20 core learning modules
AI Agility
Build your Human × AI collaboration system. Define outcomes, provide context, direct the work, and evaluate and improve the results.
Explore AI Agility
Foundational across every level
Responsible &
Ethical AI
Build trust. Reduce risk.
Create impact.
Build the judgment to use AI responsibly across enterprise systems, information, and workflows—protecting people, strengthening accountability, and earning trust.
Download the CIPS brochure (PDF)
CIPS micro-credential combines AI Agility + Responsible & Ethical AI.
For IT professionals, technology leaders, and the enterprise teams they support.
Professional standards.
Practical capability.
An industry-wide credential
Build your capability.
Earn a CIPS micro-credential.
Build practical AI capability and the judgment to use it responsibly. The CIPS micro-credential combines AI Agility + Responsible & Ethical AI—30 core learning modules and approximately 12–15 hours of learning.
Earn an industry-wide CIPS micro-credential on successful completion of both programs. Develop the capability to direct AI collaboration, evaluate its contribution, and take responsibility for the work you deliver.
Professional learning. Industry-wide credential.
AI Agility+Responsible &
Ethical AI
30 core learning modules
Approx. 12–15 hours of learning
CIPS micro-credential awarded on successful completion of both programs.
The combined micro-credential program
Two programs.
One CIPS micro-credential.
Bring AI Agility together with Responsible & Ethical AI. Build a more effective way to work with AI, grounded in professional judgment and responsibility.
- Core learning modulesAcross the combined program
- 30
- Hours of learningApproximate combined learning time
- 12–15
- Connected programsComplete both for the CIPS micro-credential
- 2
10 core learning modules
Responsible & Ethical AI
Apply professional judgment to bias, privacy, evidence, policy, and accountability. Put responsible AI principles into everyday practice.
Explore the modulesInside AI Agility
20 core learning modules to build, adapt, and improve your Human × AI collaboration system.
A useful AI conversation can help with one task. AI Agility develops a repeatable approach: define the outcome, provide context, direct the collaboration, evaluate the result, and refine what works.
Bring a task or workflow from your own work. Explore the four learning areas below to see how you’ll develop that approach.
Choose the right role for AI
Learn when AI adds value, how much structure a task deserves, which tool fits, and where human judgment belongs.
Explore: Value beyond speed · Tool and task fit · Human judgment · A collaboration brief
Make good work repeatable
Build the standing context, memory choices, project spaces, examples, and review methods that make good work easier to repeat.
Explore: Working context · Memory · Projects and workspaces · Reusable methods · Advisory boards
Use AI on complex work
Break longer work into stages, manage what AI sees, invite constructive challenge, explore missed options, and examine the people and assumptions a decision touches.
Explore: Context design · Multi-step work · Constructive challenge · New options · Assumptions and impact
Set a standard and build value
Separate evidence from inference, define what good looks like, identify new opportunities, and develop habits that create capacity for worthwhile work.
Explore: Evidence and confidence · Quality standards · Opportunity discovery · Healthy capacity
Build something you can use again.
Develop the ingredients of your own collaboration system: a clear brief, reusable context and instructions, and criteria for reviewing the work. Keep improving the approach as your work and AI change.
Inside Responsible & Ethical AI
The 10-module responsibility component of your 30-module micro-credential. Explore its two areas of study below.
Professional Judgment
1.1 Bias in AI Systems
Examine how bias can appear in AI outputs and decisions, and consider whose experiences or perspectives may be missing.
1.2 Transparency and Explainability
Explain how AI contributed to your work, what you can substantiate, and where uncertainty or limitations remain.
1.3 Privacy and Data Protection
Recognize sensitive information and make informed decisions about what to share, with which tools, and under what conditions.
1.4 Intellectual Property and Attribution
Consider ownership, permitted use, and appropriate attribution when working with source material and AI-assisted content.
1.5 Truthfulness, Hallucination, and Verification
Distinguish a confident answer from a supported one. Verify important information and respond appropriately when evidence is missing.
Knowledge check: Professional Judgment
Responsible AI at Work
2.1 Accountability in AI-Assisted Work
Identify who owns a decision, who reviews the work, and what responsibility you retain when AI contributes.
2.2 Working Within AI Policies and Guardrails
Apply your organization’s expectations to practical situations and recognize when to seek guidance or escalate a concern.
