Internal/shared Northeastern working guide · Not for external distribution
Partner Opportunity & Handoff Guide
A shared guide for Northeastern teams working with external organizations. Use it to identify the right starting point, explain the value of each engagement model, and make a clean handoff when a partner’s need changes.
Start with the partner need, not the program name. Understand the outcome, the level of accountability they need, the talent model that fits, timing and academic constraints, and whether cost is a hard constraint or simply one consideration.
Before outreach or handoff: Check Salesforce for existing activity, campaigns, opportunities, or partner engagement. Coordinate around the partner rather than approaching the same organization independently.
All three tools use the same underlying routing principles. They are different ways to synthesize and work with the information, not competing versions of the model.
Start by identifying the kind of need
Two partner pathways
Workforce learning and project delivery should not be forced onto the same staircase. First determine whether the organization primarily needs to build people capability or move a project/problem forward. Then choose the right model within that pathway.
Pathway A · Build people capability
Workforce Learning
Use this path when the primary outcome is what employees or leaders need to know, do, or adopt.
Custom Learning
Customized workforce learning built around the organization’s people, roles, tools, priorities, and adoption needs.
EDGE
Use when the organization specifically needs a formal academic structure such as credit, a degree/certificate pathway, or a larger online enterprise education model.
Key distinction: Custom Learning and EDGE are not sequential steps. Choose between them based on whether the organization needs customized workforce learning or a formal academic/credential structure.
Pathway B · Move a problem, project, or solution forward
Project + Applied AI Engagements
This is where the staircase applies. The levels reflect increasing professional accountability and intended use, not simply more hours or a higher price.
Possible entry points: academic exploration through AIIL/XN, a sponsored project through Working Labs, professional AISH roadmapping/scoping, a focused FDE engagement, or direct AISH build/integration depending on what the organization already needs.
Project + applied AI pathway
Engagement staircase
Not every organization starts at the first step, and not every project moves through every step. Start with the least intensive model that can responsibly deliver what the organization actually needs, not simply the least expensive model available.
Free does not mean low-value. Paid does not mean “more hours.”
The models make different commitments. Academic pathways can produce substantial work, but paid delivery becomes appropriate when the organization needs professional accountability for decisions, technical direction, implementation, integration, or an operational result.
Pressure-test the fit
Would the organization be satisfied if the engagement ended with what could be completed within the academic semester?
Does the work need to continue outside the course calendar?
Do they need advanced technical expertise or professional ownership of key decisions?
Does the work touch real systems, sensitive data, integrations, security, or production use?
Is “no-cost” a hard requirement, or would they invest if the business case is clear?
Level 1
Explore academically
AIIL / XN
What they get
A real organizational question brought into a credit-bearing course experience.
Talent: Students in the matched course. AIIL specifically focuses on first-year/pre-co-op students working on AI projects.
Cost / structure: AIIL is no-cost to the partner and in-course; projects may range from bite-sized to 500+ hours during one semester. XN is also course-based and no-cost.
Move up when: the partner needs someone accountable for what happens after the course, the work cannot stop at semester-end, or the project requires professional technical ownership.
Level 2
Define the path
AISH Roadmapping / Scoping
What they get
Professional assessment of opportunities, use-case prioritization, requirements, risks, and a practical roadmap.
Talent: AISH technical leadership and applied-AI expertise, with additional university talent brought in as appropriate.
Why this level: The organization is buying decision quality and professional accountability for what should happen next, not simply hours of exploration.
Move up when: a priority use case is clear and needs hands-on validation or deeper implementation.
Level 3
Test it
FDE / Focused AISH Prototype
What they get
A focused technical engagement around a specific workflow or use case to validate feasibility, requirements, and an approach.
Talent: Vetted graduate talent working with AISH technical leadership in a defined applied-AI engagement.
Why this level: The organization needs a deliberate technical test tied to business requirements, with professional oversight and a clear path into the next decision.
Move up when: the test demonstrates value and the solution now needs real integrations, reliability, security, scale, or operational ownership.
Level 4
Build + integrate
AISH
What they get
Professional engineering and delivery for a solution intended to operate in the organization’s actual environment.
Talent: Professional applied-AI technical leadership and engineering expertise, with graduate talent incorporated where appropriate.
Why this level: The organization needs accountability for integration, security, reliability, deployment, and an operational result rather than an academic or prototype output.
This is the destination when: the organization needs something it can depend on in real operations.
