Business Operations Knowledge Management Company AI Adoption

Jennifer Smith + Aaron Podolny's AI use case

Co-founders; Jennifer Smith is CEO and Aaron Podolny is CTO at Scribe

Workflow documentation and optimization workflow where Scribe captures process steps automatically, then analyzes process libraries to find automation and AI adoption opportunities.

The problem

What was broken before AI

Process knowledge often lived in employees’ heads, scattered Looms, stale wiki pages, screenshots, or one-off Slack explanations. That made it hard to onboard people, standardize best practices, spot repeated work, or know which workflows were good candidates for automation. Leaders could want AI adoption while lacking a reliable map of the underlying work.

What changed

What the use case made possible

Scribe Capture records an expert employee doing a workflow through a browser extension or desktop app, then generates a step-by-step guide with screenshots and written instructions. Scribe Optimize adds a second layer by analyzing workflow data inside a company and comparing it against Scribe’s broader workflow dataset to surface improvement, automation, and AI adoption opportunities.

Why this matters

Why this use case is worth studying

Scribe treats documentation as operational data, rather than as a static training artifact. That changes the order of AI adoption: first observe how work happens, then decide where AI belongs. For operators, this is a useful antidote to vague AI mandates because it ties automation decisions to repeated workflows, bottlenecks, software usage, and institutional know-how.

Use this when

When this pattern applies

Use this when your company wants AI adoption but lacks a clear, current map of how work actually gets done across teams, tools, and roles.

Exponential Builder analysis

Visibility comes before automation.

AI adoption improves when teams can point to actual workflows instead of abstract process names.

Documentation can become a dataset.

Once guides are structured, centralized, and reviewed, they can support training, search, standardization, workflow analysis, and AI agent context.

The best AI roadmap may start with boring work.

Repeated admin steps, onboarding flows, and support procedures often reveal clearer automation opportunities than brainstorming sessions about flashy AI projects.

Who this is for

Best fit

Operations leaders trying to find automation opportunities without relying on guesswork.

Enablement and training teams that need better onboarding materials.

IT and business systems teams managing repeated software workflows.

Customer support and success teams with recurring internal procedures.

Founders and department heads trying to turn tribal knowledge into reusable company infrastructure.

AI transformation teams that need workflow context before choosing tools or agents.

What to avoid

Mistakes and warnings

Where this pattern can go wrong if you copy it too literally.

Do not automate a messy workflow just because AI can touch it; clean up the process first.

Do not assume the captured process is the best process until the owner reviews it.

Watch for sensitive data in screenshots, customer records, HR systems, finance tools, and internal admin panels.

Avoid treating AI recommendations as final decisions for compliance, legal, security, or financial workflows.

Do not let documentation become shelfware; assign owners and update cycles.

Be careful with metrics from vendor materials unless you can verify them with your own usage data.

Public workflow preview

The shape of the workflow

A high-level look at how the use case works, with the reusable pattern made clear.

Capture the workflow

Have an expert employee perform a real task while Scribe records the steps.

Generate the guide

Turn the recording into screenshots and written instructions that can be shared with the team.

Centralize the library

Build a searchable collection of common workflows across onboarding, support, operations, training, and internal tools.

Analyze repeated work

Use Scribe Optimize to examine where workflows repeat, vary, slow down, or consume time.

Prioritize improvement

Identify which processes should be standardized, redesigned, automated, or supported with AI.

Copy the pattern

The reusable idea

Pattern in one sentence

Capture real work as documentation first, then use the resulting workflow library to decide where AI and automation should go.

Reusable idea

You do not need an enterprise rollout to borrow the pattern. Pick one recurring workflow that causes confusion, record the best version of it, and turn it into a reusable guide. Once you have ten or twenty of those, review them as a system: which steps repeat, which tools create friction, which decisions require expert judgment, and which handoffs could be automated safely?

Steal this workflow

Mini-template for a workflow-to-automation review:

Step-by-step guide link:

Most error-prone step:

Where data is copied between tools:

Automation type: standardize / checklist / deterministic automation / AI assist / leave human-owned

Review date

Suggested prompt

“Review this documented workflow and help me decide where AI or automation could safely improve it. Break the workflow into steps, label each step as human judgment, rules-based automation, AI-assisted drafting, AI-assisted analysis, data entry, approval, or exception handling. Then identify bottlenecks, duplicated work, missing context, privacy risks, and the top three improvement opportunities. Be conservative: do not recommend full automation for steps involving sensitive data, compliance, customer commitments, financial decisions, or unresolved ambiguity. Here is the workflow: [paste guide or summary].”

Field notes

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