AI Productivity at Work

Find the tasks where AI support is worth the effort and measure your starting point.

• Task inventory: frequency, effort, and pain points
• Scoring tasks on suitability, risk, and review cost
• Choosing a first workflow with low risk and clear value
• Defining a baseline: time taken and quality today
• Success measures that matter for your role
• Avoiding the trap of automating a bad process

Hands-on lab: Inventory 15 recurring tasks, score each, select one target, and record its baseline time and quality.

Make meetings produce clear records and next steps with less manual effort.

• Preparing agendas and briefing documents with an assistant
• Turning notes or transcripts into decisions and action items
• Writing follow-up emails with owners and dates
• Checking summaries for missed or invented items
• Consent and confidentiality when recording or transcribing
• Standard summary templates for your team

Hands-on lab: Take a practice meeting transcript, generate minutes and an action list, and verify every item against the source.

Speed up drafting, editing, and document review while keeping quality and accountability.

• Drafting reports, proposals, and standard operating procedures
• Summarizing and comparing long documents
• Using NotebookLM to question your own source material
• Consistent style and terminology across documents
• Redaction and anonymization before sharing with AI
• Review stages and sign-off for AI-assisted documents

Hands-on lab: Draft a real document with AI assistance, then complete a written review log of every change and correction you made.

Use AI to gather, organize, and analyze information, and confirm the findings independently.

• Structured research briefs with sources
• Comparing vendors, options, or competitors in a table
• Describing and exploring small anonymized datasets
• Formulas and checks in Google Sheets to validate AI results
• Recognizing misleading statistics and false precision
• Documenting assumptions and limits of an analysis

Hands-on lab: Analyze a small anonymized dataset with an assistant, then verify each stated finding using a spreadsheet formula.

Connect repeatable steps and automate the simple parts with no-code tools.

• Anatomy of a workflow: trigger, input, AI step, review, output
• Introduction to Zapier and Make free tiers
• Automating a form-to-summary or email-to-task flow
• Where to place a human approval step
• Error handling and what to do when output is wrong
• Security basics for connecting accounts and data

Hands-on lab: Build a simple automation that takes a form or email input, produces an AI-drafted summary, and sends it to you for approval before use.

Prove what worked, document it, and set rules for safe ongoing use.

• Before-and-after comparison of time and quality
• Documenting workflows so colleagues can reuse them
• Escalation rules for consequential or regulated work
• Team AI-use guidelines and data classification
• Bias and accuracy spot-checks as a routine
• Planning the next three workflows

Hands-on lab: Re-measure your target task, write a one-page workflow document, and present results and review rules to the cohort.