Hours are not the differentiator. Accountability and intended use are. A 500-hour academic project can create substantial value. It is still a semester-based, credit-bearing learning experience. Professional pathways are for organizations purchasing accountability for business decisions, technical direction, implementation, integration, or a production path.
Start here when you are unsure
Guided partner-routing tool
Answer a few questions about the partner’s need, academic fit, price sensitivity, talent expectations, and required level of accountability. The tool will point you toward the strongest starting lane and flag when the scope or funding does not match the delivery model.
Start
Likely route
Quick reference
Positioning guide
Use these as internal/shared elevator pitches. Each card includes the talent model and the trigger for moving the opportunity elsewhere.
A no-cost way for an organization to bring a bounded real-world project into a Northeastern course as part of the student academic experience.
Best fit
The project fits a course, can follow the academic calendar, student learning is central, and the organization can accept the course-based endpoint.
Talent: Students enrolled in the matched course; background varies by course and project.
Handoff: The project requires paid professional accountability, work beyond the semester, implementation, or operational ownership. For AI-specific first-year/pre-co-op projects, AIIL may be the better academic route.
A no-cost, AI-focused, credit-bearing, in-course project model for first-year and pre-co-op students working with industry and community partners.
Best fit
The partner has an AI project that can appropriately become part of the academic experience and accepts a semester-based, course-centered engagement. Projects may range from bite-sized to 500+ hours in one semester.
Talent: First-year/pre-co-op students in a credit-bearing course experience.
Handoff: The organization needs professional roadmapping, implementation, integration, production-oriented engineering, work beyond the semester, or an accountable delivery owner.
A sponsored graduate-team engagement for a defined organizational challenge and a useful project output.
Best fit
The partner wants supported graduate capacity around a scoped project, analysis, model, dashboard, prototype, research, or another defined deliverable.
Talent: Interdisciplinary graduate teams with faculty or expert guidance.
Handoff: An AI need requires professional strategic roadmapping, deeper technical implementation, integration, deployment, or ongoing technical ownership; move into AISH.
Professional AI opportunity assessment, use-case prioritization, requirements definition, and roadmapping before the organization commits to a build.
Best fit
The organization needs to decide what AI opportunities are worth pursuing, what should happen first, and what technical/business conditions must be true.
Talent: AISH technical leadership and applied-AI expertise, with broader university talent incorporated where appropriate.
Handoff: A priority use case is clear and needs hands-on validation or deeper implementation.
AISH can start here directly. FDE is one possible next or alternate engagement model, not a required first step.
Professional applied-AI engineering and delivery for solutions intended to operate in the organization’s real environment.
Best fit
The work needs custom development, enterprise integration, security/data considerations, deployment planning, reliability, or sustained technical ownership.
Talent: Professional applied-AI technical leadership and engineering expertise, with graduate talent incorporated where appropriate.
Handoff: This is the operational delivery lane. If the organization is not yet clear on the opportunity, enter AISH earlier through roadmapping or FDE.
An enterprise learning model that co-designs online education with employers, including degrees, certificates, and individual courses tied to workforce development.
Best fit
The employer specifically needs an academic credential/credit structure, degree/certificate pathway, or a larger formal online learning program.
Talent: Faculty/instructors and program teams aligned to the formal education model.
Handoff: The need is customized workforce training rather than a formal academic program; keep it in Custom Learning.
A supervised technology consulting model using student/co-op teams for software, data, AI, POCs, MVPs, and related project work.
Best fit
A partner specifically wants a practicum/co-op consulting structure or another Northeastern team is already working the account through this model.
Talent: Student/co-op technology consulting teams with supervision.
Handoff: For substantial AI delivery, clarify production responsibility, account ownership, and the boundary with AISH before redirecting the opportunity.
Compare the commitment, not just the hours
What changes across the models?
Use this when the partner asks why they would pay for one model when another may be no-cost.
Compare cost, talent, accountability, and when free is not enough
Professional technical accountability for operational delivery
Integrate, deploy, and operationalize
This is the lane when the solution must work reliably in the organization’s environment.
Conversation + handoff language
Explain the difference without devaluing any pathway
These talk tracks are meant for any Northeastern team that reaches the partner first.
If they ask, “Why would I pay if I can get a free project?”
Do not argue that free work is less valuable. Explain that the commitment is different.
“The academic model can create substantial value, and some projects involve significant student effort. The difference is what Northeastern is accountable for. A course project is structured around the academic experience. A paid engagement is appropriate when you need professional ownership of the roadmap, technical decisions, implementation, integration, or an operational result.”
If they say, “We have no budget.”
