Stop Overthinking Your AI Roadmap (Just Ship Something Already)

the problem: Your org doesn’t need a 10,000-step AI overhaul in Q1—it needs a roadmap + people to execute it. And after conducting hundreds of AI transformations, we’ve perfected how to run them inside literally every size company (from Series A startups to $500M giants).

So we wrote the AI Roadmap-to-Execution Playbook.

Simply put, first, you need to get executive buy-in (AKA finding the bulldozer who’s willing to unblock everything in your path). Then you mine for problems by sending out a survey and meeting with execs across the company. After that, you synthesize all your findings. And only then can you build a tailored roadmap that doesn’t fall apart the moment someone asks, “So… what do we actually do first?”

But... that’s when choice paralysis shows up and says, “Pump the brakes.” That’s why your Q1 strategy won’t kick in until January 2036.

the solution: Instead, follow our proven execution playbook to get started.

1. pick one roadmap item: Find a process in your org where a little LLM-powered thinking can go a long way in removing friction. Here’s how:

Every business workflow consists of two types of steps. Some are predictable and rule-based: if X happens, do Y (automations shine here).

Other steps rely on judgment: interpreting context, choosing between options, or drafting something new from scratch. This is where humans shine, but slow everything down (cough, cough… a great place to put AI).

2. atomize it into microsteps: Break your chosen workflow into 5–15 of those individual steps. Write them in a way that reflects how your org works today. Don’t cut corners—if a step can’t be described as one clear action, it’s still too big. Shrink it.

Then, look at your list, circle one step that is both the most repetitive and the most time-consuming. That’s the ugly duckling + where AI goes first.

3. build or buy: Okay, so you found your problem. How do you solve it? Well, the cost of building software is plummeting, which makes it very tempting to want to spin up your own AI tool. But the knee-jerk reaction should still be to buy something off the shelf. Here’s why:

4. design gutters: Backstops keep mistakes from compounding. Anytime you add an AI step, follow it up with a deterministic bumper that decides whether its output is good. This is that automation-type step from before. For example:

5. ship, measure, expand: Ship a small test to a small group. Let 3–10 people use it in the real world and see how it does in the messiness of actual work.

You can measure the things that matter, like how many hours the new workflow saves, whether tickets close faster, or whether leads truck along more reliably.

Or you can see if it’s making your team, you, or your clients happier. Those signals tell you everything you need to know. If everything’s peachy, roll it out.

➿ Before analysis paralysis pulls you down under, grab the complete playbook + actually execute this year.

NYU and Emory just tested whether AI can beat humans at ad creative.

the answer: AI apparently clears human-made ads.

The study compared three formats: human-made ads, human-made ads enhanced by genAI, and fully AI-generated ads.

All ads were in the beauty category and were evaluated through a controlled lab experiment plus a real-world Google Ads field study measuring CTR.

Fully genAI ads lifted (click through rates) CTR by 19% over human creative in the field study. GenAI-modified ads performed worse on purchase intent and showed no significant improvement in CTR.

the takeaways: If you’re using AI to produce ads, let it create from scratch. Don’t ask it to edit your existing work. According to the study, lower constraints unlock:

another note: forcing AI to preserve human scaffolding restricted its flexibility and produced visuals that felt less coherent or realistic. The two big watch-outs are:

Twenty-four pages later, the TLDR is that enterprises are using more AI than last year, getting real value from it, but the usage curve is skewed (also, the data comes from a self-reported survey of nearly 100 companies).

1. productivity gains are real: Workers using ChatGPT Enterprise report saving about an hour a day. And 75% say AI now lets them complete new tasks they previously couldn’t do—like coding, spreadsheet automation, debugging, or building small internal tools.

2. adoption is global + cross-industry: In the past 12 months, the median sector grew 6x in the number of business customers. Technology is the fastest-growing sector at 11x, but healthcare (8x) and manufacturing (7x) are right behind it.

3. a performance gap is forming: The workers using AI the most use it 6x more than everyone else.

If you’re a shadow AI user, why? Who hurt you? Reply to this email with the main reason you’re hiding your AI usage at work. We want to know the story.

background: “Agent” might be the most overused, underdefined word in the world right now. For the last three-ish years, it’s been the marketing jargon of choice for Mark Zuckerberg, Sundar Pichai, Jensen Huang, and every AI-slop influencer.

TBH, when I first heard of agents, I thought of J.A.R.V.I.S. from Iron Man or something from Isaac Asimov. That’s not what today’s agents are at all.

There’s an entire gradient that’s been lost to this collective word muddying:

0. traditional automation: You spell out every step. If X happens, do Y. Zero judgment. Think Zapier flows or the built-in rule engines inside tools like Linear, Asana, or HubSpot. We covered this up top.

1. AI-assisted automation: Same rigid steps, but one or two are replaced with an LLM’s reasoning, for example, an AI-generated message summarizes something, or interprets text. The flow itself is still rigid.

2. agentic workflow: This is that magic sweet spot that Tenex wants you to use for your 2026 AI strategy. The system can pause, fetch missing context, and make small decisions inside your structure. It stays on rails, but the AI can pause, grab context, and make a small judgment call.

3. a true agent (goal-seeking system): You give it a goal, not the steps. It decides those, pulls context, chooses tools, acts, evaluates, and loops until done. Still extremely rare in real businesses.

4. agi: A system that understands goals the way humans do and adapts to new situations without being taught or prompted. Still fiction. Not even worth diving into here.

go deeper: As autonomy rises, so do hallucinations. When Anthropic said its Sonnet 4.5 model could “work continuously for 30 hours,” it sounded incredible—like you could architect a plan, go to bed, and wake up to shippable code or your novel written.

Pipe dream.

Why? Long loops wander. One tiny misread early becomes a bigger error on the next turn, then bigger again. A 1% misunderstanding becomes 5%, then 20%, until the agent is confidently solving the wrong problem. You need a leash on the loop.

apply it: Proactivity > no activity. Tomorrow, we’re speed-building a customer-health agent. It reads every Slack thread, call transcript, and client email, compares it to your project status data, and turns the whole mess into a weekly churn-risk and sentiment-shift report—color-coded so you know exactly which fires to put out.