When every word counts: Why AI transcription accuracy is the new power move.

What’s really at stake when AI gets it wrong?

You’ve been there. You’re reading a call transcript and think, “Wait… did they really say that?”

It might be funny. It might be harmless. But in the enterprise world, “small” transcription mistakes cause big, expensive headaches. Misheard phrases disrupt workflows, fuel inaccurate insights, and erode trust in your systems.

In complex, high-volume communication environments, transcription accuracy isn’t a luxury—it’s mission-critical. Your AI-driven insights are only as good as the data you feed them. Garbage in, garbage out.

Why transcription accuracy matters for your business.

Transcription is often the first step in transforming unstructured voice data into usable intelligence. If it’s wrong at the start, everything downstream wobbles:

• Coaching insights lose meaning.
• Sentiment analysis gets twisted.
• Automation misfires.
• Compliance flags go missing.
• Agents waste time fixing avoidable errors.

This isn’t just about “pretty good” quality. It’s about fueling your business intelligence systems with precision. Poor inputs lead to poor outcomes, no matter how brilliant your AI model is.

And when agents are manually correcting transcripts—or when dashboards are built on bad data—you’re not only wasting time, you’re introducing risk into your AI and automation workflows.

The cost of getting it wrong (and the payoff of getting it right).

Voice data increasingly powers decisions, automation, and customer experiences across global teams. Accuracy doesn’t just help you “hear” better; it builds operational trust and reduces business risk.

Consider:

“I’m not happy with the billing process.” vs. “I’m happy with the billing.”

Or the even scarier:

“Cancel my subscription.” vs. “Handle my description.”

The first is an opportunity to intervene. The second? A compliance disaster waiting to happen.

When your transcription is accurate across accents, speakers, and conversation types, your AI can:

• Flag escalation signals automatically.
• Feed clean data into CRM, BI, and AI platforms.
• Enable consistent coaching at scale.
• Support audit and regulatory reporting with confidence.

AI doesn’t fix bad data—it amplifies it. Transcription accuracy isn’t about speed; it’s about building enterprise trust at scale.

Independent proof: How 8x8 stacks up.

8x8 asked The Tolly Group, a respected third-party evaluator, to independently compare transcription accuracy between 8x8, Dialpad, and RingCentral.

The test:

• 15 English-language audio samples.
• Multiple accents (British, Nigerian, Filipino, Scottish, Welsh, and more).
• Each sample was run four times to test consistency.

8x8 achieved more than 50% lower error rates than real-time transcription models used by others.

Even on the harder accents (for example, Scottish and Welsh), 8x8 showed superior performance. Its average WER across all four test runs was 4.54%, compared to Dialpad’s 8.53% and RingCentral’s 9.20%.

Why real-time isn’t always the right time.

Real-time transcription sounds impressive—and for some scenarios, it is. Live captions for meetings? Perfect.

But when you’re feeding data into AI models, compliance systems, customer experience platforms, or analytics dashboards, accuracy matters more than immediacy. Real-time models often trade fidelity for speed, producing flawed signals that ripple across your entire business stack.

Post-conversation transcription, like 8x8’s approach, allows for more robust processing and significantly higher accuracy. That means:

• AI agents aren’t trained on bad data.
• Compliance records meet regulatory expectations.
• QA and coaching programs reflect what was actually said.
• Automation triggers only on verified phrases.

In short: If the outcome depends on the transcript, accuracy must take priority over speed.

Why this matters now.

In today’s customer experience game, 32% of consumers abandon a brand after just one bad interaction. Often, the issue isn’t what the agent said; it’s what the system thought they said.

Transcription accuracy touches every layer of your CX strategy:

• Sentiment analysis.
• Automation.
• Agent performance.
• Regulatory compliance.
• Executive reporting.

Your AI and automation workflows can’t afford to run on low-accuracy, real-time shortcuts. With 8x8, you start with inputs designed to deliver trustworthy results.

Final word: It’s time to trust your transcripts.

This isn’t just speech-to-text. It’s about providing your business systems with the right information from the start.

With 8x8’s single-platform approach for voice, video, chat, and APIs, you’re not just connecting conversations—you’re empowering your teams with accurate data for AI, compliance, and CX excellence.

No more guessing. No more patch jobs. Just clean inputs, smarter insights, and outcomes that scale.

Download the full report and see why great inputs are your AI’s most valuable asset.