Test academic fit rather than automatically routing them to a free lane.
“We can absolutely look at whether the project can be shaped into an academic experience. The key question is whether you would still be satisfied with the course-based scope, timeline, and endpoint. If you need production integration, hard delivery deadlines, or ongoing technical ownership, we should be clear that those requirements point to a professional engagement.”
If they ask, “What do the students know?”
Answer with the talent model, not a generic “we use students.”
“The talent model depends on the engagement. AIIL is intentionally built around first-year and pre-co-op students in a credit-bearing course. XN depends on the matched course. Working Labs uses graduate teams with expert support. FDE uses vetted graduate talent with AISH technical leadership. AISH adds professional applied-AI ownership and engineering expertise.”
If an academic project becomes business-critical
Make the handoff feel like continuation, not a restart.
“This started as a strong academic project, but what you’re describing now involves a level of implementation, integration, or ongoing technical ownership beyond the course engagement. We can bring in the AI Solutions Hub to take the next step without starting over.”
If AISH sees a project that does not need paid delivery yet
The handoff should work in both directions.
“You may not need a professional build yet. This could be a good fit for a credit-bearing academic project where students can explore the concept with you at no cost. If it later develops into something you want to operationalize, we can pick it back up from there.”
If they need an AI roadmap before they are ready to build
Do not automatically push the opportunity into FDE.
“We can start with the strategic work first: assess the opportunities, prioritize use cases, define requirements, and build a practical roadmap. If a focused prototype is the best next step, FDE can be one way we test it.”
If they have a specific workflow they want to test
Separate focused validation from full implementation.
“If the question is whether this workflow can work and what it would take, we can scope a focused technical engagement to test it. That gives you evidence before you commit to a larger build.”
If they ask which Northeastern option is right
Do the internal routing for them.
“Tell us what you want to accomplish, what would make the project unsuccessful, and what level of accountability you need from us. We’ll identify the right Northeastern model and coordinate the handoff internally.”
Shared routing principle: Use the academic pathways when the academic model itself fits the project. Use paid professional pathways when the organization needs professional accountability for decisions, validation, implementation, integration, or operational delivery. Price sensitivity can change scope and sequencing, but it should not disguise the level of accountability the project actually requires.
Full process view
5-Step Routing Framework
A scanable view of the same routing logic, grouped into five steps so teams can understand the model without clicking through every question.
Use this for synthesis. It groups the same logic as the Guided Tool into five buckets. If you prefer a strict branch-by-branch path, use the Yes/No Decision Tree directly below it.
1
How is the partner entering the conversation?
Start with how they are talking. Do not ask them to pick a Northeastern program.
They already have a defined needClarify whether the need is learning, research, an academic project, or applied delivery, then follow the matching route below.
They are asking, “What can Northeastern do?”Use outcome-based discovery. The six choices below are the same ones surfaced in the Guided Tool.
2
If they are exploring possibilities, which outcome gets the strongest reaction?
Describe outcomes, not program names.
Build workforce capabilityUpskill employees or leaders; improve AI, data, digital, or responsible-use adoption.
→ Custom Learning / EDGE
Explore an idea academicallyThey value a course-based student experience and can accept an academic endpoint.
→ AIIL / XN
Decide what AI to pursueAssess opportunities, prioritize use cases, define requirements, or create a roadmap.
→ AISH Roadmapping / Scoping
Test a specific AI workflowValidate feasibility or prove an approach before committing to a larger build.
→ FDE / Focused AISH Prototype
Build or integrate an AI solutionThey need something intended for actual use in the organization’s environment.
→ AISH Build / Integrate / Deploy
Research something newFaculty-led exploration where the answer is not known yet.
→ Sponsored Research
3
Pressure-test the route before promising a program
The same project topic can belong in different lanes depending on the engagement model and what the partner expects Northeastern to be accountable for.
Workforce learning
Do they need a formal academic structure such as credit, a degree/certificate pathway, or a larger online enterprise program?
No: Custom Learning
Yes: EDGE
Academic experiential project
Would they be satisfied if the engagement ended with what could be completed within a credit-bearing course and the academic semester?
No: continue to a delivery model with greater accountability
Research
Is the central goal to create or validate new knowledge, technology, or IP where the answer is not known?
Yes: Sponsored Research
No: route by delivery outcome instead
Applied project that is not AI
Does the partner need supported project capacity, or another Northeastern capability?
Defined sponsored project: Working Labs
Other need: route by subject matter, talent, timing, and accountability
4
For a paid applied-AI need, choose the AISH entry point
FDE is not a required first step. The partner can enter AISH at the stage that matches what they need now.
