Integrating AI Video Generation with Slack for Team Review Workflows

The Video Review Bottleneck

Your team generates videos. Marketing needs product demos. Sales wants personalized outreach clips. Customer success creates training materials. Each video goes through the same painful process: someone creates a draft, sends it around for feedback, waits for responses, makes revisions, and repeats until everyone signs off.

This takes days. Sometimes weeks.

AI video generation changes the first part of this equation. Tools like Runway Gen-4.5, Sora 2, and Veo 3 can create professional video content in minutes instead of hours. But most teams hit a new problem: managing the review process for dozens or hundreds of AI-generated videos becomes its own bottleneck.

The solution isn't another dashboard or project management tool. It's integrating AI video generation directly with Slack, where your team already communicates. This approach turns video review from a multi-day email thread into a streamlined workflow that happens in the same place your team discusses everything else.

Why AI Video Generation Needs Better Workflows

AI video generation tools have reached production quality. In 2026, platforms can create videos with synchronized audio, realistic physics, and professional-grade output up to 4K resolution. The technical capability exists to generate hundreds of videos per week.

The problem shifts from "can we make this video?" to "how do we review and approve all these videos efficiently?"

Traditional video production involved weeks of work, so spending a few days on review made sense. When you can generate 50 video variations in an hour, that review process becomes the constraint. Teams need a way to:

• Get AI-generated videos in front of reviewers immediately
• Collect feedback without switching between tools
• Track which videos are approved, rejected, or need revision
• Maintain a record of decisions for future reference
• Route videos to the right reviewers based on content type

Email doesn't work for this. Neither does uploading files to shared drives and hoping people remember to check them. The review process needs to happen where your team already is: in Slack.

Current State of AI Video Generation

Before building a review workflow, you need to understand what AI video generation can actually do in 2026. The technology has improved dramatically from even a year ago.

Runway Gen-4.5 currently leads industry benchmarks with an Elo score of 1,247, beating Google's Veo 3. The platform handles cinematic motion, camera movement, and scene composition better than most competitors. Videos maintain consistency across frames without the jittery motion or physics problems that plagued earlier models.

Sora 2 from OpenAI comes in two variants. The standard version prioritizes speed and flexibility for rapid iteration. The Pro version produces higher-quality output suitable for final production. Both versions generate video with synchronized audio, including dialogue, sound effects, and ambient noise that matches the visuals.

Google's Veo 3 focuses on photorealistic output and longer video durations. The model can generate up to 60-second clips at 4K resolution. It includes advanced creative controls like reference image guidance and scene extension capabilities.

Smaller specialized models fill specific niches. Kling AI excels at photorealistic human characters and natural movements. Pika Labs offers rapid prototyping with quick generation times. Adobe's Firefly Video Model provides IP-friendly content trained only on licensed material, making it safer for commercial use.

Pricing varies widely. Sora 2 costs around $0.15 per 10-second clip on platforms like Kie.ai. Runway uses a credit system where costs range from $0.30 to $0.80 per video depending on settings. Veo 3 offers both fast generation at $0.30 and quality output at $2.00 for 8-second clips.

The technical capabilities matter less than how you integrate these tools into your workflow. Every AI video tool offers an API. Most support webhook callbacks to notify your systems when a video finishes generating. This makes automated integration possible.

Why Slack Makes Sense as Your Review Hub

Slack has become the default communication platform for most modern teams. Over 700 million messages get sent in Slack every day. People check Slack constantly. They don't check their email constantly. They definitely don't check specialized project management tools constantly.

This makes Slack the natural place for video review workflows. When a new AI-generated video arrives in a Slack channel, team members see it immediately. They can provide feedback in threads, use emoji reactions for quick approvals, and discuss changes without leaving their primary work environment.

Slack offers several technical features that make it ideal for review workflows:

The platform supports rich media previews. Videos posted to Slack play inline without requiring downloads or opening external players. Reviewers can watch content directly in the conversation thread.

Interactive buttons and menus enable one-click approvals or rejections. Instead of typing "approved" or "needs revision," reviewers click a button. This creates structured data your workflow can act on automatically.

Threaded conversations keep feedback organized. All comments about a specific video stay grouped together. Multiple videos can be reviewed simultaneously in the same channel without confusion.

The Slack API provides programmatic access to conversations, files, and user actions. Your integration can post videos, monitor for approval actions, and update status messages based on team feedback.

