Make the result visible
People are exploring AI across the organization. A leader can build on that energy by showing where a weekly reporting process is becoming faster, stronger, and easier to repeat.
A small scorecard brings that progress into view: compare time, output quality, review effort, and repeat use across three similar runs, then choose whether to continue, strengthen, or pause the workflow.
TL;DR
Usage shows activity; a completed workflow with a stable baseline shows whether the process changed.
Podcast Drop: Listen for one idea you can test in a real workflow, then keep only what improves the work.
Speaking Event: From Idea to Impact. An In-person luncheon, panel, and interactive workshop.
AI Use Case: Use a one-workflow value scorecard to compare a manual baseline with three AI-supported cycles.
DIRECT© Framework: Turn timing, quality, and review notes into a clear continue, strengthen, or pause recommendation.
Action Steps: Set up the scorecard in 10 minutes, run three comparable cycles, and review the evidence with the workflow owner.
AI in the News: Microsoft’s new Copilot and FinOps capabilities emphasize completed work, outcome visibility, spend controls, and organization-set boundaries.
Blog Drop: Protect quality while making each AI interaction more focused and efficient.
Watch Webinar Replay: See how assistants, agents, automation, connected tools, and human checkpoints come together around real work.
Quick List
Why this matters
Picture a weekly operations update. The dashboard shows active users and growing message volume. What it does not show is whether people spent less total time, whether the update met the same standard, or whether correction work simply moved to the manager.
That gap matters because activity is not the same as workflow value. OpenAI’s September 2026 product guidance describes usage and task data as a starting point. It says business owners still need to establish a baseline, compare outcomes over a defined period, and include review and correction time. NIST’s AI Metrology Center likewise points organizations toward field pilots, human-centered evaluation, and measurement approaches suited to the use case.
A useful workplace trial can start with consistent boundaries and four plain measures:
Time: total elapsed or hands-on time from the agreed start to the accepted output, including setup, retries, review, and corrections.
Output quality: the result against a short checklist chosen by the person accountable for the work.
Review effort: the minutes and corrections required before the output can be accepted or used.
Repeat use: whether the workflow was used again on a comparable task, plus the user’s reason for continuing or changing it.
Keep the claim modest. Three cycles can reveal useful operational friction; they do not prove universal ROI or causation. AI can organize the evidence, but the workflow owner defines quality, verifies the record, and makes the final decision.
✦ ChatGPT: Summarize + 3 action steps
◈ Copilot: Highlight Most Practical Use cases
⬡ Perplexity: Extract Key insights + Main Takeaways
✳ Claude: Identify key insights + Suggest application to my work
✨ Gemini: Build a Team Discussion Guide
Podcast drop
Use the LearnAIR™ podcast playlist as an energizing learning loop: listen once, capture one useful idea, test one small change in a real workflow, and carry forward what helps. Let the work guide which tools earn a place in the process.
Speaking Event: From Idea to Impact
Bring one business challenge. Leave with a practical AI experiment.
Join the Oregon Entrepreneurs Network on Wednesday, September 30, for From Idea to Impact: Making AI Work for Your Business Today.
This in-person luncheon brings together business leaders and AI practitioners, including panel speaker Justin Coats, for an honest look at how organizations are using AI in everyday work. After the panel, you will roll up your sleeves to identify a useful AI opportunity for your own business.
You will leave with:
Real-world lessons from businesses using AI
A practical use case connected to your work
Clear human-review and approval points
A focused 30-day experiment you can begin testing
Date: Wednesday, September 30, 2026
Time: 11:00 a.m.–3:00 p.m.
Format: In-person luncheon, panel, and interactive workshop
AI use case: Build a friday report handoff you can reuse
The workflow owner supplies the task boundary, a comparable manual example, a quality checklist, and notes from three AI-supported runs. AI helps organize the record, calculate simple differences, surface missing data, and draft a recommendation. The owner checks every input and decides whether to continue, strengthen, or pause the workflow.
The four measure scorecard
Measure | What to record | Fair comparison rule | Decision signal |
|---|---|---|---|
Time | Preparation, active work, waiting when relevant, review, corrections, failed attempts | Use the same start and accepted-finish points | Lower total time without lowering quality |
Output quality | Pass or score on 3 to 5 owner-defined criteria | Use the same checklist for baseline and trial | Meets the acceptance threshold consistently |
Review effort | Review minutes, correction count, and major issues | Do not hide cleanup inside saved time | Review burden is acceptable and trending down |
Repeat use | Used again, user rating, and reason | Count comparable runs, not casual prompts | The user willingly repeats a useful process |
The DIRECT prompt©: Run practical workflow-evaluation
Use this prompt after the workflow owner has checked the scorecard entries. Replace every square-bracket placeholder before running it.
D - Doing: Analyze one completed AI-supported workflow and turn the supplied baseline and trial records into a practical continue, strengthen, or pause recommendation.
I - Information:
Workflow: [WORKFLOW NAME AND PURPOSE].
Owner: [ACCOUNTABLE PERSON OR ROLE].
