AI in USE #65 ✨: Replace the input, not the system
🔍 Three products show how real gains come from automating the keystroke-to-decision step with tight constraints, deep integrations, and observable outcomes.
Welcome to the latest edition of AI in USE, where we explore how AI is reshaping the way products are designed, built, and experienced.
These products don’t rip and replace core tools; they add an AI control layer that turns messy intent into structured, executable work inside existing workflows.
By coupling autonomy with hard constraints—doctrine, scripted flows, personal dictionaries, offline reliability—and surfacing performance in dashboards, they deliver speed without surrendering control.
If you’re building, target the bottleneck step, ground generation in authoritative data, and instrument it end to end so operators can trust, tune, and scale it.
Wispr Flow – Cross-app AI voice dictation for knowledge workers
Organization Type: Start-up
AI Purpose: Augment
Type of AI Model: Generative AI, Supervised learning
AI Application Type: User Experience
Targeted Industry: Multi-industry
Target Group (AI User): Employees
Use Case Description:
Wispr Flow is a cross-platform voice dictation assistant that lets professionals use speech instead of typing to create polished text in any app. It addresses the bottleneck of manual typing and formatting, which slows down documentation, email, and note-taking and adds physical and cognitive strain. The product layers AI-powered dictation and editing on top of existing tools, turning natural speech into clean, context-aware text, commands, and tasks across Mac, Windows, and iPhone. Employees use it to draft emails, write docs, log CRM updates, transcribe meetings, and interact with other software tools entirely by voice while staying in their usual workflows. This delivers faster throughput on text-heavy work, reduces context switching, and expands accessibility for users who rely on hands-free input.
Key Features:
Provides system-wide voice dictation that works inside virtually any text field across Mac, Windows, and iPhone apps.
Transforms natural, rambling speech into clear, punctuated, and professionally formatted text with AI auto-editing, with dictation available in over 100 languages.
Maintains a personal dictionary that learns user-specific names, jargon, and recurring terms to improve recognition accuracy.
Captures and transcribes multi-speaker meetings and interviews in real time so teams can focus on discussion while it records notes.
Offers a snippet library and voice shortcuts so short spoken cues expand into reusable templates and standard responses.
Adapts tone and style based on context so output aligns with channels like email, chat, or documentation with minimal manual edits.
Supports a command mode that applies voice instructions to rewrite, shorten, or rephrase existing text as an editing copilot and adds device control so users can trigger actions, manage tasks, and navigate workflows without typing.
Integrates with task and calendar tools to convert spoken instructions into structured tasks, reminders, and project lists.
Results:
Enables professionals to produce written content up to 4× faster than typing according to product positioning.
Supports over 100 languages, significantly expanding the addressable user base and use cases across regions.
Delivers monthly user growth reportedly exceeding 50%, indicating strong adoption among knowledge workers.
Demonstrates up to approximately 3× faster workflows in practitioner demos when interacting with tools like Microsoft Copilot via voice instead of typing.
Launch Date: September 2024
Retrieve the case and more (including their sources) on the AI in USE website
Comand AI – AI-native command and operational planning platform
Organization Type: Start-up
AI Purpose: Augment
Type of AI Model: Generative AI, Unsupervised / Reinforcement learning
AI Application Type: Operations
Targeted Industry: Defense & Security, Government / Public Administration
Target Group (AI User): Operations teams
Use Case Description:
Comand AI’s Prevail platform accelerates military command and operational planning by transforming fragmented operational data into decision-ready battle plans. Modern multi-domain operations overwhelm staff with data and possible manoeuvres, making it hard to compare options and update plans fast enough using legacy command-and-control software. Prevail ingests mission orders, doctrine, terrain, lessons learned, and real-time field information to automatically assemble a tactical picture, generate courses of action, and run simulations that compare options. Operations teams use the system as a tactical co-planner to draft plans in minutes instead of hours, explore more manoeuvres than manual methods allow, and adapt plans quickly as conditions change. The platform also structures after-action data so units can capture and reuse lessons learned in days rather than long doctrinal cycles, turning operational experience into a continuous planning advantage.
