AI Reality Check: A Field Guide to Actually Getting Stuff Done
Or: How I learned to stop worrying about the hype and love the boring wins
Picture this: You're sitting in yet another meeting where someone breathlessly declares that "AI is going to change everything!" while you're quietly thinking, "Great, but can it help me not spend three hours categorizing these vendor names in Excel?"
Well, friend, I have good news. While everyone else is arguing about whether we're heading for an AI winter or summer, the actual builders have been quietly shipping some genuinely useful stuff. And the best part? Most of it's designed to work where you already live – no PhD required.
The Four Dials That Actually Matter (And Why You Should Care)
Here's the thing about AI progress – it's not just about making models "smarter." It's about making the whole system more... well, systematic. Think of it like upgrading a car: sure, a bigger engine is nice, but what really changes your daily drive is better steering, brakes that actually work, and a fuel gauge you can trust.
The Capability Dial: Remember when AI agents were basically digital toddlers with amnesia? Well, they're growing up. They now come with proper instruction manuals (literally – there's something called AGENTS.md that's like a README file for AI workers), and they can actually remember things between conversations. It's like finally getting an intern who takes notes.
The Speed Dial: Here's where things get interesting. NVIDIA just showed off models pumping out over 800 tokens per second. That might sound like techno-babble, but here's why it matters: speed changes everything about how you interact with AI. When responses come fast enough, you start using it differently – more like a conversation, less like sending a letter and waiting for a reply.
The Cost Dial: Google figured out that a typical AI query uses about as much power as running your microwave for a few seconds. That's... actually not bad? The point is, we're moving from "this costs a fortune" to "this costs about as much as a really fancy coffee."
The Control Dial: This is the grown-up stuff. Companies are finally building proper safety switches, spending limits, and admin controls. It's like the difference between a teenager with a credit card and an adult with a budget app.
Six Real-World Scenarios (That Don't Require a Computer Science Degree)
Scenario 1: Excel Gets Superpowers
Remember when =SUM() felt revolutionary? Well, Excel now has =COPILOT() as an actual function. You can literally type natural language into a cell and get structured answers that automatically update when your data changes.
The smart play: Pick three tedious tasks you already do in spreadsheets – maybe categorizing support tickets, summarizing meeting notes, or cleaning up messy vendor names. Wrap them in =COPILOT() formulas and add some basic validation. If it takes more than 5 minutes or 3 manual steps, there's probably a formula for that.
Pro tip: Create a hidden sheet to log what goes in and what comes out. When your error rate drops below 2%, you know you've got something worth keeping.
Resources:
Microsoft Insider announcement —
=COPILOT()in Excelhttps://techcommunity.microsoft.com/blog/microsoft365insiderblog/bring-ai-to-your-formulas-with-the-copilot-function-in-excel/4443487How‑to — Get started with Copilot in Excel
https://support.microsoft.com/en-us/office/get-started-with-copilot-in-excel-d7110502-0334-4b4f-a175-a73abdfc118aOverview — Microsoft 365 Copilot docs hub
https://learn.microsoft.com/en-us/copilot/microsoft-365/Extending Excel with agents — API plugins (OpenAPI‑based)
https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/overview-api-plugins
Scenario 2: Stop Vibing, Start Specifying
Here's where things get beautifully nerdy. Someone created AGENTS.md – basically a job description for AI workers. It includes what they can do, what tools they have access to, how to test their work, and what to do when things go sideways.
The smart play: Write down exactly what you want your AI helper to do. Not in mystical terms like "be creative" or "think outside the box," but in specific, measurable ways. Think of it like writing a really good job posting – the clearer you are upfront, the better results you get.
Resources:
OpenAI’s
AGENTS.mdformat (repo + template)https://github.com/openai/agents.mdModel Context Protocol (MCP) — spec
https://modelcontextprotocol.io/specification/2025-06-18OpenAPI Specification 3.1
https://swagger.io/specification/SwaggerMicrosoft “Agent Factory” — toolchains, governance, identity
https://azure.microsoft.com/en-us/blog/agent-factory-building-your-first-ai-agent-with-the-tools-to-deliver-real-world-outcomes/
Scenario 3: Depth Becomes a Feature, Not an Accident
New models are coming with something called "reasoning budgets" – basically a dial that lets you choose between quick-and-dirty answers or slow-and-thorough analysis. It's like having an intern who can either give you a five-minute summary or a full research report, depending on what you actually need.
The smart play: Map your reasoning budget to your actual needs. Email classification? Quick and cheap. Legal document review? Slow and careful. And always build in graceful failures – "We hit the thinking limit, want to go deeper?"
Resources:
Command A Reasoning — product note (reasoning budget & control)
https://cohere.com/blog/command-a-reasoningCommand A — model docs (context, endpoints, fit for agents)
https://docs.cohere.com/docs/command-aTechnical report (training/setup for tool‑use & reasoning)
https://cohere.com/research/papers/command-a-technical-report.pdfBedrock integration — parameters & streaming (ops reality)
https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-cohere-command.htmlAnalogy — Google “thinkingBudget” knob in Gemini API
https://ai.google.dev/gemini-api/docs/thinkingGoogle AI for
Scenario 4: Memory That Actually Works
AI systems can now remember things across sessions without turning into digital hoarders. They separate short-term "what happened in this conversation" memory from long-term "what I know about you" memory, complete with expiration dates and user controls.
The smart play: Give users a "what the AI remembers about me" dashboard where they can see, edit, and delete their stored information. If a memory influences a decision that costs real money, make the AI show its work.