AISH Roadmapping / ScopingUse when the risk is making the wrong decision: they still need to know what to pursue, prioritize, or define.
FDE / Focused AISH PrototypeUse when the risk is technical uncertainty: they need to prove a specific workflow or use case works.
AISH Build / Integrate / DeployUse when the risk is operational delivery: it must work with real systems, data, integrations, security, reliability, or deployment requirements.
Then check price sensitivityIf the organization says the work must be no-cost, re-test academic fit. If they still require professional roadmapping, hard deadlines, technical validation, real-system integration, security, production use, or ongoing ownership, the delivery need and funding constraint do not match. Reframe scope, sequencing, or funding rather than forcing the project into AIIL/XN.
5
Check the talent expectation
If the partner asks what the students know, answer with the talent model for the engagement rather than saying simply that Northeastern uses students.
AISHProfessional applied-AI leadership/engineering + graduate talent where appropriate.
Handoff rule: Move the opportunity when the engagement the partner needs changes. Academic → professional when the work requires greater accountability or operational use. Professional → academic when the partner genuinely does not need paid delivery and a course-based experience is sufficient. Preserve context so the partner does not have to start over.
Strict routing view
Yes/No Decision Tree
For people who prefer a classic branching flow. Follow the questions from top to bottom. This uses the same underlying routing principles as the Guided Tool and 5-Step Framework.
Red outline: likely destinationBlack note: stop and reframe scope/funding before promising a lane
External organization has a need or asks what Northeastern can doClarify the desired outcome before discussing program names.
Is the primary need employee or leader education/training?
Yes
Do they specifically need academic credit, a degree/certificate pathway, or a formal scalable online program?
No
Custom LearningCustomized workforce capability and adoption.
Is the need specifically an AI solution or AI-enabled workflow?
No
Working Labs or another NU serviceRoute by project type, talent, timing, and accountability.
Yes
Do they still need help deciding which AI opportunities to pursue, prioritize, or define?
Yes
AISH Roadmapping / ScopingProfessional opportunity assessment, prioritization, requirements, and roadmap.
No
Do they need to test a specific workflow or use case before committing to deeper implementation?
Yes
FDE / Focused AISH PrototypeFocused technical discovery, validation, and prototyping.
No
AISH Build / Integrate / DeployProfessional engineering for real-system and operational use.
Price-sensitivity check for any paid route: If the organization says the engagement must be no-cost, ask whether the need can genuinely be reduced to a course-based academic project. If they still require professional roadmapping, hard deadlines, technical validation, real-system integration, security, production use, or ongoing ownership, do not force the project into AIIL/XN. Reframe scope, sequencing, or funding.
Neighboring pathways
Other Northeastern models to know
Coordinate around the partner and use the handoff criteria above rather than treating every model as a competing menu choice.
AIIL ↔ AISH
AIIL is a defined no-cost academic AI pathway; AISH is the professional applied-AI pathway. The mutual handoff is based on the engagement the partner needs, not on whether students can participate. AIIL can send opportunities to AISH when the work requires professional roadmapping, testing, implementation, integration, or production ownership. AISH can send projects to AIIL when a credit-bearing first-year/pre-co-op academic experience is genuinely sufficient.
Mutual handoff
AIIL ↔ XN
Both are course-based academic experiences. AIIL is specifically AI-focused and first-year/pre-co-op. The exact operational boundary and referral rule between AIIL and XN still needs to be documented clearly.
Boundary still to clarify
NExT / Professional Practicum
A technology consulting and practicum model using student/co-op teams. For substantial AI delivery, clarify the relationship to AIIL, AISH, account ownership, and production responsibility before redirecting an opportunity.
Coordinate when relevant
Still to clarify across teams
What is the precise AIIL/XN boundary when an AI project could fit either course-based model?
What specific triggers should prompt an AIIL → AISH handoff, and how should the right AISH entry point be selected?
If an academic project later becomes a paid engagement, how should account ownership and Salesforce handoff work?
Where does NExT / Professional Practicum sit relative to AIIL for student-led AI and technology projects?
What is the expected coordination process when Salesforce shows another Northeastern team is already actively working the account?
Should cross-referrals and handoffs be tracked with a consistent Salesforce field or campaign convention?
Internal/shared working guide. Public program links open the current Northeastern page or available one-pager. “Academic,” “Professional,” “Conditional,” and “Adjacent” are working routing labels for this guide, not official Northeastern program classifications.