Real-time transcription (RTT) and AI are rapidly reshaping how enterprises interact with customers. These technologies offer real value like instant summaries, sentiment detection, agent guidance, searchable transcripts, and data-driven insights that can drive continuous improvement.
But beneath the surface, there are serious risks. Two recent lawsuits, one involving Patagonia and another targeting Genesys have brought these risks into sharp focus. For enterprise leaders, especially those responsible for customer experience, IT, or compliance, now is the time to pause and reassess how these technologies are deployed and governed.
Real-World Examples: Patagonia and Genesys
In 2024, Patagonia was named in a class-action lawsuit after customers alleged that their calls were intercepted and transcribed using AI by their contact center vendor, Talkdesk, without adequate disclosure or consent. The claim centers on violations of the California Invasion of Privacy Act (CIPA), specifically around real-time transcription and intent analysis running silently in the background.
Then just this month, Genesys faced a class-action suit for allegedly recording and analyzing deeply personal conversations from the National Domestic Violence Hotline. Plaintiffs claim their conversations some involving legal, medical, and emotional disclosures—were transcribed and potentially used for analytics or AI training, all without their knowledge or consent. The case involves both federal wiretap violations and state-level privacy concerns.
These lawsuits serve as a wake-up call: even established vendors can introduce unacceptable risk if there’s a lack of transparency, consent, or control.
The Broader Risk Landscape for Enterprises
The risks go well beyond lawsuits. Here are key areas of concern when deploying RTT and AI at scale:
1. Exposure of PII and Sensitive Data
If a customer says a Social Security number, credit card, or medical condition aloud, it’s transcribed. And if that transcript is stored, exported, or reused, your organization is now holding potentially regulated data subject to GDPR, HIPAA, CCPA, or PCI-DSS.
Even one leak or unauthorized access to this data can trigger massive fines and lasting reputational damage.
2. Consent and Compliance Challenges
Regulations like CIPA and the federal Wiretap Act require two-party consent in many jurisdictions, meaning both parties must know the conversation is being recorded or analyzed. Traditional disclaimers (“This call may be recorded…”) may not be sufficient to cover transcription and AI-based analytics.
Worse, some vendors treat consent as the customer’s problem passing responsibility downstream to the enterprise.
3. Vendor Data Practices
Most transcription and AI capabilities are delivered by third parties CCaaS platforms or cloud vendors. And unless you’re asking hard questions, you may not know:
- Where the data is stored
- Who has access to it
- Whether it’s used to train shared AI models
- If you can request deletion
If the answer to any of those questions is unclear or unsatisfactory, you’re taking on more risk than you realize.
4. Unauthorized AI and Transcription Tools
In the absence of official tools, some teams might start using free or unsanctioned AI plugins or browser-based transcription services for convenience. These tools often lack enterprise-grade security and can introduce new vectors for data leakage or compliance failures.
5. Inaccurate or Biased AI Outputs
AI-generated summaries, sentiment analysis, or intent detection may be wrong. Worse, they may be used to make decisions (like triggering escalations, categorizing cases, or assigning blame) without proper oversight or validation.
In regulated industries, bad AI outcomes can result in real legal consequences.
What Enterprises Should Do Now
Enterprises can, and should, embrace RTT and AI. But it must be done thoughtfully, with safeguards in place. Here are five steps to get started:
1. Reassess Consent Mechanisms
Update your disclosures to explicitly mention transcription and AI-based analysis. Consider in-call prompts or verbal notices for added clarity in high-risk scenarios.
2. Perform a Full Vendor Risk Assessment
Don’t assume your CCaaS or AI vendor is fully compliant. Ask questions. Review their documentation. Require formal data handling and deletion policies. And avoid tools that don’t offer data segregation and audit logging.
3. Use Separate Transcription Paths for Sensitive Interactions
Not all conversations are created equal. For financial transactions, healthcare-related discussions, or high-risk support scenarios, consider routing those interactions through a transcription engine you fully control, on-prem or private cloud, with strict access policies.
4. Treat Transcripts Like Sensitive Assets
Store them securely. Control access by role. Log every interaction. Set retention policies. Redact or mask PII automatically where possible. Don’t leave transcripts sitting in email inboxes or unmonitored cloud folders.
5. Build a Governance Program Around RTT and AI
Establish policies that cover:
- When transcription is used
- How AI models are trained and validated
- Who owns the data
- How output is reviewed and acted on
Update these policies regularly and include stakeholders from security, legal, customer success, and operations.
Final Thought: Innovation with Accountability
RTT and AI have the potential to reshape enterprise communication for the better, but only if they’re deployed with intention. The cases involving Patagonia and Genesys are reminders that enterprises can’t afford to outsource trust.
At AMC Technology, we help enterprises secure their RTT and AI environments by giving them full visibility, control, and governance over how transcription and AI are deployed. Whether you’re integrating with Microsoft Copilot, Salesforce, or your preferred CCaaS platform, our solutions are designed with privacy and compliance in mind—so you can innovate without compromising security.
FAQs: Real Time Transcription Costs for Avaya
Beyond lawsuits, RTT and AI can expose enterprises to compliance issues, data leaks involving sensitive customer information, misuse of biased or inaccurate AI outputs, and increased vulnerability from unsanctioned tools—any of which can lead to regulatory fines and reputational damage.
Standard disclaimers like “This call may be recorded…” often fail to address real-time transcription or AI-based analytics, which may require explicit and informed consent under laws like CIPA or the Wiretap Act. Enterprises must ensure their disclosures cover these technologies specifically.
These cases highlight that even well-known vendors can mishandle transcription and AI if there’s insufficient transparency or consent. Enterprises must take ownership of how these tools are deployed and ensure customers understand what’s happening during interactions.
Enterprises should reassess consent processes, thoroughly vet vendor practices, secure sensitive data, use approved transcription paths for high-risk conversations, and establish formal governance policies to manage AI usage responsibly.
Autonomous Agents Key Takeaways
Real-time transcription supercharges contact centers by enabling AI features like Agent Assist, live analytics, and compliance monitoring—all in the moment of the call.
Platform and provider choice matters: DaVinci licensing starts at $32.50/user/month, and STT service costs vary—Google being the most cost-effective, while Amazon is the priciest.
Avaya’s licensing stack is layered, requiring TSAPI, AES, SBCE, and SIPREC licenses—plus additional setup like SBCs and media capture hardware.
Maximizing ROI starts with smart planning: Pilot first, assess actual usage, audit existing licenses, and align transcription quality with business outcomes.