Resources:
MongoDB × LangGraph — new long‑term memory store (launch)
https://www.mongodb.com/company/blog/product-release-announcements/powering-long-term-memory-for-agents-langgraphLangGraph concepts — short‑ vs long‑term memory
https://langchain-ai.github.io/langgraph/concepts/memory/How‑to — add memory & MongoDB checkpointer
https://langchain-ai.github.io/langgraph/how-tos/memory/add-memory/Atlas docs — integrate MongoDB with LangGraph
https://www.mongodb.com/docs/atlas/ai-integrations/langgraph/
Scenario 5: Debugging Gets Real
Microsoft added AI debugging tools that actually work inside your regular development environment. Instead of mysterious black boxes, you get performance insights, allocation tracking, and actual useful alerts when things go wrong.
The smart play: Treat AI prompts like any other code – tag them, time them, trace them. Set up alerts for token spikes, tool failures, and users hitting the abort button. Most importantly, track "time to first response" because that's what users actually feel.
Resources:
GitHub Copilot Diagnostics for .NET (Auto‑Insights, allocation)
https://devblogs.microsoft.com/dotnet/github-copilot-diagnostics-toolset-for-dotnet-in-visual-studio/Visual Studio profiling toolbox — feature tour
https://github.com/MicrosoftDocs/visualstudio-docs/blob/main/docs/profiling/profiling-feature-tour.mdOpenTelemetry for Gen‑AI — semantic conventions (agents, LLMs)
https://opentelemetry.io/docs/specs/semconv/gen-ai/Anthropic — Claude Code + new admin controls (seats, spend caps)
https://www.anthropic.com/news/claude-code-on-team-and-enterpriseAnthropic support — extra usage & spend limits
https://support.anthropic.com/en/articles/12005970-extra-usage-for-claude-for-work-team-and-enterprise-plans
Scenario 6: The New Triangle: Fast, Cheap, and Compliant
The industry is simultaneously getting faster (800+ tokens per second), cheaper (competitive pricing in multiple markets), and more regulated (child safety probes, enterprise controls, academic oversight). This isn't a contradiction – it's maturation.
The smart play: Design your interface for speed first. Stream responses, show your thinking, let users interrupt. Price based on perceived speed tiers because fast feels premium. And build in proper escalation paths – when things go wrong, users should know why and what happens next.
Resources:
NVIDIA developer blog — GPT‑OSS perf (Blackwell/GB200; 1.5M TPS)
https://developer.nvidia.com/blog/delivering-1-5-m-tps-inference-on-nvidia-gb200-nvl72-nvidia-accelerates-openai-gpt-oss-models-from-cloud-to-edge/OpenAI — introducing GPT‑OSS (20B/120B; deployment notes)
https://openai.com/index/introducing-gpt-oss/OpenAIOpenAI Help — ChatGPT Go (₹399/mo India; what’s included)
https://help.openai.com/en/articles/11989085-what-is-chatgpt-goAnthropic — Claude can end a rare subset of conversations (policy UX)
https://www.anthropic.com/research/end-subset-conversationsElevenLabs Agents — Chat Mode (text‑only, escalate to voice later)
https://elevenlabs.io/docs/conversational-ai/guides/chat-modeAnthropic — Higher‑Education Advisory Board & free AI Fluency courses
https://www.anthropic.com/news/anthropic-higher-education-initiatives
The Big Myth (And Why It's Wrong)
"Most AI projects fail, so we should wait."
Look, MIT published a report saying 95% of AI pilots don't deliver financial returns. The community is debating the numbers, but let's assume they're right. The problem isn't that AI doesn't work – it's that most organizations are terrible at integrating new tools.
The successful projects? They start where existing systems already have a "submit" button. They work with current workflows, not against them. They solve boring problems that actually cost time and money.
Your org chart is the bottleneck, not the model.
The New Moats (Or: How to Build Something Worth Keeping)
The Physical Gates: Own your speed. Colocated inference, reserved capacity, smart caching – these make you feel faster and convert better than a slightly smarter model.
The Paper Gates: Proper specifications, tool contracts, audit trails. The boring documentation that makes you impossible to rip-and-replace.
The Human Gates: Real people with real authority to stop things when they go wrong. Not just policies in a binder, but actual humans with actual buttons.
Your 90-Day Reality Check
Weeks 1-2: Pick two workflows already living in Excel. Ship
=COPILOT()versions with proper QA. Write your first AGENTS.md file for something simple.Weeks 3-6: Set up proper memory systems with user controls and expiration dates. Make your tools explicit and logged.
Weeks 7-10: Instrument everything. Time-to-first-response, usage rates, failure modes. Add reasoning budget controls to one feature.
Weeks 11-13: Build real safety rails with human escalation paths. Prepare your "prove it" documentation for when regulators come calling.
The Million-Dollar Question
If someone with authority – a regulator, a customer, a board member – walked in tomorrow and said "prove this system is safe, fair, and auditable," would it keep running with the logs, limits, and documentation you have today?
If the answer is no, your growth is technical debt waiting to come due.
The Turn
Here's what I've learned from watching this space: The "AI winter is coming" crowd is missing the point. The real story isn't about hype cycles or market corrections. It's about steady, methodical progress on the boring infrastructure that makes advanced tools actually useful.
Excel formulas. Agent specifications. Memory management. Safety controls. Audit trails.
This isn't frantic innovation – it's engineering. It's the difference between a prototype that impresses investors and a system that helps real people get real work done.
The pace isn't slowing down because the hype is cooling off. It's speeding up because the foundation is finally solid enough to build on.
So stop waiting for permission, stop debating winter vs. summer, and start shipping. Pick the most boring, repetitive task in your workflow and make it 20% easier. Then do it again.
The future isn't coming – it's here, wearing business casual and quietly making spreadsheets less terrible.
Now go make something useful.
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