Dan Koloski leading a Northeastern learning session with participants

Northeastern University · Custom Learning

Build AI skills that outlast any single tool.

Live, expert-led programs built around your team’s roles, challenges, and questions, and taught in the tools they already use.

Build your pathway Talk with our team

For leaders, business teams, and technical staff

01

Built around your roles

Sessions reflect your team’s roles, industry, and constraints, inside the tools they already use.

02

Live discussion with experts

Learners work through real questions with colleagues and Northeastern experts, which is where the nuance comes out.

03

A path past the classroom

Ideas from training can become projects with Northeastern talent and applied AI teams. See how Bangor Savings Bank did it →

Durable AI Skills

Already have Copilot or vendor training? That’s a good start.

Tool training is perishable. Features move with every update, and tools change when your organization adopts new ones. We build skills that last: the judgment and habits to use AI well in whatever tool comes next.

We teach them hands-on in the tools your team already has, whether that’s Copilot, ChatGPT, Claude, Gemini, or a mix, built around your team’s roles and questions.

A few of the skills that carry over from one tool to the next:

Clear thinking before prompting

Framing the task, the context, and the result you want is a skill that works in any AI tool.

Knowing when to trust the output

Learners explore hallucinations and common risks, and how to recognize when a result needs a closer look before anyone relies on it.

Knowing where AI fits

Deciding which tasks to hand to AI, which to keep, and how to build AI into the way your team already works.

Dan Koloski

Information is cheap and ubiquitous, whether or not it’s correct. Experience applying it, exercising judgment, and reflecting on it is not. Our programs are built to give your team experience.

Dan Koloski Head of Learning Programs and Professor of the Practice, Roux Institute at Northeastern University

AI in Action

How do you get employees using AI effectively every day?

Zach Blattner and Berkeley Almand-Hunter talk through what helps AI learning stick, from role relevance and hands-on practice to giving people time to experiment and measuring adoption in ways that connect to real work. Three short clips, each under two minutes.

Watch the full conversation ↗

Access isn’t effective use

Clip · 1:16

Licenses and logins give people access. Getting employees to use AI well every day takes more than that.

Making time to experiment

Clip · 1:35

New habits take hold when employees have room to practice, test ideas, and build confidence with AI.

Measuring adoption that matters

Clip · 1:30

Usage counts are only a starting point. This clip looks at how to connect AI adoption back to work outcomes.

Results

What changes after the training ends.

Finishing a course and opening an AI tool are easy to count. What matters more is whether people use AI effectively: work done faster, better-informed decisions, and fewer outputs that have to be redone.

Right after every course

The signals that come first

Effective use takes time to show up in the work, so we measure what appears first: the quality of instruction, the skills learners gained, and whether they expect to work differently as a result.

97%rated instructors effective and engaging
88%said they gained knowledge and skills
80%expect the course to improve their job performance

Learner-reported, from 3,400+ post-course surveys across custom learning programs (71% response rate).

Two years in

Bangor Savings Bank: an integrated data and AI program

A two-year initiative with Northeastern that combined workforce assessment and development with a data opportunity roadmap for high-impact AI projects.

96% of program participants now use generative AI in their daily work, up from 56% before training

  • Readiness assessment

    Northeastern’s LEAP framework assessed learning culture, ecosystem, analytical architecture, and people, setting the direction for the program.

  • AI and data literacy

    A program focused on data fluency and responsible, ethical AI use across the workforce.

  • Talent pipeline

    Eight co-op students deployed across the bank on key technical needs and projects.

  • Custom AI solution

    A recommendation engine developed with the Institute for Experiential AI to support customer conversations and marketing.

Read the full case study →

What learners put into practice

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

“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

Maximizing Productivity with Generative AI

“Interactive and engaging with the ability to ‘practice’ on relevant projects to my work.”
Ann LynchExecutive, BerryDunn

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 Use Cases

“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

“Applicable knowledge and skills that can be leveraged immediately.”
Shemika ShackelfordProcess Manager, Unum

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

Use the arrows, swipe, or scroll to see more.

Build the capabilities your organization needs next.

Most organizations need different training for different groups, so leaders, business teams, and technical staff each get training that fits their role and the work AI could help them take on. Start from an example pathway or build your own from 20 courses, customized around your priorities, industry, tools, and workforce.

Examples only. Courses and sequence are adjusted for every organization.

Or build your own pathway

Most programs combine courses from different starting points. Filter by who you’re training or search any topic, skill, or role, then add courses to see your pathway take shape.

Not sure where to start? Talk with our team
Who are you training?
Topic

Showing all 20 courses

For leaders

Essential AI for Leaders

1.5–2 hrs

Help leaders build shared AI fluency, understand opportunity areas, and make better decisions about adoption and investment.

