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“Meet your users where they are” seems to be the best advice for scaling any successful product.
If we told you there was a device with:
You’d probably be jumping at the opportunity to scale your AI strategy.
Well, that’s mobile.
Ignore it at your own peril - AI on mobile is the next big wave in the current tech revolution.
But it’s also the biggest risk if you don’t execute it correctly. How many times have you uninstalled a buggy app, or immediately left a mobile website because the experience didn’t compare to the desktop version? How many brands show up in the news for silly AI mistakes like selling a car for $1?
Doing mobile AI well means thinking very carefully about some key tradeoffs.
Luckily, we’ve been there, done that. NineTwoThree has years of experience building enterprise-level AI and mobile solutions for some of the world’s most successful companies.
Let’s talk through how we approach an enterprise AI strategy for mobile. We’ll end with some of our favorite examples of cutting-edge AI mobile apps.
Five years ago, an AI strategy was forward-thinking. Now, it’s table stakes.
Lucky for you, a mobile AI strategy is forward-thinking. As our CEO has talked about many times, the AI layer on mobile is the next big opportunity for technologists to capitalize on.
AI can be a competitive advantage and true moat if done correctly. It’ll create stickier customers who come to you as a source for insights and information. Workflows they can’t get anywhere else.
But AI on mobile makes that opportunity even more substantial.
The fact is, 9 in 10 humans have a smartphone.
It’s the most successful consumer product in history. There’s no better platform to reach 90% of earth’s population. Often, our first reaction when looking for new information is to reach for our smartphone.
And thanks to recent advancements, more and more AI usage happens on these devices. Both Apple and Google spent years building their AI assistants into the core workflows of their operating systems. AI assistants are literally one click away, with a dedicated button.
Apple’s latest iOS update, iOS 18, is entirely focused on Artificial Intelligence Apple Intelligence.
(Sorry, Apple, you’re not fooling anyone.)
This is Apple’s homepage, right now.
There’s a reason companies like OpenAI are scared of newer upstarts like Perplexity. Owning the AI layer on mobile is probably the most valuable opportunity in the category.
As AI engrains itself in more smartphone interactions, user habits will change. They’ll spend more time with their AI assistant on their phone, and start to see the device as their go-to AI solution.
The proof is in the UX: both Apple and Google have made their AI capabilities (not just assistants) one-click access.
If you’re considering investing in AI capabilities on mobile, your organization probably already has a head start. You might have a mobile app, or mobile website experience.
Even if you don’t, it’s easier than ever to start. There are robust frameworks, both cross-platform and native, for building and deploying quality apps.
(NineTwoThree AI Studio has expertise in both)
It’s an excellent platform to bet on.
If you’re on board, there are a few things to consider.
It’s not as simple as taking your web or desktop strategy and porting it over to a phone app.
Sure, the mobile use case has some overlap with desktop, but there are a few areas where each excels. It’s important to think very carefully about what AI on mobile actually means, before you waste $500k and 6 months of work.
When you’re discussing adding AI to your mobile experience, consider these factors. Will it always need a high-speed internet connection? Will mouse input make the UX better? How important is drag-and-drop?
None of these are deal-breakers, but they’re important to consider if you want true seamless integration.
This is one of the first questions we get from executives who want to invest in an AI strategy.
“Should I look into developing custom models, or will something from OpenAI work?”
We’ve talked about this topic a lot. Either option can get expensive.
But before you decide, realize there might be a free option that’s more than adequate for your use case.
Both Apple and Google have developed easy ways for developers to use the on-device ML processors.
These processors can train models, perform tasks like generate images and text, and offer a low-latency way to take advantage of AI.
“Amazing! Why would I ever use an external service like GPT?” you might think.
Ask yourself these questions first:
1. What use case or Job To Be Done am I solving for?
2. What’s most important to my user?
3. What’s a priority for my team / organization / company?
4. What type of expertise do we have?
If you need extreme power and accuracy, you might want to stick to traditional models like OpenAI’s GPT or Anthropic’s Claude.
If you have a specific use case (that uses the device’s unique capabilities, for example) it may be worth considering.
These SDKs and chips have a few benefits:
As always, there are tradeoffs.
This is a difficult decision that you’ll find many articles about.
There are two paths you can take. Either develop a native application for Android and iOS, which means maintaining two separate codebases, deploying two separate apps to two separate app stores, etc.
Or, using a cross-platform development framework like React Native or Flutter. The advantage here is ease - build one app for both operating systems - but at a huge cost of native OS features.
