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AI in the organizations a project or a program?

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DORA LUZ Mejia CEO| IT Explore Envigado, Antioquia, Colombia
I am interesting in Know if organziations are moving in independent projects to test or create products with GenAI or there is a good practice to integrate a program in the organization. I am more moving to integrate a program scenario to create the concept in the organization due the bunch of initiatives the different areas are trying to move, and we really need to work clearly in governance, policies and other fundational thinks to move forward to the group of iniciatives that are going to meet clear objectives for genAI in organizations. Want to hear about cases..
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Laura Schofield
PMI Team Member
Community Specialist| Project Management Institute Newtown Square, PA, United States
Hi Dora, thanks for posting your question! I am including links to some previous discussions on AI and governance that I hope you find helpful:

https://www.projectmanagement.com/discussi...ful-integration

https://www.projectmanagement.com/discussi...ect-management-

https://www.projectmanagement.com/discussi...ect-management-
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Claudia Alcelay
PMI Team Member
Learning & Innovation Research Manager| Project Management Institute (PMI) Spain

Hi Dora,


 

This is an interesting and challenging scenario. Based on what other companies are doing, these could be some tentative steps:


 

Governance Model: As you mentioned, implementing a governance model is a crucial first step. It encourages the organization to think strategically about issues that were previously overlooked. You could start with an initial assessment to determine if your organization is ready to shift towards data-informed practices.


 

Focus on People: It's important to help your team understand how generative AI can benefit the company, rather than just individual needs. Together, you can prioritize tools and projects based on their potential positive impact on the business. Additionally, the business should be informed about how these projects align with their strategic goals.


 

Assess Impact: Consider whether a shift towards data will improve or fix current projects or the company as a whole. Any existing issues will likely become apparent in the early stages, allowing you to address them before fully implementing data-informed decisions.


 

IT Team and Tech Environment: Having a strong IT team is essential. Since you're in an IT company, this might not be the most challenging aspect. However, deciding on the right environment, tech platform, and approach can significantly impact the business.


 

I found these two resources very inspirational and useful, and they might be of help to you:



“Leading Projects with Data: Overcome Behavioral and Cultural Barriers to Unlock the Hidden Value of Data in Projects” by Marcus Glowasz
“A New Future of Work: The Race to Deploy AI and Raise Skills in Europe and Beyond” by McKinsey Global Institute, just launched
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Sergio Luis Conte Helping to create solutions for everyone| Worldwide based Organizations Buenos Aires, Argentina
It depends on your approach to create value to your internal and external customers. My recommendation after years of experience in the field (including today) is define your customer (or Persona if you will use some "fanzy" term), define your value stream then define your products. When you make that you will find that all related to generative AI most of the times deserves a program, not a project. Mainly for the level of scalation the products has themselves
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Mofazzel Hosen Morshed Engineer & Youth Researcher| Youth Policy Forum Chattogram, Bangladesh
I strongly support moving toward a program approach, primarily because generative AI models and capabilities evolve rapidly. If you view GenAI as a standalone project with a fixed start and end date, you miss the reality of maintaining and updating these tools. AI systems require ongoing model monitoring, evaluation for bias/drift, prompt tuning, and retraining.
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Md. Golam Rob Talukdar
Community Champion
Project Manager| AWR Development (BD) Ltd. Cox's Bazer , Bangladesh
Hi Dora,
The are flowering everywhere but model driven by independent projects, while stimulating initial innovation, easily leads to chaos and waste.

Establishing an integrated program centered on governance, supported by robust infrastructure, while simultaneously empowering each business unit is the correct direction to ensure your GenAI investments generate sustainable, large-scale business value.

Golam
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Robert Snyder Founder & President| Innovation Elegance, LLC Chicago, Il, United States
Dora … a question …

There’s the playful, generic contrast …

A.Do you have a solution (AI) looking for a problem? Or …
B.Do you have problems looking for a solution?

My interpretation from your post is that you hope that …

AI is a solution to foundational governance …

… because humans (CHI … Collective Human Intelligence) have not solved your governance problems.

Am I on track?

If so, would you be willing to elaborate on the governance problems?

Are the governance problems frequent enough that they qualify as culture problems?

Here is a list of 14 culture problems – culture traits – that I monitor, monthly, in a Lessons Learned template.

1.SIB (Psychological Safety, Inclusivity, Sense of Belonging)
2.Transparency
3.Simple & Straightforward
4.Accountability
5.Alignment
6.Momentum
7.Morale
8.Sustainability
9.Scalability
10. Stylishness
11. Learning
12. Emphasis
13. Balance
14. Success feels inevitable

Once we understand the foundational governance / culture problems you see, perhaps AI contains a solution. Perhaps CHI (Collective Human Intelligence) has a solution.
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Luis Branco CEO| Business Insight, Consultores de Gestão, Ldª Carcavelos, Lisboa, Portugal
An important question.
I would avoid treating AI adoption as a choice between isolated projects and a single organizational program.
Individual use cases may be managed as projects or products, while shared capabilities such as data governance, platforms, assurance, skills, policies, and organizational change may require coordinated program-level leadership.
Portfolio governance is also needed to decide which initiatives deserve investment and how their combined risks and benefits should be balanced.
Perhaps the real challenge is to design an architecture that enables local experimentation while creating the common conditions needed to learn, govern, and scale AI responsibly across the organization.

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