Which app ideas will work in 2027?

What does an app idea need to do to actually work in 2027?

For an app idea to work in 2027, we think it needs repeat usage, a believable way to make money and a reason users cannot get the same result instantly from ChatGPT, Gemini, Apple or another giant platform.

Getting downloads is too low a bar. RevenueCat’s latest State of Subscription Apps report covers more than 115,000 apps and over $16 billion in revenue. Only a small minority of subscription apps become meaningful businesses, and the gap between the winners and everyone else is huge.

For an independent founder, something around $10,000 to $100,000 in monthly revenue can already be an excellent outcome. A venture-backed startup obviously needs much more. The ideas that interest us here are the ones that can plausibly support either a strong small company or something larger, depending on how broad the workflow becomes.

The useful dividing line is repeat value. An app that gives somebody an interesting answer once may get downloads. An app that handles something the same person needs every day, every week or every month has a much better shot at becoming a business.

That becomes especially important in 2027 because building software itself is getting dramatically easier.

Is 2027 already too crowded to launch a new app?

No, 2027 is not too crowded for good apps, but mediocre ideas are going to have a much harder time surviving.

The strange thing about the app market today is that supply and spending are both rising quickly.

RevenueCat says roughly 2,000 new subscription apps were launching each month three years ago. That figure is now close to 15,000. We have gone from roughly 24,000 new subscription apps a year at that pace to something approaching 180,000.

AI coding tools are clearly helping drive that explosion.

At the same time, people are spending much more money inside apps. Sensor Tower measured $167 billion in global in-app purchases in 2025, up around 10% in one year. Spending on non-game apps grew 21% and passed games for the first time.

The latest data has stayed healthy. Adjust found global app installs up 13% year over year in the first half of 2026. Sessions rose another 5%.

So the app economy is still growing. What has disappeared is the old scarcity of software.

That pushes more of the advantage toward distribution, trust, proprietary data, specialized workflows, integrations and user history. If somebody can look at an app, recreate 80% of it over a weekend and reach the same users, we would be nervous.

A niche that looked too small to support custom software five years ago can now become attractive because the cost of serving it has collapsed. That is one of the biggest reasons we still see plenty of room for new apps in 2027.

Will generic AI apps still work in 2027?

Generic AI apps will still make money in 2027, but we would avoid building another general AI wrapper unless there is a very unusual distribution advantage.

The current demand is enormous. Sensor Tower estimates that generative-AI mobile revenue went from less than $60 million in Q1 2023 to about $1.9 billion in Q1 2026. Across the twelve months ending in Q1 2026, generative-AI apps produced roughly $6.1 billion in mobile in-app revenue.

RevenueCat also finds that AI subscription apps sell better at the beginning. Their median trial-to-paid conversion is 8.5%, compared with 5.6% for non-AI apps. First-year revenue per payer reaches $30.16 versus $21.37.

Then we get to the uncomfortable part.

After twelve months, AI apps retain only 6.1% of monthly subscribers at the median, compared with 9.5% for non-AI apps. Annual retention is 21.1% versus 30.7%. Refund rates are also higher.

Users are clearly curious about AI and willing to pay for it. There is much weaker evidence that they want to keep paying for thousands of interchangeable AI tools.

The risk gets worse as the general assistants improve. ChatGPT passed one billion monthly mobile users in 2026 according to Sensor Tower, while Gemini and Claude have been gaining quickly. More than 200,000 apps already mention AI in their store descriptions.

By 2027, “uses AI” will describe an enormous share of software. It will tell us very little about why somebody should choose one particular app.

Metric	AI subscription apps	Non-AI subscription apps
Median trial-to-paid conversion	8.5%	5.6%
First-year revenue per payer	$30.16	$21.37
12-month monthly retention	6.1%	9.5%
12-month annual retention	21.1%	30.7%
Median refund rate	4.2%	3.5%

Are vertical AI business apps the best bet for 2027?

Yes. Vertical AI business apps are our strongest broad bet for 2027.

The numbers line up unusually well.

Only 19.1% of Business subscription apps in RevenueCat’s latest dataset are classified as AI-powered. Productivity is already at 41.1%, while Photo & Video has reached 61.4%.

Yet Business has one of the best subscription funnels in the entire dataset. A median 9.1% of downloads become trials, the highest result of any category measured. Business also leads six-month monthly subscriber retention at roughly 40%.

