Luis BrancoCEO| Business Insight, Consultores de Gestão, LdªCarcavelos, Lisboa, Portugal
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.
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47 replies by ALLY MALONNJI, ANGELA ROCHA, Aamer Abbas, Abderrahim REZAK, Aristodi Fredrick Ndesuo, Beauty Ogaranya, Berenice Carmona, Carl Pro, Christopher Pollard, David Medina Gutierrez, Evans Gitau, Fady Yammine, Ganiyu Odunusi, George Mathew, Habtie Geta Nigussie, Hala Souliman, JUDSON FRY, Juan Sebastian Velasquez Montejo, Kendra Benson, Kennedy Mwanza, Kerry DeFreitas, Laetoya Curry, Linda Bullard, MARIA GONZALEZ VARGAS, Mihran Kochyan, Mohammed Munir Younis, Monica Shanivarasanthe Mohan, NIKHIL DADHICH, Nelson Paz, Patrick Owens, Paul Waggoner, Peter Ssemakula Mukiibi, Rafiat Bashiru, Robert Durham, Samuel Abbey, Shakeel Anwar Bhatti, Sherry Choi, Sijuwade Saka, Simon Tam, Sohrab Rahimi Foroushani, Timothy Tuohy, Vikash Kumar, Yadhu Guragain, anonymous, and s srinivasa rao
Mar 25, 2026 9:29 AM
Ganiyu Odunusi
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This is an impressive analysis to the problem at hand. I definitely agree to this submission.
Mar 28, 2026 5:44 PM
David Medina Gutierrez
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Exactly. Many organizations still treat automation, process improvement, analytics, and digital transformation as if they were the same as AI. They are not. Before talking about AI agents, it is worth asking whether the real need is simpler: redesigning the process, clarifying decisions, and automating repetitive tasks with reliable tools.
Mar 31, 2026 8:31 AM
Rafiat Bashiru
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I couldn't have agreed more with your submission, when suggesting the use of AI, clarity is important.
Apr 01, 2026 2:07 AM
NIKHIL DADHICH
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Brillian writeup Luis Branco Crisp, Concise, yet perfectly outlines the paradigm.
Apr 01, 2026 5:17 AM
Simon Tam
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For the seven patterns for AI, the first thing to do is to clarify what are the problems we like to solve. It is important to be aware the requirements may change and there are always new tools arising. To have the best mental model is better having a good technical skills, important steps to ask for the right question.
Apr 01, 2026 2:38 PM
MARIA GONZALEZ VARGAS
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Users may think that AI solves all possible issues for them, nevertheless it is needed humand mind behind this solution.
Apr 02, 2026 8:51 AM
Carl Pro
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Great summary
Apr 04, 2026 4:52 PM
Sohrab Rahimi Foroushani
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This is an impressive trend which seems real .
Apr 05, 2026 1:23 PM
Laetoya Curry
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Great analysis
Apr 05, 2026 1:23 PM
Laetoya Curry
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Great analysis
Apr 07, 2026 5:02 AM
JUDSON FRY
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This is an insightful analysis. Your proposal correctly identifies that the drive to adopt AI is often a response to business pressures for increased speed, efficiency, and competitive leverage, rather than a purely technological initiative. The framework you've outlined, based on the three signals of decision proximity, problem clarity, and accountability design, offers a robust method for clarifying intent. This approach is essential for aligning stakeholders and ensuring that the implementation of AI is a disciplined transformation, not just technological noise. By focusing on the deliberate design of responsibility, organizations can successfully transition from a "man in the loop" model to one of "man in control," ensuring that capability and accountability evolve in tandem.
Apr 10, 2026 2:12 AM
Peter Ssemakula Mukiibi
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I agree. The confusion will always be on why do we need this and at what level of influence do I allow the AI control my processes?
How can we best interpret the outputs, especially when summarizing them to make sense to a traditionally trained stakeholder?
I am continuosly learning on how I can improve using the available vendor based AI solutions for my precise needs in engineering consultancy and construction.
