Practical writing for sponsors, project leaders, business analysts, and process owners who need AI that survives real delivery.
Play the Game! Read about Deadline Dash
Charters, risk, status, and agile rituals where AI speeds the craft without replacing judgment.
Guided wizard, live preview, Word and PDF export. Built for project managers and project owners who need a clean charter without another enterprise license.
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Status drafts, risk scans, meeting notes, stakeholder summaries. AI can speed the paperwork. The PM still owns the hard calls so delivery is not theatre.
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AI can turn messy discovery notes into a first charter draft fast. The value is the stress test after: scope, constraints, success metrics, and political risk.
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AI can mine tickets and notes for new risks faster than a weekly spreadsheet pass. Humans still score, own, and respond. Almost automatic is the honest goal.
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Agents can split stories, flag dependencies, and draft acceptance tests. Useful. The product owner still has to refuse rubber-stamp grooming and keep product judgment.
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A weekly cadence that actually saves time: sources in, narrative out, red-flag review. Works for waterfall gates and agile demos if you refuse to ship fiction.
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Bridging the gap between strategic vision and operational reality needs more than frameworks. It needs disciplined execution and the right project structure.
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A free consulting-grade Agile and Waterfall tool with no expensive licenses. Built with AI and iteration, with screenshots and steps to get started.
Read full article →Elicitation, stories, traceability, stakeholders, and cleaner handoffs from BRD to build.
Prepare better interview questions from context packs. Keep listening for the answers that rewrite the plan.
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AI can draft stories and Gherkin. You still own scope, edge cases, and the persona that is not made up.
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Let AI propose requirement-to-test links. Review them so compliance and delivery stay honest.
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AI can summarize the threads. Mapping influence, interest, and politics still needs a person in the room.
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Cleaner BA-to-dev and BA-to-vendor handoffs: ambiguity, non-functionals, data definitions, open questions.
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Requirements, use cases, and process thinking often map well to AI products, local LLM work, and workflows with tools like LangGraph.
Read full article →Mapping, waste finding, SOPs, process mining, and root cause work with AI as a draft partner.
Workshop notes and SOP text into a draft swimlane map. Then walk it with the people who do the work so the diagram does not invent steps.
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Cluster delays, rework, and handoff failures from tickets, emails, and chat. Rank what might belong on a kaizen-style backlog.
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Safe first-draft procedures, redlines against how work actually happens, and controlled publishing so AI is not your source of truth.
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When event logs beat chat models, when gen AI helps interpret findings, and how the two can work together without confusing their jobs.
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AI can propose 5 Whys branches, fishbone categories, and evidence gaps. Facilitation and ownership stay with the people who live the process.
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A free swimlane Process Map Studio: steps, lanes, decisions, PNG/SVG export. Built with AI and iteration for consulting workshops.
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Free RCA workbench with Pareto, Fishbone, 5 Whys, and reports, without expensive quality software. Built with AI and iteration.
Read full article →How agents plan, use tools, and stay under control when work leaves the chat window.
Agentic AI is more than a flashy demo. Here is what agents that plan, call tools, and loop on work actually do, and where autonomy usually needs to stop in real delivery.
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Chatbots, copilots, and agents are not interchangeable. Here is a sponsor-friendly way to pick the right job and the risk that comes with it.
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Production agents need humans at the right checkpoints. Approval gates, review steps, and escalation keep autonomy useful without silent policy or payment changes.
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Picking a model is the easy part. Getting agents to use calendars, tickets, CRM, and internal APIs safely is where most agent projects get stuck.
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Sometimes the right answer is not an agent. Fixed workflows, RPA, and rules can be clearer, cheaper, and easier to audit than autonomy that re-plans every run.
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The industry is shifting from smarter chat to systems that can act. Why Model Context Protocol is becoming as important as the model itself.
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From prompts to ChatGPT Work and long-running agents: why the harness around the model is where practical value is being won.
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Vibe coding was great for MVPs. Superblocks on AWS is one signal that governed enterprise AI workflows may finally catch up to the speed.
Read full article →Readiness, governance, evaluation, shadow AI, and cost discipline for teams past the demo stage.
A product I built, not a client project. Firebase, Stripe, Google Calendar, and Grok Build into a live app. The PDF is on the Case Studies page.
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A Toronto law firm already had a CRM path. We skipped the firm-wide AI tour and shipped a local Lead Generator on their data. The downloadable PDF is on the Case Studies page.
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Before you approve pilot budget, run a short workshop on data access, use cases, risk, skills, and vendor questions. A checklist that keeps mid-market AI honest.
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A clever system prompt is not a control framework. Usage rules, logging, and approval standards that do not fall apart when the vendor renames the model.
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Model text is a deliverable. Use BA-style acceptance criteria: completeness, consistency, sourceability, and a definition of done that review can fail against.
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Your team is already using consumer AI on work problems. Bans rarely stick. Approved tools, clear data rules, and better internal options usually work better.
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Pilots can spike spend fast. Tokens explained simply, cost per successful outcome, and design choices that keep retries and idle agents from surprising finance.
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Smart people sit on both sides of the AI debate. Why the bubble question might miss the point, and how domain judgment plus modern models can compress months of work into days.
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Lessons from CPMAI and real client work: AI often works better when you invest in data readiness, cleansing, and ownership, not just software delivery.
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How large language models opened the door to broader AI, better consulting work, and faster iterative software delivery.
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How one afternoon with Grok replaced a paid website bill, and why small firms might rethink licenses they only use in part.
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Why a living company knowledge base often matters, and how RAG can power internal chatbots, customer assistants, and grounded automation.
Read full article →Where AI meets portfolio choices, data ownership, and the path from intent to execution.
Hands-on tools and knowledge systems that sit next to AI delivery, not in place of it.
A short browser game about decks, executives, and a partner title that only shows up after the ninth floor. Small enough to live on this site.
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Why a local Markdown vault can work for corporate knowledge, RAG pipelines, and agent memory that survives across sessions, with real limits at scale.
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