2026.10: You Lost the Plot: Feature Stuffing Is Not a Product Strategy
Every software company is cramming AI into every button, and nobody's stopping to ask if any of it solves a real problem.
You open your project management tool on a Monday morning. You've got a deadline, three fires, and a client who's been waiting since Friday.
But before you can do anything, there's a banner. A big one. "Introducing AI Summaries, AI Task Sorting, and AI Workflow Suggestions!" Fourteen new features. A guided tour you can't skip. And your actual work is now buried under three layers of menus that moved since last week.
You didn't ask for any of this. You just wanted to assign a task.
If that scenario made your eye twitch, you're not alone. And if you're a product leader who just shipped something like this, we should talk.
This week, I look at the five-stage feature stuffing playbook, three filters that separate signal from noise, and a framework I first used at Apple that tells you whether a tool is worth the money before you spend it.
Let's break it down.
The Numbers Are Real. The Strategy Isn’t.
Here’s what’s happening. SMBs are adopting AI at a pace that nobody predicted five years ago. Fifty-eight percent already use generative AI. Ninety-six percent plan to adopt it. In Canada, that number is even higher. Microsoft found that 71% of Canadian SMBs are actively using AI and GenAI tools right now.
Those numbers are real. The demand is real.
But here’s where it goes sideways. Tech companies see those adoption stats and hear one thing: “Put AI in everything.” So they do. Every button gets a copilot. Every text field gets a summary. Every dashboard gets a prediction nobody asked for.
IDC calls the winners “companies that focus on pragmatic use cases that are easy to deploy and deliver measurable ROI.” Read that again. Pragmatic. Easy to deploy. Measurable ROI. Not “fourteen features in a press release.”
The gap between what buyers need and what builders ship is getting wider, not narrower. And both sides are paying for it.
Here’s the thing that makes this moment different from previous technology cycles. When mobile apps were the hot thing, feature bloat was annoying but survivable. You ignored the features you didn’t need. AI features are different. They change your workflow whether you asked for them or not. They rewrite your interface. They insert themselves into processes that were working fine. A bloated mobile app wasted screen space. A bloated AI product wastes your time and, if you’re not careful, your judgment.
The Difference Between Feature Stuffing & Solving a Problem
I teach this concept using a simple test. Take any feature your product ships (or any feature you're evaluating as a buyer) and ask one question:
If I removed the word "AI" from this feature description, would the value still make sense?
If the answer is no, it's probably feature stuffing.
Here's what I mean. "AI task prioritization" sounds great in a press release. But what does it actually do? If the answer is "it sorts your tasks by due date and tags," that's not AI. That's a filter. You dressed up a filter in a lab coat.
Now compare that to a tool that reads your last 90 days of project data, identifies which tasks consistently get pushed, and flags the patterns causing bottlenecks. That's solving a problem. The AI part is secondary. The value is in the pattern recognition you couldn't do manually without a spreadsheet and four hours you don't have.
The difference is simple. One starts with the technology and works backward to find a use. The other starts with a problem and uses whatever tool fits best.
Or as I like to say: "We solve business problems with technology. We are not a technology company in search of a problem.
There's a model in education called SAMR, developed by Dr. Ruben Puentedura. It describes four levels of technology integration: Substitution, Augmentation, Modification, and Redefinition. Most feature stuffing lives at Substitution. You had a filter. Now you have an "AI filter." Same function, fancier label.
The features worth paying attention to live at Modification and Redefinition. They let you do something that genuinely wasn't possible before. Reading 90 days of project patterns to surface bottlenecks you didn't know existed? That's modification. Connecting customer support data to product development priorities in real time so you can fix the issue before the next ten tickets come in? That's redefinition.
The SAMR test is useful because it forces honesty. If a feature is just doing the old thing with new packaging, it doesn't matter how much AI is under the hood. It's substitution. And substitution is where digital transformation goes to die.
The Feature Stuffing Playbook (and Why It Fails)
Feature stuffing follows a predictable pattern. If you’re building products, check yourself against this list. If you’re buying them, use it as a filter.
Stage 1: The Announcement. A competitor ships an AI feature. Doesn’t matter if it works. Doesn’t matter if customers wanted it. It’s in TechCrunch. The board asks why you don’t have one.
Stage 2: The Sprint. Product team gets six weeks to “add AI.” Nobody stops to ask what problem they’re solving. The brief is the competitor’s press release, not a customer pain point.
Stage 3: The Launch. Fourteen features ship at once. There’s a banner. There’s a webinar. There’s a blog post drowning in adjectives that make a sort filter sound like a research lab. The marketing team is thrilled.
Stage 4: The Silence. Adoption is 3%. Support tickets go up 40% because the UI changed. Power users are angry. New users are confused. The AI features sit untouched while the filter sort that worked fine before is now three clicks deeper.
Stage 5: The Pivot. Six months later, they quietly remove half the features and call it “simplification.” Nobody talks about the sprint. The cycle starts again when the next competitor ships something.
Techaisle flagged this pattern directly. SMBs are now drowning in what they call “point solution sprawl.” Too many tools doing too many things, none of them connected, most of them half-finished. The response from the companies getting this right? Fewer tools. Better data. Systems that actually complete a workflow end to end instead of eight half-baked features that each do 20% of the job.