Apr 11, 2026 5:06 PM
ALLY MALONNJI
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It's true that we should use AI in project planning even in management of project but not only in that way but also we should use in different activities. Because AI help to simplify certain activities
Apr 14, 2026 2:19 PM
Kendra Benson
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Totally agree. Its a loaded response without knowing what type of "AI" would actually address the problem vs another tool that is not leveraged and therefore limit scalability
Apr 14, 2026 2:27 PM
Yadhu Guragain
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Well Said
Apr 14, 2026 8:04 PM
Juan Sebastian Velasquez Montejo
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En el desarrollo de este ejercicio, la utilización de herramientas de Inteligencia Artificial permitió evidenciar tanto su potencial como sus limitaciones en entornos corporativos reales. Si bien la IA facilita la estructuración, análisis y consolidación de información, su efectividad depende directamente del nivel de estandarización, calidad y organización de las fuentes de datos. En este caso particular, la heterogeneidad en nombres de archivos, estructuras y formatos redujo la confiabilidad de los resultados automatizados, lo que hizo necesario complementar el proceso con validación y estructuración manual. A partir de esta experiencia, se identifican dos aprendizajes clave:
La IA no sustituye el criterio analítico ni la toma de decisiones; actúa como un habilitador que requiere direccionamiento estratégico.
Su uso efectivo exige previamente la estandarización de la información (especialmente en archivos como Excel), así como la construcción de prompts estructurados y orientados a objetivos específicos.
En términos prácticos, el valor de la IA se maximiza cuando se integra dentro de un proceso organizado, donde se define claramente:
Cuándo utilizarla (procesos repetitivos, estructurados o de alto volumen)
Cuándo no utilizarla (información no estandarizada o de alta criticidad sin validación)
Esta experiencia permitió no solo avanzar en la consolidación de la información, sino también establecer una base metodológica para el uso responsable y eficiente de la IA en procesos de gestión documental y análisis de datos. En conclusión, la IA no reemplaza el proceso; lo potencia cuando existe estructura. Su adopción efectiva en la organización requiere madurez en los datos, estandarización y un enfoque estratégico en su implementación. Ing. Juan Sebastian Velasquez Montejo Colombia (+57)3203840207
Apr 15, 2026 10:29 PM
Sijuwade Saka
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We just started to discuss the use of AI by exploring the potential as well as the regulatory implications in our line of business
Apr 17, 2026 7:42 AM
Habtie Geta Nigussie
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"When someone says, “We should use AI,” they’re not giving you a requirement; they’re giving you a signal. From a PMI perspective, your role is to translate that into value by first asking what problem we’re actually trying to solve.. If the outcome isn’t clear, the solution shouldn’t be either. From there, identify the real need (automation, augmentation, insights, or user interaction), validate whether the necessary data actually exists and is usable, and define success in measurable terms. Only after assessing feasibility, technical, organizational, and governance constraints, should scope be defined. And in some cases, the right answer is not to use AI at all". I entirely agreed with above idea.
Apr 17, 2026 11:49 AM
Mihran Kochyan
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This reminds me of the origination of the relational database (yes, I am that old). Leadership would interject with the design of databases, saying they wanted to be the first to convert. Some cases caused the early deployment to RDB with a repository that was not suitably designed.
We need to be careful selecting the right tool for the right situation
Apr 19, 2026 7:15 PM
Berenice Carmona
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La claridad de propósito de lo que queremos lograr, la identificacion de con que agente de IA podems hacerlo y hasta donde se puede controlar son ahora tareas comunes, sin embargo es totalmente cierto que la responsabilidad de las respuestas y la verificacion de las mismas en la emocion de la rapidez con que se pueden lograr los datos se puede difuminar siendo un riesgo mayor a la tarea que se pretende lograr
Apr 21, 2026 7:24 AM
ANGELA ROCHA
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div class="ql-code-block-container" spellcheck="false"div class="ql-code-block" data-language="plain"I believe the biggest question is figuring out where AI would fit in: automating tasks, supporting decisions, or creating something entirely new... the challenge is knowing how to make "AI" cease to be abstract and become something concrete./div/div
Apr 22, 2026 12:48 PM
Patrick Owens
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Luis, your summary and analysis of the problem is 'spot on.' There is great value in developing a common understanding of the problem you are trying to solve before throwing AI Solutions at all parts of it.
May 16, 2026 12:16 AM
Nelson Paz
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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.
Apr 27, 2026 9:12 PM
Mohammed Munir Younis
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I think one of the basic issues with failures in adopting AI solutions (process wise), is that teams fail adopt the technology, without really looking at whether processes and people are not usually automation ready. This starts with a good change management plan that takes into consideration the assessment of the readiness of the current state (both people and process wise) before making a decision on what is the logical next step.
