From Passive Transcription to Autonomous Agency – The Rise of Agentic Meeting Assistants (2025–2026)

Executive Summary

The global productivity landscape stands on the precipice of a fundamental structural transformation, driven by the rapid maturation of "Agentic Artificial Intelligence." As we transition from 2025 into 2026, the paradigm of the "meeting assistant" is being redefined. No longer satisfied with the passive role of transcription and summarization—capabilities that defined the Generative AI (GenAI) boom of 2023–2024—the market is shifting decisively toward autonomous agents capable of independent perception, reasoning, planning, and execution. This report, grounded in an exhaustive analysis of current technological trajectories and market data, posits that by 2026, the primary value driver for enterprise software will be "agency": the ability of software to close the loop between decision and action without human intervention.

Current market indicators suggest a massive adoption curve, with 75% of global manufacturers expecting AI to contribute significantly to operating margins by 2026 ¹, and nearly 40% of all G2000 job roles projected to involve direct collaboration with AI agents.² This shift is powered by the emergence of Large Action Models (LAMs), which extend the semantic capabilities of Large Language Models (LLMs) into the realm of digital execution. Unlike their predecessors, which could only describe work, LAMs can perform it—navigating user interfaces, executing API calls, and orchestrating multi-step workflows across disparate systems.³

This report provides a granular analysis of this burgeoning ecosystem. We examine the rise of general-purpose agents like Manus AI, which utilize novel architectures like "Wide Research" and "Browser Operators" to perform massive parallel processing and direct browser manipulation.⁵ We analyze the strategic positioning of hardware-integrated solutions like UMEVO, which bridge the gap between physical conversations and digital workflows.⁷ We contrast these with the specialized intelligence of platforms like Fireflies.ai and Otter.ai, which are evolving into semantic routers for enterprise data ⁸, and the platform dominance strategies of Microsoft and Zoom, who aim to make agentic capabilities a native feature of the workplace operating system.¹⁰

Furthermore, we explore the critical "middleware" role of orchestration platforms like Zapier Central, which allow businesses to define the logic that governs these digital workers.¹² Finally, we address the profound implications for the human workforce, discussing the necessary pivot from "execution" to "Agent Ops"—the management and auditing of digital labor—and the looming security and governance challenges posed by autonomous systems with access to sensitive corporate data.² This document serves as a comprehensive guide for enterprise leaders navigating the transition from the era of copilot assistance to the era of agentic autonomy.

1. The Paradigm Shift: From Generative to Agentic AI

To fully appreciate the trajectory of meeting automation through 2026, it is imperative to first delineate the architectural and functional schism occurring within the field of artificial intelligence. The industry is rapidly moving beyond the "chatbot" interface, which relies on synchronous, turn-by-turn human prompting, toward "agentic" interfaces that operate asynchronously, proactively, and autonomously. This is not merely a feature update; it is a rewriting of the contract between human and machine.

1.1 The Limitations of the Generative Era

The period from 2023 to 2024 was defined by Generative AI. Technologies based on Large Language Models (LLMs) demonstrated an unprecedented ability to generate text, code, and images based on learned patterns.¹⁴ In the context of meetings, this manifested as the "AI Note-Taker." These tools could ingest audio, transcribe it with high accuracy, and produce coherent summaries.

However, Generative AI is fundamentally reactive and constrained. It is a "brain in a jar"—highly intelligent but disconnected from the world. It produces a summary that says, "Action Item: Schedule a follow-up with the client," but it cannot act on that information. It relies on a human to read the text, interpret the intent, switch context to a calendar application, and execute the task. This "Action Gap" means that while the cognitive load of remembering the meeting is reduced, the administrative load of acting on it remains.¹⁶

Furthermore, Generative AI operates in a closed loop of content creation. It excels at drafting an email but lacks the autonomy to send it, track the response, and schedule a subsequent meeting based on that response. It is a tool for creation, not completion.¹⁵

1.2 Defining the Agentic Difference

Agentic AI represents the functional evolution of the technology. An "agent" is distinguished by its capacity for agency—the ability to pursue complex, high-level goals without continuous human oversight.¹⁴

Table 1: The Structural Divergence of Generative vs. Agentic AI

The defining characteristic of an agentic system is its ability to reason and plan. When given a goal—such as "Plan a marketing campaign based on this meeting's transcript"—an agent does not simply write a plan. It breaks the objective down into a series of logical steps:

1. Analyze the transcript for key requirements.

2. Research competitor campaigns (using web browsing tools).

3. Draft the copy (using generative capabilities).

4. Create the visual assets (using image generation tools).

5. Schedule the posts in a social media management platform (using API integrations).

6. Monitor for errors and report completion.¹⁶

This "looping" capability—Perception -> Planning -> Action -> Observation -> Correction—allows agents to function as digital employees rather than just digital tools. They can recover from errors, adapt to changing information, and persist over time until the goal is achieved.¹⁶

1.3 The Technological Enabler: Large Action Models (LAMs)

The engine driving this shift is the Large Action Model (LAM). While LLMs are trained to predict the next token in a sequence of text, LAMs are trained to understand and replicate user interactions with software interfaces.³ LAMs bridge the gap between semantic understanding and digital execution.

Traditional automation (RPA) relies on brittle, hard-coded scripts that break if a button moves a few pixels. LAMs, however, utilize a neuro-symbolic approach. They combine the computer vision and semantic understanding of neural networks to "read" a Graphical User Interface (GUI) like a human does, with the logical planning of symbolic AI to determine the correct sequence of actions.⁴

This allows LAMs to navigate complex enterprise environments—clicking buttons in Salesforce, navigating dynamic forms in Jira, or interacting with legacy banking portals—without requiring a custom API integration for every single action. By 2025, LAMs are transforming meeting assistants from passive recorders into active participants that can operate any software the human user can operate.¹⁹

1.4 The Economic Imperative: Closing the Action Gap

The transition to Agentic AI is driven by a stark economic reality: the "Action Gap" in traditional transcription is a productivity leak. Every summary generated by a GenAI tool creates a "to-do" list that requires human labor to execute. By automating the "last mile" of the meeting workflow, Agentic AI promises to recapture this lost time.

Forecasts for 2026 suggest that organizations leveraging agentic AI will realize significantly higher ROI than those stuck in the generative phase. The metric of success will shift from "word error rate" (transcription accuracy) to "task completion rate" (successful execution of follow-up items). Organizations that fail to prioritize high-quality, AI-ready data for these agents risk a 15% productivity loss relative to their peers.²

2. The General Purpose Agent: A Case Study of Manus AI

In the vanguard of this transition, Manus AI has emerged as a representative of the "General Purpose Agent" philosophy. Unlike specialized tools that live within a specific application (like a Zoom plugin), Manus positions itself as an overarching operating layer—a digital worker that sits above the software stack and orchestrates tasks across the entire digital environment. Its architecture and feature set offer a glimpse into the sophisticated capabilities that will define the high-end of the market in 2026.

2.1 Architecture of Autonomy: The "Manus Computer" Paradigm

One of the most significant barriers to the adoption of autonomous agents is the "Black Box Problem." When an AI system executes tasks invisibly in the background, users struggle to trust its output or verify its actions. Manus AI addresses this through a novel interface paradigm known as "Manus's Computer."

Rather than hiding the execution, Manus visualizes it. Users are presented with a view of a virtual desktop where the agent operates. They can watch in real-time as the agent opens a browser, navigates to a URL, moves the cursor, clicks links, and types text.²¹ This transparency serves a dual purpose:

1. Trust Verification: It allows the user to audit the agent's behavior instantly. If the agent is researching a competitor, the user can see exactly which sources it is visiting, ensuring that the data is drawn from authoritative sites rather than hallucinations or low-quality content.²³

2. Collaborative Intervention: The interface enables a "human-on-the-loop" workflow. If the agent begins to deviate from the intended path—perhaps misinterpreting a nuanced instruction—the user can intervene, guiding the agent back on track. This collaborative model is essential for complex, high-stakes workflows where total autonomy is not yet feasible or desirable.²¹

This "transparent agency" contrasts sharply with the opaque operations of traditional backend automation, positioning Manus as a true digital collaborator rather than just a script runner.