This is exactly the type of gap we want to find: relatively limited AI penetration inside a category where customers already pay and stick around.

The next wave should go much further than adding a chat box to existing business software.

Think about what happens after a plumber visits a house. The plumber may need to record the problem, organize photographs, calculate materials, prepare an estimate, update the customer record, send the quote and schedule a follow-up.

A specialized app can now listen to the site visit, inspect the images, pull prices from the company’s own history and prepare most of that work automatically.

The same structure appears in insurance inspections, property management, field sales, logistics, restaurants, construction and dozens of other industries.

These businesses contain huge amounts of messy administrative work that traditional software has never handled particularly well. Voice, images and documents give AI a much better way into those workflows.

We would rather build there than fight the 40th AI note-taking app.

Which small-business app ideas look strongest right now?

The strongest small-business apps right now turn messy real-world inputs such as conversations, photographs and documents directly into finished work.

That sounds simple, but it changes what software can handle.

Traditional business software usually waits for somebody to type clean information into a form. Real work rarely begins that way. A roofer walks around a property. A restaurant receives invoices from different suppliers. A property manager gets tenant photos and WhatsApp messages. A salesperson comes out of a meeting with ten minutes of spoken information.

Multimodal models can now process much more of that raw material.

We especially like workflows where the same conversion happens several times a day. The more frequently somebody turns a photo, conversation, PDF or handwritten note into administrative work, the easier it becomes to justify a subscription.

We would also look for a clear financial outcome. Saving five minutes sounds nice. Producing quotes faster, preventing missed invoices, filling empty appointments or reducing a manager’s evening paperwork is easier to sell.

The best opportunities will often look boring from the outside. That is fine. A small vertical where thousands of businesses happily pay $50 or $100 every month can be far more attractive than a glamorous consumer idea fighting for millions of free users.

App idea	What goes in	What should come out
Trades quoting app	Voice, site photos, previous jobs	Scope, materials, estimate, customer quote
Property inspection app	Photos, tenant messages, inspection notes	Repair list, work orders, contractor messages
Field-sales app	Customer conversation, CRM history	CRM update, proposal, follow-up
Restaurant purchasing app	Invoices, stock photos, sales history	Inventory update, reorder suggestions
Freelancer admin app	Messages, completed work, receipts	Client update, invoice, payment follow-up

Are AI fitness and nutrition apps still worth building?

Yes, AI fitness and nutrition apps still look unusually attractive, although we would stay away from broad “AI wellness coach” products.

Health & Fitness currently has some of the strongest subscription economics in mobile.

Sensor Tower measured a record $4.5 billion in Health & Fitness in-app purchases in 2025, up 13%. Downloads increased only 0.8% to 3.96 billion.

That gap is interesting. The category did not need a massive wave of new users to produce much more revenue.

RevenueCat sees the same strength from another angle. Health & Fitness has the highest median download-to-paid conversion in its category comparison at 2.9%. It also produces around $0.48 of revenue per install after 14 days and $0.66 after 60 days. Gaming reaches only about $0.14 after 60 days in the same subscription dataset.

AI is already helping specific products. Sensor Tower has pointed to Cal AI, MacroFactor and YAZIO in nutrition, while running products such as Runna have also been growing. Strava acquired Runna in 2025, which gives us another concrete sign that personalized training software has strategic value beyond simple subscription revenue.

The ideas we like have a continuous data loop.

A nutrition app sees new meals every day. A running app receives every workout. A lifting app sees weights, repetitions and fatigue. Each new piece of data gives the product another chance to make a useful decision.

That makes the app harder to replace with an occasional ChatGPT conversation.

Can AI education apps still win against ChatGPT?

Yes, specialized AI education apps can still beat ChatGPT when they own the practice loop rather than simply answering questions.

Duolingo is the clearest reality check.

In its latest quarterly results, Duolingo reported 58.7 million daily active users, up 23% year over year. Paid subscribers reached 12.7 million, up 17%, while quarterly revenue climbed 18% to $298.5 million.

Those numbers arrived well after ChatGPT became capable of explaining grammar, translating sentences and holding conversations in foreign languages.

People kept using Duolingo because learning involves far more than getting an answer.

A good learning product decides what to practice next, remembers mistakes, adjusts difficulty, forces repetition, measures progress and gives the user a reason to return tomorrow.