Slack's permission system lets you control who can approve videos. You can route different content types to different channels with different reviewers. Marketing videos go to the marketing team. Sales videos go to sales managers. Training content goes to subject matter experts.

The platform integrates with over 2,600 other applications. Your video review workflow can connect to Google Drive for storage, Jira for task tracking, or your CMS for publishing approved content.

Building the Integration: Technical Architecture

A complete AI video generation and Slack review workflow involves several components working together. The basic architecture looks like this:

Trigger: Something initiates video generation. This might be a form submission, a scheduled job, a Slack command, or an API call from another system.

Video Generation: Your chosen AI video tool (Runway, Sora, Veo, etc.) receives the prompt and generates content. This happens asynchronously since video generation takes 30 seconds to several minutes.

Workflow Orchestration: A system manages the steps between triggering generation and posting to Slack. This handles status polling, error recovery, and routing decisions.

Slack Integration: The orchestration system posts videos to appropriate Slack channels with context and interactive elements for review.

Feedback Collection: The system monitors for review actions (approvals, rejections, comments) and routes videos accordingly.

Downstream Actions: Approved videos get published, stored, or processed further. Rejected videos return for revision or get archived.

Most teams use a workflow automation platform to orchestrate these steps. MindStudio offers no-code AI agent building that can connect video generation APIs with Slack in a visual workflow builder. The platform provides access to 200+ AI models without requiring separate API key management, which simplifies integration significantly.

Alternatives include n8n for self-hosted workflows, Zapier for plug-and-play simplicity, or custom code using the various APIs directly. The choice depends on technical resources, budget, and how much control you need over the process.

Step-by-Step Implementation

Here's how to build a working AI video generation and Slack review workflow from scratch.

Step 1: Set Up Your AI Video Generation API

Choose your AI video generation tool and get API access. For Runway, sign up for their API program and receive authentication credentials. For Sora 2 via OpenAI, you need an API key with appropriate billing set up. For Veo 3, access comes through Google Cloud with your project credentials.

Test basic generation with a simple API call. Most services work similarly: you submit a text prompt, receive a task ID, poll for completion status, and download the finished video. Confirm you can generate videos programmatically before building the full workflow.

Note the API rate limits and pricing. Runway limits concurrent generations. Sora has usage tiers based on your subscription. Veo charges per generation. Your workflow needs to respect these constraints.

Step 2: Create Slack App and Bot

Go to api.slack.com and create a new Slack app in your workspace. Enable bot functionality and add these OAuth scopes:

• chat:write - Post messages to channels
• files:write - Upload video files
• channels:read - Access channel information
• reactions:read - Monitor emoji reactions
• im:write - Send direct messages

Install the app to your workspace and save the bot token. You'll use this token to authenticate API calls from your workflow orchestration system.

Create dedicated Slack channels for video review. Use naming conventions like #video-review-marketing, #video-review-sales, #video-review-training. This keeps content organized and lets you route videos to appropriate reviewers.

Step 3: Build the Core Workflow

The workflow logic follows this pattern:

Receive Trigger: Accept input that includes the video prompt, target Slack channel, and any metadata (video type, campaign name, due date, etc.).

Generate Video: Call the AI video generation API with the prompt. Store the task ID and initial status.

Poll for Completion: Check the generation status every 10-30 seconds. Handle three states: processing, completed, or failed. If processing, wait and check again. If failed, log the error and notify stakeholders. If completed, proceed to next step.

Download Video: Retrieve the generated video file from the API. Some services provide direct download URLs. Others require you to fetch from cloud storage.

Post to Slack: Upload the video to the designated Slack channel. Include context in the message: the prompt used, video duration, timestamp, and any relevant metadata. Add interactive buttons for "Approve", "Reject", and "Request Changes".

Monitor Responses: Watch for interactions with the posted message. When someone clicks "Approve", mark the video as approved and trigger downstream actions. When someone clicks "Reject", archive the video or route it back for regeneration. When someone clicks "Request Changes", notify the appropriate team member.

Step 4: Add Human-in-the-Loop Controls

Not every video should go straight to production after a single approval. Implement approval workflows based on content type and stakes.

For low-stakes content like internal training videos, a single approval from a team lead might suffice. For high-stakes content like customer-facing marketing materials, require multiple approvals from different stakeholders.