What learners leave with
  • Size up real AI use cases in your industry
  • Weigh risks and ethics before you invest
  • Leave with a framework for evaluating AI opportunities
“Providing tools to help leaders wrap their heads around using and implementing AI without much knowledge of the nuts and bolts.”
Scott McAuliffe, Digital Product Manager, GMRI
View course

For leaders and project managers

Setting Your AI Strategy

4 hrs

Build a shared foundation in AI, then work through a facilitated session to start shaping your organization’s AI strategy.

What learners leave with
  • Assess the risks of AI solutions and how to mitigate them
  • Apply a framework for AI talent readiness and learning pathways
  • Plan first steps toward an AI initiative or strategy
View course

For leaders

Identifying AI Use Cases

2 hrs

Bring leaders together to brainstorm where AI could address real business challenges, then refine the strongest ideas.

What learners leave with
  • Generate AI project ideas tied to your business strengths and challenges
  • Work through data and ethical considerations early
  • Leave with a curated list of opportunities ready to evaluate
View course

For leaders

Evaluating AI Opportunities

2 hrs

Give teams a structured way to identify, evaluate, and prioritize AI opportunities tied to business value.

What learners leave with
  • Pressure-test 3-4 of your own AI initiatives
  • Rank them by impact and feasibility
  • Leave with sharper business cases and known risks
“It broke the process of analyzing an AI opportunity down into practical steps. It’s been difficult to know where to start.”
Maureen Flaherty, Chief Strategy Officer, Kennebunk Savings Bank
View course

For leaders

Building an AI-Ready Workforce

2 hrs

Use Northeastern’s LEAP methodology to assess AI and analytics readiness, pinpoint skill gaps, and map development pathways by role.

What learners leave with
  • Assess organizational and role readiness for AI and analytics
  • Establish a repeatable process for ongoing skill assessment
  • Identify learning pathways that close capability gaps
View course

For leaders, data teams, and project managers

Leveraging AI and Machine Learning at Work

2–12 hrs

Give leaders and product teams a working understanding of machine learning, how AI solutions are deployed, and the risks to plan for.

What learners leave with
  • Explain the main types of machine learning
  • Compare common algorithms and where each fits
  • Identify AI risks and approaches to mitigate them
View course

For leaders, business teams, data teams, and project managers

Maximizing Productivity with Generative AI

2 hrs

Help employees use generative AI tools more effectively, safely, and consistently in everyday workflows.

What learners leave with
  • Write prompts that get usable results
  • Recognize and reduce hallucinations
  • Leave with ways to use AI in your daily work
“It gave some basic and easily understandable steps to create useful AI prompts for my work. It was engaging, interactive, and gave us time to experiment.”
Christina Stacey, Permitting Manager, City of Portland
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For business teams, data teams, and project managers

Turning Your LLM Into Your Work Assistant

2–6 hrs

Use personalization, grounded workspaces, documents, spreadsheets, and lightweight automations more intentionally.

What learners leave with
  • Set up personalization so AI works the way your team does
  • Ground AI in your own documents and workspaces
  • Put AI to work in spreadsheets, presentations, and simple automations
View course

For leaders and data teams

Establishing RAI Governance and Data Ethics

2–12 hrs

Support teams responsible for deploying AI in ways that maximize positive impact, reduce risk, and address emerging regulations.

What learners leave with
  • Understand key RAI frameworks and regulations
  • Apply RAI checks at each stage of the AI lifecycle
  • Outline or draft an RAI blueprint for your organization
“The session forced us to think about considerations I hadn’t thought about previously, such as the need for an AI ethics committee and how AI can be governed appropriately.”
Diane Porter, Director of Financial Crimes, Bangor Savings Bank
View course

For data teams and technical staffTechnical

Foundations in Responsible AI Architectures: Tools, Metrics, and Audits

2–4 hrs

Give engineers and technically fluent teams a grounding in responsible AI: where bias enters the ML lifecycle and how to measure it.

Requires experience in AI engineering or research.

What learners leave with
  • Build responsible AI into the ML development lifecycle
  • Apply technical approaches to assessing fairness and bias
  • Spot the types of bias that can enter a model, and address them early
View course

For leaders and data teams

Developing an Organizational Data Strategy

4 hrs

Align leaders around the strategic and operational pillars needed to become a more data-driven organization.

What learners leave with
  • Align leaders on the core pillars of a data strategy
  • Work through real data tradeoffs together
  • Leave with a priority focus area and starting action plan
“Good collaboration and an opportunity to have data discussions with bank peers.”
Sean Reardon, Data and Analytics Manager, Bangor Savings Bank
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For leaders and data teams

Improving Data Quality and Compliance Through Governance

4–8 hrs

Help data, risk, and compliance leaders put governance in place that protects data quality and meets compliance requirements.

