KAZI SAIFUL HOQUECEO& Co founder| HCAP, Human Capital BangladeshDhaka, N/A-Outside Usa / Canada, Bangladesh
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Sergio Luis ConteHelping to create solutions for everyone| Worldwide based OrganizationsBuenos Aires, Argentina
It is a board question because AI is a board term, no matter today most of the people and organizations confuse generative AI with AI. Adding to that, what it does mean in your question post-delivery state? For example, in my past work places and in my today workplace business analyst and product managers are using AI based tools to evaluate if the solution is achieving the expected results, infrastructure people is using AI assistance to prevent any service down, etc etc. But it is not new. Organizations are using it from more than 30 years ago.
Program Manager| HARPER SRLSanto Domingo / Distrito Nacional, Dominican Republic
Hello KAZI SAIFUL HOQUE It would be helpful to add a bit more context to the question, since “post-delivery” can mean different things depending on the project or environment.
But from my perspective, in the post-delivery stage AI is most useful for tracking outcomes and improving what was delivered. For example, analyzing user adoption, detecting issues early in production, and comparing expected vs actual benefits. It can also help identify patterns from feedback and lessons learned, allowing teams to adjust faster instead of waiting for formal reviews.
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2 replies by Alaa Alnafori and KAZI SAIFUL HOQUE
Mar 24, 2026 3:38 AM
KAZI SAIFUL HOQUE
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Thank you for your insightful answer. In my country it has been a big headache to manage a public sector infrastructure project after delivery because of lack of prudent policy taken affront. I shall keep your suggestions in mind.
Mar 25, 2026 6:13 AM
Alaa Alnafori
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uLissette Indhira Pimentel Sosa /uI completely agree with this perspective. Indeed, in the post-delivery stage, AI is a powerful tool for tracking outcomes, improving performance, and detecting issues early, enabling teams to adapt quickly and leverage lessons learned without waiting for formal reviews.
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KAZI SAIFUL HOQUECEO& Co founder| HCAP, Human Capital BangladeshDhaka, N/A-Outside Usa / Canada, Bangladesh
Mar 23, 2026 8:31 PM
Replying to Lissette Indhira Pimentel Sosa
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Hello KAZI SAIFUL HOQUE It would be helpful to add a bit more context to the question, since “post-delivery” can mean different things depending on the project or environment.
But from my perspective, in the post-delivery stage AI is most useful for tracking outcomes and improving what was delivered. For example, analyzing user adoption, detecting issues early in production, and comparing expected vs actual benefits. It can also help identify patterns from feedback and lessons learned, allowing teams to adjust faster instead of waiting for formal reviews.
Thank you for your insightful answer. In my country it has been a big headache to manage a public sector infrastructure project after delivery because of lack of prudent policy taken affront. I shall keep your suggestions in mind. Saving Changes...
PM Consultant| CLOUD SAFE CO., LTD.New Taipei City, NWT, Taiwan
I believe AI can add strong value in the post-delivery stage, especially in areas like issue trend analysis, lessons learned consolidation, and stakeholder feedback summarization. That said, the key is not only efficiency, but also governance — ensuring the outputs are traceable, reviewable, and aligned with project controls. AI should support decision-making, not replace it.
Like Chia mentioned above, AI can be really interesting to use when it comes to lessons learned and stakeholder feedback. One way that we've been leveraging AI is to generate insights from lessons learned and stakeholder feedback so that we can use it for future projects and to help project managers make data-driven decisions that make material business impact and have even built a tool for this, called WorkshopIQ. Saving Changes...
I believe AI can add strong value in the post-delivery stage, especially in areas like issue trend analysis, lessons learned consolidation, and stakeholder feedback summarization. That said, the key is not only efficiency, but also governance — ensuring the outputs are traceable, reviewable, and aligned with project controls. AI should support decision-making, not replace it.
I agree with you Chia Fang Chang Saving Changes...
Hello KAZI SAIFUL HOQUE It would be helpful to add a bit more context to the question, since “post-delivery” can mean different things depending on the project or environment.
But from my perspective, in the post-delivery stage AI is most useful for tracking outcomes and improving what was delivered. For example, analyzing user adoption, detecting issues early in production, and comparing expected vs actual benefits. It can also help identify patterns from feedback and lessons learned, allowing teams to adjust faster instead of waiting for formal reviews.
uLissette Indhira Pimentel Sosa /uI completely agree with this perspective. Indeed, in the post-delivery stage, AI is a powerful tool for tracking outcomes, improving performance, and detecting issues early, enabling teams to adapt quickly and leverage lessons learned without waiting for formal reviews. Saving Changes...
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