It’s the first week of 2026. Like every other leader right now, you’re probably looking at your dashboard and asking: “Am I behind on AI? How do I catch up?”
Here’s the truth: nobody is really “ahead” yet. We aren’t even in the second iteration of this tech (generative AI). But with over 70% of AI projects failing to deliver an ROI, the real question isn’t how fast you can move; it’s where you’re placing your bets.
Most orgs are throwing money at AI tooling based on “hopes and dreams.” Others are investing just to say they did, knowing they’ll get zero results but hoping for a “learning experience.” Then there’s the majority: sitting on the sidelines, waiting to see if the dust settles, or worse, stuck in analysis paralysis.
Waiting is a mistake. AI isn’t going away. Whether it delivers AGI or just becomes a more efficient power tool next to us doesn’t matter. What matters is that you stop treating it like a novelty toy and start treating it like a business fundamental.
The “Kindling” Behind the Fire
We have a massive problem in tech culture: we ship MVPs (Minimum Viable Products) or POCs (Proof of Concepts), declare them a success, and then never go back to harden them. We “operationalize” them without actually operationalizing them.
AI is about to perpetuate that at an exponential pace. We’re developing “AI ADHD,” hopping from one fire to the next because the tech makes it easy to bang out a solution. But look behind you: there’s a massive pile of kindling in the form of technical debt, disconnected platforms, and processes that just don’t jive.
“If you don’t iterate based on real feedback, you’re not building an MVP; you’ve built your final product. Sometimes we need to be honest about that rather than hope we’ll return to harden our first attempt.”
The winners in 2026 won’t be the ones with the most agents; they’ll be the ones who realize that a POC is just a hypothesis. It’s a first attempt. In the scientific method, you have a control and variables. Your MVP is designed to test that hypothesis and refine your build. If you don’t iterate, you’ve just shipped your legacy code on day one.
Back to the Basics: The Magical Triangle
Regardless of your AI roadmap, you need to look at the “magical triangle” of People, Process, and Technology. The reality is that the more things change, the more they stay the same. High impact vs low effort, stakeholder alignment; these rules still apply. We’ve just spent years pushing them aside because it’s easier to buy a tool than to do the “grunt work” of talking to humans and building alignment.
1. Technology: Stop the Bloat
Before you add an “AI layer,” look at your underlying stack. Most orgs view tech through a cost-cutting lens; removing seats or making blanket calls like, “I don’t know why Marketing uses that tool instead of ours.” Stop guessing. Ask why they use it. Where’s the overlap?
Follow the 80/20 rule: If you’re paying for a platform but only using 10% of the features, and it’s not delivering outsized value, cut the footprint now so your AI doesn’t have to navigate a digital junkyard later.
2. Process: Capturing “Unwritten Logic”
This is the biggest lever you have. You need to document your critical workflows in detail. I’ve sat in rooms where we locked Operations, Product, and Engineering together and had Ops walk through their actual day-to-day.
I watched engineers who swore, “That’s not how it works,” eat their hats in real time. They had no idea how much time was wasted on manual workarounds. Instantly, they came up with solutions that reprioritized the entire roadmap.
When you do these process interviews, two things happen:
You realize half the bottlenecks don’t even need an “agent.” They just need a better workflow.
You build the documentation that becomes the “brain” for your future agents.
Our people use “unwritten logic,” tribal knowledge, quick Slacks, and those “gut moments” that agents don’t know how to do unless we program them in. Without this documentation, agentic implementations fail every time because the agent expects one thing, while the day-to-day reality is something else entirely.
3. People: Expanding the Pie
We need to move away from “AI replaces people” and toward “AI empowers people.” You have to give your team a safe space to learn.
Don’t just turn ChatGPT or Gemini on for every employee and get frustrated when they don’t use it. We’ve seen this movie before with Salesforce, email, and Excel; we train them once and never talk about it again.
“Expand the pie with AI. Increase revenue and decrease non-personnel expenses; instead of just trying to cut the headcount to drive a bottom line.”
The reality? Most employees today still struggle with right-clicking a mouse or writing an Excel formula. You cannot expect them to navigate complex, language-specific AI software without guidance. Share best practices. Leverage their skills.
Use AI to Get Ready for AI
You don’t have to reinvent the wheel. Use the tools you have to prepare for the future tooling you want.
Start recording your process interviews on Zoom or Google Meet. Use Gemini to transform those transcriptions into actual documentation. That’s a proper way to use AI today to be ready for the agents of tomorrow.
We shouldn’t insist that our employees learn on their own or after hours. We should instead carve out time in our day-to-day to assist, to drive conversations so that folks know they’re not alone in their journey, to share best practices, and to drive innovation.
The Bottom Line
Don’t try to keep up with the Joneses. Be your company. Remember the secret sauce that got you here. Don’t pull resources from the “fires” that are actually working just to chase hype driven by FUD or FOMO.
“The code was never the hard part. The hard part is education, understanding, alignment, and prioritization. AI can accelerate what we do, but it can’t fix a broken business model.”
Do the grunt work. Harden your prototypes. And for everyone’s sake, stop rushing.
Let’s Move the Needle
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