She Leads AI Academy | AI Consulting Accelerator | Teaching Case Study
NOTE — This is a fictionalized case study based on a real client engagement. All names, organizations, and identifying details have been changed. The methodology, outcomes, and teaching patterns are real.
Meridian Consulting Group is a philanthropy consulting firm specializing in campaign counsel, feasibility studies, and grateful patient programs for hospitals and higher education institutions. The firm has three principals and several independent contractors, all working remotely.
Key people
Before any work begins, a Statement of Work is signed. This is the standard modular structure.
Prepared for: [Client Name], [Organization], [Address]
Prepared by: Anne Murphy Philanthropy LLC dba She Leads AI, [Address]
Primary Contacts: Anne Murphy (Founder and CEO), Don Wackerly (VP Strategic Partnerships)
Date: [Date]; Effective upon execution by both parties
Block 1 — AI Impact Assessment ($2,000)
Block 2 — Responsible AI Academy ($2,000/participant, minimum 5; $500/person thereafter)
Block 3 — AI Policy Review and Advisory Support ($4,000)
Block 4 — Custom AI Solutions (variable pricing)
Payment terms — Fixed fees, invoiced per agreed services, due within 15 days of receipt. Pricing valid 30 days.
Term and termination — Begins upon execution, concludes upon completion. Either party may terminate with 30 days written notice; Client invoiced for completed services.
Confidentiality — Both parties maintain confidentiality. Consultant retains rights to proprietary frameworks, materials, and methodologies. Will not retain client data beyond what is necessary.
Responsible AI commitment — Consultant committed to ethical, transparent, secure, equitable AI practices. Client agrees to good-faith responsible AI efforts.
The SOW is modular. Some clients need all four blocks. Some need only the assessment. The assessment always comes first because it reveals what the other blocks should contain. Block 3 (AI Policy) can be offered at no cost as a bonus to close the deal — Anne did this with one client to sweeten the package.
Six individual interviews, 30 minutes each, all recorded. Interviewed Joan (founder), Laura Chen (president), Sarah Oakes (VP ops), Karen Walsh, Tom Bennett, and Rachel Diaz. Let leadership nominate who to interview, including resistors.
| Category | Time Savings | Priority Items | Biggest Finding |
|---|---|---|---|
| Stakeholder trust and data handling | 20-30% | 3 high | No formal data handling policies. No protocols for AI meeting recorders when consultants share confidential donor information. |
| Service delivery and operations | 20-30% | 2 high | Inconsistent approaches across consultants. Interview analysis is a major time drain — budgeting 1 hour per interview plus 1 hour to write the summary. |
| Client and stakeholder communications | 50-70% | 2 high | Total customization of every proposal creates bottlenecks. Logjams from never standardizing proposal language. |
| Internal operations | 50-80% | 2 high | Last-minute template requests. 2-week turnaround on final reports with heavy designer back-and-forth. Rush job culture eroding goodwill. |
| Knowledge management | 30-70% | 1 high | Clumsy document management. Template disagreements. 100+ campaign documents with no sharing system. |
| Future considerations | 40-60% | 0 high | Business development and content strategy as growth opportunities. Bespoke tools to explore later. |
Overall average: 35-45% potential time savings across all categories.
#### Laura Chen — President / Integrator
[Inbound Lead] → [Discovery Call] → [Handwritten Notes]
↓ ↓ ↓
[Notes to Sarah] → [Proposal Drafting] → [Proposal Review]
↓ ↓ ↓
[Pitch] → [Contract/Kick-off] → [Ongoing Management]
Bottlenecks: Proposal iterations (designer + Sarah + consultant + Laura), Joan unresponsive to leadership decisions, core processes never finalized.
#### Sarah Oakes — VP Client Services (single point of failure)
[Lead Arrives] → [Needs Assessment] → [Template Selection]
↓ ↓ ↓
[Proposal Drafting] → [Designer Formatting] → [Review Rounds]
↓ ↓ ↓
[Pitch Support] → [Contract Execution] → [Engagement Kick-off]
Bottlenecks: PDF comment revision cycle, last-minute edits from consultants, ALL proposals funnel through Sarah, cannot edit PDF directly.
