As more tools add AI to their offerings, promising more data insight but effectively remaining siloed from each other, how is this affecting your projects? What are you doing to combat AI sprawl? Are you manually consolidating the data or are there projects, pending or in progress, to automate the integration of the data from these tools into a unified view for more comprehensive reporting and effective decision-making? Saving Changes...
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Sergio Luis ConteHelping to create solutions for everyone| Worldwide based OrganizationsBuenos Aires, Argentina
We are using AI, mainly embedded in tools, from more than 30 years ago. Nothing to combat. Generative AI, from the time the new model was published in 2017, helps to consolidate data from multiple sources. But nothing new below the sun. The key is to understand that all related to AI is a data endeavor. No more than that.
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2 replies by Aaron Porter and Md. Golam Rob Talukdar
Jun 14, 2025 5:48 AM
Md. Golam Rob Talukdar
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Hi Aaron Porter,
Great question. Our organization is still in the observation and learning phase of AI tools.
We haven't moved to full automation or integration yet due to some internal concerns about data reliability.
Currently, we manually aggregate most of the data, but we plan to transition to a unified, effective AI platform in the future.
Golam
Jun 15, 2025 1:29 PM
Aaron Porter
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Are you manually consolidating data using GenAI, create a custom API (or other internal tool), or are you using a third party tool to automate it?
Saving Changes...
Luis BrancoCEO| Business Insight, Consultores de Gestão, LdªCarcavelos, Lisboa, Portugal
Aaron Porter This is a timely and critical question.
AI sprawl is emerging as one of the biggest hidden challenges in digital project environments.
Many organizations are excited by AI-enhanced tools promising better forecasting, risk detection, and decision support—but we’re seeing a proliferation of disconnected intelligence rather than true transformation.
The real danger lies in the illusion of insight.
Siloed AI outputs can create fragmented truths, leading to inconsistent reporting and decision misalignment at both project and portfolio levels.
Some organizations attempt to manually reconcile these differences, but this is reactive, labor-intensive, and prone to bias.
What we’re implementing is a strategic AI orchestration layer—a programmatic initiative to: - Map which tools are generating what types of insights;
- Prioritize which AI outputs are most critical for strategic and operational decisions;
- Build APIs or use middleware to automate data consolidation into a unified project intelligence dashboard;
- And most importantly, establish a data governance model that treats AI outputs as just one (auditable) layer in the broader decision architecture.
AI should augment clarity, not fragment it.
Addressing AI sprawl isn’t just an IT issue—it’s now a project leadership imperative.
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1 reply by Aaron Porter
Jun 16, 2025 9:16 AM
Aaron Porter
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"...illusion of insight..."
I like that and may have to borrow it. I'd be interested in your lessons learned, once you get to that point.
Project Manager| AWR Development (BD) Ltd. Cox's Bazer , Bangladesh
Jun 14, 2025 3:46 AM
Replying to Sergio Luis Conte
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We are using AI, mainly embedded in tools, from more than 30 years ago. Nothing to combat. Generative AI, from the time the new model was published in 2017, helps to consolidate data from multiple sources. But nothing new below the sun. The key is to understand that all related to AI is a data endeavor. No more than that.
Hi Aaron Porter,
Great question. Our organization is still in the observation and learning phase of AI tools.
We haven't moved to full automation or integration yet due to some internal concerns about data reliability.
Currently, we manually aggregate most of the data, but we plan to transition to a unified, effective AI platform in the future.
Golam
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1 reply by Aaron Porter
Jun 15, 2025 1:37 PM
Aaron Porter
...
I can understand the concerns with data reliability - the models are getting better, but I still find occasional issues in the results.
I'm looking into low cost solutions. So far, I like Ollama, so that we can have local/private AI tools. One of the challenges is that the average user would struggle if we went with the Ollama interface. MSTY.app provides a free interface that looks similar to most GenAI interfaces, but it's intended for one user:one source, and the free version is only for personal use. You can have multiple desktop clients hitting the same source, but then resource requirements/costs grow.
We are using AI, mainly embedded in tools, from more than 30 years ago. Nothing to combat. Generative AI, from the time the new model was published in 2017, helps to consolidate data from multiple sources. But nothing new below the sun. The key is to understand that all related to AI is a data endeavor. No more than that.
Are you manually consolidating data using GenAI, create a custom API (or other internal tool), or are you using a third party tool to automate it? Saving Changes...
Great question. Our organization is still in the observation and learning phase of AI tools.
We haven't moved to full automation or integration yet due to some internal concerns about data reliability.
Currently, we manually aggregate most of the data, but we plan to transition to a unified, effective AI platform in the future.
Golam
I can understand the concerns with data reliability - the models are getting better, but I still find occasional issues in the results.
I'm looking into low cost solutions. So far, I like Ollama, so that we can have local/private AI tools. One of the challenges is that the average user would struggle if we went with the Ollama interface. MSTY.app provides a free interface that looks similar to most GenAI interfaces, but it's intended for one user:one source, and the free version is only for personal use. You can have multiple desktop clients hitting the same source, but then resource requirements/costs grow. Saving Changes...
Aaron Porter This is a timely and critical question.
AI sprawl is emerging as one of the biggest hidden challenges in digital project environments.
Many organizations are excited by AI-enhanced tools promising better forecasting, risk detection, and decision support—but we’re seeing a proliferation of disconnected intelligence rather than true transformation.
The real danger lies in the illusion of insight.
Siloed AI outputs can create fragmented truths, leading to inconsistent reporting and decision misalignment at both project and portfolio levels.
Some organizations attempt to manually reconcile these differences, but this is reactive, labor-intensive, and prone to bias.
What we’re implementing is a strategic AI orchestration layer—a programmatic initiative to: - Map which tools are generating what types of insights;
- Prioritize which AI outputs are most critical for strategic and operational decisions;
- Build APIs or use middleware to automate data consolidation into a unified project intelligence dashboard;
- And most importantly, establish a data governance model that treats AI outputs as just one (auditable) layer in the broader decision architecture.
AI should augment clarity, not fragment it.
Addressing AI sprawl isn’t just an IT issue—it’s now a project leadership imperative.
"...illusion of insight..."
I like that and may have to borrow it. I'd be interested in your lessons learned, once you get to that point. Saving Changes...
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