What learners leave with
  • Build a basic governance framework for one of your own datasets
  • Classify data by sensitivity, value, and compliance needs
  • In longer formats, map data lineage and plan for risk
View course

For business teams, data teams, and project managers

Cultivating Data-Driven Decision Making

4–6 hrs

Help functional teams ask stronger questions, interpret data, and use insights to drive business decisions.

What learners leave with
  • Ask sharper questions of your data
  • Spot and fix common data quality issues
  • Turn findings into visuals and a clear call to action
“I found the content relevant to my role and am now better equipped to analyze and present data.”
Ilayda Demirdag, Practice Analyst, Orrick
View course

For leaders, business teams, data teams, and project managers

Communicating Effectively with Data Visualizations

4–12 hrs

Help anyone who presents data turn analysis into clear visual stories that move a specific audience to act.

What learners leave with
  • Plan a data story around your audience and the action you want
  • Build visualizations in Excel, BI tools, R, or Python
  • Tailor the message for technical and non-technical audiences
View course

For data teamsTechnical

Empowering Self-Service Data Warehouse Access with SQL

8–12 hrs

Teach analysts to find, combine, and summarize warehouse data with SQL before it ever reaches their BI tools.

What learners leave with
  • Find the data you need by reading entity relationship diagrams
  • Combine, filter, and summarize large datasets in SQL
  • Catch data integrity issues before they reach dashboards
View course

For data teams and technical staffTechnical

Foundations in Python for Scaling Data Analysis

9–12 hrs

Build Python skills for cleaning, analyzing, and visualizing data, working hands-on with your own company data.

What learners leave with
  • Explore, clean, and summarize data with pandas
  • Create visualizations with Matplotlib and seaborn
  • Run an exploratory analysis on company-provided data
View course

For data teamsTechnical

Foundations in R for Scaling Data Analysis

9–12 hrs

Build R skills for cleaning, analyzing, and visualizing data, working hands-on with your own company data.

What learners leave with
  • Explore, clean, and summarize data with the tidyverse
  • Create visualizations with ggplot2
  • Run an exploratory analysis on company-provided data
View course

For data teams and technical staffTechnical

Building Predictive and Prescriptive AI Data Models

9–12 hrs

Build technical capability in Python-based machine learning for predictive and prescriptive data modeling.

What learners leave with
  • Prepare data for machine learning in Python
  • Train and test your own ML model
  • Choose metrics that fit your use case
“Machine learning and analysis were taught in an approachable manner. It was useful to see practical examples.”
Bryan Adams, Site Reliability Engineer, Bangor Savings Bank
View course

For technical staffTechnical

Maximizing Productivity with GitHub Copilot

2 hrs

Support developers in using AI-assisted coding tools more effectively inside real engineering workflows.

What learners leave with
  • Use ghost text, inline prompts, and chat effectively
  • Generate documentation, unit tests, and wrapper functions
  • Catch and reduce AI coding risks
View course

For leaders, business teams, and project managers

Advancing Effective Project Management Practices

4–12 hrs

Help project managers and team leads strengthen planning, communication, stakeholder management, risk management, and execution.

What learners leave with
  • Apply consistent practices across the project lifecycle
  • Adapt the process to how your organization works
  • Leave with reusable project templates and assets
“Well-presented information, assignments clearly tied to work, and lots of opportunities for participation.”
Colby Santana, Practice Analyst, Orrick
View course

How it works

How custom learning comes together

We don’t start with a generic course and hope it fits. Engagements range from a single two-hour workshop to multi-year initiatives, and each one is shaped around your audience, tools, and priorities.

Start a conversation Meet our experts
  1. 1

    Assess needs and current capabilities

    We start with your organization’s goals, roles, skill gaps, priorities, and learning constraints.

  2. 2

    Recommend a learning sequence

    We map course progressions to your business priorities and workforce needs.

  3. 3

    Co-design around your context

    Examples, scenarios, and exercises are tailored to your roles, industry, and constraints, using the tools your team already has.

  4. 4

    Deliver live, interactive sessions

    Sessions are scheduled around your team’s capacity, with time for practice, discussion, and questions.

  5. 5

    Share feedback and next steps

    Participant feedback informs future training, expansion, or follow-on support.

Inside a session

Live sessions built around your roles and your team’s questions.

AI tools can generate examples for any role on demand. What they can’t do is talk through the gray areas with your team: when an answer is good enough, when it needs a closer look, and where AI fits in the way a department already works.

  1. Concepts

    Enough understanding of how the tool works that people still know what to do when features move or the interface changes.

  2. Application

    Experts show how it applies to work in your function, with scenarios built around your team’s roles.