Use Slack's threading feature to collect feedback. When reviewers request changes, they comment in a thread below the video post. The workflow monitors these threads and can summarize feedback, notify the content creator, or automatically regenerate the video with revised prompts based on the comments.

Set time-based escalation rules. If a video sits unreviewed for 24 hours, send a reminder. If it sits for 48 hours, escalate to a manager. This prevents videos from getting stuck in the review queue.

Step 5: Implement Status Tracking

Maintain a database or spreadsheet that tracks every generated video. Include fields for:

• Video ID (unique identifier)
• Original prompt
• Generation timestamp
• Current status (pending review, approved, rejected, published)
• Slack message link
• Approver names and timestamps
• Final destination (YouTube, landing page, email campaign, etc.)

Update this tracking system as videos move through the workflow. This creates an audit trail and lets you analyze bottlenecks in your review process.

Post summary updates to Slack periodically. A daily message showing "5 videos pending review, 12 approved, 2 rejected" helps keep the team aware of workflow status.

Step 6: Connect Downstream Systems

Approved videos need to go somewhere. Build integrations to your final destinations.

For videos going to YouTube, use the YouTube Data API to upload approved content automatically. Include metadata like title, description, and tags derived from the original prompt.

For videos used in email campaigns, upload them to your email service provider's asset library and update the campaign template with the video URL.

For videos published to your website, push them to your CMS or update your CDN with the new content.

For videos shared externally, generate shareable links with appropriate permissions and post them back to the Slack channel for the team to use.

Advanced Workflow Patterns

Once the basic workflow runs reliably, add sophistication to handle complex scenarios.

Conditional Routing Based on Content

Different video types need different review processes. Use AI to analyze generated videos and route them accordingly.

Send the video thumbnail and prompt to a language model. Ask it to categorize the content: Is this a product demo? A testimonial? Training material? Marketing content? Based on the category, route the video to the appropriate Slack channel and reviewer group.

This automatic routing ensures videos get reviewed by people with relevant expertise. Marketing videos go to marketing managers. Technical content goes to engineers. Customer-facing content goes through brand review.

Batch Processing with Progress Updates

When generating many videos at once (like variations for A/B testing), batch the work and provide progress updates in Slack.

Post an initial message: "Generating 20 video variations for Campaign X. This will take about 15 minutes." Update the message periodically: "10/20 complete..." When all videos finish generating, post them to the review channel together with a summary message.

This prevents notification spam while keeping stakeholders informed about long-running processes.

Automated Quality Checks

Add automated checks before human review. Analyze generated videos for technical issues:

Check video duration matches expectations. Flag videos that are too short or too long.

Verify audio sync. Use speech-to-text on the audio track and compare timing to the video duration. Flag videos where audio cuts off early or runs past the video end.

Scan for visual artifacts. Use computer vision to detect corrupted frames, flickering, or other rendering problems.

Test text rendering if the video includes on-screen text. OCR the video and verify any text is legible and matches expected content.

Videos that fail automated checks go to a separate Slack channel for technical review before reaching content reviewers. This saves time by catching obvious problems early.

Version Control and Iteration

When reviewers request changes, generate a new version and post it in the same Slack thread as the original. This creates a version history visible to everyone.

Track how prompts evolve across iterations. If version 1 used prompt A, version 2 used prompt B with requested changes, and version 3 got approved, save this progression. This data helps improve prompt engineering over time.

Link related videos. If you generate multiple variations of the same concept, post them as a group. Use Slack's message blocks to create a structured layout showing all variations side by side.

Best Practices for Team Review Workflows

Technical implementation matters, but process design matters more. Follow these practices to keep your video review workflow running smoothly.

Set Clear Review Criteria

Create a documented checklist for video reviewers. What makes a video approved versus rejected? Common criteria include:

• Brand alignment - Does the video match brand guidelines for style, tone, and messaging?
• Technical quality - Is the video resolution acceptable? Is audio clear? Are there visual artifacts?
• Content accuracy - Does the video accurately represent the product, service, or information?
• Legal compliance - Does the video avoid copyright issues, false claims, or regulatory problems?
• Audience appropriateness - Is the content suitable for the intended audience?

Pin this checklist in your Slack review channels so reviewers can reference it easily.

Define Response Time Expectations

How quickly should reviewers respond to new videos? Set clear SLAs.

