Julia KhokhlovaProduct Delivery Manager Lead| EvolutionWarsaw, Poland
It all starts from the perspective, experience, occupied role and tasks at hand. As a manager, I mostly work with the prompt engineering part, trying to simplify my routine and automate regular tasks. Software developers would imagine a different approach hearing AI. Say nothing about specific tools... Saving Changes...
In the construction industry, “AI” can mean anything from simple automation to BIM, forecasting, or smart site monitoring. The biggest clue is the problem being discussed — saving admin time, reducing delays, or improving project decisions. What usually goes wrong is when everything gets labeled as AI, creating unrealistic expectations without proper systems or accurate data in place. Saving Changes...
AI is necessary so the real question before asking where to use AI, we should ask what are we actually trying to improve, and where is the issue in the workflow today? Saving Changes...
ELMUGHDAD ELTAHIRConstruction Manager | Dorsch Holding Groupe Dubai, Az, United Arab Emirates
Great point about scoping ,defining success in measurable Saving Changes...
Nelson PazIngeniero TI| EcuTagQuito, Pichincha/Quito, Ecuador
Feb 19, 2026 1:05 PM
Replying to Luis Branco
...
Great question.
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.
We had the same problem at my company. Due to the need to integrate AI into the process, it was decided to incorporate a bot, but the sponsor never gave us the acceptance criteria for the bot, and in the end it was an effort that led nowhere, and we had to start from scratch.
When someone says, ‘we should use AI,’ I first try to understand the actual business problem behind the statement rather than jumping directly to technology. In many organizations, AI is often mentioned as a solution before the root operational challenge is clearly defined. I usually unpack the request by asking questions around efficiency, risk, quality, decision-making, customer experience, and scalability.
For example, in insurance operations and claims processing, leadership may say ‘we should use AI’ because turnaround times are increasing or manual reviews are creating bottlenecks. Instead of immediately proposing a chatbot or automation tool, I would analyse where delays occur, what type of decisions are repetitive, whether the issue is data quality, workload distribution, compliance risk, or lack of workflow visibility.
In one operational scenario, AI may be best suited for intelligent document classification and prioritization, while in another case a simpler workflow automation or rules-based RPA solution may provide better ROI than advanced AI. Therefore, I believe the right approach is to first identify the business objective, measurable pain points, stakeholders impacted, and expected outcomes before deciding which AI pattern or technology is actually appropriate.
To me, successful AI adoption is not about using AI because it is trending; it is about aligning AI capabilities with operational value, process maturity, and business goals.”
Saving Changes...
JOSE MAURICIO PECHIMPROJECT MANAGER| CONSTRUSOYO CONSTRUÇÃO CIVIL LDARUA MONTE DOS BURGOS 248 4250-309 PORTO PORTUGAL, Portugal
hi, You unpack it by asking what problem they want to solve, what outcome they expect, and what workflows actually need support — because “use AI ” is not a strategy, it’s a placeholder for an unclear need. AI will definetly provides support iin speed solving problems, and good and fast results. progress will come fast. Saving Changes...
Jeff RoseProject, Program & Portfolio Managment Consultant| Independent ConsultantKingwood, Tx, United States
We should seek to understand the business problem/opportunity and the value that is expected prior to jumping straight into the solution design. Saving Changes...
AI Is a more sophisticated tool that the ones we as Project Managers have been used in the past. It's key to use It, to give us decition making criteria for speed, Innovative and effciency to mitigate project risks and make potential the opportunitties
Saving Changes...
Paul WaggonerProgram Manager| Consultant - FreelancePapillion, Ne, United States
After identifying the real business problem, you will want to identify the data that can be used with one or more of the seven AI patterns. Excellent comments from everyone! Saving Changes...
PMI Team Member