Duolingo has also been adding generative AI itself, including conversational features. That is probably a preview of how the category develops: the specialized learning product remains the home of the curriculum while the model makes practice much more flexible.

We particularly like narrow outcomes. Speaking English for hotel work is clearer than “learn English.” Passing a specific oral exam is clearer than “AI tutor.” Sales-call practice, interview training, children’s reading fluency, pronunciation and professional vocabulary all have the same advantage.

RevenueCat also finds Education among the better subscription categories. Median download-to-trial conversion reaches 6.5%, and annual pricing sits near the top of the categories it tracks.

The lesson for 2027 is straightforward: teach one thing extremely well and keep score.

Are finance apps becoming more attractive again?

Yes, finance apps look more attractive now because usage is accelerating much faster than new-user acquisition.

Adjust’s newest data shows finance-app installs up 5% year over year in the first half of 2026. Sessions jumped 29%.

That means session growth was almost six times faster than install growth.

The earlier full-year dataset had already shown finance sessions growing 21%, so this does not look like a one-quarter blip. People who already have finance apps are using them more.

We would still avoid areas where the product needs to make speculative investment calls or win users’ trust with vague AI recommendations. Administrative finance looks much cleaner.

Freelancers still chase invoices. Small companies still lose receipts. Families still forget subscriptions. Businesses still wonder how much cash will remain after taxes, payroll and upcoming bills. Many of these jobs are handled through spreadsheets, bank apps, accounting tools and manual reminders at the same time.

AI can help join those pieces.

A useful finance app could spot an unpaid invoice, match receipts to transactions, explain the next week’s cash obligations and prepare what the accountant needs.

The value comes from understanding the user’s own financial situation and helping them act on it. That gives a specialized product something a general chatbot lacks until the user manually reconstructs the whole situation in every conversation.

Is private on-device AI a real app opportunity now?

Yes, private on-device AI has become a much more credible app opportunity, and Apple’s latest developer tools make it especially interesting for 2027.

Apple now gives developers direct access to the on-device model behind Apple Intelligence through its Foundation Models framework. The newest version accepts image input and can call tools.

Apple also introduced Core AI, which lets developers run their own supported models entirely on Apple Silicon with zero server dependency and zero token cost.

There is an even bigger change for small developers. Apps in Apple’s Small Business Program with fewer than two million first-time App Store downloads can access Apple’s more powerful Foundation Model on Private Cloud Compute without normal cloud API charges.

That removes two problems at once: privacy concerns and potentially large inference bills.

It opens up products that would feel uncomfortable if every interaction required continuously uploading personal data to an unknown server.

We could imagine an app that understands years of receipts and warranties, a private searchable archive of personal documents, an inventory of everything inside a home or a local assistant that understands screenshots and files stored on the device.

The opportunity is still early, so our confidence here is lower than for vertical business software or Health & Fitness. We do think the underlying economics have changed enough to take the category seriously.

Should a 2027 app work inside ChatGPT and Siri too?

For many 2027 apps, yes: the strongest products should let users call their data and actions from ChatGPT, Siri and other assistants without forcing them to open the full app every time.

The distribution model is already moving this way.

OpenAI has shifted ChatGPT integrations into its broader Plugin system, where external services can expose data and actions directly inside ChatGPT and Codex. Apple’s App Intents framework similarly lets apps expose functionality to Apple Intelligence and Siri.

That gives us a useful way to think about app design.

A property manager may still need a full application to see buildings, tenants, contractors and repair history. But asking an assistant to “show me every unresolved plumbing issue and follow up with the contractors” should eventually work without tapping through six screens.

A finance app might keep its dashboard while also allowing “which invoices are overdue?” from an assistant.

The apps in the best position are the ones that control valuable authenticated data and reliable actions. Better assistants can actually make those products more useful because the assistant becomes another way to reach them.

Products built mostly around displaying information face more pressure. Once an assistant can retrieve the same answer directly, opening a separate app becomes harder to justify.

For 2027, we would increasingly ask what useful verbs an app owns: quote, book, reconcile, send, schedule, file, order, inspect, compare or update.

Those verbs are harder to commoditize than another chat interface.

Are AI photo and video apps already overcrowded?

Yes, generic AI photo and video apps already look overcrowded, and we would need a very specific angle before building another one.

RevenueCat classifies 61.4% of subscription Photo & Video apps as AI-powered. That is by far the highest penetration of any category it measures. Productivity comes next at 41.1%.

