A recent workplace survey showed that over 70% of professionals are now actively using AI tools during daily work. What’s more interesting: Most of them are not developers. They’re marketers, freelancers, creators, business owners, and operators using AI to save time and increase output. If you're trying to understand how people are actually integrating AI into real workflows instead of casually testing tools, this gives a practical direction:👉 https://tryaiprofitboardroom.com The biggest shift happening right now is not “AI replacing jobs.” It’s people with AI workflows outperforming people without them. Curious to know from newer members here: What AI tool are you currently using the most? And what’s the ONE task you wish you could automate completely?

AI tools are everywhere now, but most people usually stick with 1–2 tools they use daily. For me, the biggest value comes from tools that save time and simplify workflows. What’s the AI tool you use the most right now and why? 👇

You know that task where you go back through past client deals, look for patterns, and try to figure out what's actually working and why. It takes hours. Sometimes a full day. This week I let Gemini 3.1 Pro handle it. Because it's connected to my own files and the web at the same time, it spotted patterns across my past deals and matched them against what's happening in the market right now. Things I would have completely missed on my own. That's the difference between spending your Friday guessing what to do next week and actually knowing. If you want to explore building workflows like this into your own work, you can join here: https://www.skool.com/ai-profit-lab-7462/about?ref=fc2bc25e21b64a5382b9d0af171ed85f

What’s still unclear for you right now?

AI feels like it’s entering a new phase where infrastructure matters just as much as the models themselves. A year ago, most conversations were focused on which AI model was the smartest or had the best benchmark scores. Now the bigger differentiators seem to be things like latency, orchestration, context management, reliability, inference costs, developer workflow, and deployment flexibility. Model quality across the industry is improving so fast that having the highest benchmark score no longer automatically means delivering the best real-world experience. More teams are starting to optimize around workload routing, hybrid local and cloud setups, smaller specialized models, faster iteration cycles, and predictable scaling costs. In many ways, AI is beginning to feel less like a pure model competition and more like a systems and infrastructure challenge. Curious if others are seeing the same shift, or if frontier model capability still dominates most decisions in your workflows.