For urgent content (like social media responses to trending topics), expect reviews within 2 hours. For standard marketing content, allow 24 hours. For evergreen training materials, 48 hours is reasonable.

Communicate these timelines when posting videos for review. Include due dates in the Slack message: "Please review by end of day Tuesday for Thursday campaign launch."

Use Emoji Reactions Strategically

Slack emoji reactions provide quick, informal feedback. Establish conventions:

• 👍 = "Looks good to me"
• 👀 = "I'm reviewing this now"
• ❓ = "I have questions"
• ⚠️ = "I see potential issues"
• ✅ = "Approved" (for reviewers with approval authority)

Your workflow can monitor for the ✅ emoji and treat it as an approval signal. This gives reviewers a fast way to approve without clicking buttons.

Minimize Context Switching

Keep everything related to a video in one place. Don't make reviewers open external links to see additional context.

Include the original brief or prompt in the Slack post. If the video relates to a specific campaign or project, link to the planning doc or campaign page. If there are multiple versions, show them all in the same thread.

When reviewers comment with feedback, acknowledge their input directly in the Slack thread. If you make changes based on their feedback, post a follow-up: "Updated version incorporating Sarah's feedback about the call-to-action timing."

Track Metrics That Matter

Measure your workflow efficiency with quantitative data:

• Average time from generation to first review
• Average time from first review to approval
• Percentage of videos approved on first submission
• Number of revision rounds per video
• Videos stuck in review (pending more than 48 hours)
• Reviewer workload distribution

Post monthly summaries to your team. Identify bottlenecks and address them. If certain reviewers take much longer than others, investigate whether they're overloaded or need more training on review criteria.

Common Challenges and Solutions

Every team encounters obstacles when implementing AI video review workflows. Here's how to handle the most common issues.

Challenge: Review Fatigue

When you can generate dozens of videos quickly, reviewers get overwhelmed. They start approving everything without careful review just to clear the queue.

Solution: Implement batching and prioritization. Group related videos and schedule specific review sessions. Use priority flags to mark urgent content. Rotate review responsibilities across team members to prevent any one person from burning out.

Add automated pre-filtering to reduce the review burden. If you're generating 50 variations for A/B testing, use automated quality checks to eliminate obviously flawed videos before human review. This might cut the review queue from 50 videos to 20 viable candidates.

Challenge: Inconsistent Standards

Different reviewers apply different standards. One person approves content that another would reject. This creates confusion and delays.

Solution: Document clear rubrics with examples. Show side-by-side comparisons of approved versus rejected videos with explanations. Run calibration sessions where the team reviews sample videos together and discusses why they should be approved or rejected.

For critical content, require reviews from multiple people and implement voting. Three reviewers must all approve, or 2 out of 3 must approve, depending on your threshold. This ensures outlier opinions don't block good content or let bad content through.

Challenge: Lost Context

Videos get generated, posted to Slack, reviewed, and approved. Six months later, someone asks "Why did we make this video? What was it for?" Nobody remembers.

Solution: Include comprehensive metadata with every video post. Tag it with campaign name, target audience, intended use, and creation date. Link to the original request or brief. When the video gets approved and published, update the Slack thread with where it went and how it performed.

Maintain a searchable database or wiki that indexes all generated videos. Use Slack's search functionality to find videos by keyword, but also keep structured records outside Slack for long-term reference.

Challenge: Technical Failures

API calls fail. Video generation times out. Slack webhooks don't fire. Something breaks in the workflow.

Solution: Build robust error handling and monitoring. When a video generation fails, don't silently skip it. Post an error message to Slack notifying the team. Include details: which API failed, what error occurred, whether the system will retry automatically.

Implement exponential backoff for retries. If an API call fails, wait 30 seconds and try again. If it fails again, wait 60 seconds. If it keeps failing, alert someone to investigate.

Set up health check monitoring. Ping your workflow orchestration system regularly. If it stops responding, send alerts via a separate channel (email, PagerDuty, SMS) since Slack won't work if your Slack integration is what's broken.

Challenge: Security and Permissions

Sensitive content gets generated (internal strategy videos, unannounced product demos) and you need to control who can see it.

Solution: Use private Slack channels for sensitive content. Only add reviewers who need access. For extremely sensitive material, use direct messages instead of channels.

Implement approval hierarchies. Junior team members can approve routine content. Managers must approve customer-facing marketing. Executives must approve investor relations content. Your workflow routes videos to the right approval level automatically based on content tags.

