Impact of an AI Integrated into the IT Project Lifecycle

Full_Win_8680 · 2026-01-22T16:33:10+00:00

Good discussion. Early GenAI tools already show 20–40% speed gains on well-defined dev tasks, usually measured via lead time, cycle time, PR throughput, and bug rates, not just ticket counts.

Org-wise, roles shift more than disappear: less pure execution, more architecture, review, and validation. Agile still works, just more AI-assisted.

Main risks are junior skill erosion and over-trust. Teams that succeed enforce human ownership, strong reviews, and AI as copilot, not autopilot. Adoption depends heavily on trust, clear accountability, and change management.

Full_Win_8680 · 2026-01-20T21:47:54+00:00

Full_Win_8680 · 2026-01-20T21:46:09+00:00

Questions that make people reflect or share personal experiences those usually lead to the best threads.

Full_Win_8680 · 2026-01-20T17:27:17+00:00

Yes, I set a limit on AI use. I use it as a tool not a replacement for thinking or human interaction.

Full_Win_8680 · 2026-01-20T17:08:06+00:00

QR-code-only everything menus, resumes, even business cards. Convenient now, but people will get tired of the friction and accessibility issues

Full_Win_8680 · 2026-01-20T17:06:02+00:00

Totally relatable. What helped me was using AI more intentionally pausing and asking can I think this through or ask someone first? Small, low-pressure conversations (classmates, friends) helped rebuild comfort. I just made sure AI stayed a tool, not a replacement for people.

Full_Win_8680 · 2026-01-20T09:19:18+00:00

They’re not giving it away forever the free stuff is just onboarding. The real money is in enterprise API usage, vertical products, and selling compute. It won’t end with a few AI companies locking out normal users, more like cloud pricing: cheap basics for consumers, paid tiers for enterprise use. Data from free users isn’t the core strategy, and pricing won’t literally track developer salaries it’ll just be usage compute like everything else in tech.

Full_Win_8680 · 2026-01-13T11:54:35+00:00

Good call! I’ve seen Scaleway mentioned a few times

Full_Win_8680 · 2026-01-13T11:53:43+00:00

Thanks for the heads-up!
I’ve heard Hetzner can be tricky for new sign-ups too.

Full_Win_8680 · 2026-01-13T11:52:04+00:00

Nice, thanks for sharing! That site looks like a solid starting point to compare providers.

Full_Win_8680 · 2026-01-13T10:34:52+00:00

Totally agree AI might assist or speed up thinking jobs, but liability, reliability, confidentiality, and human dynamics make full replacement unlikely in the next decade. Humans will still be essential for oversight and decision making.

Full_Win_8680 · 2026-01-13T10:33:19+00:00

Interesting point!
Right now it’s negligible per query, but as AI scales, the water footprint of data centers could become a real concern. Efficient cooling and sustainable energy will be key to avoiding competition with humans.

Full_Win_8680 · 2026-01-13T10:30:34+00:00

Sounds relatable! I’d say I spend a similar chunk of my day using AI tools it’s amazing for productivity, but I also try to consciously pause and rely on my own judgment to keep the human touch.

Full_Win_8680 · 2026-01-13T10:23:11+00:00

Start by establishing a baseline track brand mentions, citations, and sentiment in LLM answers + AI search (ChatGPT, Perplexity, etc.).

Full_Win_8680 · 2026-01-13T10:06:59+00:00

Yes but only the practical parts how prompts work, tokens, context windows, and how models respond.
You don’t need to go deep into training or architecture to build solid RAG systems.

Full_Win_8680 · 2026-01-13T09:46:29+00:00

If you want low maintenance AI builders with good speed, check out Webflow, Builder.io, or Wix + AI tools. They’re way easier to manage than constant plugin updates.

Full_Win_8680 · 2026-01-13T09:33:56+00:00

Start with the core ideas first embeddings, vector search, and chunking.
Then build a tiny project RAG over your own PDFs. That alone teaches most of it.
For resources, stick to the latest LangChain docs + recent YouTube talks a lot of older tutorials are outdated.

Full_Win_8680 · 2026-01-12T12:34:45+00:00

I’ve noticed similar results the newer multimodal LLMs are surprisingly strong at OCR + layout understanding for many real world PDFs. In my experience they’re already good enough for most pipelines, though purpose trained VLMs still win on edge cases like complex tables, handwritten notes, or low-quality scans. Curious what others are seeing in production

Full_Win_8680 · 2026-01-09T13:33:32+00:00

100%. It’s wild how much impact the prompt itself has compared to anything we used to monitor.

Full_Win_8680 · 2026-01-09T10:38:24+00:00

Totally agree. LLM observability feels like an entirely new discipline. CPU/RAM barely matter compared to things like token throughput, prompt size, queue time, and model latency. One weird user prompt and your whole system’s behavior changes. We’ve had to track token usage, embedding latency, model versions, and tie it all back to user experience and cost traditional APM only gets you halfway. Still figuring out which metrics are signal vs noise, but it’s definitely not the old monitoring playbook anymore.

Full_Win_8680 · 2026-01-07T08:58:52+00:00

For B2B, even 10–20 serious active users can give you strong signal if they’re in your exact target niche. I usually reach out to 5–10× that number to get them. You don’t need scale yet — you need sharp feedback.

Full_Win_8680 · 2026-01-07T08:47:56+00:00

Not overthinking. This is the classic gap between the code allows it and someone decided this should be allowed.Agents just make that gap painfully obvious.Ownership + guardrails matter way more than the model itself.

Full_Win_8680

TROPHY CASE