May 06, 2026 2:48 PM
Timothy Tuohy
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I enjoyed reading your structured view of the issue. I too have found that a structured approach is necessary. I think the oldest problem remains the problem still. What do you want? Until the PM is able to clearly define the desired outcome there is no way to help guide the project in the correct direction.
May 01, 2026 12:58 AM
George Mathew
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The biggest question should be on client or user requirement. AI may help solve it or may not help. That is upto the design and technical team to decide based on the requirement.
May 09, 2026 4:59 AM
s srinivasa rao
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Very nicely illustrated. Simple answer is AI is solving the real problem within time, within quality and sustainable for future. Not about choosing any technology as priority.
May 29, 2026 10:17 PM
Robert Durham
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I agree with your observations. The first point of failure I have experienced each time is when the business leader wants us to leverage an AI solution (wanting speed and faster time to market) but when IT leaders hear the request they see the need for training AI, added cost for new experienced resources because the time to learn AI isn't available on the current team.
May 06, 2026 7:17 PM
anonymous
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in our organisation i have been asked to find ways of using ai to support our pmo teams resource capacity and capabilities by automating repetitive tasks.
May 15, 2026 12:45 AM
Fady Yammine
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Great comment. The key takeaway from this discussion is that AI isn’t just an evolution in technology, but an evolution in decision-making and responsibility.
If you don’t have ownership, governance, and agreement on objectives, the greater your ability, the greater your risk.
May 28, 2026 7:39 AM
Kennedy Mwanza
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Great stuff!
Apr 30, 2026 6:12 PM
Sherry Choi
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This is a great response to a project ask - with great granular detail! I love these requests for more tools, but have tended to ask more open ended questions of "why this tool in particular" or "which problems & issues do you think it will help with"?
May 14, 2026 11:03 AM
anonymous
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Great Analysis
May 07, 2026 1:03 AM
Abderrahim REZAK
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A targeted reflection before starting the process can make a big difference
Jun 02, 2026 7:11 AM
Vikash Kumar
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Well Said !
Jun 03, 2026 4:45 AM
Aristodi Fredrick Ndesuo
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I agree with you.
May 28, 2026 5:04 AM
Samuel Abbey
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I haven't had anyone break it down in the manner that you did, Sir. It covers all the necessary questions before any AI project initiation.
Jun 02, 2026 3:00 PM
Kerry DeFreitas
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Nicely written.
May 04, 2026 2:17 PM
Hala Souliman
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Totally agree
May 07, 2026 5:13 AM
Monica Shanivarasanthe Mohan
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Well said. I totally agree. It’s about saving time, reducing repetitive work, improving decisions, or scaling faster. The biggest challenge is translating the excitement into a clear business problem and measurable outcome before choosing the tool.
May 19, 2026 1:11 PM
Beauty Ogaranya
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Nice perspective
May 20, 2026 9:22 AM
Christopher Pollard
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I agree 100%. Well done.
May 22, 2026 2:55 AM
Evans Gitau
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Love the detailed explaination and how you've broken things down.
May 10, 2026 4:14 AM
Shakeel Anwar Bhatti
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Very well described.
Apr 29, 2026 2:10 AM
Aamer Abbas
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Can't agree more. However, in my experience, most of the time when business says "Let's use AI", they usually mean quick turnaround.
Apr 23, 2026 1:38 PM
Linda Bullard
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This is great stuff. Yes, your three subtopics are great for making this a better place in our workplaces.
May 17, 2026 11:28 PM
Paul Waggoner
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From a PMP, its better to slow down and follow the several steps recommended, be patient. Excellent recommendations included that should be followed.
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Mohamed AbdelhafezPM I| Forward construction and real stateCAIRO, C, Egypt
well done
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1 reply by Mohammed AlShahrani
May 11, 2026 12:22 AM
Mohammed AlShahrani
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excellent
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Sergio Luis ConteHelping to create solutions for everyone| Worldwide based OrganizationsBuenos Aires, Argentina
The first thing is to clarify what AI means. Human beings are using AI from more than 50 years ago. We are surrounded of AI entities embeded inside refrigerators, air conditioners, cell phones, etc, etc, Unfortunately in the last time some people and organizations are contributing to the general confusion using generative AI as a synonim of AI.
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6 replies by Funmilola Iyiola, Jeronimo Sanchez, Michael Hood, Ontresicia Averette, Renea Anderson, and Teresa Peterson
May 02, 2026 12:18 PM
Teresa Peterson
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Agree.
