversation across dozens of required Salesforce fields: next steps, decision process, competitors, and deal risks.
In practice, reps found ways to bypass or shortcut this task because it required spending valuable time on administrative work instead of selling and building customer relationships. Many fields were left incomplete or filled inaccurately, and sales operations lacked trustworthy data for forecasting, pipeline reviews, and deal support.
Their CRM became a reporting requirement rather than a system that actively helped deals move forward.
Starting Small: Automating Sales Notes and CRM Updates
The company began with a focused Phase I deployment of Squid AI’s DealFlow, the agentic solution for sales, deal desk, and sales ops, starting with the most painful operational bottleneck: automating how sales conversation notes were captured and entered into Salesforce.
Instead of asking reps to manually summarize each interaction, DealFlow extracted structured information from sales notes and conversations, automatically populated the correct Salesforce fields, and standardized how opportunity data was recorded across the team.
This eliminated the need for reps to translate conversations into dozens of form fields and ensured that opportunity records reflected what actually happened in customer discussions.
Importantly, the deployment was fast and low-friction. DealFlow worked behind the scenes, within Salesforce itself, to populate fields — operating inside existing workflows rather than introducing another tool — and never interacting with the sales team unless absolutely necessary.
The DealFlow worked behind the scenes, within Salesforce itself, to populate fields, never interacting with the sales team unless absolutely necessary. In just a few weeks, a solution was rolled out to a small group of sales reps and then expanded to hundreds of users within once results were validated.
Expanding Value: Using Call Intelligence and Sentiment Analysis
After Phase I, sales reps strongly supported expanding the automation.
In Phase II, DealFlow was integrated with the company’s call recording and transcription system, allowing it to analyze live sales conversations directly rather than relying only on post-call notes.
DealFlow extracted structured deal information and customer sentiments directly from transcripts, identify buying signals, hesitation, and risk factors, update Salesforce with both factual data and sentiment indicators, and help prioritize follow-ups while surfacing deal health earlier in the sales cycle. This reduced guesswork in pipeline reviews and helped managers intervene earlier when deals were at risk.
Sales leaders gained clearer visibility into pipeline quality, while reps benefited from more accurate deal records without additional effort.
Importantly, this automation did not replace sales judgment. Instead, it made their CRM much more effective by reflecting real customer conversations and that follow-up actions were based on consistent, timely information.
Business Impact
Within weeks of deployment and expansion across the sales organization, the company achieved:
- 28× return on investment
- $3.3M in annualized savings
- Full payback in under two weeks
- 60% reduction in time spent on manual CRM updates
- Improved pipeline accuracy and proposal quality
Sales operations teams also reported more reliable opportunity data for forecasting, faster deal progression due to clearer next steps and approvals, and reduced back-and-forth between sales and deal desk teams.
Why It Worked
The system succeeded because it adapted to how sales already worked, pulled in data from different systems and sources including from outside the CRM, and operated inside existing CRM and call systems. Sales reps didn’t need to learn or adopt another tool. And data quality improved as a by-product of everyday conversations, not as a separate compliance task.
Instead of forcing behavior change, the DealFlow removed friction from workflows that already existed and automated the most time-consuming administrative steps.
Feedback from Sales Operations Leadership
“The feedback from our sales team has been tremendous. What stands out most is the accuracy of Squid AI’s results — it consistently delivers the right insights at the right time. Since rolling it out, our reps have seen a 60% reduction in time spent on manual updates and much greater confidence in their opportunity data. It’s improved the quality of proposals and customer conversations, driving stronger responses and greater trust from customers.”
— Senior Director of Sales Operations
Applicable Use Cases
This approach applies to organizations facing:
- High CRM data entry burden for sales teams
- Poor pipeline data quality affecting forecasting and deal strategy
- Deal desk bottlenecks caused by manual approvals and pricing workflows
- Revenue operations teams overwhelmed by inconsistent opportunity data
Common starting points include sales note automation, opportunity qualification and routing, pricing and approval workflows, and forecast risk detection.