Marico PurbaProject Manager| Tracon IndustriBEKASI, JB, Indonesia
In my experience, when someine start saying "we should AI", usually what comes in their mind is that everything is automatic, you just click one button and then see the result. They necer thought whats behind that machine.
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1 reply by Anil Pradhan
Oct 29, 2026 11:01 PM
Anil Pradhan
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Agreed. It requires lot of consideration before taking the first step.
Saving Changes...
Anil PradhanInfosysAllambie Heights, NSW, Australia
Oct 29, 2026 9:22 PM
Replying to Marico Purba
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In my experience, when someine start saying "we should AI", usually what comes in their mind is that everything is automatic, you just click one button and then see the result. They necer thought whats behind that machine.
Agreed. It requires lot of consideration before taking the first step. Saving Changes...
Debasis SenguptaDirector| Advice & ImpactKolkata, West Bengal, India
Are we sometimes choosing the technology before clearly defining the problem we want to solve? Saving Changes...
AMIT SIRSIKARProject Leader| M.C.DeanAshburn, Va, United States
Coming from an electrical construction background, I see tremendous potential for AI in industries where project execution involves complex processes, technical requirements, and multiple stakeholders. In electrical construction, we manage everything from engineering validation, submittals, procurement, and logistics to installation, quality control, commissioning, and closeout. The challenge is not always the lack of information. It is connecting information, understanding dependencies, and making the right decisions at the right time. When someone says, "We should use AI," my first question is: What problem are we trying to solve? Are we looking to automate repetitive tasks, improve technical validation, identify risks, or integrate information across the project lifecycle? For example, approving a switchgear submittal is one activity. But connecting that approval to procurement, delivery schedules, installation readiness, and commissioning requirements is where AI could deliver much greater value. I believe we need to distinguish between AI for task automation and AI for systems integration and decision support. More importantly, industry professionals must play an active role in developing these solutions. Technology experts understand AI, but construction professionals understand the practical challenges, dependencies, and consequences of decisions in the field. The real opportunity is not simply doing individual tasks faster. It is connecting people, processes, and information to improve the execution of an entire project. Saving Changes...
when someone suggests using AI, I unpack it through four key steps:
Identify the Core Need & Expectations: I start by asking why AI is needed for this specific project and what clear outcomes or expectations we hope to achieve.
Establish a Shared Language & Toolset: I align the team on terminology and evaluate which specific AI tools are actually suitable for addressing our challenges.
Define Scope, Inputs, and Outputs: Scoping the project scope by clearly defining the required inputs, expected outputs, and how to navigate changing AI technologies.
Set Up Early Review Loops: I ensure continuous early reviews to catch framework shifts quickly, allowing us to make informed decisions and prevent stalled or broken processes."
Working at an advertising agency, I've noticed a really refreshing shift lately. People aren't just throwing around a vague 'let's use AI' anymore—they’re coming to the table with actual problems to solve. Now, the conversations sound a lot more like:
'Can we use an AI model to streamline how we process client feedback and cut down the manual spreadsheet grind?'
'Can we use gen AI tools to quickly rough out storyboards for this pitch?'
It makes a world of difference when you start with an actual bottleneck instead of trying to force-fit a trendy tool where it isn't needed.
In past roles, it was the exact opposite. Leadership would often say things like 'let’s use AI to solve this' or 'we need an AI initiative,' without having any clue what the actual problem or execution looked like. Unpacking those vague mandates was always a gamble. Honestly, we spent so much time trying to reverse-engineer use cases that we ended up wasting hours and creating more friction instead of making anything faster or better. Saving Changes...
Sheraz QureshiTechnical Project Manager| Nokia, CanadaOttawa, ON, CAN, Canada
how better a process could be improved to facilitate the user's while ensuring Business not only flourishes, but do have a clear roadmap going forward in utilizing AI,
the expectation from AI does not end at any point Saving Changes...
Deema AldowilaFunctional Manager| King Saud UniversityRiyadh, 1, Saudi Arabia
Thanks for you Saving Changes...
Eva CarterEnterprise Portfolio Project Manager| US ArmySan Antonio, United States
In my experience, one of the first signals is listening closely to what people actually mean when they say “AI.” Different people may be using the same term while envisioning very different capabilities or outcomes.
I would start by clarifying the problem we are trying to solve, what we expect AI to do, and what value we expect it to create. When everything gets lumped together under “AI,” requirements and expectations can become unclear and stakeholders can easily become misaligned.
I have seen similar challenges in project work when people use the same terminology but have different interpretations of what the work actually represents. I also think responsible AI use requires human judgment. AI should help refine and strengthen our work, not replace the critical thinking and decision-making behind it. Saving Changes...
We need to first understand why? In otherwards we need to ask the question why we are using AI. Is it to automate repetitive tasks? Then how we are going to use AI and then lastly what we are going to use AI for. Saving Changes...
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