Add watermarking to pre-approval videos. Before a video gets final approval, overlay a watermark indicating it's draft content not for distribution. Remove the watermark only after approval and before publishing.

Measuring Success

How do you know if your AI video generation and Slack review workflow actually improves your team's productivity? Track these metrics.

Time Savings

Compare the old process to the new one. How long did video review take before automation? How long does it take now?

Organizations implementing structured review processes see up to 50% faster approvals and 50% fewer revision cycles. If your previous workflow took 3-5 days from draft to approval and your new workflow takes 1-2 days, quantify that savings across all videos produced.

Multiply time saved per video by number of videos per month. If you save 2 days per video and produce 20 videos monthly, that's 40 days of saved effort. At typical loaded labor costs, that represents significant value.

Quality Improvements

Track rejection rates and revision cycles. When you first implement AI video generation, rejection rates might be high because prompts need refinement. Over time, as your team learns better prompting and establishes standards, rejection rates should decrease.

Measure final content quality through downstream metrics. Do videos with streamlined review processes perform better or worse than videos created through traditional methods? Look at engagement rates, view duration, conversion rates, or whatever KPIs matter for your use case.

Team Satisfaction

Survey your team quarterly. Are reviewers satisfied with the workflow? Do content creators feel supported? Do stakeholders have visibility into the video pipeline?

The best workflows feel invisible. People just do their work without thinking about the underlying systems. If your team complains about the workflow constantly, something needs adjustment even if quantitative metrics look good.

Utilization Rates

How much of your AI video generation capacity gets used? If you can generate 100 videos per month but only produce 20, investigate why. Are review bottlenecks limiting production? Is there insufficient demand? Are people unaware they can request videos through the automated system?

Track request patterns. Which teams use AI video generation most? Which content types get generated most frequently? Use this data to optimize your workflow for the most common use cases.

Security and Compliance Considerations

AI-generated content and automated workflows introduce security and compliance requirements that traditional video production didn't face.

Data Privacy

Video generation APIs send your prompts to external services. Those prompts might contain sensitive information. Review the privacy policies and data handling practices of your chosen AI video platforms.

Slack conversations about videos might contain confidential information. Use Slack's data retention and export controls appropriately. For regulated industries, ensure your Slack workspace meets compliance requirements (HIPAA, GDPR, SOC 2, etc.).

When videos contain personally identifiable information (PII), implement additional controls. Don't generate videos that include real employee faces without consent. Be careful with customer data. Follow your organization's data handling policies.

Content Authenticity

AI-generated videos can be mistaken for real footage. This creates legal and ethical concerns, especially for content representing real people or events.

By August 2026, the EU AI Act requires machine-readable marking for AI-generated content. Implement watermarking or metadata tagging now to get ahead of regulatory requirements. Several technical standards exist:

C2PA (Coalition for Content Provenance and Authenticity) provides metadata-based watermarking. Information about content origin gets embedded in the file's metadata.

Google's SynthID embeds watermarks directly into the video at a pixel level. These watermarks survive common modifications like compression and resizing.

Choose a watermarking approach and implement it in your workflow. All generated videos should be marked before they leave your review process.

Copyright and Licensing

AI video models train on vast datasets that might include copyrighted material. This creates potential liability when using generated content commercially.

Adobe Firefly Video Model trains only on licensed content, making it safer for commercial use. If your organization is risk-averse, consider using Firefly despite its higher cost and limited capabilities compared to other models.

For other models, review the terms of service carefully. Understand what rights you have to generated content and what restrictions apply. Some platforms prohibit commercial use without enterprise licenses. Others require attribution. Get legal review if you're unsure.

Access Controls

Limit who can trigger video generation. If anyone can submit prompts, you might end up generating inappropriate content or burning through your API budget quickly.

Implement approval requirements for video generation requests. New content types or high-volume batches need manager approval before generation begins. This prevents accidental waste and gives stakeholders visibility into what's being created.

Log all generation activity. Track who requested which videos, when they were generated, and what happened to them. This audit trail helps investigate issues and demonstrates compliance with internal policies.

Future Developments to Watch

AI video generation and review workflows continue to advance quickly. Several trends will likely impact how teams work in the coming years.

Longer Video Durations

Current AI video models max out around 60 seconds. By late 2026, expect models that can generate coherent 2-5 minute videos. This expands use cases from short clips to complete presentations, training modules, and marketing videos.

