When someone says, “we should use AI,” how do you unpack what’s really being asked?

When someone says, “we should use AI,” how do you unpack what’s really being asked? With project management continuously evolve, adapting to new process always created blockage. First thing comes first in mind as it's another way to waste time. Like the common phrase of " if not broke don't fix it". But nowadays, PMOs always looking for something to improve process. AI is one of the new trends that most of my colleagues still continuedly deny. Not because of laziness, but merely misinformed. So, in laymen's term, when somebody asked if we have to use AI, the right response is "is there a room in our project for more growth and expansion?". What kind of AI our current project need? What AI can possibly make our project worth more and excel its productivity? AI only works if all stakeholders understand what the project needs and in turn, correct AI approach must be introduce.

One of the most important lessons I've learned is that when people say, "We should use AI," they are often expressing a desired outcome rather than defining an actual problem.

The critical first step is establishing the right mental model. AI is not the objective; it is a tool. The real questions should be; what problem are we trying to solve? what decision are we trying to improve? what process are we trying to optimize? and what value are we trying to create for stakeholders?

Without this mental model, organizations risk treating AI as a technology project rather than a business transformation initiative.

The second aspect is understanding the relationship between planning, delivery, and value creation. Successful AI initiatives begin with strategic planning that clearly defines business objectives, data requirements, governance structures, risks, and success metrics. Delivery then becomes the disciplined execution of that strategy through appropriate technology, people, and processes. Ultimately, value creation is measured not by the sophistication of the AI solution, but by tangible outcomes such as improved efficiency, better decision-making, enhanced customer experience, reduced costs, increased compliance, or new revenue opportunities.

A useful way to think about it is:

Mental Model → Planning → Delivery → Value Creation

If any link in this chain is weak, the initiative is unlikely to achieve meaningful results.

The cost of overlooking this sequence can be significant. Organizations may invest heavily in AI tools that are poorly aligned with business needs, generate low user adoption, create governance and compliance risks, or fail to produce measurable returns. In many cases, teams become focused on deploying technology rather than solving the underlying problem, leading to wasted resources, stakeholder frustration, and reduced confidence in future innovation efforts.

From an information and knowledge management perspective, AI delivers its greatest value when it is integrated into a broader organizational strategy. The conversation should therefore move beyond "How can we use AI?" to "How can AI help us deliver measurable value and achieve our strategic objectives?" That shift in thinking often determines whether AI becomes an expensive experiment or a genuine driver of organizational performance and transformation.

Response to Q1:

There are specific AI tools available that explain what they are best at. When using a specific AI tool, I believe individual business streams can become lumped together.

Response to Q2:

AI is a gigantic source of power at the individual level, depending on how it is used and perceived.

Response to Q3:

Should we use AI individually or collectively?