  3. Practice

    Learners work hands-on with a role-relevant document or a pre-built project, in the tools your team already uses.

  4. Momentum

    Learners compare what worked and trade ideas for where else it applies. Experts close with the takeaways that still hold when the tool changes, so the learning keeps going after the session ends.

Live virtual sessions put every learner hands-on at their own keyboard, with no travel for your team. In-person and hybrid sessions are available too.

83%–97%

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%.

Training comparison

How Northeastern University Custom Learning compares to other common training options.

Most training options now offer role-relevant examples and hands-on exercises. The difference is live discussion with experts, skills that carry over when tools change, and a path to what your organization does next.

What to compareNortheastern Custom LearningSelf-paced online coursesGeneralist training vendorsNo Training Support Provided
Decide how AI fits your work✓
Teams work out which tasks to hand to AI and which to keep		◐
Tool features and suggested uses		◐
Varies by vendor		–
Left to individuals
Skills that transfer across tools✓
Built around your roles and context, taught in the tools you use		◐
Often tied to one tool’s features		◐
Sometimes		–
Trial and error
Live discussion with experts✓
Questions, nuance, and edge cases worked through in real time		–
Automated or none		◐
Varies by instructor		–
Not available
Judge AI output✓
Where AI falls short and when to check its output		◐
Covered as a concept		◐
Covered as a concept		–
Errors found after the fact
Shared team practice✓
Colleagues learn together and compare approaches		–
Individual learning		◐
Sometimes		–
Inconsistent across teams
A path past training✓
Connects to talent, applied AI, and responsible AI support		–
Ends at the course		◐
Sometimes connected		–
Not applicable
✓Strong fit◐Partial fit–Limited fit

Talk with our custom learning team

Exploring AI, data, or technology training for your organization? Tell us where you are, and we’ll connect you with the right person.

Where are you in planning training?

Already working with someone on our team? Book directly with Colleen, Lauren, or Nicole.

The experts behind our programs and frameworks

Berkeley Almand-Hunter, Dan Koloski, and Zach Blattner shape our custom learning programs and teaching approach, and helped build the two frameworks our team uses with partners to set AI priorities and plan upskilling.

Our experts

Berkeley Almand-Hunter

Berkeley Almand-Hunter

AI adoption, data literacy, and responsible use

Helps teams understand how AI and data tools can support real workflows, better decisions, and practical adoption.

View profile
Dan Koloski

Dan Koloski

Workforce transformation, AI strategy, and intrapreneurship

Helps organizations build AI and data capability from within by connecting learning, innovation, and workforce strategy.

View profile
Zach Blattner

Zach Blattner

Learning design, facilitation, and enterprise AI upskilling

Leads AI upskilling for financial, legal, and professional services organizations, including the multi-year Bangor Savings Bank program, using practice-based, role-focused training.

View profile

Our frameworks

TRIAD Framework™

Evaluate AI opportunities through strategic questions, moving from reactive experimentation to clearer value delivery.

Explore the framework →

Analytical LEAP Framework

Map current skills against future needs and identify targeted upskilling recommendations by role.

Explore the framework →

Both frameworks were developed with Berkeley, Dan, and Zach.

Connect learning to what comes next.

Northeastern brings workforce learning, graduate talent, applied AI, and research together. As your needs develop, we can help you move from building capability to governance, use-case development, prototyping, implementation, or custom research.

Common questions about custom learning

Don’t see your question? Talk with our team.

Can we use workforce funding for this?

For Maine employers, often yes. The Roux Institute is an accredited training provider for the Harold Alfond Center Workforce Development Grant, a 50/50 match of up to $1,200 per eligible worker, and for the Dirigo Business Incentives program for qualifying for-profit companies. See funding details

How long are courses?

Courses run from 1.5-hour workshops to 12-hour programs, depending on the topic, audience, and how much applied work is needed. Each course in the pathway builder lists its time range.

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.

What if the course we need is not listed?

Reach out to the custom learning team. Programs can be shaped around your organization’s specific needs, roles, tools, and priorities.

Do learners receive proof of attendance?

Many learning experiences include a digital badge that learners can share as a visual micro-credential.

Ready to build workforce capability?

Start with a short conversation about your team, goals, and learning needs. Maine employers can often offset part of the cost through Maine workforce funding programs.

Talk with our team Build a pathway Email us

Responsible AI Education

Help your teams put Responsible AI into practice.

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.

Download Responsible AI catalog Talk with our team

From awareness to action

Northeastern’s Responsible AI education helps organizations move beyond high-level principles and build the skills, structures, and practices needed to manage AI responsibly.

Explore Responsible AI Practice
Leaders

Responsible AI for decision-makers

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 Highlighted 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.

View Course

Ethics and Governance in the Age of Generative AI

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.

View Course