2.2 Massive Parallelism: The "Wide Research" Breakthrough

A critical limitation of traditional LLMs is the "context window." When tasked with processing large volumes of data—such as analyzing the financial reports of 100 companies or vetting 50 vendor proposals—a standard model processes items sequentially. As the conversation history grows, the model suffers from "context drift," often forgetting earlier instructions or hallucinating details as the context window fills up.⁵

Manus AI introduces "Wide Research" to solve this scalability challenge. Instead of using a single agent to process a list of 100 items sequentially, Manus orchestrates a massive parallel operation. It spawns hundreds of independent sub-agents, each allocated its own dedicated virtual machine and fresh context window.⁵

Table 2: Comparative Analysis of Research Capabilities

This architecture allows for the generation of exhaustive, enterprise-grade reports in minutes. In a meeting context, an executive could ask Manus, "Research the background, recent news, and strategic priorities of every participant on this invite list." Manus would instantly spin up a dedicated agent for each participant, executing simultaneous web searches and synthesizing the data into a unified briefing document before the meeting even begins.²⁶ This capability fundamentally alters the economics of information gathering, turning what was once a week-long analyst project into a five-minute automated task.

2.3 The "Browser Operator" and the Security Frontier

Perhaps the most powerful—and controversial—feature of the Manus ecosystem is the "Browser Operator." This browser extension allows the Manus cloud service to take full remote control of the user's local browser instance. By leveraging high-level permissions (debugger, cookies, all_urls), the agent can "ride" the user's authenticated sessions.⁶

This solves the perennial problem of "integration hell." Traditional automation requires API keys and complex setups for every tool (Salesforce, Jira, Workday, internal banking portals). The Browser Operator bypasses this by interacting with the software exactly as the user does. If the user is logged in, the agent is logged in.

However, this capability introduces significant security risks. It effectively functions as a sanctioned Remote Access Tool (RAT), granting a cloud provider deep access to the user's browsing context, including session tokens and sensitive internal data.⁶ For enterprise InfoSec teams, this presents a dilemma: the utility of a universal agent versus the risk of a "god-mode" extension. By 2026, we anticipate this tension will drive the development of "Agent Browsers"—secure, sandboxed environments specifically designed for AI agents to operate in, separated from the user's primary personal browsing context.

2.4 Nano Banana Pro: Visual Intelligence

Finally, Manus acknowledges that business communication is not solely textual. The "Nano Banana Pro" model adds a visual dimension to the agent's capabilities. Unlike general-purpose image generators (like Midjourney) that often struggle with text rendering, Nano Banana Pro is fine-tuned for business utility.²⁷

It excels at generating presentation slides, charts, and diagrams where text legibility and data accuracy are paramount. In a post-meeting workflow, this allows the agent to go beyond a written summary. It can autonomously generate a visual PowerPoint deck that outlines the strategy discussed, complete with accurate infographics and timelines, ready for presentation.²⁷ This closes the loop on the "creation" aspect of meeting follow-ups, further reducing the manual burden on employees.

3. The Hardware Frontier: UMEVO and the Edge of Capture

While cloud-based software agents dominate the discussion, the physical capture of meeting data remains a critical frontier. The "Action Gap" cannot be closed if the data is never captured in the first place. UMEVO represents the hardware-centric approach to the agentic market, addressing the reality that critical business decisions often happen outside of formal Zoom rooms—in hallways, coffee shops, and impromptu desk-side chats.

3.1 The UMEVO Note Plus: An AI Agent in Your Pocket

The UMEVO Note Plus is marketed not merely as a digital voice recorder but as a portable "AI Meeting Agent." It differentiates itself through a form factor designed for ubiquity and "always-on" readiness. Its magnetic design allows it to attach to the back of a smartphone, bridging the gap between mobile communications and high-fidelity audio capture.²⁸

Key capabilities that define its role in the 2026 ecosystem include:

• Dual-Mode Recording: The device seamlessly switches between recording the room environment (for in-person meetings) and capturing vibration-based audio from the phone it is attached to (for calls), ensuring no conversation is lost.²⁸

• Edge-Enhanced AI Noise Cancellation: Operating in non-studio environments requires robust noise suppression to ensure transcription accuracy. UMEVO integrates AI-driven noise cancellation at the hardware level, ensuring that the input fed to the agentic cloud layer is clean and actionable.⁷

• GPT-4 Integration: While the device captures the audio, it pairs with a cloud platform powered by GPT-4 to provide immediate value—summaries, mind maps, and translation across 112+ languages.⁷

3.2 The Strategic Role of Hardware in an Agentic World

In the agentic era, hardware devices like the UMEVO Note Plus serve as "edge nodes" for the broader AI system. They ensure that the "context" available to the agent is not limited to scheduled virtual meetings. By capturing the offline world, they complete the data picture.

