NEIL PRINCEShip Manageer| Perez Y Cia Ja. LtdArraijan, 8, Panama
When A customer suggests, "We should use AI". this not a project requirement, similar to a request for working in Agile Methodology, we as project managers will have to know, first the issue or problem that the project will solve, and second, to see if an AI or Agile solution will be a good fit in solving the issues Saving Changes...
Penso na necessidade primeiro, IA certa depois, e visão de conjunto sempre no radar. Antes de falar em IA, eu quero entender a necessidade. Qual é o problema, onde ele dói, o que muda se resolver isso. Só depois dessa parte eu pergunto se IA é a solução — e qual IA, porque isso muda tudo. Sou super a favor de usar IA. Cada necessidade pede um tipo diferente, e escolher errado é pior do que não escolher nada. O que eu realmente defendo é ir além de aplicar IA em pontos isolados e soltos pela empresa. Prefiro pensar em construir algo mais parecido com um cérebro central — que entende o contexto todo do negócio, conecta as pontas, e pensa em tudo, não só resolve aquele problema de forma isolada é inteligência que enxerga o sistema inteiro. Saving Changes...
Vandana BahetiProgram Manager| Tata Consultancy ServicesHyderabad, Andhra Pradesh, India
We Should use AI should be considered an ambiguous statement. AI is such an ocean and we do not need ocean for our business. Starting from basics may not apply as well === we already have something built by thinking about the basics and it's working. If AI had not advented what would that system currently support and what are it current underlying issues that is stopping us from generating value for the business. I think this becomes the starting point for any such conversation. Then we start identifying the gaps and issues of the current system which can be either resolved or augmented by AI and that is where the conversation begins towards adoption of AI. Saving Changes...
I unpack it by turning “use AI” into a clear use case: what problem? What data? Who benefits? What risk? And what measurable outcome? AI should not start with the tool; it should start with the business plan and need. Saving Changes...
As we are living in an underdeveloped country, we are slowly and gradually moving toward the use of AI. In reality, AI is already embedded in our daily lives. Most mobile applications rely heavily on AI, but many people are unaware of it. Even on our laptops and computers, AI is integrated into many of the applications we use every day. When people say “we should use AI,” they are usually referring to adopting more advanced technologies to reduce response time, shorten cycle time, and improve efficiency in routine tasks. In most cases, the intention is not about AI itself, but about achieving faster results, minimizing manual work, and improving overall productivity. Saving Changes...
NONYELUM OBIDIMMAHEAD OF OPERATIONS| ZENITH BANK PLCNEW KARMO, FC, Nigeria
When someone says we should use AI, what comes to mind is firstly to go to ChatGPT, Gemini, ETC. Then the next thought is "to what extent" .
are we going to automate the whole process?
will it be used as a backup to human process ie to fine tune human effort
or will it be used to get more answers as problems come up.
When someone says “we should use AI,” the conversation is rarely about technology itself. It is usually about pressure for speed, efficiency, innovation, or competitive leverage. The first step is to clarify intent.
Three signals help distinguish what is really being asked.
First, decision proximity. Is AI automating a task, augmenting human judgment, or moving toward managing objectives autonomously? These are fundamentally different categories of work. The closer AI gets to consequential decisions, the stronger the need for governance, traceability, and explicit oversight.
Second, problem clarity. Is there a clearly defined business problem with measurable impact, or is AI being treated as the starting point? When the solution precedes the problem, misalignment and inflated expectations follow.
Third, accountability design. Who owns the outcome if an AI-driven recommendation fails? When responsibility becomes diffuse, risk scales faster than performance.
In many organizations, “AI” simultaneously means efficiency, experimentation, and cost reduction to different stakeholders. Misalignment becomes visible when decision flows and ownership are unclear. A common tipping point is when stakeholders use the same word “AI” but describe different success metrics.
The real shift is not from manual to automated. It is from “man in the loop” to “man in control.” Without deliberate design of responsibility, capability increases while accountability erodes.
Clarity of purpose, category of AI work, and ownership separates disciplined transformation from technological noise.
The Objective, goal, and problem definition indicate whether AI adoption is needed. It further informs the project manager and their team of the pattern or combination of patterns that will deliver the required solution after the associated problem-statement data has been analyzed, verified, and validated against measurable metrics. Any text, image, audio, or video dataset, properly labeled and segmented, that captures the defined problems should provide the required starting point for the solution. Saving Changes...
Many AI projects struggle not because the technology is lacking, but because teams never establish a shared understanding of what "AI" means in the context of the problem.
The conversation stays at the buzzword level instead of moving to the specific capability and desired outcome.
A useful habit is to replace "AI" with the actual capability. Instead of saying "we need AI," say "we need to automatically classify customer feedback," "predict demand," or "generate first drafts of reports." Once the capability is clear, it's much easier to decide whether AI is the right tool—or whether a simpler solution would achieve the same result. Saving Changes...
Since lots of projects face different kind of problems, using AI is not just a matter of using the tool, but using the right framework for the solution of the problem. Saving Changes...
Tyrell SiagiMorgan StanleyChino, Ca, United States
When someone suggests "let's use AI!" I often find myself thinking mostly about the potential for automation, but I also acknowledge the risks involved in implementing such an automated tool. This means considering how the AI might be created, whether through generative models or whether there's a need for a more strategic approach to its implementation. Saving Changes...
PMI Team Member