Photo & Video also has the weakest median trial-to-paid conversion in RevenueCat’s category data at 22.2%. Health & Fitness reaches 37.7% and Travel reaches 43.5%.

The category can still create huge winners. The problem is how quickly the basic features spread.

Image generation, background removal, object editing, avatars, video generation and automatic captions now appear across specialist startups, Adobe, Canva, ChatGPT, Gemini and other major platforms.

We would go further down the workflow.

A real-estate product could turn listing photos and property information into platform-ready vertical videos, captions and scheduled posts. An e-commerce app could take a merchant’s catalog and continuously create advertising variations around inventory and campaigns.

Those products use generative media, but users are paying to finish a commercial job.

That distinction should become even more important in 2027.

Can AI companion apps still become big businesses?

Yes, AI companion apps can still become very big businesses, but we would classify them as high-upside and high-risk rather than an obvious opportunity.

Demand is already large enough to take seriously.

Sensor Tower estimates AI companion mobile revenue reached roughly $150 million in Q1 2026. The category has grown more than twelvefold since Q1 2023.

People are therefore spending real money on AI relationships, characters and companionship.

The open question is how durable that spending becomes.

Conversation quality itself will keep improving across every frontier model. A companion app needs additional reasons for somebody to care about one particular product: long-term memory, recognizable characters, relationship progression, shared history, community or unusual social mechanics.

There is also more safety and moderation work here than in a calorie tracker or quoting tool. Users can form strong emotional relationships with these products, which creates responsibilities that a small team should take seriously.

Demand is clear. The economics for the average new companion app are much less clear.

The few products that create characters people genuinely care about could become enormous.

Can travel apps work if people only need them a few times a year?

Yes, travel apps can work in 2027, but the business model has to accept that most travel problems are temporary.

RevenueCat gives us an unusually clear picture.

Travel has the highest median trial-to-paid conversion among the categories it measures at 43.5%. Users facing an immediate trip clearly have strong purchase intent.

At the same time, Travel has relatively low download-to-trial conversion at 4.1%, and annual pricing sits toward the lower end of subscription categories.

That combination makes sense. A person planning a complicated trip may happily pay today and have almost no reason to keep paying six months later.

We would design around the episode.

A relocation app could manage visa documents, appointments, housing tasks and deadlines until the move is complete. A complex-trip assistant could track bookings and rebuild plans when flights or reservations change. A group-travel product could keep reservations, expenses and decisions synchronized across several people.

Generic itinerary generation feels much weaker. ChatGPT, Gemini, Google and the travel platforms themselves can already produce increasingly decent travel suggestions.

There is more room in everything that happens after somebody says, “Fine, this is the trip. Now help me actually manage it.”

Are AI shopping apps worth building?

Some AI shopping apps are worth building, especially when the buying decision is complicated enough that generic search still performs badly.

We already have evidence that conversational shopping can affect real purchases.

Sensor Tower found that Amazon sessions using Rufus, Amazon’s AI shopping assistant, convert at roughly twice the rate of sessions without Rufus. The pattern appears across both web and app traffic.

We should be careful with what that proves. Sensor Tower’s later analysis found that Rufus users also tend to show stronger buying intent, so the assistant itself cannot claim all of that conversion lift.

Still, people are clearly becoming comfortable using AI while deciding what to buy.

An independent company will struggle to beat Amazon, Google or ChatGPT at general product discovery. A narrow product can go deeper.

Furniture is a good example. An app could remember the dimensions, photographs and style of every room in a house, then rule out items that physically or visually make no sense.

Professional equipment has similar possibilities because compatibility matters. Clothing could work if the product accumulates measurements, fit preferences and return history across brands.

The strongest shopping apps will probably know something important about the buyer that a general search engine does not know yet.

Are mobile games a bad bet for indie founders in 2027?

For most indie software founders, mobile games are a worse 2027 bet than business, health or education apps.

Gaming remains huge, and individual winners can reach a scale almost no utility app will ever touch. But current market data looks much harsher for new entrants.

Sensor Tower measured global game downloads falling 10.7% year over year in Q4 2025, with every major game genre declining.

The newest Adjust data is more encouraging on engagement. Gaming sessions grew 6% year over year in the first half of 2026, and casual-game sessions jumped 55%.

The combined picture is tougher for a new entrant: established games can keep users playing while acquiring large numbers of new players has become harder.