Longer videos create new review challenges. Watching a 10-second clip takes 10 seconds. Watching a 5-minute video takes 5 minutes. Review time scales linearly with duration. Consider implementing time-stamped feedback tools that let reviewers comment on specific moments without writing out timestamps manually.

Real-Time Generation

Some researchers predict near-real-time video generation within the next year. Instead of waiting 30-60 seconds for a video, you might wait 3-5 seconds.

This enables interactive workflows where you generate a video, get immediate feedback, regenerate with changes, and iterate quickly. Your review process might shift from async batch reviews to synchronous sessions where stakeholders watch videos generate live and provide instant direction.

Multi-Agent Orchestration

Current workflows typically use a single AI model for video generation. Future workflows might coordinate multiple specialized models: one for visual generation, another for audio, a third for voice synthesis, and a fourth for music composition.

This modular approach provides more control but adds complexity. Your workflow orchestration needs to manage dependencies between models and combine their outputs correctly. Slack becomes even more important as the central place where humans review and approve each component before final assembly.

Improved Consistency

Current AI video models struggle with consistency across multiple videos. Generating a series of videos with the same characters, settings, or style requires careful prompt engineering and often produces inconsistent results.

New techniques like reference image guidance and style transfer will improve consistency. You'll be able to generate dozens of videos that look like they belong to the same campaign without manual post-processing.

This makes batch generation more valuable. Instead of generating individual videos one at a time, you can generate entire video series and review them as a cohesive set.

Getting Started

If you want to implement AI video generation with Slack review workflows in your organization, start small and iterate.

Week 1: Proof of Concept

Pick a single use case. Maybe it's generating social media clips, or training video thumbnails, or product demo variations. Choose something that happens regularly and currently takes significant time.

Set up manual generation using one AI video tool. Create 5-10 sample videos. Post them to a test Slack channel. Get feedback from a few team members. Validate that the output quality meets your needs.

Week 2: Automate Generation

Build a simple workflow that generates videos programmatically. Use a workflow platform (MindStudio, n8n, Zapier) or write custom scripts. The goal is to go from "person manually submits prompts to a web interface" to "system automatically generates videos from structured input."

Test the automation with 20-30 videos. Verify it works reliably. Fix any bugs or edge cases.

Week 3: Integrate Slack

Add Slack posting to your workflow. When a video generates successfully, post it to a designated review channel. Include basic context (the prompt, timestamp) and a link to download the video.

Don't worry about interactive buttons or fancy features yet. Just get videos flowing into Slack automatically.

Week 4: Add Review Process

Implement interactive elements. Add approve/reject buttons. Monitor for emoji reactions. Route approved videos to their final destination (whether that's a storage system, CMS, or social media platform).

Document the workflow for your team. Explain how to use it, what to expect, and who to contact if something breaks.

Month 2: Refine and Expand

Based on the first month's usage, identify improvements. Maybe you need better error handling. Maybe reviewers want more context in Slack posts. Maybe certain video types need different review processes.

Add one refinement per week. Don't try to build everything at once. Incremental improvements based on real usage are more valuable than speculative features.

Once the workflow runs smoothly for one use case, expand to a second use case. Apply lessons learned from the first implementation.

Month 3: Scale

Open the workflow to more team members. Provide training on how to request videos and review content. Create templates for common video types to make requesting new content faster.

Start tracking metrics systematically. How many videos are being generated? How long do reviews take? What's the approval rate? Use this data to optimize the workflow further.

Conclusion

AI video generation technology has reached a point where creating video content is no longer the bottleneck. The bottleneck is reviewing and approving that content efficiently.

Integrating AI video generation with Slack solves this problem by bringing review workflows into the tool your team already uses constantly. Videos get reviewed faster. Feedback stays organized. Approvals happen with a single click. The entire process becomes visible and trackable.

The technical implementation is straightforward: connect your AI video generation API to Slack through a workflow orchestration platform. The hard part is designing processes that work for your team's specific needs and maintaining those processes as usage scales.

Start with a small pilot focused on one use case. Learn what works and what doesn't. Iterate based on real feedback. Expand gradually as confidence builds.

The teams that figure this out early will have a significant advantage. They'll produce more video content, review it faster, and publish higher-quality results than competitors still using email threads and shared drive folders.

The technology exists today. The question is whether you'll use it.