#### Tom Bennett — Consultant (Campaign)
[Client Assignment] → [Initial Setup] → [Case Development]
↓ ↓ ↓
[Prospect ID] → [Interviews (80+)] → [Manual Analysis]
↓ ↓ ↓
[Report Writing] → [Campaign Tracking] → [Materials Library]
Bottlenecks: Reading 80 interview responses manually, inconsistent report format, no knowledge sharing system.
This is the granular breakdown delivered to the client — every task scored by priority and estimated time savings.
Priority 1 (address immediately)
| Task | Time Saved | Rationale |
|---|---|---|
| Internal Communications | 50-70% | AI can draft memos, newsletters, announcements with clear, consistent messaging |
| Data Privacy and Compliance | 20-40% | AI can research regulations, ensure compliance, draft privacy policies |
| Market Research | 30-50% | AI gathers and summarizes market data, competitor analysis, customer insights |
| Strategic Planning | 20-40% | AI brainstorms strategies, runs SWOT analysis, predicts outcomes from data |
| Campaign Implementation | 20-30% | AI schedules campaigns, coordinates tasks, generates performance reports |
| Content Creation | 40-60% | AI drafts copy, posts, articles, scripts. Image generation adds visual capability |
| Customer Relations | 30-50% | AI drafts emails, handles inquiries, analyzes feedback. Advanced: sentiment analysis |
| Long-term Strategic Planning | 30-50% | AI models scenarios, assesses risks, optimizes multi-year strategies |
| Complex Decision Making | 10-20% | AI weighs options with multiple variables, provides data-driven recommendations |
| Creative Problem Solving | 10-30% | AI generates ideas, brainstorms solutions, evaluates feasibility |
| Negotiation and Deal-Making | 10-20% | AI analyzes terms, identifies leverage points, suggests strategies |
Priority 2 (next wave)
| Task | Time Saved | Rationale |
|---|---|---|
| Crisis Management | 20-30% | AI drafts statements, responses, communication plans |
| Budgeting | 50-70% | AI generates templates, allocates resources, tracks spending |
| Sales Forecasting | 40-60% | AI analyzes historical data, trends, seasonality |
| Performance Analysis and Reporting | 60-80% | AI collects, analyzes campaign data, generates comprehensive reports |
| Email Marketing | 50-70% | AI writes copy, personalizes subject lines, segments audiences |
| Event Planning | 20-40% | AI helps with ideation, promotional materials, follow-up communications |
| Marketing Automation | 40-60% | AI creates automated workflows for nurturing, email, social |
| Lead Generation and Nurturing | 30-50% | AI writes lead magnets, creates drip campaigns, scores leads |
| Product Launch | 30-50% | AI develops strategies, generates promotional materials, writes descriptions |
| Adapting to Emerging Trends | 20-40% | AI identifies and analyzes trends, predicts impact, recommends strategies |
| Resource Allocation Optimization | 20-40% | AI develops models considering budget, demographics, and historical performance |
Priority 3 (foundational)
| Task | Time Saved | Rationale |
|---|---|---|
| Website Optimization | 20-40% | AI suggests improvements to copy, navigation, CTAs |
| Competitive Analysis | 40-60% | AI gathers and summarizes competitor products, pricing, strategies |
| SEO Optimization | 20-40% | AI suggests keywords, optimizes content, analyzes traffic |
| Partnerships and Influencer Marketing | 20-30% | AI drafts outreach, tracks performance |
| Predictive Analytics and Modeling | 40-60% | AI refines statistical models, uncovers patterns |
Priority 4-5 (lower urgency)
| Task | Time Saved | Rationale |
|---|---|---|
| Social Media Management | 40-60% | AI schedules posts, drafts content, analyzes performance |
| A/B Testing | 30-50% | AI designs tests, analyzes results, recommends winners |
| Public Relations | 30-50% | AI drafts press releases, pitches, talking points |
| Brand Management | 20-40% | AI maintains voice and consistency, monitors mentions |
| Attribution Modeling | 20-40% | AI assesses marketing touchpoint impact, optimizes budget allocation |
E0 — AI cannot help
| Task | Time Saved | Why |
|---|---|---|
| Team Management | 0% | Requires interpersonal communication and leadership |
After the assessment, the client receives a one-page service overview. This is the sales tool, not the full report. It frames four service offerings that flow from the assessment findings.