May 03, 2026 12:56 AM
Jeronimo Sanchez
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I agree, as in every era when there is a trend, the use of certain terms becomes dangerous, and in this digital age, one must be very careful with the ideas that are shared.
May 04, 2026 3:33 PM
Ontresicia Averette
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When someone suggests we use AI, the first thought is that we can use it to forecast budgets and develop clearer, more detailed measures of the project's status and success.
May 12, 2026 12:54 PM
Michael Hood
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Agree
May 21, 2026 8:36 AM
Renea Anderson
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Yes, performing a more deeper dive to ensure scope has been outlined and identified.
Jun 01, 2026 7:09 PM
Funmilola Iyiola
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I quite agree with you. We first need to clarify and establish what AI means in any task or project we are handling to avoid confusion.
Product Operations Program ManagerBarcelona, Cataluña, Spain
Most individuals relate AI to LLM lihe ChatGPT. There are very few individuals who realize that AI is on an "agentization" process, evolving from the current assistant status.
Agentization refers to the process of turning an AI system (such as a LLM) into an autonomous agent that can:
Perceive its environment (through inputs, data, APIs, sensors, etc.)
Make decisions based on goals
Take actions using tools or external systems
Adapt based on feedback or changing conditions
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7 replies by Ahmed Alfahal, Berenice Carmona, Eduard Anubis Hernandez Rincon, Gobikumar B, Kimberley Seals, PIYUSHKUMAR RAVAL, and Paul Waggoner
Mar 27, 2026 9:40 AM
Ahmed Alfahal
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Wel done
Apr 05, 2026 5:46 AM
Gobikumar B
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absouletly correct on agentization is happening in some of crtical industries (service operations)-supply chain area where refer to service request to delivery process are automated through agentization as mention
Apr 05, 2026 1:17 PM
PIYUSHKUMAR RAVAL
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Well said. Also, the biggest challenge in using AI in business environment is how to protect the business information and adhere to the privacy laws. Not only the success rate of choosing AI model but these considerations also play as an important consideration while choosing AI options.
Apr 07, 2026 6:59 PM
Eduard Anubis Hernandez Rincon
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Totalmente de acuerdo. La Inteligencia artificial debe ser vista como algo mas allá de los prompt y los grandes modelos de lenguaje (LLM). Excelente punto de vista Eduard.
Apr 16, 2026 3:50 PM
Kimberley Seals
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I agree with this assessment of agentic ai as well. Utilizing ai supports the need for teams to work smarter, increasing productivity as well as creativity. Ai is not a cure-all, however, a human-first approach should improve ai output.
Apr 19, 2026 7:10 PM
Berenice Carmona
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totalmente de acuerdo contigo , creo que el concepto de agentizacion describe totalmente lo que podemos de manera preliminar preguntarnos,, mi agente de IA puede gestionar esta tarea? y a partir de ahi, estabecer las directrices de ue debemos hacer nosotros para que la IA lo logre
May 22, 2026 12:29 PM
Paul Waggoner
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Thanks for your advice. Your helping me gain a better understanding of how to approach the use of AI as a business solution.
One of the biggest signals for distinguishing different types of AI work is the expected outcome—whether the goal is automation, prediction, or content generation. For example, if the focus is on insights and forecasting, it’s likely predictive AI; if it’s about creating text, images, or code, it points to generative AI. What often goes wrong is when everything gets labeled simply as “AI” without clarifying the use case. This can lead to unrealistic expectations, poor tool selection, and misalignment with business objectives. I’ve definitely been in conversations where “AI” meant different things to different stakeholders. Usually, I notice it when requirements are vague—like “we should use AI to improve efficiency” without defining how. That’s when I step in to ask clarifying questions about the problem we’re trying to solve, the data available, and the desired outcomes. In my experience, the key is to shift the conversation from “using AI” to “solving a specific business problem with the right AI approach.”
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6 replies by Brenda Hanson, Carmela Villuga, Dinarte Bairos, Pritesh Kumar Srivastava, Sahara Ibarra, and Shivaramu Banasamudra Veeregowda
Apr 07, 2026 3:50 AM
Shivaramu Banasamudra Veeregowda
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Yes, we need to shift the conversation to "solve a business problem with right AI approach". I agree to this submission.
Apr 09, 2026 12:23 PM
Brenda Hanson
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I agree, the focus needs to be on the problem, not the solution. This is a problem we've been having in projects since ... forever! We have a new, powerful tool, AI, and everyone wants to jump on the bandwagon because it's money saved, right? But the focus needs to be on the problem, and how AI will solve it.