Key Features:
Builds a holistic tactical situation picture in seconds from mission orders, doctrine, terrain, lessons learned, and open-source data.
Generates and compares alternative manoeuvre options using a reinforcement learning tactical agent fed by real-time field information.
Runs AI-driven simulations that explore a wide range of manoeuvre possibilities before commanders commit forces.
Enables mission plan generation in minutes with workflows that support adherence to or deviation from existing doctrine.
Accelerates lessons-learned workflows by turning post-operation data into structured, searchable insights for future planning.
Provides an AI-native workflow designed for field reliability, interpretability, encrypted-by-design security, and offline or disconnected operations.
Results:
Reduced mission planning time by up to 4× in exercises, with mission plans produced in minutes instead of hours.
Achieved up to 10× planning time reduction in a surveillance operation scenario compared with traditional planning processes.
Improved decision speed by up to 4× for commanders evaluating and selecting courses of action.
Enabled distribution of major lessons learned to operational units within a few days rather than traditional doctrinal cycles.
Secured early contracts with German and French armed forces, including evaluations during field exercises.
Launch Date: January 2025
Retrieve the case and more (including their sources) on the AI in USE website
Zendesk – AI email reply agents and article translations
Organization Type: Tech giant
AI Purpose: Automate
Type of AI Model: Generative AI, Supervised learning
AI Application Type: Customer Care
Targeted Industry: Multi-industry
Target Group (AI User): Customer support agents, Operations teams, Knowledge managers, IT administrators / System integrators
Use Case Description:
Zendesk uses AI to automate factual customer email responses and to translate help center articles directly in the editor, eliminating a major bottleneck in multilingual support. Support teams previously spent significant time reading repetitive emails, drafting near-identical replies, routing complex cases, and absorbing the cost, latency, and inconsistency of manually translating and updating content across languages. With AI agents for email, the system reads incoming messages, generates complete responses grounded in connected knowledge sources, and follows scripted flows or escalates to humans when needed. In Knowledge, authors generate and re-generate full translations inside the editor for any enabled language, review before publishing, and apply term-exclusion lists to preserve brand terminology. Operations teams, knowledge managers, and admins configure flows, connect data sources and integrations, and monitor automation metrics in the AI agent performance dashboard. This raises automated resolution for factual queries and speeds multilingual content rollout, allowing agents to focus on exceptions while global support scales without proportional headcount.
Key Features:
Generates complete, factual email replies using configured knowledge sources so many tickets are resolved end-to-end without agent intervention.
Understands customer intent in email and either answers directly, follows a predefined conversation flow, or routes the request to a human for complex handling.
Lets admins design hybrid flows that mix scripted steps with generative answers to keep control over authentication, data collection, and policy-sensitive interactions.
Connects via integration tools to CRM and external systems so AI agents can perform actions like updating orders or creating cases, not just answering questions.
Provides an AI agent performance dashboard that reports on automated resolutions, deflection, and efficiency to demonstrate ROI and guide tuning.
Generates and re-generates full article translations inside the editor with preview and publishing in standard Knowledge workflows, enabling fast updates when source content changes and minimizing process disruption.
Results:
Increased share of customer emails fully resolved by AI agents, raising the overall automation rate for factual support queries.
Reduced volume of agent-touched tickets by resolving repetitive status, policy, and how-to questions without human involvement.
Lower average handle time for remaining human-routed email tickets, as agents focus on complex cases rather than repetitive replies.
Improved visibility into AI performance through automated resolution and deflection metrics tied directly to usage-based pricing units.
Accelerated rollout of multilingual help center content by generating translations inside the editor instead of using external vendors or tools.
Reduced translation turnaround time and operational effort for maintaining aligned content across all supported languages.
Launch Date: July 2025
Retrieve the case and more (including their sources) on the AI in USE website
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Disclaimer: This content was (obviously 😉) built with the assistance of AI.