AI Adoption Roadmap: Delivering on the Promise of AI While Protecting Stakeholder Trust
The Roadmap positions the assessment as the entry point and shows the client the full journey. It answers the question "what happens after the assessment" before they ask it.
The Playbook is the lasting artifact. After the Academy ends, this is what the team refers back to. For Meridian Consulting Group, the Playbook covered:
The Playbook is titled "Using AI for Improved Outcomes in Consulting Projects (and life stuff, too!)." It is based on the actual Academy sessions, customized with the client's specific use cases and workflows.
When you take this framework to a different client in a different industry, you do not rebuild from scratch. You adapt. Here is the actual adaptation document used to move this framework from a philanthropy consulting firm to a property management company.
| # | What changes | From (philanthropy consulting) | To (property management) |
|---|---|---|---|
| 1 | Opening/welcome | "Laura and Joan and crew" | "Steve and the [Company] team" |
| 2 | Industry experience | "Nonprofit and higher education advancement" | "Small business operations and real estate management" |
| 3 | AI expertise framing | AI for nonprofits | AI for "property management and real estate businesses" |
| 4 | Use case examples | Grant writing, feasibility studies, donor relations | Lease agreement generation, maintenance request triage, tenant screening, market analysis, compliance monitoring, owner communication |
| 5 | Target audience | "Small businesses inundated with opportunities" | "Property management companies managing multiple properties and stakeholders" |
| 6 | Key outcomes | Donor trust, campaign efficiency | Faster maintenance response, rental compliance, resident retention, vendor coordination |
| 7 | Academy session topics | Consultancy ethics, donor data | Property management business ethics, protecting owners/residents/vendors |
| 8 | Governance framing | Protecting donor trust | Protecting property owners, residents, and vendors' interests |
| 9 | Title/subtitle | "Responsible AI for consultancies" | "Responsible AI for Property Management" |
| 10 | Pricing | No changes | Same structure ($2,000 assessment, $2,000/person Academy, $4,000 governance) |
The property management client had specific attributes that made the framework fit:
The teaching point for the cohort: The framework is the same. The 6 assessment dimensions are the same. The SOW structure is the same. The Academy curriculum structure is the same. You swap the industry language and use cases. That is it.
Anne opened with a hands-on exercise designed to eliminate intimidation immediately. Rather than starting with theory, she had participants download ChatGPT on their phones, photograph their pantries, and generate dinner recipes — establishing multimodal AI use within the first 15 minutes. She escalated the exercise into repurposing (recipe → dinner invitation), introduced tone and audience customization, then framed the five-session curriculum. The middle portion addressed the emotional and ethical landscape of AI adoption. The session closed with a tour of the ChatGPT ecosystem and a live demo of the Fathom meeting recorder workflow.
Rebecca Lane's pregnancy prompt breakthrough. She told ChatGPT she was 34 weeks pregnant and needed protein. It explained why certain meals mattered, gave three options, and recommended option two with reasoning. The group saw personal context unlock personalized, useful output.
Laura Chen's street corn tacos. She photographed her fridge and got a creative meal suggestion. Her reaction ("That sounds really good") was genuine delight, not polite interest.
The "I'm not special" preemptive move. Anne opened her bio with "There is nothing special about my background or my education or my aptitude for me to be good at AI. This is all self-taught." This removed the expertise barrier before anyone could feel it.