Apr 11, 2026 9:55 PM
Sahara Ibarra
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Kumar, I've been in a similar situation. It is crucial to be up to date not only with the latest information, but aware of how AI is evolving. This will help tailor conversations and provide direction to the team.
May 07, 2026 5:51 AM
Pritesh Kumar Srivastava
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What outcome need to get will define which AI we should use
May 12, 2026 7:12 PM
Carmela Villuga
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Thanks for your post. This is exactly what I am experiencing in my collaborations. What is lacking is the intention. What specific outputs do they want from AI is the critical information we need to pull.
May 30, 2026 7:30 AM
Dinarte Bairos
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Should we being multiple AI models at the same time. Models may quantify risk significantly different.
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Omar JabbarProject Management and Digital Transformation Consultant| OGreen IT Service Inc.Ontario, Canada
I’ve been asked this many times, and my first response is always: what do you want to achieve with AI? Once the outcome is clear, we can define the right approach, tools, and path forward.
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13 replies by Angel Feliberty, Emily Ngamau, Funmilola Iyiola, Gonzalo Valenti, Htar Htar Ei, Janhavi Chavan, Judith Nyabuto, Mahmood Chauhan, Parin Ratansi, Qi Chen, REBECCA OWUSUA, Sourabh Malviya, and Virginia Kern
Apr 02, 2026 4:36 AM
Judith Nyabuto
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I completely agree with this. There needs to be clarity on what AI needs to do or what problem a business is trying to solve using AI!
Apr 06, 2026 12:31 AM
Janhavi Chavan
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Also, to add, Do we see this as a cost saving initiative or a growth investment - how does that influence your budget?
Apr 11, 2026 9:51 AM
Qi Chen
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There’s no doubt that AI has powerful data processing capabilities and can help us save a significant amount of time on data analysis.
Apr 11, 2026 1:43 PM
Angel Feliberty
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Define first the issue and what it wants to achieve, without it the group/department or organization will be on a path to fail on the AI project.
Apr 16, 2026 8:37 AM
Emily Ngamau
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Completely agree, the need to understand the need for the AI, often brings insights to what AI means to people. It often comes up as a solution to simplify a repetitive annoying task/process. Many a times the need for AI comes after broken/stalled processes block agility and scale. Forcing the organization to manually solve urgent issues while simultaneously trying to include AI.
Apr 17, 2026 4:28 PM
Virginia Kern
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agree!!
Apr 27, 2026 10:22 AM
Mahmood Chauhan
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Completely agree
May 03, 2026 6:57 AM
Sourabh Malviya
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Completely agree, one should not depend solely upon the outcome generated by AI, but one should be aware and can able to judge the AI generated outcome, based on the work requirement. Validation, and performance achievement in AI is always linked with the Model training and utilization of GPUs. It will impact the time duration of the result generation as well. So. one should optimize the interaction with AI tools in every aspects. Especially for Automation.
May 09, 2026 12:06 PM
Gonzalo Valenti
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Sure, the fundamentals of business is the strategy and the client. If we dont know what the client wants, any IA projects becones only a technology projecr without value generation.
May 26, 2026 6:59 AM
Htar Htar Ei
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I completely agree. AI is just a tool — the real starting point is understanding the goal. Once the desired outcome is clear, it becomes much easier to identify the right approach, choose the appropriate tools, and define a practical path forward.
Jun 01, 2026 7:11 PM
Funmilola Iyiola
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Yes, I agreed with you on this.
Jun 02, 2026 1:13 PM
Parin Ratansi
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Totally agree on getting clarification on the outcomes expected
Jun 03, 2026 9:49 AM
REBECCA OWUSUA
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Totally agree. The goal is the major determinant
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Ibrahim BushraProject Manager| Al Sahraa Holding GroupAbu Dhabi, AZ, United Arab Emirates
I had came cross of a lot's of people who have concern about Ai in a way of imagining in the skaynet arrival and the other kind of people who are ambitious about using it . Personally i get garbed by topic discussions that include Ai. It represent to me the vast amount of technology and intelligence that available right now in the world and how it's continues growing , once the discussion is over immediately i will start using that Ai tools mentioned in the topic after evaluating it objective and i will start to understand about it and figuring out a way how i can harness this intelligence and use it to make me more efficient . THOSE WHO STAND AGAINST THE ADVANCE WILL STUCK IN THE PAST. AND THE HISTORY ALAWYSE PROVE THAT TECHNOLOGY REVELOTION ALAWYES WIN.
