7 min read
Published on May 15, 2026
Robin Bunevich is a Product Marketing Manager at Zoom. She oversees product marketing and strategy for Zoom AI. After three years of leading marketing for Zoom’s Event Solution products, and launching one of the fastest growing products at Zoom, Zoom Events, she is now focused on helping organizations seamlessly adopt AI into their workflows. Prior to Zoom, she ran marketing for live events at The New York Times, and was instrumental in helping the organization transition to a fully virtual events program in March of 2020. At Zoom, Robin uses her 15 plus years of marketing and advertising experience to drive awareness and adoption for Zoom’s AI solutions.
A practical guide to how AI transcription works, what accuracy and privacy benchmarks to evaluate, and how to deploy it across your organization.
Meeting overload is a real productivity drain — and the cost often lands squarely on IT. When knowledge workers can't find decisions, action items, or context from meetings they couldn't attend, they create support tickets, duplicate work, and ask the same questions twice. Zoom changes that equation by allowing users to automatically transcribe meetings in real time with AI Companion and to capture key moments in My Notes, so IT decision-makers can help ensure their teams never miss critical information.
This guide is written for IT and platform decision-makers evaluating AI transcription tools at scale. You'll learn how the underlying technology works, what accuracy benchmarks actually mean, which compliance requirements to check, and how to build a vendor evaluation framework — so you can make a defensible, well-scoped deployment decision.
AI transcription is an automated process that converts spoken audio or video into written text using artificial intelligence — specifically, automatic speech recognition (ASR) models and natural language processing (NLP) — for organization that needs to capture, search, or act on spoken information at scale.
Unlike manual transcription, AI systems typically analyze audio in real time or post-recording to detect speech patterns, identify individual speakers, and handle accents and domain-specific vocabulary, to produce output structured, searchable text. Modern AI transcription tools go further still: many layer large language models (LLMs) on top of ASR output to generate summaries, extract action items, and answer questions from transcript content.
For IT decision-makers, AI transcription isn't a single point solution — it's a foundational capability that determines how well your organization captures institutional knowledge from every conversation. Zoom AI Companion is built directly into Zoom Workplace, which means transcription, meeting summaries, and My Notes are available without a separate tool, a third-party bot joining the call, or audio leaving the Zoom platform.
The core of any AI transcription system is an automatic speech recognition (ASR) model — a deep learning system trained on thousands of hours of spoken audio to map acoustic signals to words. ASR is the technology that converts raw audio into a raw text stream. Here's how a modern AI transcription pipeline generally works end to end:
Accuracy is measured by word error rate (WER) — the percentage of words the system gets wrong compared to a reference transcript. Lower WER is better. Independent benchmarking has found that Zoom AI transcription achieves the lowest word error rate at 7.40%, ahead of competing platforms — a meaningful in situations gap in enterprise scenarios where rare words, technical vocabulary, and multiple speakers can increase transcription complexity.
Multilingual support is an increasingly important variable for global IT deployments. AI transcription systems vary significantly in non-English accuracy, translation fidelity, and the ability to handle code-switching (speakers alternating between languages mid-conversation). Ethical considerations matter too: ASR models trained primarily on certain accents or dialects can exhibit higher error rates for speakers with non-dominant accents — a factor worth raising with any vendor during evaluation.
The decision between AI and manual transcription isn't binary — many enterprise deployments use AI as the primary method, with human review reserved for high-stakes or regulated content. Here's how the two approaches compare across dimensions that often matter to IT decision-makers:
| Dimension | AI transcription | AI transcription | Zoom AI Companion |
| Speed | Real-time or minutes post-meeting | Hours to days | Real-time, in-meeting |
| Cost | Low per-minute cost at scale | High labor cost | Included in Zoom Workplace |
| Accuracy (WER) | 7–15% WER for leading tools | 2–4% WER (optimal conditions) | 7.40% WER (lowest among major platforms) |
| Speaker identification | Automated diarization | Manual labeling | Automatic, named participants |
| Scalability | Unlimited concurrent sessions | Limited by headcount | Scales across all Zoom meetings |
| Data privacy | Varies by vendor | Human reviewer has access | No customer audio/video used to train AI models |
| Compliance support | Varies by vendor | Depends on reviewer agreements | HIPAA-eligible, supports GDPR requirements |
| Integration depth | API-dependent | Manual export | Native: transcripts → summaries → action items → My Notes |
| Multilingual support | Varies by platform | Requires bilingual staff | Supports 30+ languages with translation |
Ratings reflect Zoom's assessment based on publicly available documentation as of April 2026. Verify current capabilities directly with each vendor.
Key differentiator: Unlike standalone AI transcription tools, Zoom AI Companion is natively built into Zoom Workplace. The native integration also means transcripts flow directly into meeting summaries, My Notes, and action items without a manual export step.
Zoom AI Companion takes a fundamentally different architectural approach from standalone transcription tools. Rather than routing audio to a single external model, Zoom uses a federated AI architecture — meaning it can select from multiple AI models (including Zoom's own models and third-party providers) based on the task, the user's data residency requirements, and the context of the conversation. This design is built to support both accuracy optimization and data governance at the same time.
In practice, this means IT teams deploying Zoom AI Companion get:
No superlatives needed here: the combination of native integration, federated model architecture, and a documented no-training-on-customer-data policy addresses the three most common objections IT decision-makers raise when evaluating AI transcription tools — accuracy, security, and vendor lock-in.