For 2026, we anticipate a tighter integration where such devices evolve from passive recorders to active interfaces. Future iterations may include real-time feedback mechanisms—a haptic buzz or LED indicator—to signal that the agent has recognized an action item or requires clarification. The device becomes a physical interface for the digital worker, allowing a user to tap a button and say, "UMEVO, task the design team with the changes we just discussed," instantly triggering the downstream workflow.

4. Specialized Meeting Intelligence: Fireflies.ai and Otter.ai

While Manus aims for general utility, specialized meeting agents have evolved from simple transcription services into robust platforms for conversation intelligence and deep workflow automation. Companies like Fireflies.ai and Otter.ai are leading this vertical by integrating deeply with existing enterprise stacks and turning meeting data into structured business signals.

4.1 Fireflies.ai: The Semantic Router

Fireflies.ai has effectively positioned itself as a "Semantic Router" for the enterprise. Its core value proposition is not just recording, but understanding the type of information being discussed and routing it to the correct system of record.

The Power of Topic Trackers:

A standout feature for agentic workflows is the "Topic Tracker." This allows organizations to program the agent's attention. Users can define custom triggers based on keywords or semantic concepts (e.g., "competitor mention," "pricing objection," "deadline," "bug report").8

• Engineering Workflows: When a "bug" is described in a stand-up, Fireflies can automatically create a Jira ticket, populating it with the relevant transcript segment. This ensures that technical debt is logged without the engineer needing to open Jira.³¹

• Project Management: Integration with Monday.com, Trello, and Asana allows Fireflies to turn verbal commitments into digital cards. If a user says, "I'll have the draft by Friday," the system recognizes the intent, the owner, and the deadline, and creates the task autonomously.³³

AskFred: The Conversational Intelligence:

The "AskFred" feature, powered by GPT-4, transforms the meeting from a static artifact into a queryable database. Users can interact with the meeting history conversationally: "Did we decide on a budget for the Q3 campaign?" Fred retrieves the specific answer from the transcript, synthesizing context from multiple speakers.35 This capability is crucial for "institutional memory," allowing teams to recall decisions without re-listening to hours of audio.

4.2 Otter.ai: Automation via Orchestration

Otter.ai, a pioneer in the transcription space, has adopted a strategy of "Orchestrated Automation." While its native features focus heavily on collaborative transcription and real-time summarization (OtterPilot), its connectivity via platforms like Zapier allows it to serve as a trigger for complex, multi-step workflows.⁹

Through Zapier, an Otter.ai transcript completion can trigger a cascade of actions:

1. Trigger: Meeting recording finishes processing in Otter.

2. Action 1: A summary is generated and posted to a specific Slack channel for team visibility.⁹

3. Action 2: "Action Items" are extracted and added to a Notion database for tracking.³⁷

4. Action 3: A follow-up email draft is created in Gmail, ready for the user's review.³⁸

This "modular agency" allows organizations to build custom agents using Otter as the sensory input. It relies on the user to define the logic, contrasting with the more "out-of-the-box" autonomy of general agents, but offering greater flexibility for established workflows. It allows Otter to fit into any stack, provided the orchestration layer (Zapier) supports it.

5. The Enterprise Giants: Microsoft Copilot and Zoom AI

The major platform holders—Microsoft and Zoom—are integrating agentic capabilities directly into the infrastructure of work. Their strategy is one of "ecosystem dominance," threatening to squeeze out standalone tools that do not offer significant differentiation by making agency a native feature of the workplace operating system.

5.1 Microsoft Copilot: The "System of Work"

Microsoft's strategy with Copilot is leveraged on its massive "data gravity." Because it owns the email (Outlook), the document (Word), the meeting (Teams), and the CRM (Dynamics), its agents have a native advantage in context that third-party tools struggle to match.¹⁰

Agent 365 and Cross-App Intelligence:

Copilot does not view a meeting in isolation. It views it as one data point in the Microsoft Graph.