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1 reply by Karen Armstrong
May 12, 2026 3:42 PM
Karen Armstrong
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Totally agree. I’m just starting to learn how AI can help me. I’ve used it for simple tasks so far like summarizing complex material. Looking forward to learning more!
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Douglas BoydQuantity Surveyor| CTP Consulting EngineersLiverpool, United Kingdom
It is recognised that AI can assist, but we need to obtain clarity as to what AI system is to be used as there are many.
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3 replies by Carletta Calahan, Oscar Migani, and Pouviraj Shamboo
Apr 18, 2026 3:48 PM
Oscar Migani
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it's true that AI are so many, as user we must know which one gives expected outcome, unless otherwise it will be considered as AI does not add value.
Apr 29, 2026 1:59 AM
Pouviraj Shamboo
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Indeed, value added and risks minimization are great benefits for an organization. However, we will need to define the problem well.
May 30, 2026 9:54 PM
Carletta Calahan
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Initial Planning should includes several clarifying questions, including which tools should be used.
When someone says, “We should use AI,” they’re not giving you a requirement; they’re giving you a signal. From a PMI perspective, your role is to translate that into value by first asking what problem we’re actually trying to solve.. If the outcome isn’t clear, the solution shouldn’t be either. From there, identify the real need (automation, augmentation, insights, or user interaction), validate whether the necessary data actually exists and is usable, and define success in measurable terms. Only after assessing feasibility, technical, organizational, and governance constraints, should scope be defined. And in some cases, the right answer is not to use AI at all.
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10 replies by Anthony Scarpace, Caroline Njura Wambui, Dephney Mabuela, Gerben Duijster, Itumeleng Mokgotsi, Paul Waggoner, Paulo Crisóstomo, Pranjal Adurkar, and anonymous
Mar 25, 2026 11:29 AM
anonymous
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I agree with you, many times people are just pressured to use AI, but it is necessary to get the requirements clear first.
Mar 30, 2026 9:01 AM
Gerben Duijster
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We have seen this happen many times, not just for AI, also for (chat)bots, based on no experience and without a problem they are trying to solve. Some companies want bots and AI because every other company is using it and we cannot "stay behind". Eventually the actual problem is not solved by implementing AI or bots because the requirements for this kind of solution cannot be met with the existing data.
Vendors often show you great demos that are set in a controlled environment, using perfectly formatted and clean data. In real live situations this demo would never have worked.
Apr 02, 2026 2:30 PM
Paulo Crisóstomo
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Absolutely. “We should use AI” is a directional impulse, not a requirement. It’s no different from someone saying “We need automation” or “We need a dashboard.” It signals ambition, not clarity. The real work begins when we translate that impulse into something operationally meaningful:
What outcome are we trying to improve—speed, accuracy, cost, experience, or decision‑making
What process is actually breaking or underperforming
What data exists, what condition it’s in, and whether it can support the ambition
What constraints—technical, organizational, ethical, or governance—shape the solution space
Only after that discovery can we define whether AI is the right tool, one of the tools, or not needed at all. In practice, the most responsible AI decisions come from teams that are willing to say “AI isn’t the answer here” just as confidently as they say “AI can help.”
Apr 16, 2026 2:00 PM
Pranjal Adurkar
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I agree with this statement wholeheartedly. Over dependence on AI is possible when we underestimate human potential and overestimate the use of tools.
Apr 25, 2026 11:56 PM
Itumeleng Mokgotsi
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That's correct
Apr 26, 2026 11:59 PM
Paul Waggoner
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Very good points, AI is a very powerful tool, but not required for every project. This is where the sutdy of AI and understanding its application comes in handy.
Apr 27, 2026 4:20 AM
Caroline Njura Wambui
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I agree, problem first, fit second, technology third.
May 04, 2026 10:58 AM
Anthony Scarpace
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agreed
May 24, 2026 9:14 AM
anonymous
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Agree
May 26, 2026 4:28 AM
Dephney Mabuela
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Very profound, sometimes the use of AI can be misinterpreted as a substitution for human intellectual capability, when its aimed at enhancing workflow efficiency when applied appropriately.
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.
This is an impressive analysis to the problem at hand. I definitely agree to this submission. Saving Changes...
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