u/MimirLearning

I’ve been noticing that thanks to AI, development teams are moving insanely fast now. Faster builds, faster releases, faster execution in general. (I wonder if the 2 weeks sprint still make sense nowadays!!!)... but not sure if this high speed means become faster at cost of any alignment.

I mean the users or stakeholders can keep the pace with this velocity?

Until few years ago the execution was the "bottleneck", With AI there is less "friction" from that side but if priorities aren’t clear, AI doesn’t fix that, and I wonder if we are just building the wrong thing faster.

I’ve been noticing that thanks to AI, development teams are moving insanely fast now. Faster builds, faster releases, faster execution in general. (I wonder if the 2 weeks sprint still make sense nowadays!!!)... but not sure if this high speed means become faster at cost of any alignment.

I mean the users or stakeholders can keep the pace with this velocity?

Until few years ago the execution was the "bottleneck", With AI there is less "friction" from that side but if priorities aren’t clear, AI doesn’t fix that, and I wonder if we are just building the wrong thing faster. Feels like a lot of orgs are confusing speed with agility.

The ITIL® Product (Version 5) certification focuses on digital product management across the full product and service lifecycle. It is designed for professionals who want to understand how products and services are managed from initial idea through design, development, delivery, support, and continual improvement.

This certification helps professionals apply ITIL principles in modern digital environments where product thinking, service management, customer value, and business alignment are critical.

Exam Format & Requirements

📝 40 multiple-choice questions
⏱️ 90 minutes
✅ Pass mark: 70% (28/40)
📘 Open book

The exam consists of a mix of:

• knowledge-based questions
• scenario-based questions

The assessment includes approximately:

• 65% Bloom Level 2 (understanding concepts)
• 35% Bloom Level 3 (applying concepts in practical scenarios)

What the Exam Covers

The exam covers the complete ITIL Product and Service Lifecycle, focusing on how organizations manage digital products and services from concept to support.

Key areas include:

• Digital Products and Services: Understanding the foundations of product management and digital service delivery.
• Discover: Learning how to identify opportunities, customer needs, and business value.
• Design: Designing products and services that meet customer expectations and business objectives.
• Acquire: Understanding how to acquire technology, people, and third-party services.
• Build: Developing and integrating product capabilities effectively.
• Transition: Managing the movement of products and services into live environments.
• Operate: Ensuring stable and efficient product and service operations.
• Deliver: Providing value through effective service delivery and customer engagement.
• Support: Maintaining service quality and resolving issues efficiently.

The exam also includes advanced topics such as:

• end-to-end lifecycle management
• operating models and value streams
• digital product management success factors
• organizational enablement
• AI and automation in product management
• integration with Agile, DevOps, and PRINCE2®

Preparation Tips

To prepare effectively for the ITIL® Product exam:

✅ Study the official syllabus thoroughly
✅ Focus on understanding lifecycle stages and their outputs
✅ Practice applying concepts in real-world scenarios
✅ Take mock exams to improve speed and confidence

Recommended preparation approach:

• Start with core concepts
• Study each lifecycle stage in detail
• Use mock exams to simulate exam conditions
• Practice scenario-based questions

Since the exam includes practical application questions, understanding how concepts work in real business environments is essential.

Exam Strategy Tips

Because the ITIL® Product exam is heavily scenario-based, having a good exam strategy can significantly improve both speed and accuracy.

• Read the scenario carefully before starting scenario-based questions

You will usually receive a scenario (for example, the ICR Care Rental case study) that is used across multiple questions.

Read the full scenario carefully before starting the exam. It is the one included in the official manual

This helps you:

• understand the business context
• identify key stakeholders
• recognize challenges and objectives
• avoid wasting time re-reading during the exam

The better you understand the scenario upfront, the easier it becomes to answer related questions quickly.

• Answer non-scenario questions first

A smart approach is to answer all non-scenario-based questions first.

