If you are not already using AI meeting transcription tools, you are leaving leverage on the table. For me, tools like Gemini in Google Meet and Granola are not about creating perfect meeting notes or checking a documentation box. They are about getting more value out of the conversations I am already having. As a delivery lead, a large portion of my day is spent in meetings with clients, my project team, and other stakeholders. AI meeting transcription tools help me do that work without slowing everything else down.

Using an AI transcript tool allows me to stay fully present in meetings instead of splitting my attention between listening and note-taking. It also lets me move quickly once the meeting ends. Within minutes, I can generate summaries, pull out action items, or reflect decisions back to the client while the conversation is still fresh.

Over time, transcripts become something even more valuable: a project memory library. I can go back and ask questions like, “When did we first talk about this?” or “What did the client say about this feature last month?” That kind of recall matters on long-running projects where context can easily fade or get distorted.

Where AI Transcription Tools Fit Into My Delivery Lead Workflow

Today, I primarily use Gemini in Google Meet and Granola, and I get the most value from them in client-facing meetings. That includes:

• Daily standups with the client and project team • Feature discovery and requirements discussions • Client conversations that inform scope decisions or statements of work

These are the meetings where accuracy, clarity, and follow-through matter most. Having a transcript gives me confidence that I can capture what was said without derailing the conversation to take notes.

I will occasionally use transcript tools for internal planning sessions, but I have found the return on investment is highest when I am interacting with clients. Client conversations tend to be longer, more nuanced, and more likely to surface future work, risks, or open questions. Transcripts help me make sense of that complexity after the fact.

What AI Transcripts Help Me Do Faster

Once I have a meeting transcript, several delivery lead tasks immediately become easier. First, I can summarize daily standups within minutes of the meeting ending and share that summary with the team and the client. This keeps everyone aligned and reduces the need for follow-up clarification.

Second, I use transcripts to reflect development requirements back to the client. In situations where requirements are discussed verbally but not written down, I can pull out what was said, organize it, and confirm that we are aligned on what will actually go into development.

Transcripts also help me add meaningful context to backlog stories. Developers and testers benefit from understanding why something was requested and how it was described, not just the final backlog story text. Having that context readily available improves downstream implementation and acceptance.

Finally, transcripts make it much easier to summarize long or complex discussions for executives and to spot emerging themes when the same topics come up repeatedly across meetings. Instead of relying on memory or scattered notes, I can query past conversations to identify patterns.

5 Pro Tips That Make AI Transcripts Super Useful

I didn’t get much value from AI meeting transcripts right away. The tools worked, but the output was only okay until I changed how I used them. These are the practices that have made the biggest difference for me.

Prime the AI with attendee names

At the beginning of a meeting, I make a point to say who is on the call. This helps the transcript tool associate voices with people early and reduces the chances that comments are attributed to the wrong person later. This is especially important when I am the meeting host. Without this step, tools like Gemini often assume I said something simply because I created the meeting. Taking a few seconds to name attendees saves time and confusion when reviewing the transcript.

Give the AI a glossary for client-specific language

Every project has its own vocabulary. Some terms have a very specific meaning to the client and may not be obvious to the AI. I define these terms directly in the transcript notes. For example, I might clarify that “collabs” means “collaborations,” or explain how the team refers to a particular system or physical location. Adding a small glossary helps keep terminology consistent in summaries and reduces cleanup before sharing notes or turning conversations into backlog items.

Type real-time context into the transcript.

During meetings, I will type short notes directly into the transcript window. These are not polished notes. They are reminders and context. This might include Jira ticket numbers, known decisions such as already having talked to someone about a requirement change, or reminders about follow-up conversations. These notes give the AI more context when it generates a summary and noticeably improve the quality of action items and backlog suggestions.

Query transcripts instead of rereading them.

I rarely reread a full transcript. Instead, I ask targeted questions. I’ll ask things like what the client said about implementing a particular feature, or who the attendees should be for a meeting we need to have to refine requirements. This turns transcripts into a thinking tool. I am not just reviewing what happened instead I am using past conversations to help decide what to do next.

Tell the AI exactly what format I want the summary in.

I don’t let the AI guess how to structure a meeting summary. I tell it exactly what I want. For instance, this is a prompt I regularly use in Granola: “Give me a summary of this meeting and structure the format by name of individual, their action items, and what they updated the team about. In a general section at the end, add topics that are not attributed to an individual. List items that need to be added to the development backlog.”When I do this, the output is immediately usable by the team. I do not have to reformat it or hunt for the actionable parts before sharing.

Security and Client Considerations

Using AI transcript tools in client-facing work requires more discipline than using them internally. It’s good practice to first ask clients for permission to record a meeting. Fortunately, at the start of my current project, the client explicitly requested that we record our Google Meet meetings. That explicit direction made recording and transcription a regular part of our working agreement.

This particular client also shares meeting transcripts with members of their extended team who are not directly involved in the day-to-day work. Because of that, I treat every transcript as a client-visible artifact. Before anything is shared, I review it for accuracy and context, especially around speaker attribution.

One important setting to be aware of when using Gemini in Google Meet is “Send notes to.” This setting determines who receives the meeting notes. I’m intentional about using this feature and often choose hosts and co-hosts only so I can review the notes before anyone else sees them.

Another consideration is to be mindful of what is said before and after the client comes on the call. Gemini and Granola will continue taking notes as long as it is enabled. If your internal team joins early or stays on after the client drops off, those conversations will be captured unless you stop note-taking.

Conclusion

AI transcript tools have become a necessary part of how I work as a delivery lead. They do not replace judgment, experience, or good facilitation. They help me stay present in conversations, move faster after meetings, and build a reliable project memory that I can return to when context starts to fade.

The real value does not come from turning transcription on and zoning out. It comes from being intentional about how the tools are used. Naming attendees, adding context in real time, teaching the AI project-specific language, and asking better questions of transcripts all make a noticeable difference in the quality of the output.

I am especially interested in pushing this further by using transcripts to generate placeholder backlog items and then refining them with the team. The AI helps surface raw material, and the team brings clarity, prioritization, and shared understanding. That feels like a natural extension of the way I already work.