RevenueCat’s subscription data also shows how different gaming economics are. Gaming produces only around $0.08 of subscription revenue per install after 14 days and $0.14 after 60 days, although that understates games that make money through advertising and consumable purchases.

A founder who understands game loops, live operations, paid acquisition and virtual economies may see things differently. That expertise is a real edge.

For somebody whose main advantage is being able to build software quickly, we see much easier places to compete.

Which app ideas have the best odds of making real money?

Vertical business AI, specialized fitness, narrow education and finance administration currently give us the best combination of recurring demand and believable monetization.

We would put vertical business software first.

Business apps combine low current AI penetration with strong trial conversion and unusually good six-month retention. They also allow much higher pricing than many consumer utilities when the app saves labor or helps the customer make money.

Health & Fitness comes next because the category already converts and monetizes extremely well. Education has similar advantages when there is a concrete learning goal.

Private on-device utilities are more speculative, although Apple’s new economics make them much more interesting than they were a year ago.

Travel and shopping both work better when pricing follows the actual event. We would happily charge per trip, per case or per transaction rather than force an artificial monthly subscription onto occasional usage.

AI-heavy products with significant inference costs may also need credits or usage limits alongside the subscription.

There is no reason every successful 2027 app should charge $9.99 a month forever.

Rank	App idea	Why we like it	Sensible starting model
1	Vertical AI business operator	Repeated work, strong retention, relatively low AI penetration	$30–$150+ monthly
2	AI fitness or nutrition app	Excellent consumer monetization and frequent new data	Annual-first subscription
3	AI coach for one specific skill	Clear progress loop and strong Education economics	Monthly/annual
4	Finance admin app	Finance usage is currently growing very quickly	Monthly subscription
5	Private on-device personal utility	Better privacy and falling inference cost	Paid or subscription
6	Travel/relocation operator	Very high purchase intent	Per trip/case
7	Specialized shopping agent	Can own difficult purchase context	Premium, affiliate or transaction

Which app ideas should we avoid in 2027?

We would avoid generic AI chatbots, simple AI wrappers, broad AI image generators, generic itinerary planners and interchangeable productivity apps in 2027.

The common problem is easy substitution.

If the user can upload the same document to ChatGPT and get nearly the same result, the standalone app needs an extraordinary acquisition advantage to compensate.

The same concern applies to simple summarizers, generic writing assistants, “chat with your PDF” products and basic AI note tools. Many can still make money. We just dislike the direction of competition because the foundation-model companies are improving precisely those capabilities.

Generic creative apps face another problem: AI penetration is already extraordinarily high in Photo & Video, so the founder competes against both large incumbents and a flood of nearly identical startups.

We would also hesitate before building another general consumer social network because AI makes content production cheaper. Cheap content does very little to solve the hard part of a social product: getting the right people into the same network at the same time.

Our biggest warning sign is simple. If removing the underlying AI API removes nearly all the product’s value, we probably want a different idea.

So, which app ideas will actually work in 2027?

The apps most likely to work in 2027 are vertical AI business tools first, followed by specialized health and fitness apps, narrow learning products, finance administration tools and a new wave of private AI utilities.

The latest evidence gives us a much clearer answer than “build something with AI.”

AI subscription apps currently acquire and monetize users better, yet they retain them significantly worse. Generic AI assistants are reaching enormous scale. Creative AI is already crowded. Meanwhile, Business apps remain relatively underpenetrated by AI while showing some of the strongest subscription retention and conversion data.

That puts the opportunity one layer deeper.

A roofer can already ask ChatGPT how to write a quote. The useful app sees the site photographs, hears the inspection, knows the company’s previous prices and prepares the actual quote.

A language learner can already ask Gemini what a Spanish phrase means. The useful app remembers the learner’s mistakes for six months and decides what they should practice tomorrow.

A runner can already ask Claude for a marathon plan. The useful app sees every completed workout and changes the next one automatically.

A freelancer can already ask an AI how to improve cash flow. The useful app knows which invoice remains unpaid and which bills arrive next week.

That is where we would place the 2027 bet.

The strongest new apps will know something specific about the user, see new information continuously and help complete a recurring job. AI will increasingly sit underneath that experience, almost like databases and cloud hosting do today.

By 2027, simply having AI will be ordinary. Owning the context and getting the work done should be much harder to copy.