Why?

• they are usually faster to complete

• they help secure quick marks early

• they build confidence at the start of the exam

• Complete scenario questions together

Once non-scenario questions are done, move to the scenario-based questions as a group.

This strategy helps because:

• all scenario questions share the same context
• your focus stays on one business case
• you avoid repeatedly switching between question styles

This reduces mental fatigue and improves concentration.

I've been involved in projects where there were stakeholder maps, communication plans, workshops, surveys, and regular updates.

Yet when the project went live, some of the people most affected felt blindsided. It made me wonder:

What's the difference between stakeholder engagement and stakeholder communication?

Is keeping people informed enough, or does engagement only count if stakeholder feedback actually changes decisions?

I've seen situations where feedback was collected, documented, and acknowledged but the outcome never changed. On the other hand, involving everyone in every decision can make projects grind to a halt.

Where do you draw the line?

Have you experienced stakeholder engagement that improved a project? Or have you been on the receiving end of a process that felt like the decision had already been made?

Collecting input is only half the system. The real value comes when it reaches decision-makers consistently.

This is where collective intelligence becomes operational advantage.

Goal: Turn employee input into leadership insight

Time: Weekly executive integration

A simple structure that works:

• Themes from Start/Stop/Keep conversations
• Recurring employee concerns
• Repeated obstacles across teams
• Emerging opportunities from the field
• Signals from frontline/customer-facing teams
• Patterns (not isolated comments)

Why this works

One comment is noise.

Ten similar comments are a signal.

When leadership aggregates input:

• Patterns become visible
• Blind spots are reduced
• Decisions become grounded in reality
• Priorities are validated or challenged

The idea is simple:
👉 Individual feedback matters less than repeated patterns across people.

The rule that matters most:

➡️ Look for repetition, not exceptions

Common failure modes

1. Cherry-picking feedback Leadership reacts to the loudest voice → skewed priorities and reactive decisions
2. No synthesis layer Feedback is shared raw, without interpretation → executives get overwhelmed, not informed

Bottom line

Employee input only becomes powerful when it is synthesized into patterns that shape leadership thinking every week.

The most overlooked source of clarity in any organization is not dashboards or reports, it’s employees.

But only if leaders consistently ask.

A simple but powerful practice is the Start/Stop/Keep conversation, done regularly by executives and middle managers with employees.

This can be weekly, monthly, or embedded into structured 1:1s, what matters is the continuity, not the exact cadence.

Goal: Surface real friction and real opportunities

Why this works

Most organizations underestimate how much signal is already inside the company.

When leaders consistently ask:

• Hidden friction becomes visible
• Small inefficiencies surface early
• Opportunities emerge from frontline experience
• People feel heard in a structured way

The idea is simple:
👉 The closer you are to real work, the clearer the truth becomes.

The rule that matters most:

➡️ Don’t just ask, repeat the conversation consistently over time

Common failure modes

1. One-off listening sessions Leadership collects feedback once and moves on → no trust, no continuity, no real insight
2. Turning it into a survey only Forms replace conversations → context and nuance are lost

Bottom line

Start/Stop/Keep is not a tool.

It’s a leadership rhythm that turns everyday conversations into a continuous intelligence system.

1) What is AI Project Governance Framework?

The AI Project Governance Framework (AIPGF®) is a structured framework designed to help organizations govern, manage, and oversee Artificial Intelligence (AI) initiatives effectively and responsibly.

It provides guidance for ensuring that AI projects are:

• aligned with organizational objectives
• managed ethically and transparently
• compliant with legal and regulatory requirements
• controlled through effective governance structures
• delivered with appropriate risk management and accountability

As AI adoption continues to grow, organizations need clear governance mechanisms to ensure that AI solutions deliver business value while maintaining trust, security, and compliance.

2) What is APMG International?

