AYCE AI Service Layer
One AI standard for every MSP in the portfolio.
AYCE ALIGN is a shared AI operating layer for MSP portfolios. AYCE builds the standard once. MSPs use it to sell and deliver governed AI services. Clients get practical adoption, clearer risk controls, and measurable outcomes without fourteen different versions of “AI strategy.”
Workflow before tool
Security before scale
Human review before automation
ROI before expansion
Private Preview
MSP Partners
Executive Summary
AYCE does not need fourteen MSPs hiring fourteen AI leads, building fourteen policies, and vetting fourteen vendor stacks. It needs a PE-level Centre of Excellence that defines the standard for readiness, vendor approval, service packaging, sales language, implementation handoff, and ROI reporting. The value is consistency, faster go-to-market, cleaner delivery, lower risk, and a stronger portfolio story.
Product Definition
What AYCE ALIGN is, what it is not, and why it matters now.
What it is
AYCE ALIGN is a portfolio-wide AI Centre of Excellence and service layer. It sets the operating standard for AI readiness, vendor evaluation, workflow mapping, sales enablement, governance, implementation rules, and outcome reporting across MSPs serving SMB and mid-market clients.
What it is not
It is not a random tool catalogue, a hype program, or a demand that every MSP becomes a full AI consultancy. It does not replace local client trust. It standardizes how that trust is turned into practical AI revenue.
Who it serves
AYCE leadership, MSP owners and account teams, implementation leads, and portfolio clients in healthcare, legal, finance, manufacturing, retail, professional services, and local business operations.
Why now
AI demand is already showing up in client conversations. Without a shared model, each MSP will answer differently, vendors will set the agenda, and delivery quality will drift. This is the point where operating discipline becomes a growth advantage.
Three-Layer Model
AYCE owns the standard. MSPs sell and deliver it. Clients use it safely.
AYCE Layer
- Centre of Excellence leadership and policy ownership
- Vendor gold standard and approval process
- Package design, sales templates, and ROI model
- Security, governance, and escalation rules
- Portfolio dashboard and reporting cadence
MSP Layer
- Client discovery and relationship management
- Workflow mapping and service packaging at account level
- Implementation coordination and local supportability
- Executive briefings, proposals, and account expansion
- Feedback loop back to AYCE COE
Client Layer
- Readiness assessment and current-state review
- Safe AI use, documented policies, and workflow priorities
- Human-reviewed deployment decisions
- Measured business outcomes and ROI checkpoints
- Clear rules on what should not be automated
Product Modules
Six sellable packages MSPs can take to market without inventing them from scratch.
AI Readiness Brief $4K-$8K
Target client
SMBs and mid-market firms with active AI interest but no shared operating view.
Problem solved
No clear starting point, unclear use cases, scattered staff experimentation.
Deliverables
Leadership brief, current-state assessment, top workflow shortlist, risk notes, 90-day recommendation.
Timeline
2-3 weeks.
MSP role
Lead discovery, stakeholder interviews, local account context.
AYCE COE role
Assessment framework, scoring model, review, final recommendation.
Client role
Provide access to process owners and current tool list.
Human-reviewed
Use-case prioritization and final rollout recommendation.
Success metrics
Assessment completion, approved roadmap, first package conversion.
Follow-on sale
AI Waste Audit or Department Workflow AI Map.
AI Waste Audit $6K-$12K
Target client
Businesses already paying for AI tools, overlapping SaaS, or informal team usage.
Problem solved
Tool sprawl, duplicate spend, unclear value, unmanaged shadow AI.
Deliverables
Tool inventory, usage map, overlap analysis, vendor rationalization plan, cost-risk summary.
Timeline
3-4 weeks.
MSP role
Gather tenant, support, and procurement context.
AYCE COE role
Audit method, evaluation framework, recommendation review.
Client role
Supply licences, ownership map, and workflow pain points.
Human-reviewed
Vendor retention/removal decisions and cost reduction plan.
Success metrics
Waste identified, duplicate tools removed, approved consolidation plan.
Follow-on sale
Secure AI Use Policy or Copilot in Context.
