Build AI, data, and digital skills your team can use at work.
The Roux Institute delivers customized, instructor-led learning experiences that help organizations close skill gaps, apply new tools, and make progress on real business challenges.
Northeastern brings workforce learning, graduate talent, applied AI,
and research together. As your needs develop, we can help move from
capability building to governance, use-case development, prototyping,
implementation, or custom research.
Graduate talentApplied AI solutionsWorking LabsResponsible AICustom research
Build the capabilities your organization needs next.
Explore a recommended learning sequence based on what you are trying to accomplish
or who you need to prepare. Each pathway can be customized around your priorities,
industry, tools, and workforce.
What are you trying to do?
Who are you training?
Pathway 01
AI Strategy
Equip leaders and decision-makers to make informed choices about AI
investments, identify and assess valuable opportunities, and prepare
prioritized initiatives for implementation.
Primary audience
Executives, organizational leaders, strategy and innovation leaders,
department heads, and teams responsible for AI initiatives.
Prepare teams to define roles, success measures, functional requirements, and the components of an MVP.
Available through a custom program
✓
Pathway outcome
A shortlist of AI initiatives assessed for feasibility, business value,
risk, and organizational readiness, with a clearer path from awareness
to implementation.
Pathway 02
AI Enablement
Build practical AI capability across the workforce, moving employees
from basic awareness and effective prompting to confident use of AI
tools in everyday work.
Primary audience
Employees across the organization, especially frontline, operational,
and line-of-business teams applying AI to everyday workflows.
ProgressionBuild strong foundations→Apply AI to real work
Use personalization, grounded workspaces, documents, spreadsheets, and lightweight automations more intentionally.
Available through a custom program
✓
Pathway outcome
Broader, more confident use of approved AI tools in real workflows,
supporting productivity gains and practical adoption rather than
tool access alone.
Not sure which pathway fits?
We can recommend a sequence based on your audience, goals, current
capabilities, available tools, and organizational priorities.
Participants leave with practical methods, frameworks, and skills they can apply to real work.
Maximizing Productivity with Generative AI
“Great job of breaking it down and progressing through examples- it was helpful to digest and then practice! I actually was able to solve a real problem I have been fighting with for months!”
Heather HodgeIT Helpdesk Supervisor, Bangor Savings Bank
Maximizing Productivity with Generative AI
“Very engaging - leaving course with tangible skills that I can use right away and also with concepts that can be applied broadly.”
Sarah RinesSenior Program Manager, MaineHealth
Maximizing Productivity with Generative AI
“Applicable knowledge and skills that can be leveraged immediately.”
Shemika ShackelfordProcess Manager, Unum
Evaluating AI Opportunities
“Practical way to break down potential AI use cases that could benefit the organization.”
Eileen RiveraHead of Global Total Rewards & Talent Acquisition, Biogen
Identifying AI Opportunities
“Really helpful and practical framework which supported engaging conversations in the breakout rooms.”
Michael CatoSenior VP and CIO, Bowdoin College
Maximizing Productivity with Generative AI
“The examples for the exercises were very applicable to my job, and I realized tasks AI could assist with that I was not already utilizing it for.”
Brittney TomaselloAVP, Senior Marketing Coordinator, Bangor Savings Bank
Evaluating AI Opportunities
“I like that it broke the process of analyzing an AI opportunity down into practical steps. It’s been difficult to know where to start.”
Maureen FlahertyChief Strategy Officer, Kennebunk Savings Bank
Maximizing Productivity with Generative AI
“Interactive and engaging with the ability to ‘practice’ on relevant projects to my work.”
Ann LynchExecutive, BerryDunn
Use the arrows, swipe, or scroll horizontally to see more comments.
Learning in action
See what practical AI learning can look like.
Custom learning connects AI concepts to the real decisions, tools,
and workflows people encounter in their work.
Free AI resource
The Power of Better AI Prompts
Download Zach’s AI Prompt Engineering 101 Guide, a quick-start PDF with practical strategies, examples, and frameworks to help professionals get better results from AI tools.
Our programs are designed for direct application, live engagement, and organizational relevance. That means learners work with real scenarios, relevant tools, and guided practice that connects back to the work they do every day.
1
Applied, not passive
Participants apply what they learn directly to day-to-day work instead of sitting through passive lectures.
2
Instructor-led and interactive
Live instruction, discussion, hands-on activities, breakouts, and feedback keep learners engaged.
3
Built around your context
Courses can reflect your business priorities, roles, tech stack, data environment, and industry scenarios.
4
Backed by university expertise
Northeastern faculty and staff bring academic depth into programs designed for workplace application.
5
Designed to scale
Support one team, one function, a leadership cohort, technical staff, or broader workforce capability building.
6
Measured for impact
We collect and share learner feedback to help evaluate program effectiveness and inform next steps.
Training comparison
How Northeastern University Custom Learning compares to other common training options.
Many options introduce AI, data, and digital topics. Roux custom learning is designed to connect live instruction, customization, and practical application to the realities of your organization.
What to compare
Northeastern Custom Learning
Self-paced online courses
Generalist training vendors
No Training Support Provided
Customized to your organization
✓ Built around your goals, roles, and context
– Usually standardized
◐ Some tailoring
◐ Left to individuals to seek upskilling
Live expert faculty instruction
✓ Facilitated collaboration, discussion, coaching, and Q&A
– Typically solo learning
◐ Atypical
- Not applicable
Applied to real workflows
✓ Designed to change the way teams approach real challenges
– Often one-size-fits-all
◐ May not reflect your workflow
◐ Skills learned for generic application
Role-based learning paths
✓ Supports leaders, technical staff, business teams and end-users
◐ Some options exist
◐ May not reflect actual use cases
– Possible for self-motivated individuals
Broader AI readiness support
✓ Aligns with adoption and workforce goals
– Standalone learning
◐ Sometimes connected
– Not scalable
✓Strong fit◐Partial fit–Limited fit
Need help figuring out the right training path?