• Sales Execution: Copilot can analyze a Teams meeting transcript, cross-reference it with historical CRM data in Dynamics 365, and autonomously generate a sales quote or a follow-up email that references specific objections raised during the call.³⁹

• Meeting Prep: Before a meeting, Copilot serves as a research agent, summarizing past email threads, documents, and public information to prepare the user. It creates a "briefing dossier" automatically.³⁹

Autonomic Security:

A critical differentiator for 2026 is Microsoft's focus on "autonomic security." The "Agent 365" framework creates a secure perimeter where agents can operate. Enterprises are far more likely to trust a Microsoft agent, which adheres to their existing compliance and governance policies (Entra ID), with sensitive internal data than a third-party extension.41 The introduction of "Copilot Studio" allows organizations to build custom departmental agents (e.g., an "HR Onboarding Agent") that live within this secure perimeter.42

5.2 Zoom AI Companion: The "Switzerland" Strategy

Zoom, lacking the full office suite of Microsoft, adopts an open platform strategy. The Zoom AI Companion aims to be the interface through which other agents are commanded, acting as a neutral "Switzerland" in the platform wars.¹¹

Agent2Agent (A2A) Protocol:

Zoom's "Agent2Agent" (A2A) protocol is a forward-looking standard that acknowledges a multi-agent future. It allows the Zoom AI to communicate with and hand off tasks to third-party agents.

• Cross-Platform Action: A user in a Zoom meeting can say, "Create a Jira ticket for this bug," and the AI Companion can invoke the Jira agent to execute the task. It can pull context from the Zoom conversation and pass it to the external agent, bridging the gap between communication and execution.¹¹

• Customization: Zoom allows organizations to build custom skills for the AI Companion, connecting it to proprietary knowledge bases or internal tools. This extensibility is key to its survival strategy, ensuring it remains relevant even in a Microsoft-dominated world.⁴³

6. Orchestration and Logic: The Middleware Layer (Zapier Central)

As the number of specialized agents grows, the need for an orchestration layer becomes critical. Zapier Central represents the "middleware" of the agentic era. It is not just an automation tool but a platform for building custom logic bots that sit on top of 6,000+ app integrations.⁴⁵

6.1 The Logic of Agency

Zapier Central allows users to train assistants on specific data sources (e.g., a Google Sheet of leads, a Notion database of tasks) and define complex behaviors using natural language logic.¹²

• Behavioral Training: Users can teach the agent: "When a new lead is added to this sheet, research them on LinkedIn, draft a personalized email based on their recent posts, and save the draft in Gmail." This moves beyond simple "if this, then that" triggers into the realm of semantic processing and decision-making.¹²

• Meeting Workflows: For meetings, Zapier Central acts as the "brain" that processes the raw output from a transcription tool. It can apply complex business logic: "If the meeting transcript mentions a budget over $10,000, alert the VP of Sales via Slack; otherwise, just log it in the CRM." This logic layer is essential for converting raw data into intelligent business processes, filtering noise and prioritizing high-value actions.¹²

By 2026, platforms like Zapier Central will likely evolve into the "control plane" for enterprise AI, giving managers a central dashboard to monitor, audit, and optimize the fleet of agents running across their organization.

7. Vertical Transformations: Agentic AI in Practice

The impact of Agentic AI is not uniform; it varies significantly by vertical. The general capability of "taking actions" manifests as highly specific workflows in different departments.

7.1 Sales: The Autonomous Closer

In sales, speed is currency. Agentic AI transforms the sales cycle by removing latency.

• Real-time CRM Hygiene: Agents like Fireflies and Copilot ensure that the CRM is never out of date. Every call logs the outcome, sentiment, and next steps automatically.

• Automated Follow-up: The "Action Gap" in sales—the delay between a call and the follow-up email—is eliminated. Agents draft and even send personalized follow-ups immediately after the call concludes, referencing specific client needs.³⁹

• Deal Intelligence: Agents monitor the health of deals by analyzing the sentiment of conversations and alerting managers to "at-risk" accounts before they churn.⁸

7.2 Engineering: The Technical Scribe

For engineering teams, the friction lies in documenting technical decisions.

• Bug Triage: Agents listen to stand-ups and bug bashes, extracting technical details and automatically populating Jira tickets with reproduction steps and severity levels inferred from the conversation.³²

• Knowledge Base Maintenance: Agents can identify when a new architectural decision is made and automatically update the internal wiki (e.g., Confluence or Notion), preventing the documentation rot that plagues fast-moving teams.³²

7.3 HR and Recruitment: The Candidate Concierge

Recruitment involves massive data processing (resumes) and coordination (scheduling).

