The Intelligence Trapped in Your Sales Calls

Andrew Ng, one of AI's most respected voices, recently discovered a SaaS vendor wanted to charge his team over $20,000 for an API key to access their own customer data.

His conclusion? "This high cost, no doubt intentionally designed to make it hard for customers to get their data out, is adding a barrier to implementing agentic workflows."[1]

This isn't about API pricing. It's a systemic strategy across sales tech vendors to block a transformative opportunity: AI agents that connect dots across your entire revenue tech stack.

"AI agents are getting better at looking at different types of data in businesses to spot patterns and create value. This is making data silos increasingly painful," Ng explains.

Picture this: An email click is logged in your marketing platform. A pricing objection is captured in your conversation intelligence tool. An opportunity is created in your CRM. A contract negotiation is tracked in your document system.

An AI agent connecting these dots would tell you: "In the last quarter, when your reps used the pricing justification framework from the Q2 training within 48 hours of a prospect clicking the ROI calculator link, close rates jumped 23%."

That's possible today.

Here's the problem: Your vendors are building walls to prevent it.

Ng warns: "Some SaaS vendors are seeing AI agents coming for this data and working to make it harder for you to efficiently access it."

Every sales conversation contains intelligence that could transform multiple functions:

For Sales: Who's executing methodology? Where are the coaching gaps? Which reps need what training? What do top performers say in discovery that middle performers don't?

For Product: What features get requested? What's confusing in the demo? Where's the product-market fit?

For Marketing: Which messaging resonates? What pain points land? Which segments convert?

For Competitive Intelligence: What are buyers comparing you to? What objections surface by competitor? Where do you win vs. lose?

For Customer Success: Where are the upsell signals? Which accounts are at risk? What drives renewals?

For Revenue Operations: Where are deals stalling? Which stages leak? Where's the forecast risk?

All of this is in your calls. Right now. Across every function.

The challenge: It's locked behind multiple vendor barriers.

Leading platforms across your revenue tech stack share a pattern: they make data capture effortless and extraction nearly impossible.

In October 2025, Salesforce introduced new restrictions:[10][11]

One Reddit admin responded: "We are considering investing in OwnBackup or a similar tool."

This establishes the pattern. When your conversation intelligence platform writes data to Salesforce, friction compounds at both ends.

Leading CI platforms like Gong illustrate how this plays out in practice. While these tools excel at capturing insights, data extraction reveals significant barriers.

API Restrictions:

For a mid-sized sales org with 2,000 historical calls, you'd need a minimum of one day just to retrieve call metadata—before even accessing transcripts or recordings.[4]

Kevin Mead, a sales consultant, described a real scenario: "My client cancelled Gong and had 30 days to migrate their recordings to HubSpot. The catch? They had 30,000 recordings."[5]

The API limits meant this required custom Python scripting and 14 days of dedicated developer time for what should be a standard export.

No Bulk Export: Users across G2, Capterra, and Reddit report the same reality: "It requires downloading calls individually, which is impractical and inefficient for a large volume of data."[6][8]

Compare this to platforms with native bulk export:

When confronted about data access, vendors provide carefully worded responses: "We are in full compliance with the California Consumer Privacy Act (CCPA) and all related data accessibility and portability requirements."[9]

Then comes the qualifier: "This means no further customization or support is available if you need bulk access to your call data."

Data is technically accessible (satisfying CCPA), but accessing it at scale requires significant technical investment that vendors explicitly refuse to facilitate.

When conversation intelligence platforms write data to Salesforce, it creates "25MB a day of records… 12,000 records a day" —approximately 9GB annually in additional storage costs.

You must either purchase expensive Salesforce storage, reduce CI platform functionality, or accept incomplete data integration.

You're now locked into TWO platforms: one holding your conversation data, another holding your pipeline data. Neither makes connecting them for AI agent development straightforward.

Let's quantify what these barriers actually cost:

Developer Time for Data Migration:

This is for one data migration. If you're evaluating multiple platforms or building custom AI agents, multiply these costs.

Industry analysis suggests $15,000-$50,000 in total developer costs when factoring in:

One Sales Ops Manager explained the impact: "This lack of flexibility has required us to engage our development team at additional cost, adding significant operational and opportunity costs just to extract data we already own."

The calculation for your mid-market sales org:

For perspective: You pay $500-$1,000+ per user annually for conversation intelligence. Then potentially pay another $15,000-$50,000 to access that data for custom AI development.

That's vendor lock-in by design.

This systemic vendor lock-in prevents two critical AI use cases:

Custom AI Agents on Your Sales Methodology: Your high-performing team has proprietary methodology—your qualification framework (MEDDPICC, SPIN, BANT), your objection handling approaches, your industry-specific talk tracks. You could train AI agents on your methodology using your conversation data to provide coaching that reflects what actually works in your market.