Three Questions That Separate Signal from Noise
Whether you’re building features or buying software, these three filters will save you time and money.
1. Does this move a line on my P&L within 90 days?
If a feature can't be connected to revenue, cost, or capacity within a quarter, it's a nice-to-have. Nice-to-haves are fine. But they're not your priority, and they're definitely not worth disrupting your workflow for.
Alex Bratton figured this out during the mobile app craze. I worked with Alex during my time at Apple, and his framework stuck with me. In Billion Dollar Apps, he built something called Return on App (ROA) that forces you to calculate two things before spending a dollar: the New Revenue Potential (how much more revenue your team can generate with the freed-up time) and the Cost Savings Potential (how much you'll save by cutting process time and labor). Those two numbers, plus the organizational side effects, give you a real return calculation before you write the check.
The math works just as well for AI tools as it did for mobile apps. I broke down the full ROA framework in this week's Deep Dive, including the formulas, a worked example, and the five mistakes that blow up the calculation. Deep Dive: How to Tell If a Feature Is Worth Your Money (Before You Spend It)
For builders: if you can't articulate the P&L impact in one sentence, the feature isn't ready to ship. For buyers: if the vendor can't tell you specifically what changes in your business after 90 days, keep walking.
2. Does this complete a workflow, or does it add a step?
The OnDeck and Ocrolus research on SMBs entering 2026 made this point clearly. The businesses getting results from AI are the ones using it to improve real outcomes, things like cash-flow visibility, decision speed, and lending accuracy. Complete workflows. Not half-built processes that still require you to copy-paste between three tabs.
A feature that saves you from opening a second application is valuable. A feature that requires you to open a second application to verify its output is a cost wearing a benefit’s clothing.
3. Would this exist if AI didn’t?
This is the gut check. Some features exist because they genuinely solve a problem better than the previous approach. Others exist because someone needed to check the “AI” box on a product roadmap.
If the underlying need would still exist without AI, and AI just makes the solution faster or more accurate, you’re looking at real value. If the feature only makes sense because AI makes it technically possible, but nobody was asking for it before, that’s a solution looking for a problem.
Think about it this way. Businesses have always needed to categorize expenses, route invoices, and flag anomalies in spending. AI does all of that faster and with fewer errors than a human scanning a spreadsheet at 4pm on a Friday. The need existed before the tool. AI just made the execution better.
Now contrast that with “AI-generated mood analysis of your Slack channels.” Was anyone struggling to figure out team morale before this existed? No. They talked to their people. This is a feature built because the technology made it possible, not because the problem demanded it.
The Mistakes Both Sides Make
Builders: Shipping for the press release, not the user.
Your AI feature doesn’t need to be impressive. It needs to be useful. A tool that auto-categorizes expense receipts with 95% accuracy and saves an office manager four hours a week will never make TechCrunch. But that office manager will never leave your platform. That’s the feature that builds a business.
IDC’s research is clear on this: successful implementations start with honest assessments of infrastructure, skills, and current gaps. Then they focus on a few high-value workflows. Not a buffet. A few things done well.
Buyers: Confusing “more features” with “better product.”
When a vendor shows you a demo with forty features, your instinct might be to feel like you’re getting more value. You’re not. You’re getting more complexity. More training. More things that can break. More surface area for something to go wrong.
The better question when evaluating any tool: how many of these features will my team actually use in the first 60 days? If the answer is three, buy the product that does those three things well. Skip the one that does forty things you’ll never touch.
Both: Ignoring the switching cost.
Every new feature changes the product. For builders, that means every feature ships with hidden costs: documentation, support load, UI complexity, performance impact. For buyers, every update means retraining, workflow disruption, and the very real chance that the thing you loved about the product just got buried under something you didn’t ask for.
The best software companies I’ve worked with understand this. They ship less, not more. They run honest assessments before adding anything. And they ask the uncomfortable question before every release: are we building this because our customers need it, or because our competitors have it?
Where to Start
If you’re buying software right now, here’s a simple filter you can use this week.
Take every tool you’re currently paying for. For each one, write down the one thing it does that you can’t live without. Just one. If you can’t name it, that’s a problem. If you can, you’ve just identified what you’re actually paying for. Everything else is noise.
Then look at any new tools you’re evaluating. Apply the same test. What’s the one thing? If the sales pitch focuses on a list of features instead of one clear problem solved, you’re looking at feature stuffing.
For builders, the filter is even simpler. Before any feature gets on the roadmap, require a one-sentence answer to this: “What does this let a customer stop doing?” If the answer is vague, the feature isn’t ready.
Fewer tools. Fewer features. More outcomes. That’s not a slogan. It’s math.
Deep Dive
Want the actual math? I broke down Bratton's full ROA framework, including the formulas, a worked example, and the five mistakes that blow up the calculation, in this week's Deep Dive.
Thanks for reading!
If this saved you from one bad software purchase or one unnecessary feature sprint, it did its job.
I'd love to hear where you're at. Got a feature-stuffing horror story? I want to hear it. Hit reply.
See you next Friday.
Best,
JT
P.S. — If your project management tool just added a feature that predicts your mood based on how aggressively you click, we need to talk.
