Learning & Innovation Research Manager| Project Management Institute (PMI)Spain
Are you utilizing any specific checklists or protocols within your projects or company to assess your readiness for working with Generative AI data? I'm curious to know what strategies or tools you've implemented to prepare for integrating Gen AI into your workflows. Please share your approaches in the comments below! Saving Changes...
This is a very relevant question, Claudia. From my perspective as a PMP practitioner working in Retail & Projects, I believe GenAI readiness should start with one simple question: are we clear about the problem we are trying to solve?
In project environments, it is very easy to get excited about the tool first. But the real value comes when GenAI is applied to a clear business or project need, such as improving lessons learned analysis, preparing stakeholder communication, supporting risk identification, reviewing project documents, or identifying recurring issues across past projects.
For me, a practical checklist would include the following checkpoints:
1.Define the business problem clearly The team should be able to explain what outcome they expect from using GenAI and how it will improve the project or decision-making process.
2.Check data readiness In retail technology projects, data often comes from different systems, countries, vendors, brands, and operating teams. If the data is incomplete, outdated, inconsistent, or not properly understood, the AI output may look convincing but still lead to the wrong conclusion.
3.Confirm governance and security upfront Project teams need to know what data can be used, what must be anonymised, who owns the data, who can access it, and whether the selected tool is approved for internal or confidential information. This is especially important when dealing with vendor proposals, financial details, system architecture, customer information, project risks, or incident records.
4.Keep human review in the process I see GenAI as a strong assistant, not a final decision-maker. It can help us draft, summarise, compare, and highlight patterns, but the project manager and subject matter experts still need to validate the context, challenge the assumptions, and take accountability for the final decision.
5. Start with low-risk, useful use cases A good starting point would be summarising meeting notes, extracting key themes from lessons learned, drafting project updates, preparing checklists, or identifying repeated risk patterns from past project records. These small wins can help build confidence while allowing the organisation to mature its governance approach.
6. Apply a simple decision gate before use Before applying GenAI, I would ask:
Can this data be used safely?
Is the business problem clearly defined?
Is the AI tool approved for this type of information?
Has sensitive or confidential data been removed or protected?
Is there a subject matter expert review step?
Is the final decision still owned by a responsible human?
7. Learn from real-world examples I would also be very interested to see more practical examples from other industries. I believe project managers have an important role to play here, not only by using GenAI, but by helping organisations introduce it in a structured, responsible, and value-driven way. Saving Changes...
Kiba MUVUNYIExpertise France - AFD GroupKigali, 01, Rwanda
Evaluate existing IT infrastructure for compatibility with GenAI requirements.
Ensure adequate computing power and storage capacity.
Assess network capabilities for handling GenAI data processing.
Data Management
Saving Changes...
Kiba MUVUNYIExpertise France - AFD GroupKigali, 01, Rwanda
Evaluate existing IT infrastructure for compatibility with GenAI requirements.
Ensure adequate computing power and storage capacity.
Assess network capabilities for handling GenAI data processing.
Data Management
Saving Changes...
Ronitia HodgesSr. Project Manager| C4Jacksonville, FL, United States
Honestly, we don't have formal checklists or protocols in place yet, and I think that's true for a lot of organizations doing this kind of work. What we do have is a culture of intentionality, so the questions we're sitting with are less about technical readiness and more about values alignment: Who owns the outputs? How do we protect community data? Where does AI support the work versus flatten it?
I'm genuinely curious what a readiness framework looks like for organizations focused on power-building and narrative change, where the "data" is often people's stories and lived experience. Would love to learn from what others are building. Saving Changes...
PMI Team Member