But when conversation data is locked with $20,000+ extraction costs, you're forced to use generic vendor-provided AI instead. Your competitive advantage is trapped in a vendor's database.

Cross-System Pattern Recognition: Which discovery call patterns correlate with closed deals? What kinds of pricing conversations predict expansion? These insights require connecting conversation intelligence, CRM data, and product usage. When each system creates barriers, you either accept siloed vendor AI or spend $50,000+ on custom development to extract and normalize data across platforms.

Meanwhile, companies who chose data-portable platforms can build these capabilities today.

Ng's recommendation is straightforward: "I often advise [businesses] to try to control their own data… you can hire a SaaS vendor to record and operate on your data, but ultimately you decide how to route it to the appropriate human or AI system.”

His example: Obsidian saves notes as Markdown files in his file system, and he's built AI agents that read and write to them. "This is a small example of how controlling my own notes data lets me do more with AI agents."

Make data portability a top-three requirement. Include these provisions:

Key evaluation questions before signing:

Standards like the Model Context Protocol (MCP) and Agent2Agent (A2A) represent the future: open protocols allowing AI agents to access data across systems without vendor barriers.

Salesforce announced support through Agentforce 3.0. Leading conversation intelligence platforms have made no such commitments—their business models depend on data silos.

The sales tech market faces a fork in the road. One path leads to vendor-controlled AI, where you're trapped paying premium prices regardless of performance. The other leads to customer-empowered agents, where you build competitive advantages through proprietary AI trained on your methodology and your data.

Andrew Ng's warning is clear: "Some SaaS vendors are seeing AI agents coming for this data and working to make it harder for you (and your AI agents) to efficiently access it.”

The competitive advantage of AI agents won't come from your vendors. It will come from your ability to connect your data across systems in ways only you understand.

Don't let vendor lock-in prevent you from building it.

[1]: Ng, Andrew. "Issue 326: Data Silos and AI Agents." The Batch, DeepLearning.AI, November 2025. https://www.deeplearning.ai/the-batch/issue-326/

[2]: "What the Gong API provides." Gong Help Center. https://help.gong.io/docs/what-the-gong-api-provides

[3]: "Gong - Nango Docs." Nango Documentation. https://nango.dev/docs/integrations/all/gong

[4]: "Why Gong Implementation Takes 6 Months (And Costs $200K)?" Oliv.ai Blog. https://www.oliv.ai/blog/gong-implementation-timeline

[5]: Mead, Kevin. LinkedIn Post. December 2023. https://www.linkedin.com/posts/kevin-mead_my-client-cancelled-gong-and-had-30-days-activity-7147989343276572672-nbHV

[6]: "How Does Gong Work? - You Need To Know This Before You Buy." tl;dv Blog. https://tldv.io/blog/how-does-gong-work/

[7]: "I Summarized 20 Real Gong Reviews – Is It Worth It?" tl;dv Blog. https://tldv.io/blog/gong-review/

[8]: "Gong Smart Trackers Exposed: 7 Critical Limitations Sales Teams D." Oliv.ai Blog. https://www.oliv.ai/blog/what-are-gong-smart-trackers

[9]: "Gong.io Reviews 2025. Verified Reviews, Pros & Cons." Capterra. https://www.capterra.com/p/157969/Gong-io/reviews/

[10]: "Standard Setup Data Exports Now Rate Limited with Winter '26." Reddit r/salesforce, October 2025. https://www.reddit.com/r/salesforce/comments/1nzo1b8/

[11]: "Understand Changes to Data Export Download Limits." Salesforce Help, Winter '26 Release Notes. https://help.salesforce.com/apex/HTViewHelpDoc?id=release-notes.rn_data_export_rate_limit.htm

[12]: "Quick & Easy Gong Salesforce Integration 2025." Pixel Consulting. https://www.pixelconsulting.io/post/gong-salesforce-integration

[13]: "Hitting Data Storage Limits with Email Messages." Reddit r/salesforce. https://www.reddit.com/r/r/salesforce/comments/rhvhj0/

[14]: "Agents Unleashed: Interoperability to Power a $6 Trillion…" Salesforce News. https://www.salesforce.com/news/stories/agentic-interoperability-powers-ai-agent-market/

[15]: "Open Protocols Can Prevent AI Monopolies." AI Frontiers, July 2025. https://ai-frontiers.org/articles/open-protocols-prevent-ai-monopolies

Victor Adefuye is a B2B sales consultant and founder of Dana Consulting, helping organizations implement AI into sales processes through methodology-first approaches. Get in touch if you'd like to assess how AI could improve your team's process!