Frequency: [HOW OFTEN].
Agreed start: [START EVENT].
Accepted finish: [FINISH EVENT].
Manual baseline: [TOTAL TIME, QUALITY RESULTS, REVIEW TIME, CORRECTIONS, DATE, AND WHETHER VALUES ARE MEASURED OR ESTIMATED].
AI-supported cycles: [THREE COMPARABLE RUNS WITH PREPARATION TIME, AI RUN OR WAIT TIME, REVIEW TIME, CORRECTION TIME, FAILED ATTEMPTS, QUALITY RESULTS, AND REPEAT-USE NOTE].
Quality checklist and pass threshold: [3 TO 5 CRITERIA AND MINIMUM ACCEPTABLE RESULT].
User feedback: [WHAT HELPED, WHAT CREATED FRICTION, WHETHER THEY WOULD REPEAT IT, AND WHY].
Tool and support cost if known: [ACTUAL COST OR UNKNOWN].
Available resources: [APPROVED TOOLS, PEOPLE, DOCUMENTS, AND TIME].
Constraints: [BUDGET], [TIME], [ACCESSIBILITY NEEDS], [POLICY], [APPROVALS], and [DATA CLASSIFICATION].
R - Role Persona: Act as a practical workflow-evaluation coach. Be precise about missing evidence and uncertainty. Do not act as the workflow owner, financial approver, privacy officer, legal reviewer, or subject-matter expert.
E - End Goal Result: Return:
a baseline-versus-trial comparison table;
findings for time, quality, review effort, and repeat use;
missing or non-comparable data;
a continue, strengthen, or pause recommendation with confidence labeled low, medium, or high;
the smallest next test with owner, time box, success signal, and review date; and
three questions the human owner must answer before any scale decision.
C - Context: Use only the information supplied. Treat estimates as estimates. Do not invent costs, convert saved time into revenue, or assume that correlation proves causation. Do not include personal, customer, employee, health, financial, legal, security-sensitive, or confidential information beyond what is approved for this tool. Flag privacy, fairness, accessibility, safety, or policy concerns for human review. Keep consequential decisions and final approval with the named owner. A good answer makes review work visible and explains why the evidence does or does not support the recommendation.
T - Tone Style Format: Use warm, direct business language. Keep the response under 700 words.
Use these exact headings: Comparison, Four Measures, Evidence Gaps, Recommendation, Next Test, Human Review Questions. Put numbers in a compact table, label every estimate, and avoid hype, jargon, and unsupported ROI claims.
Action steps: Run the three cycle test
Five practical steps
Choose one finished workflow in 10 minutes. Pick a recurring task with a clear trigger and an accepted output, such as a weekly status brief. Name the workflow owner and keep high-risk decisions out of the first trial.
Write the quality check before using AI. Choose three to five criteria the output must meet. Success signal: the owner can say pass or revise without changing the rules after seeing the result.
Record one comparable baseline. Log total time, review time, corrections, and quality for a recent manual example. Mark any estimate. Do not reconstruct a precise number from memory if you do not have one.
Run three comparable AI-supported cycles. Record preparation, AI processing or waiting when relevant, review, corrections, failed attempts, quality, and whether the user chose to repeat the workflow. Use approved tools and approved information only.
Hold a 15-minute review with the workflow owner. Choose to continue, strengthen, or pause. If you strengthen it, change one important variable, name the next owner, and set a review date after three more cycles. Expand the workflow after the output meets the quality threshold and the review burden is understood.
Success signal: You finish with a decision and a next test, not merely a chart.
Review point: after three comparable cycles, or immediately if the workflow crosses a privacy, safety, policy, or approval boundary.
AI in the News
Microsoft introduces new Copilot and FinOps for AI capabilities
Microsoft announced a new Copilot experience with Home, Code, and Autopilot, alongside expanded FinOps for AI capabilities across Agent 365, Insights, and Microsoft Copilot. The announcement frames Cowork around end-to-end delegated work that returns a completed result and says business leaders and admins will be able to see which Cowork tasks are generating the strongest outcomes so they can sustain, expand, or optimize usage.
The announcement moves the unit of attention from isolated prompts toward completed work, outcome visibility, and controlled spend. That makes a one-workflow scorecard especially useful: leaders can test whether a finished result is faster and acceptable after human review before expanding usage.
Blog drop: Turn visible progress into workflow value
A faster first draft is useful only when the final output still meets the required standard. Your scorecard gives you a practical starting point.
Add tool or support cost when known, label estimates, and avoid converting saved minutes into revenue without a defensible model. The goal is not a dramatic ROI claim. The goal is a decision about what to continue, strengthen, or pause.
Explore the complete workflow economics perspective:
Watch webinar replay: From AI assistant to digital employee: automating real workflows
See how the workflow develops in practice:
The LearnAIR™ webinar explores the progression from an AI assistant to an agent, an automated workflow, and a Digital Employee. Use the replay to examine how triggers, decisions, actions, connected tools, and human checkpoints come together around real work.
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