We can live without Generative AI and it has not always been here as several posters have asserted. Sensor code was limited, did not proliferate itself or create new functionality on the fly until recently. Software did not have destructive hallucinations or generate torture videos human trainers have to review and correct for accuracy, but that's happening now in an ethics-free cowboy marketplace. I am disgusted the AI conversations in boardrooms take no harm into account. No permanent harm to aquifers, the air and noise pollution, the vibration, the lost property value to homeowners nearby, and now whole cities like Tahoe have lost residential power supply to satisfy a data center. Who will they sell to for fair market value now? The health toll caused by living too close is permanent. The unreversible harm done to natural preserves, plant and animal species shouldn't be dismissed like it's an 1800s coal mine operation. The complete absence of ethics, oversight, and control in the name of "not stifling growth" is unethical. It's disingenuous not to factor the harm into the equation. It is frightening to think these systems are now woven into law enforcement and defense systems with very high false positives for matching and that school children are being taught to prompt engineer around topics instead of learning actual topic material. Insurance coverage is managed by AI and declining covered healthcare to real human beings. There's nothing to be calm about here right now and no benefit greater than the harm being done. The bad clearly outweighs the good and it's not under professional control when OpenAI/Chat GPT can get away with not correcting code after 7+ families in 2025 won litigation because it talked their loved one into ceasing their life functions under false pretense of having human mental health experts in the chat. We are not doing anything good with this technology, and it is doing nothing good for us right now. Saving Changes...
I agree with Douglas Boyd, how do we know which AI system to use?
The same way you determine which new application to build or buy: requirements, requirements, requirements. Business process analysis and optimization. Fit-gap analysis. The core processes and solid business analysis will never let you down regardless of what technology happens to be doing at the moment. AI is not the answer to many problems. Saving Changes...
To echo what others have said, when my teammates have talked about using AI, it really only seems to mean using ChatGPT to look something up. That’s all well and good, but making full use of AI is a bit more comprehensive than that. It likely also doesn’t help that only a handful of my team members have received training on putting together prompts (and let’s just say they’re the folks who are least likely to use AI in the first place).
ChatGPT is the world's most expensive search engine that has a steep environmental and quality of life cost, and it was never meant to be a little lookup tool. Frankly, I can get the work done in less time than engineering a prompt because I have put in the effort to become a professional the hard way - reading, writing, being mentored, and doing for 30 years. I do not need to hand my thinking and cognitive processes over to an overgrown Google. GenAI's value is in large dataset analysis. If you don't have very large data to analyze for specific outputs, it's a very, very expensive solution looking for a problem. Saving Changes...
The biggest signal is whether people mean strategy, automation, analytics, generative AI, or ML operations — because each needs a different problem statement, data shape, and success metric. What goes wrong is treating ‘use AI’ like a single solution, which leads to mismatched expectations, bad vendors, wasted budget, and disappointment when the tool solves the wrong problem. I usually ask: what decision or workflow are we trying to improve, what data do we have, and what does ‘success’ look like?
And what will the organization do to offset the environmental harm and quality of life impact to the people living by a huge polluting data center? What budgetary constraints are there on future litigations because of using GenAI and the definitely harmful infrastructure it requires? What will the organization's insurance company think about entering such high-risk solution space? IT has got to step up and do better vs. being enamored with a new and very harmful animal in the zoo. The security considerations are not trivial. Saving Changes...
What’s really being asked when someone says, “We should use AI!”? What’s really being asked is: We have a business problem and want a smarter automation, faster decisions, or greater efficiency. Saving Changes...
Anonymous
Agree Saving Changes...
Anonymous
Mar 25, 2026 10:02 PM
Replying to Sibaliwe Pali
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Distinguishing AI types involves identifying signals like memory retention (reactive vs. limited memory), architectural complexity, and whether it operates as a "tool" (isolated task) or an "operator" (embedded in workflows). Misclassifying these—or lumping them together—causes AI to underperform, becoming expensive, unreliable, or hallucination-prone when simple reactive tools are tasked with complex reasoning
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.
PMI Team Member