APMG International is the global examination institute that:

• administers AI Project Governance Framework certification exams
• accredits training providers
• ensures consistency and quality of exams worldwide

Think of APMG as the official certification authority, similar to its role in frameworks such as AgilePM or Change Management.

3) Official Sources

Here are the official references if you want to explore further:

• AI Project Governance Framework (AIPGF) Official Page

4) AI Project Governance Framework Foundation (AIPGF Foundation)

This is the entry-level certification.

📝 40 multiple-choice questions
⏱️ 40 minutes
✅ Pass mark: 50% (20/40)
📘 Closed book
❌ No prerequisites

It focuses on:

• AI governance principles and concepts
• ethical and responsible AI practices
• governance structures and accountability
• AI risk management fundamentals
• key terminology and framework components

5) AI Project Governance Framework Practitioner (AIPGF Practitioner)

This is the advanced certification.

📝 4 complex scenario-based questions
⏱️ 120 minutes
✅ Pass mark: 50% (40/80)
📘 Closed book
❌ Requires Foundation certification

It tests your ability to:

• apply AI governance principles in real-world scenarios
• manage AI-related risks and compliance requirements
• establish governance frameworks for AI initiatives
• align AI projects with organizational strategy and regulatory expectations

6) Sample Exams (Highly Recommended)

Practice exams are essential, especially for Practitioner-level certifications.

👉 Official sample papers:
https://oea2022.apmg-international.com/Marlin/SamplePapers.aspx?_gl=1*idfmor*_gcl_au*MTA0MTA0Mzc3MS4xNzczNjU1MDM1

💡 Practicing helps you:

• understand exam structure and question styles
• improve time management
• prepare for scenario-based questions
• build confidence before the real exam

7) Final Thoughts + Training

AI Project Governance Framework is a strong certification if you:

• work in AI, data, governance, risk, or compliance environments
• want to ensure AI initiatives are delivered responsibly and effectively
• aim to establish governance and oversight for AI projects
• need a globally recognized AI governance certification

It’s particularly valuable for organizations pursuing responsible AI adoption, regulatory compliance, and trustworthy innovation.

If you’re interested in training, certification paths, or exam preparation support, feel free to ask about MIMIR learning 👍

Happy to guide you through:

Certification

Practice

Development

1) What is AI Project Governance Framework?

The AI Project Governance Framework (AIPGF®) is a structured framework designed to help organizations govern, manage, and oversee Artificial Intelligence (AI) initiatives effectively and responsibly.

It provides guidance for ensuring that AI projects are:

• aligned with organizational objectives
• managed ethically and transparently
• compliant with legal and regulatory requirements
• controlled through effective governance structures
• delivered with appropriate risk management and accountability

As AI adoption continues to grow, organizations need clear governance mechanisms to ensure that AI solutions deliver business value while maintaining trust, security, and compliance.

2) What is APMG International?

APMG International is the global examination institute that:

• administers AI Project Governance Framework certification exams
• accredits training providers
• ensures consistency and quality of exams worldwide

Think of APMG as the official certification authority, similar to its role in frameworks such as AgilePM or Change Management.

3) Official Sources

Here are the official references if you want to explore further:

• AI Project Governance Framework (AIPGF) Official Page

4) AI Project Governance Framework Foundation (AIPGF Foundation)

This is the entry-level certification.

📝 40 multiple-choice questions
⏱️ 40 minutes
✅ Pass mark: 50% (20/40)
📘 Closed book
❌ No prerequisites

It focuses on:

• AI governance principles and concepts
• ethical and responsible AI practices
• governance structures and accountability
• AI risk management fundamentals
• key terminology and framework components

5) AI Project Governance Framework Practitioner (AIPGF Practitioner)

This is the advanced certification.