Opened with AI image bias and ethical storytelling, then moved into a live proposal-writing exercise. Participants transformed a deliberately mediocre proposal into a polished deliverable using the role/task/context prompting structure. Anne demonstrated re-prompting, memory logging, and the proper noun anonymization rule.
Tom Bennett's critic pivot. Instead of following the exercise as prescribed, Tom asked ChatGPT to evaluate the proposal for weaknesses rather than rewrite it. His readback of the AI's critique was a breakthrough — the group saw AI as an analytical partner, not just a content generator.
Rachel Diaz's eagerness. "I can't wait. I really want to put in one of the proposals and be like, what is wrong with this?" She was already bridging from exercise to real workflow.
The bribery technique. Anne taught "I know you have this. You are brilliant. My boss really wants me to get this right. I'll give you $100 if you do this right." She was visibly uncomfortable ("I hate that this works") but shared it because it is effective.
Anne invited Cindy Nakamura to walk the group through her board retreat project — a full consulting deliverable built end-to-end in ChatGPT over four hours. This peer teaching moment was the session's centerpiece. It led organically into data privacy practices, competitive research capabilities, and the trust/verification problem.
Cindy Nakamura's board retreat walkthrough. She designed a complete board retreat for a foundation with three hospitals: design thinking exercises, hospital-specific scenarios, facilitator script with timing, handouts, supply checklist, post-retreat debrief from whiteboard photos, and a follow-up email. Four hours total. The group saw the full arc of what is possible.
Rachel Diaz on earned confidence. Her friend asked "Don't you feel bad not thinking of it yourself?" Rachel replied: "I've been thinking of it myself for 20 years. I'm allowed to get a little help at this point in my career."
Karen Walsh's trust problem. She said "I don't believe anything that I read on it." Anne validated it, reframed verification as a professional standard, and scheduled a targeted office hour for Karen and Sarah Oakes together.
Rachel Diaz's role-play dodge. Had to deliver a training on calling patients as a fundraiser. Was "dying inside" at role-play. Fed her existing resource into ChatGPT and got back scripts, questions, and transition phrases so thorough that the role-play was unnecessary.
$2,000 assessment → $10,000+ Academy + Playbook + Office Hours + ongoing relationship
The assessment revealed what they needed. The Academy delivered it. The Playbook made it stick.
The engagement does not end when the Academy ends. Here is the menu of ongoing revenue streams. Each is a natural extension of the assessment and Academy work.
| Offering | When | Revenue |
|---|---|---|
| Office hours (monthly drop-in) | Months 2-6 | Included in SOW, then $500/session |
| 30-day Playbook check-in | Month 1 | Free (maintains relationship) |
| AI Council formation facilitation | Months 4-5 | $2,000-4,000 |
| Quarterly policy review retainer | Ongoing | $1,000-2,000/quarter |
| Custom GPT maintenance | Ongoing | $2,000/year |
| Annual re-assessment | Month 12 | $2,000-4,000 |
| Phase 2 Academy (advanced) | Month 12+ | $2,000/person |
| Referral pipeline | Month 6+ | "Who else in your network is wrestling with this?" |
| Phase | Revenue | Timeline |
|---|---|---|
| Assessment | $2,000 | Month 0 |
| Academy (5 sessions, 5 participants) | $10,000 | Months 1-2 |
| Policy framework (waived as bonus) | $0 (would be $4,000) | Month 2 |
| AI Council facilitation | $3,000 | Months 4-5 |
| Custom GPT maintenance | $2,000/year | Ongoing |
| Annual re-assessment | $3,000 | Month 12 |
| Phase 2 Academy | $10,000 | Month 12-14 |
| Total 18-month relationship | $28,000+ |
The $2,000 assessment opened a $28,000+ relationship.
[NOTE: Full cultivation strategy and the Executive Roundtable demand generation plan are in Module 7 materials — see `aca-module7-cultivation-and-roundtable.md`]
She Leads AI Academy | AI Consulting Accelerator | Cohort 1
Teaching Case Study — Fictionalized from real engagement