We can help you decide what makes sense for your audience, goals, tools, and current level of AI or data maturity.
Training is one part of building an AI-ready organization.
AI readiness is not built through one-off courses or tool access alone. Organizations need shared understanding, practical application, and support structures that help teams use AI responsibly and effectively in their daily work.
01
Build shared fluency
Leaders and teams need a common language for AI, data, risk, opportunity, and responsible use. This foundation helps organizations make better decisions and reduces confusion across functions.
02
Apply learning to real work
Training should connect directly to the roles, workflows, tools, and business challenges employees already face. That is where AI moves from abstract concept to practical capability.
03
Sustain adoption over time
AI capability grows when organizations pair training with leadership alignment, responsible use guidance, peer learning, measurement, and continued support after the first session ends.
Roux custom learning is designed around this progression.
We help organizations move from awareness to applied use by tailoring programs to the audience, maturity level, tools, business priorities, and adoption goals.
Learners reported practical AI confidence after training.
In a recent cohort, post-training confidence reached 83%–97% across measured AI skill areas, including RAG, AI agents, system prompts, LLM memory, and personalization settings.
Based on learners who completed both the pre-survey and post-survey. Pre-training confidence ranged from 0%–41%.
How custom learning works
We do not start with a generic course and hope it fits. We work with partners to assess needs, recommend learning paths, customize content, deliver live learning, and evaluate participant feedback.
We begin by understanding your organization’s goals, roles, skill gaps, priorities, and learning constraints.
2
Recommend the right learning sequence
We identify course progressions and learning experiences aligned to your business priorities and workforce needs.
3
Co-design the learning experience
We customize content with your input so examples, tools, and scenarios feel relevant and useful to learners.
4
Deliver live, applied sessions
Programs are scheduled around your team’s capacity and designed to prioritize practice, discussion, and application.
5
Share feedback and next steps
Participant feedback helps evaluate the experience and inform future training, expansion, or follow-on support.
Course catalog
Training options for leaders, functional teams, and technical staff.
Courses can be customized for different levels, departments, and roles. Explore common starting points below, then download the full catalog for more options.
Structured thinking for AI and analytics readiness.
Our custom learning team uses practical frameworks to help organizations move from broad interest to clearer priorities, targeted upskilling, and measurable business value.
Exploring AI, data, or technology training for your organization?
Connect with our team to discuss custom programs, role-based learning,
and practical ways to help employees apply new skills in their work.
Learn from experts helping organizations use AI and data at work
Our programs are shaped by Roux Institute practitioners working across AI adoption, data literacy, workforce transformation, responsible use, and practical implementation.
AI Adoption, Data Literacy, and Responsible Use
Berkeley Almand-Hunter
Berkeley helps teams understand how AI and data tools can support real workflows, better decision-making, and practical adoption.
Reach out to the custom learning team. Programs can be shaped around your organization’s specific needs, roles, tools, and priorities.
How long are courses?
Course length can range from short workshops to more in-depth learning experiences, depending on the topic, audience, and amount of applied work needed.
How many people can participate?
Many virtual classes can run as cohorts of up to 50 learners, though smaller groups may be recommended depending on the content and goals.
Who are these courses for?
Courses support employees from frontline staff to executives, including technical, non-technical, functional, and leadership audiences.
Do learners receive proof of attendance?
Many learning experiences include a digital badge that learners can share as a visual micro-credential.
Is funding available?
Maine-based employers may be able to explore workforce training funding through the Harold Alfond Center Workforce Development Grant.
Ready to build workforce capability?
Start with a short conversation about your team, goals, and learning needs.
Organizations work with Northeastern to build shared AI understanding,
strengthen role-specific capabilities, and connect learning to broader
adoption and implementation efforts.
Organization-wide adoption56% → 96%
increase in reported employee generative AI use
Bangor Savings Bank
A two-year initiative combined workforce assessment, custom AI and
data literacy training, graduate talent, and applied AI support.
Eight co-op students deployed to address technical needs
Responsible AI connected to customer trust and adoption
Applied AI work designed to strengthen customer engagement
Responsible AI is not only a policy conversation. It requires shared language,
practical decision-making, governance structures, and role-specific training
for the people building, buying, managing, and using AI systems.
Northeastern’s Responsible AI education helps organizations move beyond high-level principles and build the skills, structures, and practices needed to manage AI responsibly.
Build shared understanding of AI risk, oversight, accountability, and governance so leaders can make better adoption decisions.
Governance
Policy-to-practice training
Help teams translate Responsible AI principles into workflows, review processes, deployment gates, and evidence-based decision-making.
Product & Technical
Risk and impact assessment
Train teams to identify, evaluate, and document risks related to fairness, robustness, explainability, privacy, safety, and intended use.
Workforce
Responsible use of AI tools
Support employees using generative AI with practical guidance around privacy, accuracy, human oversight, bias, and appropriate use.
Responsible AI education can be delivered as stand-alone training or paired with broader AI strategy, governance, workforce development, or applied AI initiatives.
Responsible AI Highlighed Courses
Responsible AI Executive Education
This course equips leaders with an actionable blueprint to drive RAI strategy and advance their businesses in the digital age. Ideal for those leading company strategy, digital transformation, or innovation initiatives.
Explore the ethical and technical foundations of generative AI and learn best practices for its responsible integration. Ideal for individuals seeking to navigate AI development and deployment responsibly.
