Which AI Features Are Worth Building First, and Which Ones to Skip?
Build the narrow assist your users can check. Skip the autonomous agent, the chat box on every screen, and the pilot with no path to production.
You have a list of AI features. Most teams do. The hard part is not generating ideas, it is picking the one that ships first and cutting the rest without regret.
The evidence is unusually clear about which ones survive contact with real users. Narrow, assistive features that speed up work people already do tend to land. Autonomous, everywhere-at-once, demo-first features tend to get canceled. Here is how we sort them.
Build first: the task your users already do by hand
Find the thing your users repeat every week and quietly resent. Drafting the same reply. Retyping data from one screen into another. Sorting a list they already know how to sort. That is your first AI feature, because the value is measurable the day it ships: the task took twelve minutes, now it takes two.
This also avoids the most common failure mode. Reporting on MIT's NANDA research found that 95% of enterprise generative AI pilots fail to deliver measurable ROI, and the failure was rooted in integration and misaligned priorities rather than model quality. The model is rarely the problem. The workflow you dropped it into usually is.
Build first: the assist, not the decision
People will let AI narrow the field. They will not let it sign off. A Gartner consumer survey found that willingness to let AI make purchase decisions topped out at 11%, while roughly three in ten consumers were open to AI narrowing their choices.
That gap is a product spec. Shortlist, draft, rank, summarize, pre-fill. Then hand the last click to the user. The same feature that gets rejected as a decision gets adopted as a suggestion.
Build first: the AI that runs on your own data, behind the scenes
Not every AI feature needs a cursor blinking in front of a user. Some of the best ones run on a schedule, on data you already own, and show up as finished output.
We build these for clients and for ourselves. Goblyn is our autonomous AI platform that plans, writes, publishes, and optimizes a full brand blog, with multi-tenant architecture, custom domains, subscriptions, and the publishing pipeline behind it. MagicTrips is our AI travel planner, live in production and generating affiliate revenue, built on Google Places integration and programmatic SEO content produced at scale. AI-native builds are our specialty, and running our own in production is how we know what holds up.
Skip: the autonomous agent you cannot afford to babysit
Agents demo beautifully. They are also the category with the worst survival rate. Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, blaming escalating costs, unclear business value, and inadequate risk controls, and its recommendation is to pursue agentic AI only where it delivers clear value or ROI.
So ask the unglamorous questions before you scope one. What does a run cost at your volume? Who is accountable when it acts wrong? What does it do that a well-placed suggestion could not? If the answers are fuzzy, the agent is a version two.
| Build first | Skip for now | |
|---|---|---|
| Who decides | The user, with AI suggesting | The AI, acting on its own |
| Scope | One workflow users already repeat | Every screen, all at once |
| What users accept | Roughly three in ten consumers are open to AI narrowing their choices | Willingness to let AI make the purchase decision tops out at 11% |
| Track record | Small enough to measure, keep, or kill | Over 40% of agentic AI projects predicted canceled by end of 2027 |
| Verdict | Assistive and narrow wins the first release. Autonomous earns its turn later, once the assist proves the value. | |
Skip: the chat box bolted onto every screen
"AI everywhere" is a roadmap that produces a chat icon in nine places and adoption in none. It spreads effort across surfaces where nobody asked for help, and it makes measurement impossible, because no single screen owns the outcome.
The pilots that fail do not fail on model quality, they fail on integration and misaligned priorities. A chat box is the least integrated thing you can ship: it sits next to the workflow instead of inside it. If the answer would be more useful as a pre-filled field, a ranked list, or a draft the user edits, build that instead.
Skip: the demo-driven pilot with no path to production
A pilot built to impress a stakeholder is not a feature, it is a slide. S&P Global Market Intelligence found the share of companies abandoning most of their AI initiatives before production jumped from 17% to 42%, with the average organization scrapping 46% of its proof-of-concept projects.
Before you start, write down what production looks like: who uses it, what it costs to run, what number moves, and what happens when the model is wrong. If you cannot answer those in a paragraph, you are building a demo.
Then scope it like any other feature
AI features are not exempt from normal product discipline. We map projects into fixed milestones with clear pricing, so you know what ships and when before work starts, and we run regular demos until the product is live.
You work directly with Alec from first idea through launch. No account managers, no hand-offs. Strategy, design, and full-stack engineering under one roof, which matters more on AI work than anywhere else, because the model, the interface, and the data pipeline are the same decision.
Assistive lands. Autonomous stalls. Ship the version your user can check.
- Build the narrow assist first: one repeated task, one measurable time saving, output the user verifies before it counts.
- Users accept help, not authority. Gartner found willingness to let AI make purchase decisions topped out at 11%, while about three in ten were open to AI narrowing choices.
- Skip autonomous agents until the value is obvious. Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027 on cost, unclear value, and weak risk controls.
- Skip pilots with no production plan. The share of companies abandoning most AI initiatives before production rose from 17% to 42%.
- Scope AI work in fixed milestones with clear pricing, and demo it until it is live.
- Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, and inadequate risk controls, and recommends pursuing agentic AI only where it delivers clear value or ROI.
- S&P Global Market Intelligence found the share of companies abandoning most AI initiatives before production rose from 17% to 42%, with the average organization scrapping 46% of proof-of-concept projects.
- A Gartner consumer survey found willingness to let AI make purchase decisions topped out at 11%, while roughly three in ten consumers were open to AI narrowing their choices.
- Reporting on MIT's NANDA research found 95% of enterprise generative AI pilots fail to deliver measurable ROI, rooted in integration and misaligned priorities rather than model quality.
Should we build an AI agent for our product?
Only where the value is clear enough to defend on cost. Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027 because of escalating costs, unclear business value, and inadequate risk controls, and recommends pursuing agentic AI only where it delivers clear value or ROI. Start with an assistive version of the same job, measure it, then decide.
How do we know an AI feature is actually working?
Pick the number before you build. Reporting on MIT's NANDA research found 95% of enterprise generative AI pilots fail to deliver measurable ROI, with the cause traced to integration and misaligned priorities rather than model quality. If you cannot name the task, the baseline, and the target, the feature has no way to prove itself.
How do we get a first AI feature scoped?
Send us the idea. We read every inbound message and reply within two business days, then map the work into fixed milestones with pricing agreed before the build starts.