Put simply, it’s much harder (or even impossible) to use a lot of the on-device hardware capabilities with a cross-platform app. iOS and Android simply don’t give React Native the same level of hardware and software access as it does to their native development languages.
Be sure to consider this before going down the wrong path.
Mobile operating systems become another attack vector for bad actors. Yes, it’s true that devices like Apple’s iPhone generally lock down the operating system.
But today’s consumers take their phones everywhere, and will inevitably lose them.
HIPAA and GDPR don’t cease to apply when you’re developing mobile apps. Plus, you’ll be sending data to and from the device.
Investing in enterprise-grade security is all-the-more critical for mobile applications. Especially when consumers treat AI agents as an extension of the company.
There’s no question that adding AI capabilities increases your chances of an embarrassing, brand-threatening incident…if you don’t do it carefully and correctly.
A few companies learned the hard way that AI agents can’t be trusted to handle everything themselves.
If you build any AI agent, you have to decide what happens if it:
On top of that, you have to contend with all the issues on mobile:
You’re dealing with two types of risk: users trusting your app in the wrong ways, and hackers targeting it.
Prompt engineering is the next major attack vector for hackers, and protecting against this makes sure your brand stays intact as you scale.
With most AI apps on the desktop, you need to think about internet connectivity and file upload failures.
These problems aren’t unsolveable, but they’re at the very least UX concerns you’ll need to effectively communicate to your users.
AI products are significant time-and-money investments for your team.
Make sure you’re closely tracking mobile KPIs to prevent this becoming a years-long effort with no payback.
Track these carefully BEFORE you start working on an AI feature.
We had to mention some of our own work!
We worked closely with a leading golf app, SWEE, to leverage the iPhone’s on-device ML chip. Using just the iPhone’s camera and LiDAR system, we trained a model to help golfers improve their swing.
Apple’s latest iOS 18 update included a key AI feature: use the Camera Control button to quickly learn more about anything the camera can see.
You can get information about a business, search Google for similar items, and even ask ChatGPT about what it can see.
Apps like Adobe Scan let you use AI to detect and extract text from documents.
Socratic lets you take a photo of homework, and turn it into an interactive lesson. Concepts are explained to you, rather than solved for you using something like ChatGPT.
This is an excellent example of making AI’s ease-of-access a learning opportunity, not a shortcut.
Amazon’s in an excellent position to build an in-app shopping assistant. Customers are already browsing, searching for items, and reading through reviews.
Amazon makes those insights a tap away, instead of something they have to manually do themselves.
Talk about niches. CountThings is an incredible example of using AI on a mobile phone to drive enterprise results.
Manual counting is a common task in industrial settings (logs, pipes, pallets), but CountThings uses the mobile phone camera to count from just one photo. You’d think this is a niche use case, but the app has found huge success.
Voice AI models have gotten incredibly accurate at transcription. Otter’s app is a sales-focused meeting recording tool, and their mobile app lets you record anything.
Agentic money and financial advice is a great use case for AI, done right. Cleo uses a chat interface to get insights into large amounts of unstructured financial data.
This wouldn’t be an AI on mobile article if we didn’t talk about the AI search and chat apps like Perplexity and ChatGPT. Both offer AI-powered search engines that include LLMs and information retrieval.
Perplexity is especially helpful for research - they include many citations in a Wikipedia-style summary.
Another example of AI models being one interaction away. The UX of Bodyful makes it so easy to log meals - take a photo of your meal, AI analyzes it and calculates the calories.
One of the biggest pain points of tracking calories is manually inputting everything, and AI makes it 10x easier.
This is the type of leap you should be looking for when working with AI on mobile.
Lastly, let’s touch on how to start. If you’ve decided to build an AI product, you should try to validate the smallest testable version of that product first.
It could be that the AI feature you’re building is completely different from the AI feature that will improve your product experience. Finding that out early can be a budget-saving insight.
Done right, this is a decade-long journey that will continue to pay dividends. But only if you focus on the right, iterative process to test and deploy on mobile.
If you’re ready to start integrating AI into your mobile solutions, it helps to hit the ground running.
NineTwoThree has years of experience developing innovative mobile apps and enterprise AI solutions.
Our unique approach leverages state-of-the-art development processes to ensure you’re not wasting your investment. We’ll find the most cost-effective way to validate your AI strategy, do it, and then scale it up with you every step of the way.
We’ve got experts in mobile development and machine learning, ready to help. And yes, we’ve built custom models AND worked with on-device ML chips.
