Sample deliverable
Enterprise AI Scenario Map Sample
The paid report is not a longer free report. It turns the AI first-cut judgment into a lightweight consulting memo your leadership and team can forward: value chain, scenario map, priority matrix, and roadmap.
Consulting report preview
AI job-candidate matching
The value and frequency are clear enough to justify a small pilot because matching, evaluation, and candidate-pool reuse are repeated expert decisions with clear human review points. Sample quality and ownership should be proven before system integration.
3
Executive memo
31
Scenario map
2
Risk controls
What changes after unlock
Free gives the aha. Paid gives the meeting-ready plan.
The sample below shows why the deep report is positioned as a lightweight consulting deliverable instead of a gated score page.
Direction call
Free report
Enough to create the first aha: what to try first, what not to start with, the biggest missing condition, and three priority scenarios.
One company-level first-cut call
3 priority AI scenarios
Biggest risk and next action
Forwardable plan
$9.90 deep report
Turns the direction into a lightweight consulting report: full scenario queue, evidence chain, matrix, roadmap, risk controls, and internal pitch.
31+ mapped scenarios
Priority matrix and evidence
4-8 week roadmap and internal memo
Report structure
Company profile and value chain
An HR services company where delivery value depends on client understanding, candidate-data reuse, consultant quality, and candidate experience.
The first AI cut should be frequent, reviewable, and able to turn repeated work into reusable data assets.
Business value-chain view
AI scenario-map overview
Client development
Total 4Quick 2Mid 2Long 0Requirement understanding
Total 4Quick 1Mid 3Long 0Talent sourcing
Total 8Quick 3Mid 4Long 1Candidate evaluation
Total 5Quick 2Mid 2Long 1Interview coordination
Total 4Quick 2Mid 2Long 0Support system
Total 6Quick 2Mid 3Long 1Executive Summary
The first cut should be "AI job-candidate matching", not a company-wide automation platform.
Executive Summary
An HR services company where delivery value depends on client understanding, candidate-data reuse, consultant quality, and candidate experience.
Executive Summary
Before any tool purchase, prepare 20-50 desensitized examples from resumes, job descriptions, interview notes, and past evaluation reports, the current human decision standard, one recruiting delivery owner, and the fields/systems that the first loop can safely read.
Forwardable
One-page conclusion for leadership and team
The deep report compresses the recommendation, risk boundary, and organizational ask into a copyable internal memo for meetings or project approval.
Decision: Do not buy a tool first. Validate "AI job-candidate matching" as our first AI cut.
Priority first cut: AI job-candidate matching
Main risk boundary: Pilot scope is too broad.
Support needed: We need one recruiting delivery owner, 20-50 desensitized samples from resumes, job descriptions, interview notes, and past evaluation reports, and a weekly 30-minute review window.
AI first-cut recommendation
AI job-candidate matching
The value and frequency are clear enough to justify a small pilot because matching, evaluation, and candidate-pool reuse are repeated expert decisions with clear human review points. Sample quality and ownership should be proven before system integration.
Expected impact
Reduce screening time and improve consultant throughput.
Owner hint
Assign one recruiting delivery owner who can provide samples and review output quality weekly.
Evidence chain
Company-level value
82/100 · Strong
Clear enough to support an internal pilot discussion.
Scenario-map coverage
31 scenarios
Scenarios are grouped into quick starts, mid-term builds, and long-term bets.
Owner and budget window
56/100
The next step is to clarify who owns samples, review, and adoption.
Value / feasibility / readiness matrix
AI job-candidate matching
First priority scenario
AI candidate evaluation report
Second priority scenario
Cross-system full automation
Not recommended as the first step
Risk controls
Pilot scope is too broad.
Limit the first test to one workflow, one recruiting delivery owner, and one success metric.
Output quality has no measurable standard.
Track adoption, edit reasons, accuracy, and failure cases from the first week.
Focus scenario deep dive
Talent sourcing · 1-3 months
AI job-candidate matching
Expert time is consumed by repeated classification, review, and prioritization work.
Validate ranking or drafting quality with 20-50 desensitized samples before system integration.
Success metric: Processing time, adoption rate, edit reasons, and accuracy
Validation premise: Resume/JD samples, matching criteria, and human review
Candidate evaluation · 1-3 months
AI candidate evaluation report
Reports and summaries depend on personal style and are slow to standardize.
Generate reviewable drafts from a fixed template and have experts approve the final output.
Success metric: Drafting time, rework rate, user satisfaction, and reuse rate
Validation premise: Evaluation templates, sample reports, and review rubrics
Talent database · 3-4 months
AI talent-pool activation
Historical data and know-how are not reused consistently.
Start with tagging, retrieval, and recommendation rather than end-to-end automation.
Success metric: Drafting time, rework rate, user satisfaction, and reuse rate
Validation premise: Candidate database, update history, and tagging rules
4-8 week action roadmap
1-2 weeks
Define the cut
Choose one high-frequency sub-workflow.
Collect 20-50 desensitized samples and current human decisions.
Write down rules, exceptions, and failure costs.
Success metric: Sample set, rules, and metrics are ready.
3-4 weeks
Validate the small loop
Generate drafts or rankings, then keep human review.
Track time saved, edits, adoption, and failure cases.
Review output quality with the business owner weekly.
Success metric: First sample run completed with review notes.
5-8 weeks
Decide whether to expand
Feed edit reasons back into SOP and data definitions.
Decide whether to integrate systems or expand to adjacent workflows.
Choose standardization, deeper co-creation, or pause.
Success metric: A clear expand / standardize / pause decision is made.
Internal pitch
Do not buy a tool first. Validate "AI job-candidate matching" as our first AI cut.
Use a small sample set to prove business value.
Keep AI as assistive and reviewable in the first loop.
Use 4-8 weeks to decide whether to expand.
Support needed: We need one recruiting delivery owner, 20-50 desensitized samples from resumes, job descriptions, interview notes, and past evaluation reports, and a weekly 30-minute review window.
Next meeting questions
Which three workflows inside the candidate-to-placement value chain most affect revenue, cost, risk, or customer experience?
Which data can be safely exported or desensitized for the first pilot?
Who will own review quality and adoption?
Next step
Map your company before choosing the first AI project
Use one lightweight page to generate the free scenario-map summary first. Add details later only when you need the deep report.