Practical guides for working better with AI
Learn practical AI workflows for marketing, sales, research and project delivery, then explore related skills, prompts and templates.
How to Synthesize Customer Interviews Without Flattening the Evidence
A practical customer-interview synthesis method for turning qualitative notes into patterns, preserving context, and connecting research to a real product or service decision.
Weekly Founder Review: What to Review, Decide, and Stop
A practical weekly founder review for separating activity from progress, surfacing decisions, reviewing evidence, stopping low-value work, and setting a small number of priorities.
How to Build a Marketing Measurement Plan Before You Track Everything
A step-by-step marketing measurement plan covering business questions, KPI definitions, events, data sources, segmentation, attribution assumptions, QA, and decision-ready reporting.
How to Prioritize Features with Evidence, Not Scoring Theater
A practical feature-prioritization process using customer problems, evidence, strategic fit, cost, risk, confidence, reversibility, and explicit opportunity cost.
How to Build an AI Marketing Workflow Without Losing Human Judgment
A step-by-step guide to using AI for marketing research, briefs, messaging, content adaptation, and review without handing strategy or claims to the model.
B2B Discovery Call Preparation Checklist: Research, Questions & Next Steps
Prepare a B2B discovery call with a practical checklist for account context, hypotheses, problem questions, decision context, and a mutually clear next step.
How to Refresh Old Content for SEO Without Creating Duplicate Pages
A practical content-refresh process for deciding what to update, consolidate, redirect, or leave alone while protecting search intent and internal relevance.
What Should You Automate First? A Practical AI Workflow Checklist
A practical framework for choosing the first business process to automate: score repetition, stability, cost, risk, exceptions, and the need for human judgment.
A manual step is not automatically a problem
Manual work is only a problem when it creates avoidable cost, delay, error, or inconsistency. Use this framework to decide what deserves automation.
The best automation is often invisible
The most useful automation rarely feels like a robot taking over. It quietly removes copying, waiting, and preventable handoff work from an existing process.
Do not automate a broken handoff
Automation does not repair an unclear handoff. Map ownership, inputs, exceptions, and the definition of done before you connect another tool.
What a strategy document must make easier
A strategy document is useful when it helps people choose: what to prioritize, what to reject, which customer to serve, and which trade-offs are intentional.
The founder’s job is often to reduce ambiguity
Founders create leverage by turning uncertainty into clear priorities, decisions, ownership, and constraints. More answers are not always required; clearer questions often are.
A busy week can still be strategically empty
A founder can complete dozens of tasks and still avoid the decisions that matter. Use a weekly review to separate activity from strategic progress.
Service design is a team sport
Customers experience one service even when the company is split across marketing, sales, product, support, and operations. Map the journey across handoffs, not departments.
Retention starts before the customer wants to leave
Retention work starts with expectation, activation, repeated value, and recovery from friction — long before a cancellation screen or churn email appears.
Feedback is not a vote count
Customer feedback should be grouped by problem, context, severity, frequency, and business relevance. Counting requests alone can turn noise into a roadmap.
How to write an experiment result people can trust
A trustworthy experiment readout separates setup, observed result, limitations, interpretation, and next decision. Make it easy for a reader to see what the test did — and did not — prove.
The metric is not the decision
Metrics describe part of reality. Decisions require context, thresholds, trade-offs, and ownership. Keep the number separate from the action you choose because of it.
A dashboard cannot fix an unclear question
Before building another dashboard, write the decision it should support. A clear question determines the metric, segment, time window, and level of detail you actually need.
The audience feedback worth paying attention to
Not every comment should change your content strategy. Learn to separate preference, confusion, repeated demand, and high-signal audience behavior before reacting.
A content niche should create useful constraints
A useful content niche narrows the audience, problems, evidence, and point of view enough to make better ideas easier to choose — without trapping the creator in one format.
Consistency is not posting every day
Content consistency is a reliable system for producing useful work at a pace you can sustain. Frequency is only one variable, and it is rarely the most important one.
Prioritization gets easier when trade-offs are visible
Prioritization is not a perfect score. Make value, evidence, cost, risk, reversibility, and what you are delaying visible so the team can make an explicit choice.
The first five minutes shape the product experience
Early onboarding should help a user understand value, required effort, and the next meaningful action. Audit the first five minutes for decision friction, not visual polish.
A roadmap item is not a customer problem
A roadmap item is a proposed solution. Good product discovery works backward to the customer situation, evidence, and outcome before treating the request as a priority.
Why the safest first AI agent is often the most useful
Your first AI agent should have a narrow job, clear evidence, visible output, and an easy human override. That constraint is a feature, not a limitation.
A useful meeting recap does not invent commitment
A meeting recap should separate decisions, open questions, and explicit commitments. Never let an AI summary turn discussion into promises nobody made.
Good operations make the next step obvious
Operational clarity comes from making state, ownership, and the next action visible. If people have to ask what happens next, the system is carrying hidden work.
A discovery call is not a product presentation
A discovery call should reduce uncertainty, not compress a demo into the first meeting. Research the account, test hypotheses, and leave with a mutually useful next step.
Find the next step for your work
Move from an idea to a practical use case, then find ready-to-use AI assets that help you put it into action.