Secure AI Use Policy $5K-$10K
Target client
Regulated or trust-sensitive organizations with staff already using AI tools.
Problem solved
No clear rules on approved tools, allowed data, or required human review.
Deliverables
Policy draft, role guidance, approved-use matrix, incident path, executive summary.
Timeline
2-4 weeks.
MSP role
Surface real support patterns and existing security posture.
AYCE COE role
Policy template, compliance logic, governance review.
Client role
Validate risk tolerance and approval workflow.
Human-reviewed
Policy sign-off, exception handling, high-risk use approvals.
Success metrics
Policy adoption, approved tool list, reduced shadow AI behavior.
Follow-on sale
Support Intelligence Operating Model.
Copilot in Context / Microsoft 365 AI Readiness $8K-$18K
Target client
Microsoft 365 clients evaluating Copilot or struggling to operationalize it.
Problem solved
Licensing interest without data hygiene, permission readiness, or use-case discipline.
Deliverables
Tenant readiness review, permission risks, file architecture concerns, role-based use-case map, rollout guidance.
Timeline
3-5 weeks.
MSP role
Tenant access, M365 admin collaboration, account-level rollout support.
AYCE COE role
Assessment model, use-case prioritization, governance checklist.
Client role
Assign business owners and validate high-value use cases.
Human-reviewed
Prompt use guidance, access remediation priorities, deployment phases.
Success metrics
Readiness gaps closed, pilot adoption, use-case usage clarity.
Follow-on sale
Department Workflow AI Map.
Support Intelligence Operating Model $10K-$22K
Target client
Organizations with recurring support, service desk, or intake inefficiencies.
Problem solved
Repeated triage, poor ticket summaries, weak knowledge reuse, inconsistent routing.
Deliverables
Support workflow map, AI-assisted triage model, escalation rules, KPI template, human review rules.
Timeline
4-6 weeks.
MSP role
Operational discovery, implementation planning, support tooling alignment.
AYCE COE role
Operating model template, governance guardrails, reporting design.
Client role
Process walkthroughs, routing exceptions, operational sign-off.
Human-reviewed
High-risk routing, exception handling, knowledge updates.
Success metrics
Ticket deflection, response speed, fewer handoffs, summary quality.
Follow-on sale
Client retainer and workflow expansion.
Department Workflow AI Map $7K-$15K
Target client
Departments with repetitive admin work in HR, finance, operations, marketing, or compliance.
Problem solved
No clear view of which workflows are worth automating and which must remain human-led.
Deliverables
Workflow map, human review points, tool options, automation boundaries, quick-win roadmap.
Timeline
3-4 weeks.
MSP role
Facilitate workshops and link outcomes to stack reality.
AYCE COE role
Framework, review, package recommendations.
Client role
Provide process owners and sample workflow artifacts.
Human-reviewed
Decision points, exceptions, final output release.
Success metrics
Workflows mapped, quick wins approved, roadmap accepted.
Follow-on sale
Implementation package or AI Readiness retainer.
Vendor Gold Standard
Vendors do not enter the portfolio story until they meet the AYCE standard.
Evaluation framework
Data privacy
Data residency
Security posture
Audit logs
Role-based access
Admin controls
Microsoft 365 fit
Integration requirements
MSP supportability
Pricing transparency
Exit plan
Human review features
Compliance fit
ROI evidence
Risk profile
Approval tiers
- Tier 1 Approved: cleared for portfolio-wide recommendation and integration.
- Tier 2 Conditional: use-case limited, extra review required.
- Tier 3 Observe: not approved, monitor only.
- Tier 4 Reject: risk, opacity, pricing, or supportability fails the standard.
Vendor intake questionnaire
- What data enters the system and where is it processed?
- What admin controls exist for access, logging, and permissions?
- How does the product support human review and rollback?
- What integration model applies for Microsoft 365 and common MSP stacks?
- What evidence exists for outcomes, supportability, and pricing stability?
Red flags and conflict rules
- No clear data handling answer or vague residency language.
- No export path, no exit plan, or punitive lock-in pricing.
- No admin visibility for MSPs supporting the client.