The right AI transcription tool for your organization depends on more than the marketing accuracy claim on a vendor's homepage. Here's a practical evaluation framework for IT and platform decision-makers:
Key question to ask any vendor: "Can you provide word error rate benchmarks from real multi-speaker meeting recordings — not controlled test audio — and tell me exactly where that audio is processed and whether it's used to train your models?"
For IT decision-makers, this outcome illustrates a pattern worth modeling: the cost of third-party AI transcription tools isn't just the licensing fee — it's the fragmented data, the integration maintenance, and the security review overhead. Consolidating on a natively integrated tool like Zoom AI Companion can help eliminate that entire cost category.
Lake|Flato's experience is a useful benchmark for organizations where meetings are frequent and project-critical — architectural firms, professional services firms, and consultancies where every meeting generates decisions that need to be traceable. The firm-wide 100 hours per week figure reflects what's possible when AI transcription is deployed consistently across an organization rather than adopted ad hoc by individual teams.
Enterprise meeting intelligence: Deploy Zoom AI Companion across your organization so every meeting — internal standups, customer calls, executive briefings — automatically generates a searchable transcript and summary. IT teams can use this to build a searchable institutional knowledge base without any custom development.
Eliminating shadow IT transcription tools: When employees adopt individual AI note-taking apps to fill gaps in official tooling, IT teams face uncontrolled data flows, unsanctioned vendor relationships, and audit risk. Deploying Zoom AI Companion as the standard transcription layer removes the incentive for shadow adoption — and the Gainsight case study shows the cost savings that can follow.
Cross-platform meeting capture: For organizations running hybrid meeting environments (Zoom, Teams, Google Meet, in-person), Zoom AI Companion's cross-platform support can capture and summarize conversations acrossregardless of the meeting platforms, giving IT a single AI layer to manage rather than multiple vendor-specific tools.
Compliance documentation and audit trails: Regulated industries — healthcare, financial services, legal — increasingly need documented records of key decisions and communications. AI transcription can creates a timestamped , speaker-attributed record of every meeting, which can be retained, exported, or reviewed according to your data governance policy.
For IT decision-makers, AI transcription has moved from a nice-to-have to a foundational productivity and compliance capability. The evaluation criteria that matter most — word error rate on real meeting audio, data governance policies, compliance support, integration depth, and total cost of ownership — are the criteria where tools differ most significantly.
Zoom AI Companion is built natively into Zoom Workplace, which means transcription, summaries, and My Notes work together as a single system — not a collection of integrated point tools. For organizations that want to capture institutional knowledge, support accessibility, and reduceeliminate shadow IT transcription tools, that native integration is what makes the difference.
See how Zoom AI Companion can help your organization turn every meeting into a searchable, actionable record — [request a personalized demo for your IT team].
AI transcription is an automated process that uses artificial intelligence — specifically automatic speech recognition (ASR) models and natural language processing — to convert spoken audio or video into written text. It works in real time or post-recording, identifies individual speakers, and can generate summaries and action items from transcript content. Organizations use it to capture meeting decisions, support accessibility needs, and build searchable records of spoken communications.
Zoom AI Companion transcribes meetings in real time within the Zoom Workplace app, attributing speech to named participants automatically. Transcripts feed directly into automated meeting summaries and My Notes — a persistent AI note-taking workspace — without any manual export or third-party tool. Zoom does not use customer audio, video, or transcript content to train its AI models, which may beis a relevant policy distinction for IT teams managing data governance requirements.
AI transcription is generally the right choice for enterprise meeting capture because it's faster, scales to unlimited concurrent sessions, and costs significantly less per minute than human transcription. Manual transcription achieves lower word error rates (2–4% under optimal conditions) and is better suited for high-stakes regulated content — legal depositions, medical records, compliance-critical documentation — where maximum accuracy and a human review layer are required. Most enterprise IT teams use AI as the default and reserve human review for specific regulated workflows.
Word error rate is a metric that measures the percentage of words an ASR system transcribes incorrectly compared to a reference transcript. Lower WER means more accurate transcription. WER matters to IT decision-makers because vendor accuracy claims (such as "99% accurate") are often measured on clean, single-speaker audio — not the multi-speaker, background-noise, domain-vocabulary conditions of real enterprise meetings. Always ask vendors for WER benchmarks on realistic meeting audio before making a deployment decision.
It depends on the vendor and their data handling policies. For HIPAA compliance, the key questions includeare whether the vendor will sign a Business Associate Agreement (BAA) and where audio and transcript data are processed and stored. For GDPR, the relevant questions should concern data residency, retention policies, and whether transcript data is used to train AI models. Zoom AI Companion is designed to support HIPAA compliance requirementsoffers HIPAA-eligible configurations and Zoom does not use customer audio or video content to train its AI models — both relevant factors for regulated industry deployments.
Most enterprise-grade AI transcription tools support multiple languages, but accuracy can varyies significantly across languages and accents. English typically achieves the lowest word error rates; accuracy in other languages depends on the size and diversity of the training data. For global deployments, test transcription accuracy in each language your teams use and ask vendors specifically about translation fidelity and code-switching support (handling speakers who alternate between languages in a single conversation). Zoom AI Companion supports 30+ languages.
Real-time transcription converts speech to text as it happens, allowing meeting participants to easily follow the conversation — essential for live captions, in-meeting search, and ADA/WCAG accessibility compliance. Asynchronous transcription processes a recording after the meeting ends, which can allow for higher accuracy at lower computational cost. Zoom AI Companion supports both: live captions appear during the meeting, while full transcripts and summaries are generated and available in My Notes shortly after the meeting ends.
