In my experience, one of the first challenges with AI is that “AI” rarely means the same thing to everyone in the room. When someone says, “We should use AI,” I first try to understand the actual business need rather than immediately discussing technology. The signals I look for are the problem being solved, the expected outcome, the type and quality of data available, the level of human judgment required, and how success will be measured. These usually reveal whether the need is really AI, automation, analytics, a GenAI assistant, predictive modeling, or simply a process improvement. For example, a request to “use AI to improve customer service” could mean very different things: automating repetitive workflows, deploying a conversational assistant, predicting customer behavior, analyzing sentiment, or helping employees make faster decisions. Problems arise when all of these are grouped under one “AI initiative.” Expectations become unclear, architecture and governance requirements get mixed together, costs increase, and teams may select technology before defining the problem. The result can be an impressive AI demonstration that delivers very little measurable business value. My approach is: Business Problem → Use Case → Data Readiness → AI/Automation Fit → Risk & Governance → Measurable Value → Scale The key question is not “Where can we use AI?” but rather “What business outcome are we trying to achieve, and is AI actually the right tool to achieve it?” That distinction has been particularly important in enterprise and digital transformation programs, where AI should be treated as a capability within the transformation—not as the transformation itself Saving Changes...
I’ve seen “AI” used as a broad term for very different needs, from automating repetitive tasks to analyzing data and making predictions. What usually signals the difference is the expected outcome: are we trying to generate something, predict something, classify information, or automate a process? When all of these are simply labeled as “AI,” teams can focus on the technology instead of the actual problem, leading to unclear requirements, unrealistic expectations, and solutions that do not address the real need. Saving Changes...
Stephen RileyPMO Portfolio Manager| Retired HP Program Manager (P&G Global Account)West Chester, Oh, United States
Most recently I have been working with a group 7 senior IT leaders, CFO, CSO, etc. so asking this group what AI means to them goes all over the map. In general our Security Officer is the most technical so he sees AI helping ID threats at the bit/byte/network level. Our CFO is mostly row/column/dbase analysis based so sees AI helping develop tools to provide insights buried in reams of data. The application lead sees AI as the tool to catch up the software backlog. etc. Getting this group to align on company priorities is probably the hardest task and an area where I can see PM Infinity being a good sounding board to help structure separate and group planning. Saving Changes...
When someone says “we should use AI,” I think the first thing we need to understand is what they actually want to achieve. In my experience, we sometimes get excited about a new tool before clearly defining the problem. I prefer to start by understanding what we need to improve. Maybe we want to save time, analyze information faster, identify risks, or make better decisions. Once that is clear, we can see where AI can really help. I also believe we should not forget our responsibility as project managers. AI can help us work faster and see things we might otherwise miss, but we still need to understand the context and decide whether the result makes sense. For me, the important question is not how we can use AI, but where it can actually help us do a better job. Saving Changes...
CLAUDIA PARAMOProject Engineer| industry TelecommunicationsBogota, Colombia, Colombia
Feb 19, 2026 1:05 PM
Replying to Luis Branco
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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.
Cuando alguien propone incorporar IA, mi primera reacción como Gerente de Proyectos es preguntar: ¿qué problema queremos resolver?, ¿qué proceso podemos mejorar?, ¿qué datos necesitamos y qué indicador de negocio esperamos impactar? A partir de estas respuestas, podemos evaluar alternativas, viabilidad técnica, costos, riesgos, seguridad y beneficios esperados. También considero importante comenzar con un piloto controlado, medir sus resultados y validar su escalabilidad antes de realizar una implementación amplia. Mi conclusión es que la IA no debe ser el objetivo del proyecto, sino un habilitador para generar valor, mejorar la eficiencia y optimizar la toma de decisiones.
First clarify the business objective, identify the root problem, and define measurable success criteria. Then I assess whether AI is the right solution based on feasibility, data readiness, cost, risks, and expected ROI. Finally, I recommend a proof of concept to validate the value before scaling. Saving Changes...
Shabbir AhamadManagement| Hill InternationalTamil Nadu, TN, India
I support using AI, but before selecting a tool, let's clearly define the business process, current pain points, available data sources, expected benefits, and success criteria. Once those are identified, we can determine where AI will deliver measurable value to the project rather than implementing AI for the sake of AI Saving Changes...
Mohammed JillaniSr Project Manager| Grit Innovation Software Pvt LtdTandur, Telangana, India
“When we say we should use AI, I suggest we first identify the specific business problem and the expected outcome. We should understand the current process, establish baseline metrics, and assess whether AI is actually the right solution or whether conventional automation would be more effective. Once we identify a promising use case, we can evaluate data readiness, technical feasibility, cost, security, and risks. We should then run a small proof of concept with measurable success criteria before committing to a full-scale implementation. As the project manager, I will coordinate the stakeholders, define scope and KPIs, establish ownership, monitor risks and costs, and present a recommendation based on the PoC results.” Saving Changes...
Shabbir AhamadManagement| Hill InternationalTamil Nadu, TN, India
I support using AI, but before selecting a tool, let's clearly define the business process, current pain points, available data sources, expected benefits, and success criteria. Once those are identified, we can determine where AI will deliver measurable value to the project rather than implementing AI for the sake of AI. Saving Changes...
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