- No human review controls for material outputs.
- No pay-to-play recommendations. Referral economics must be disclosed and separated from approval decisions.
MSP Sales Enablement Kit
Give account teams a clear way to talk about AI without overpromising it.
Discovery questions
- Where is work repeating every day?
- Where are staff already using AI informally?
- What decisions must remain human-reviewed?
- Which tools create confusion, waste, or duplicate effort?
Client call script
“We are not here to sell random AI tools. We are here to identify where AI can reduce repeat work safely, where your controls need to tighten, and how to build a realistic first package.”
Executive briefing outline
- Current-state risk and opportunity
- Top workflows to assess first
- Policy and vendor concerns
- Recommended 90-day move
Proposal language
“This engagement is designed to improve one or more specific workflows, document safe use, and produce measurable operational outcomes under defined review controls.”
Objection handling
- “We are not ready.” That is why the first package is readiness, not full deployment.
- “We already use AI.” Good. Now we can assess whether it is governed, useful, and measurable.
- “We do not want automation mistakes.” Human review is built into the model.
What not to promise
- No promise of full automation.
- No promise that every workflow is worth changing.
- No promise of ROI without baseline and measurement.
- No promise that a vendor is approved before AYCE review.
Follow-up email
“Thanks for the conversation. Based on what we heard, the right next step is a focused readiness and workflow review, not a broad AI rollout. We will come back with a scoped brief, risk lens, and recommended first package.”
One-page readiness offer
A short paid assessment that surfaces current AI usage, workflow opportunities, policy gaps, and a realistic 90-day action plan.
Simple AI risk explanation
AI risk is not only about the model. It is about what data enters it, who can use it, what it is allowed to decide, and whether a human reviews the result before it affects the business.
Client-Facing Case Study
Mock example: 50-person medical clinic.
Current situation
Front-desk staff are rewriting intake notes, handling repetitive follow-up requests, and manually routing billing, referral, and appointment issues. Some staff are already using public AI tools informally to draft summaries.
Key workflow issues
- Appointment intake is incomplete and inconsistent.
- Call summaries are manually reconstructed.
- Referral and billing questions bounce between teams.
- Knowledge lives in staff habits, not structured workflows.
Privacy concerns
- Staff are unsure what information can be entered into outside AI tools.
- Patient-related tasks require strict human review.
- Any tool must fit clinic privacy expectations and audit requirements.
Recommended first use cases
- Structured intake summarization
- Internal support triage for recurring admin requests
- Referral preparation checklists
- Knowledge retrieval for policy and billing guidance
What should not be automated
- Clinical decision-making
- Final patient communication without staff review
- Any sensitive data action without access controls and audit visibility
90-day roadmap and ROI story
First 30 days: readiness review, policy guardrails, workflow mapping. Next 30: pilot intake summaries and support triage. Final 30: measure time saved, reduced handoffs, clearer documentation quality, and training completion.
MSP value: trusted advisor, structured rollout, governed tool support. Client value: less repeat work, lower privacy risk, cleaner service flow.
Operating Model
How AYCE runs this internally without creating portfolio drag.
90-Day Build Plan
Build once, pressure test fast, then roll across the portfolio.
- Inventory MSP stacks and common client environments
- Interview MSP owners and account leaders
- Identify recurring client pain points
- Build first readiness model
- Draft vendor gold standard
- Select first 2 pilot MSPs
- Run pilot assessments
- Test sales language and proposal flow
- Build first client-facing packages
- Create reporting templates
- Refine vendor criteria
- Train MSP account teams
- Launch first standardized packages
- Build dashboard
- Create final sales kit
- Approve first vendor list
- Document playbook
- Prepare portfolio rollout
Pricing and Revenue Model
Value for AYCE, margin for MSPs, practical pricing for clients.