📝 4 complex scenario-based questions
⏱️ 120 minutes
✅ Pass mark: 50% (40/80)
📘 Closed book
❌ Requires Foundation certification

It tests your ability to:

• apply AI governance principles in real-world scenarios
• manage AI-related risks and compliance requirements
• establish governance frameworks for AI initiatives
• align AI projects with organizational strategy and regulatory expectations

6) Sample Exams (Highly Recommended)

Practice exams are essential, especially for Practitioner-level certifications.

👉 Official sample papers:
https://oea2022.apmg-international.com/Marlin/SamplePapers.aspx?_gl=1*idfmor*_gcl_au*MTA0MTA0Mzc3MS4xNzczNjU1MDM1

💡 Practicing helps you:

• understand exam structure and question styles
• improve time management
• prepare for scenario-based questions
• build confidence before the real exam

7) Final Thoughts + Training

AI Project Governance Framework is a strong certification if you:

• work in AI, data, governance, risk, or compliance environments
• want to ensure AI initiatives are delivered responsibly and effectively
• aim to establish governance and oversight for AI projects
• need a globally recognized AI governance certification

It’s particularly valuable for organizations pursuing responsible AI adoption, regulatory compliance, and trustworthy innovation.

If you’re interested in training, certification paths, or exam preparation support, feel free to ask about MIMIR learning 👍

Happy to guide you through:

Certification

Practice

Development

I've been involved in projects where there were stakeholder maps, communication plans, workshops, surveys, and regular updates.

Yet when the project went live, some of the people most affected felt blindsided. It made me wonder:

What's the difference between stakeholder engagement and stakeholder communication?

Is keeping people informed enough, or does engagement only count if stakeholder feedback actually changes decisions?

I've seen situations where feedback was collected, documented, and acknowledged but the outcome never changed. On the other hand, involving everyone in every decision can make projects grind to a halt.

Where do you draw the line?

Have you experienced stakeholder engagement that improved a project? Or have you been on the receiving end of a process that felt like the decision had already been made?

A lot of organisations think AI governance is about restricting AI use. That's not quite right, and misunderstanding that is where most problems begin.

The AI Project Governance Framework (AIPGF) isn't designed to slow AI adoption down. It's designed to help organisations use AI confidently, responsibly, and at scale.

Without governance, AI quickly becomes fragmented. Different teams use different tools, decisions become difficult to explain, and risks start appearing in places nobody expected.

The framework is really about balancing innovation with accountability. If you only focus on AI capabilities, you're missing the governance side. If you only focus on governance, you're missing the value AI can create.

The AIPGF brings both together.

At its core are three principles. They're not independent ideas; they reinforce each other.

① Human-Centricity → AI supports people, people remain accountable

A common antipattern: Teams begin relying heavily on AI-generated outputs and recommendations. Over time, nobody can clearly explain how decisions were reached.

How the principle helps: AI assists, humans decide. Accountability remains with people, not algorithms. The framework reinforces that AI should enhance human capability, not replace judgement.

② Transparency → decisions must be explainable

Antipattern: A project team uses AI to generate plans, forecasts, or recommendations, but stakeholders cannot understand where the outputs came from.

Reversal: AI-supported decisions are documented, explainable, and auditable. Trust increases because stakeholders can see not only the outcome but also the reasoning behind it.

③ Adaptability → governance evolves with AI maturity

Antipattern: Organisations create rigid governance structures that quickly become obsolete as AI tools evolve.

Reversal: Governance scales alongside AI adoption. Controls become more sophisticated as usage grows, allowing organisations to remain compliant without limiting innovation.

The framework also promotes five core values that shape how AI is used in projects.

• Accountability → every AI-supported decision has an owner

Antipattern: When something goes wrong, responsibility becomes unclear because "the AI suggested it."

Reversal: Every AI-assisted output remains attributable to a person or role. Ownership never disappears.

• Sensibility → balance AI outputs with human judgement

Antipattern: Teams treat AI recommendations as automatically correct.

Reversal: AI becomes one input among many. Human experience, context, and critical thinking remain essential.