2.3 Choosing and Using AI Tools Responsibly
Evaluate whether a tool fits the task, the information involved, and the consequences of getting something wrong.
2.4 Workforce Wellbeing and Human Agency
Consider how AI changes people’s work, wellbeing, and ability to make decisions, contribute, and exercise judgment.
2.5 Building a Responsible AI Culture from Your Seat
Make responsible practice visible through the questions you ask, the standards you uphold, and the example you set.
Knowledge check / Responsible AI Action Brief
10+ practical tools.
In Responsible & Ethical AI, use templates, checklists, frameworks, and guides to examine risks, challenge assumptions, explain decisions, and follow through on your responsibilities.
A basis for your next decision.
Bring the learning together in a Responsible AI Action Brief: connect what you’ve examined to the decisions, responsibilities, and next steps in your own context.
Two knowledge checks sit alongside this component’s ten core learning modules.
Ready to build this capability?
Explore the combined CIPS micro-credential for yourself or your team.
The case for responsible AI training
AI is already at work.
Judgment needs investment.
Technical capability is one part of enterprise AI adoption. IT teams also need the judgment to question outputs, protect information, and account for decisions.
KPMG · Transforming the Enterprise · 2026
- of organizations reported a workforce highly proficient in using AI tools.
- 19%
- had proactively integrated AI risk management into strategy and the technology lifecycle.
- 24%
- tracked operational or revenue outcomes linked to trusted AI.
- 28%
Survey of 1,750 senior transformation leaders across 20 countries and territories, fielded February 2026. Self-reported organizational findings across sectors; they do not measure this program’s impact.
Read the 2026 researchTraining is part of effective governance.
NIST’s AI Risk Management Framework calls for AI risk management training for personnel and partners so they can carry out their responsibilities. This program builds practical judgment around the decisions those responsibilities involve. NIST · GOVERN 2.2
Where responsibility becomes practice
The next decision
is where it counts.
From incident summaries to service decisions, AI can help IT teams work more effectively. Professional judgment determines what information it can use, how outputs are checked, and who remains accountable.
Before you shareA faster summary.
A faster summary.
Sensitive incident data.
An AI assistant could summarize an incident report. Logs and support tickets include user identifiers, system details, and information your organization must protect.
What would responsible practice involve?
Check whether the tool and data use are approved. Remove unnecessary identifiers and secrets, respect access boundaries, and use only the information needed for the task. Escalate when the permitted use is unclear.
Before you rely on itA credible recommendation.
A credible recommendation.
A production decision.
An AI assistant recommends a configuration change to resolve a service issue. The explanation is convincing, but a mistake could affect availability or access.
What would responsible practice involve?
Verify the recommendation against authoritative documentation and your environment. Test through approved change controls, check for unintended effects, and keep a person accountable for the decision.
Before you put it to workA faster service desk.
A faster service desk.
A human consequence.
Your team wants AI to triage employee support requests. It could speed up responses, but changes who gets attention, who reviews exceptions, and how people challenge a decision.
What would responsible practice involve?
Define ownership, human review, and escalation. Examine who could be underserved or unfairly prioritized. Explain AI’s role and preserve a meaningful way for employees to question or correct the result.
Illustrative, AI-generated scenes. The program helps you develop judgment for the context, policies, and responsibilities of your own work.
The organizational value
10 ways responsible AI
governance adds value.
Good governance gives people a basis for moving forward: a clear purpose, appropriate boundaries, and a way to learn from what happens.
Our synthesis of NIST, ISO, and OECD guidance. These are potential benefits to pursue and assess in your context.
Select a benefit to explore its value and supporting guidance.
Earn stakeholder trust
Make responsible AI use visible through clear expectations and evidence of how decisions are governed.
Look for: confidence supported by evidence.
ISO · trust and reputationImprove decision quality
Set expectations for reliable information, traceable inputs, and review before consequential outputs are used.
Look for: better-supported decisions.
ISO · traceability and reliabilityProtect people’s information
Build privacy and data protection into decisions about how AI is used and what information it receives.
Look for: appropriate information boundaries.
OECD · privacy and data protectionReduce unfair outcomes
Consider whose interests are affected and create ways to identify, challenge, and address harmful bias.
Look for: concerns identified and acted on.
OECD · fairness and accountabilityMake accountability clear
Define who owns a decision, who reviews the work, and how concerns move to the people able to act.