• Screening at Scale: "Wide Research" agents can screen hundreds of applicants in minutes, comparing their resumes against the job description and ranking them based on fit.⁴⁸

• Interview Logistics: Agents handle the scheduling negotiation with candidates, finding times that work for the entire panel, booking the rooms, and sending the invites.

• Onboarding: Post-hire, agents can guide new employees through the onboarding checklist, provisioning accounts and answering FAQs, acting as a 24/7 HR buddy.⁴⁷

8. The Human Element: Workforce, Ethics, and Governance

The integration of agentic AI will fundamentally reshape workforce dynamics, economic models of productivity, and corporate governance. We are moving from a world of "human execution" to "human orchestration."

8.1 Workforce Transformation: The Rise of "Agent Ops"

The role of the human employee is shifting. With AI agents expected to handle 11–50% of routine decisions by 2028 ¹, the human worker's primary responsibility becomes defining goals and auditing agent performance.

• New Roles: We will see the emergence of "Agent Ops" teams responsible for the lifecycle management of digital workers—provisioning, monitoring, debugging, and decommissioning agents.⁴⁹

• Reskilling: This shift requires massive reskilling. 81% of HR chiefs are already planning programs to prepare employees for working alongside digital colleagues.¹³ The ability to effective prompt, guide, and audit an AI agent—"Managerial Prompting"—will become a core competency for every knowledge worker.

8.2 The Economics of Autonomy

The cost model of work is shifting. "Wide Research" and parallel agent processing dramatically reduce the time-cost of information gathering.²⁶ What previously took a team of analysts weeks can be accomplished by a swarm of agents in minutes.

• Productivity Gains: The upside is immense. Organizations that successfully adapt expect meaningful productivity gains of 30% or more.¹ Conversely, those that fail to prioritize high-quality data for their agents face a 15% productivity penalty.²

• Shift to Value: As execution costs drop to near zero for administrative tasks, human value capture shifts to strategy, creativity, and relationship management—areas where human judgment and empathy remain superior to algorithmic logic.

8.3 Governance, Security, and "Workslop"

The proliferation of agents introduces new risks.

• Shadow AI: Unauthorized agents running on corporate networks—like the "Browser Operator" accessing internal tools—will be a major security headache.⁶

• Workslop: There is a risk of "workslop"—low-quality, hallucinated, or redundant AI output clogging enterprise systems. By 2026, organizations will implement strict "Agent IDs" and governance frameworks to track who (human or agent) performed an action and why.⁵⁰

• Liability: With agents making autonomous decisions (e.g., approving a refund, scheduling a high-stakes meeting), legal frameworks will be tested. Gartner predicts substantial fines and lawsuits related to agent governance failures by 2030.² Organizations must establish "Human-in-the-Loop" guardrails for high-stakes actions to mitigate this liability.

9. Future Outlook: 2026 and Beyond

As we look toward 2026 and beyond, the trajectory is clear. The "Meeting Assistant" is dissolving as a distinct category and merging into the broader concept of the "Digital Employee."

• Ubiquitous Agency: Agency will not be a feature of a specific app; it will be a layer of the internet. We will move from "using an agent" to "living in an agentic web," where software negotiates with software on our behalf.

• Multimodal Fluency: Agents will be as comfortable with video, audio, and images as they are with text. They will "watch" meetings, "read" screens, and "draw" slides with equal facility.

• The Trust Economy: The defining competitive advantage for AI vendors will be trust. The platform that can guarantee its agents act safely, securely, and in alignment with human values will win the enterprise.

Conclusion

The transition from 2024 to 2026 marks the maturation of the "Agentic Era." Meeting assistants are evolving from tools that listen to teammates that act. Whether through the general-purpose autonomy of Manus AI, the hardware-integrated precision of UMEVO, the specialized workflow intelligence of Fireflies.ai, or the ecosystem power of Microsoft Copilot, the common thread is the move toward Large Action Models (LAMs) and autonomous execution.

For organizations, the imperative is clear: the passive accumulation of meeting transcripts is no longer sufficient. To realize the productivity gains of the next decade, businesses must adopt and govern systems that close the loop—turning spoken words into digital actions, and freeing human capital to focus on the creative and strategic challenges that no agent can yet solve. The future of meetings is not about better notes; it is about better work.

Reference

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