• Collaboration → humans and AI work together

Antipattern: AI adoption becomes isolated, with individual users experimenting independently and creating inconsistent practices.

Reversal: Teams develop shared approaches to AI use, creating consistency, learning, and better outcomes.

• Curiosity → innovate responsibly

Antipattern: Organisations either ban AI completely or adopt every new tool without evaluation.

Reversal: Teams actively explore AI opportunities while assessing risks, benefits, and suitability before widespread adoption.

• Continuous Improvement → governance is never finished

Antipattern: AI policies are created once and forgotten.

Reversal: AI use is regularly reviewed, lessons are captured, and governance evolves alongside technology.

Where AIPGF really separates itself from generic AI governance discussions is in its lifecycle.

Rather than treating governance as a compliance exercise, it embeds governance directly into project delivery through three stages:

Foundation → establish the conditions for success

Before AI is used:

• Define objectives and scope
• Select appropriate AI tools
• Assess data quality
• Enable the team
• Identify AI-related risks

The focus is preparation rather than reaction.

Activation → govern AI while work is happening

Antipattern: Teams implement AI and only discover issues after problems emerge.

Reversal: AI usage is monitored continuously. Risks, issues, and benefits are actively managed while the project is being delivered.

Governance becomes part of day-to-day delivery.

Evaluation → learn and improve

Antipattern: Projects finish and AI performance is never reviewed.

Reversal: Teams evaluate impact, transparency, accountability, and outcomes. Lessons learned feed directly into future projects.

This creates a continuous improvement cycle regardless of whether projects use Agile, Waterfall, or hybrid approaches.

Curious how this plays out in other organisations:

Where are you seeing the biggest challenge with AI adoption?

Is it governance, accountability, transparency, data quality, or simply getting people comfortable working alongside AI?

Collecting input is easy. Closing the loop is where most organizations fail. Without it, feedback becomes noise and trust disappears.

This is why a mid-management layer must own the system end-to-end.

Goal: Ensure every insight is acknowledged and resolved or explained

Time: Ongoing accountability system

A simple structure that works:

• Track all employee inputs (Start/Stop/Keep, surveys, conversations)
• Categorize: actioned / not actioned / deferred
• Assign ownership for follow-up
• Communicate decisions clearly back to employees
• Explain why something is or isn’t being acted on
• Review unresolved items in leadership meetings

Why this works

Closing the loop creates:

• Trust in leadership
• Psychological safety to speak up again
• Clarity on priorities and constraints
• Reduction in repeated frustration cycles

The key idea:
👉 People don’t expect every idea to be implemented, they expect to be heard and understood.

The rule that matters most:

➡️ Every input deserves a response, even if the answer is “not now” or “not aligned”

Common failure modes

1. Silent absorption of feedback Input is collected but never acknowledged → employees stop sharing honestly
2. No ownership of follow-through Everyone assumes someone else is handling it → nothing gets resolved

Bottom line

Without closing the loop, feedback systems collapse.

With it, they become a trust-building engine that continuously improves the organization from within.

The final sign of a healthy executive team is not agreement.

It’s the ability to disagree, openly, productively, and without fear.

Again, Lencioni’s model is direct:

• Absence of trust leads to fear of conflict
• Fear of conflict leads to fake alignment
• Fake alignment destroys execution

Goal: Create productive conflict that improves decisions

Time: Embedded in every strategic discussion

A simple structure that works:

• Encourage multiple viewpoints before decisions
• Separate ideas from individuals
• Use facts and data as grounding points
• Pressure-test assumptions openly
• Assign a “devil’s advocate” when needed
• End debates with clear decisions and ownership

Why this works

Without constructive debate:

• Problems stay hidden too long
• Weak ideas survive unchallenged
• Decisions get made politically, not logically
• Execution suffers downstream

With healthy debate:

• Better decisions emerge faster
• Risks surface earlier
• Alignment becomes real, not performative

The idea is simple:
👉 Conflict is not a sign of dysfunction, it’s a sign of engagement.