Pricing model
- AYCE setup fee for initial COE build and package design
- Monthly enablement fee per activated MSP
- Per-assessment fee charged to MSPs or netted into resale margin
- Package resale margin on approved modules
- Vendor evaluation fee only where neutral review economics are clearly disclosed
- Optional client retainer for monitoring, policy refresh, and workflow expansion
Illustrative portfolio math
Example: 14 MSPs activated. If each MSP sells 2 readiness packages per quarter at an average client value of $7,500, that is $210,000 in quarterly client package revenue before follow-on work. Add enablement fees, expanded workflow packages, and retainers, and the portfolio develops both service revenue and a stronger advisory position.
Internal portfolio value
AYCE gains consistency, faster sales cycles, less vendor chaos, stronger account control, cleaner reporting, and a repeatable AI narrative that improves portfolio quality rather than fragmenting it.
KPI and ROI Dashboard
Measure activation, delivery quality, and outcome clarity at every layer.
AYCE KPIs
- MSPs activated
- Packages sold
- Vendor reviews completed
- Revenue generated
- Risk reduced
- Portfolio consistency score
MSP KPIs
- Leads generated
- Assessments sold
- AI projects converted
- Client retention
- Time to proposal
- Implementation clarity score
Client KPIs
- Hours saved
- Tickets reduced
- Manual steps removed
- Tool waste identified
- Staff trained
- Risks addressed and human review compliance
Sales Narrative
Use one core story, tailored for each audience.
30-second pitch
AYCE ALIGN helps MSPs sell and deliver practical AI services under one portfolio-wide standard. AYCE owns the model, MSPs own the client relationship, and clients get governed, measurable AI adoption instead of random tool experiments.
2-minute pitch
AI demand is already showing up in client accounts, but most MSPs do not want to build their own AI practice from scratch. AYCE ALIGN solves that by creating a shared Centre of Excellence: vendor rules, readiness assessments, service packages, sales language, implementation guardrails, and reporting. The result is faster go-to-market, lower delivery chaos, and better client trust.
5-minute executive pitch
The strategic risk is not missing AI headlines. It is letting every MSP in the portfolio create its own answer, its own vendors, and its own risk posture. AYCE ALIGN builds once and adapts many times. That means a common vendor gold standard, repeatable service packages, a clean MSP handoff model, and measurable client outcomes. It gives AYCE a portfolio operating advantage, not just a marketing line.
MSP owner pitch
You do not need to become an AI consultancy overnight. You need a standard way to talk about AI, package it, govern it, and deliver it so your account teams can lead the conversation with confidence.
Vendor pitch
If you want access to the AYCE ecosystem, meet the standard first. Approval depends on security, supportability, transparency, and measurable fit, not sales theatre.
Client pitch
We are not here to sell you more AI tools. We are here to help you decide what is worth improving, what must stay human-reviewed, and how to do it in a way your business can actually support.
Deck Outline
Ten slides to take the operating case to AYCE leadership.
AI demand is outpacing MSP operating models
AYCE should not solve AI 14 different ways
The opportunity: build once, adapt many times
AYCE AI Service Layer
The three-layer model
Product modules
Vendor gold standard
How MSPs sell it
90-day rollout
Why AYCE wins
Risks and Failure Modes
Know where this can break before the portfolio finds out the hard way.
COE becomes bureaucracy
Mitigation: keep review SLAs short, publish decision rules, and tie every standard to a package or risk outcome.
MSPs ignore the standard
Mitigation: link approved packages, enablement, and portfolio reporting to standard adoption.
Vendors bypass the process
Mitigation: no portfolio recommendation without documented review and approval tier.
Clients expect full automation
Mitigation: train MSPs to sell human-reviewed workflow improvement, not magic.
Security review is weak
Mitigation: assign named review ownership and stop approvals where evidence is incomplete.
ROI is vague
Mitigation: define baselines at assessment stage and standardize package metrics.
Packages become too custom
Mitigation: treat exceptions as tracked variants, not new products every time.
Technical teams get overloaded
Mitigation: stage implementation, gate vendor complexity, and separate discovery from deployment capacity.
Final Strategic POV
AYCE does not need to chase the AI market. It can define the standard its MSPs, vendors, and clients use to navigate it.
The win is not being loud about AI. The win is owning the operating model: one standard, repeatable revenue, cleaner delivery, lower risk, and a portfolio narrative that clients can actually trust.