Look for: decisions with an accountable owner.
NIST · Meaure 2.8Strengthen compliance readiness
Connect AI policies and documented practices to relevant obligations, with a clearer basis for review.
Look for: responsibilities and evidence in place.
ISO · governance and complianceEnable purposeful innovation
Give teams a structured way to explore AI opportunities while managing the risks of new uses.
Look for: experiments with clear boundaries.
ISO · opportunities and innovationMake better tool and supplier choices
Examine third-party risks and responsibilities before relying on an AI product or service.
Look for: supplier decisions with documented review.
NIST · GOVERN 6Preserve human agency
Keep meaningful human oversight and ways to question AI-assisted decisions, protecting people’s ability to contribute and act.
Look for: usable review and challenge routes.
OECD · agency and transparencyLearn from problems earlier
Use monitoring, incident reporting, and regular review to detect issues and improve the way AI is used.
Look for: timely correction and follow-through.
NIST · GOVERN 1.5 and 4.3People make governance work. Training equips them to apply it. Leadership, policies, technical controls, and ongoing monitoring put that capability into practice.
Build your team’s capability
Partnership announcement
Professional standards.
Practical capability.
A shared commitment.
CIPS and humanskills.ai are partnering to help professionals build the capability to use AI effectively, exercise sound judgment, and take responsibility for the results.
The micro-credential brings together AI Agility and Responsible & Ethical AI: 30 core learning modules and approximately 12–15 hours of learning. Successful completion of both programs leads to the industry-wide CIPS micro-credential.
Since 1958, CIPS has advanced standards, best practices, and integrity in Canada’s IT profession. As AI changes how we work, that responsibility extends to how we direct it, evaluate its contribution, and consider the people affected.
Professional competenceIntegrityPublic trust
“Public trust depends on the choices professionals make. Through our partnership with humanskills.ai, we want to help our members turn ethical principles into everyday practice: questioning outputs, protecting people, and taking responsibility for the work they put into the world.”
Responsible & Ethical AI’s ethical foundation is the CIPS Code of Ethics, adapted from the ACM Code of Ethics and Professional Conduct. Practical learning helps you apply that foundation to your own work. Explore the Code
Featured partner / Excell IT
Responsible AI belongs
in the way work gets done.
Our partner Excell IT brings a perspective grounded in the technology, systems, and information businesses depend on.
With services spanning managed IT, cybersecurity, cloud, and digital transformation, Excell IT works where technology decisions meet operational responsibilities. That context matters when people begin using AI in their everyday work.
Responsible & Ethical AI builds the judgment people need alongside their organization’s tools, policies, and technical safeguards.
“When a business brings AI into its work, the everyday decisions matter: what information people share, which outputs they trust, and when they ask for help. Our partnership with humanskills.ai supports the practical judgment that helps teams use AI with confidence and care.”
A few useful answers
Make an
informed choice.
Find the learning that fits your work, your responsibilities, and the people who rely on your decisions.
Who is this program for?
IT professionals, technology leaders, and enterprise teams who use AI, review its outputs, or shape how it is used in organizational workflows. The combined program develops practical AI collaboration alongside judgment around evidence, fairness, privacy, policy, accountability, and the effects on people.
How does it fit the wider humanskills.ai offering?
Responsible & Ethical AI provides 10 core learning modules on professional judgment and responsible AI at work. For the CIPS micro-credential, it combines with the 20 core learning modules in AI Agility. Together, the two programs comprise 30 core learning modules and approximately 12–15 hours of learning. Explore all HumanSkills programs
What is the ethical foundation?
Responsible & Ethical AI is grounded in the CIPS Code of Ethics, adapted from the ACM Code of Ethics and Professional Conduct. You apply that foundation to practical questions about AI use in your own context.
What do I complete to earn the CIPS micro-credential?
The CIPS micro-credential requires successful completion of both AI Agility and Responsible & Ethical AI. The combined program comprises 30 core learning modules and approximately 12–15 hours of learning. Completing both programs leads to the industry-wide CIPS micro-credential.
Can we explore this for a team?
Yes. Share the roles involved, approximate team size, and the AI decisions your people face. We’ll discuss the program and an approach that fits your organization’s context. Tell us about your team
What should I bring to the learning?
A decision, task, or workflow you want to examine, using a fictionalized or appropriately shareable example. Have relevant organizational policies available and consider who could be affected by the work.