Most conversations about AI adoption start with the wrong question. “Do you trust the output?” gets asked constantly, in transcription, in captioning, in every corner of enterprise software. Yet across a run of recent conversations that we’ve had spanning education, insurance, legal, and government, the actual sticking point wasn’t accuracy. It was closer to: who decides when AI is involved, and can that decision be made job by job, department by department, instead of once, for everything.
In one recent conversation, we were asked whether AI-generated summaries could be turned off entirely at the account level – not because of fear that the summaries were wrong or simply unhelpful, but because the organization’s internal generative-AI policy hadn’t caught up yet. In another call, a prospect named the ability to switch between fully automated and human-reviewed transcription as the single best part of our product demo, better received than any accuracy number. Neither of these was a trust question in the “is AI good enough” sense. Both were control questions: how much say do we get over where AI shows up in our workflow.
Is Trust Really the Right Question to Ask About AI?
Recent research suggests the answer is no. A 2026 TeamViewer study on workplace AI found that 75% of employees now use AI daily, but 61% still want human oversight before it acts autonomously, what the researchers call an “AI confidence gap.” Tellingly, only 5% of respondents said they have no concerns about AI operating without human oversight at all, while 70% said they’re fine with autonomous action as long as they can step in when needed. That’s not a rejection of AI. It’s a request for a steering wheel.
Verbit’s own research into AI trust by sector points to the same pattern: trust in AI varies less by how capable a tool is and more by how much a person understands and can override what it’s doing. People “trust AI more when they understand how it works and can override its decisions.” That’s a control problem dressed up as a trust problem, and it shows up constantly in how organizations evaluate AI-powered transcription and captioning tools, not as “will this be accurate?” but as “will we be stuck with it once it’s on?”
AI-Powered Transcription Doesn't Have to Mean All-In on AI
It’s easy to picture this as an either/or choice: use AI transcription and accept whatever comes out, or use human transcription and pay for it accordingly. In practice, it works better as a spectrum, and it’s one that already exists in how AI-powered transcription tools are built. Verbit’s automatic speech recognition technology, Captivate, is the adaptive AI base layer behind our transcription platform, with an optional human review layer available on top for compliance-critical, legal, or otherwise high-stakes content. That means the choice isn’t AI or human, it’s AI by default with the option to add human review or human-produced exactly where the stakes are highest, deposition transcripts, regulated communications, anything where a mistake is expensive, while leaving lower-stakes work fully automated for speed and cost.
That flexibility matters more than any single accuracy claim, because it means an organization doesn’t have to negotiate its AI policy once for every use case. A legal team can route depositions through human review while letting internal meeting notes run fully automated. A university can lean on AI captioning for lecture capture at scale while keeping human oversight available for accommodations work. A post-production media team can choose how to transcribe their content with flexibility. The AI adoption conversation stops being “are we an AI-first organization or not” and becomes “where, specifically, do we want a human in the loop this week.”
As one of our customers, Jeffrey Lehman, Post Supervisor at Alfred Street Industries who uses Verbit to produce transcripts for the TV hit Project Runway, puts it: “What ultimately made Verbit stand out to us wasn’t simply the use of AI transcription — there are many AI-only options available now — but the flexibility of having both AI and human transcription services within the same platform.”
“We relied on AI transcription for the majority of our material because the turnaround time and cost savings compared to traditional human-only workflows were substantial,” he continued. “At the same time, having the ability to shift to human transcription whenever we encountered difficult audio, heavy accents, or accuracy concerns gave us an important level of confidence during post-production.”
What Control Should Actually Look Like When Evaluating an AI Vendor
If control, not raw trust, is the real barrier, then the questions worth asking a transcription or captioning vendor change. A few worth pushing on directly:
- Can automated and human-reviewed transcription be selected per job, or is it an all-or-nothing account setting?
- If a team needs to dial back AI-generated features (summaries, insights) for part of the organization, is that actually possible, or is it a workaround?
- Are data-handling commitments, the agreement itself, who’s accountable for it, put in writing, or just described as “secure” in general terms? Documented data-handling commitments are the difference between an answer and a reassurance.
- Are accuracy and turnaround tiers clearly defined, so “human-reviewed” and “fully automated” come with real, comparable expectations rather than vague marketing language? Our own guide to boosting transcription accuracy walks through what actually moves the needle.
A vendor that can answer these specifically, rather than reassuring in general terms, is one an organization can bring into a cautious AI rollout without having to relitigate the whole policy every time a new team wants to use it.
How to Choose an AI Transcription and Captioning Partner That Gives You Control
Output quality is still part of the AI adoption conversation. Plenty of teams are right to ask hard questions about accuracy before they rely on AI transcription or captioning. But a lot of the hesitation we’re seeing lately isn’t really about that. It’s about “turn on AI” so often meaning turning it on everywhere, for everyone, with no easy way to dial it back for the one team, the one document type, or the one compliance concern that needs a human in the loop. The organizations moving fastest right now aren’t the ones with the least caution about AI, they’re the ones that found a vendor built around adjustable, job-by-job control instead of an all-or-nothing switch.
If your team is evaluating an AI-powered transcription or captioning vendor and wants that kind of flexibility, book a demo with Verbit, and we’ll walk through how our automated and human-reviewed workflows work side-by-side in the same platform.
FAQs on AI Adoption in Transcription and Captioning
Is AI transcription accurate enough to trust for compliance-critical work?
It depends on the workflow, not just the model. Automated transcription alone is often sufficient for internal or low-stakes content, while an optional human review layer is available for any high-stakes scenarios, including broadcasts, depositions, regulated communications, and other documentation. AI transcription accuracy also varies vendor-to-vendor and platform to platform. You should be wary of free, automatic AI transcription tools and seek out those that allow for prep and customization to pre-identify terms correctly.
Is human review available instead of fully automated AI transcription?
Yes, in a well-built AI transcription platform like Verbit’s this should be selectable per job rather than a fixed account-wide setting, letting teams route sensitive work to human review while keeping routine work fully automated.
What's actually blocking AI adoption inside most organizations right now?
The actual blocker is that most tools are built as a single on/off switch for AI, not a set of controls a team using a platform can adjust job by job. It’s less a lack of capability than a lack of granularity. A 2026 workplace study found 75% of employees already use AI daily, yet 61% still want human oversight before it acts on its own, and separately, we’ve heard from accounts who wanted a way to turn off AI-generated features for one department without shutting them off company-wide.
How is generative AI different from the AI used in transcription and captioning?
Generative AI creates new content (summaries, insights, drafted text) on top of the transcripts or captions being generated. The standard AI behind transcription and captioning primarily converts speech to text. Verbit’s own platform touches both, in that order: Captivate, our proprietary automatic speech recognition technology, produces the speech-to-text, the captions, the transcript itself, and from there, Verbit’s generative AI features can turn that same transcript into summaries, chapters, and other insights with a single click. That second layer is where the payoff shows up: a student gets a study-ready summary instead of scrolling through a full lecture transcript, a corporate team walks out of a meeting with clear takeaways instead of digging back through notes, and a court reporter or attorney can pull the key moments out of hours of testimony without rereading all of it. Keeping those two layers distinct, and letting a team turn on the generative AI piece without changing how the core transcription works, is exactly why this control question matters.