The rule that matters most:

➡️ Debate hard on ideas, commit fully after decisions

Common failure modes

1. Avoiding conflict to stay “nice” Leaders hold back disagreement → leads to weak decisions and silent frustration
2. Personalizing disagreements Debate becomes emotional or political → trust erodes, conversations shut down

Bottom line

High-performing executive teams don’t avoid conflict.

They normalize it, structure it, and use it to improve every major decision.

Hi everyone,

We're currently using an LMS, but we're exploring the possibility of moving towards a more integrated setup with Odoo. (using odoo for finance and sales already)

Our goal is to have one unified system instead of managing separate platforms. We're considering two approaches:, Integrating actual LMS with Odoo through APIs or Expanding and adapting Odoo's eLearning App to cover our LMS requirements.

From what we've seen, the standard Odoo eLearning module seems relatively basic compared to dedicated LMS platforms. Some of the features that are important for us include:

• SCORM support
• Course tracking and reporting
• Certifications
• User learning paths
• Advanced assessments/quizzes

We're interested in hearing from anyone who has experience with such projects.

How was your experience? Did you build custom modules, use third-party apps, or keep a separate LMS connected through APIs?

Any lessons learned, recommendations, or pitfalls to avoid would be greatly appreciated.

Thanks!

The ITIL Foundation (Version 5) E-learning course is designed to help professionals understand the latest ITIL framework and modern service management practices.

✅ 100% online & self-paced
✅ 12 months access
✅ Interactive video lessons
✅ Official digital manual included
✅ Practice tests & simulated mock exams
✅ Online certification exam included
✅ Available in English

👉 Free demo lessons available now.

MIMIR Learning | ITIL Foundation (Version 5) demo

Already certified in a previous ITIL version?

You can upgrade your knowledge through the new ITIL Foundation Bridge Exam, designed for professionals moving from ITIL 4 certifications to Version 5

🎁 Special Launch Offer:
➡️ Pay only for the exam
➡️ E-learning course included FREE

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🎓 ITIL Foundation (Version 5) Exam Voucher + eLearning
€690 €490 for Reddit members

🎓 ITIL Foundation Bridge (Version 5) Exam Voucher + eLearning
€290 €190 for Reddit members

These prices are based on Euopean Region price, Every region has a different Exam voucher price.

If you have questions, please let us know.

A great opportunity to update your certification at minimal cost.

Most companies reports 100% completion of security awareness training but employees continue clicking phishing emails.

It got me wondering how much security training actually changes behaviour versus simply checking a compliance box.

Most people know they're not supposed to click suspicious links, share credentials, or bypass verification processes. Yet incidents still happen, often involving people who completed all the required training.

For those working in IT, security, compliance, or management:

Have you seen a disconnect between training completion rates and real-world security outcomes?

What has actually worked to reduce risky behaviour?

And if trained employees still make mistakes, where should accountability sit: the individual, the training programme, or the organisation's security design?

Healthy executive teams don’t just execute well, they evolve together.

That requires structured learning.

Monthly Meetings : r/BestPracticesMgmt

Goal: Improve thinking quality as a leadership unit

Why this works

Most teams plateau because they repeat the same thinking patterns.

Monthly learning breaks that cycle by:

• Exposing blind spots
• Upgrading decision-making frameworks
• Aligning mental models across leaders
• Preventing “local optimization” thinking

The idea is simple:
👉 Teams don’t just need better information, they need better thinking tools.

The rule that matters most:

➡️ If you’re not learning, you’re repeating

Common failure modes

1. Treating it like a lecture One person teaches, others passively listen → no real behavioral change
2. No application layer Ideas are discussed but never applied → learning without transformation

Bottom line

Executive education is not optional development.

It’s a system for improving how leadership decisions get made over time.