A product owner's guide to what AI actually is

Let’s start with a reality check: AI isn’t magic. It’s not some all-knowing robot brain waiting to take over your app and run the show. It’s maths. It’s data. And when used properly, it’s a powerful tool that can help your app do smarter things, faster.

But before we dive into what AI can do, let’s decode what it actually is and why it matters for you as an app owner.

You’ll hear a lot of acronyms thrown around. Here’s what they boil down to:

• AI (Artificial intelligence) is the umbrella term. It refers to machines that can “think” in a way that mimics human decision-making, spotting patterns, making predictions, or generating content.
• ML (Machine learning) is a type of AI. It uses data to “train” a model so it can make predictions or decisions without being explicitly programmed to do so.
• LLMs (Large language models) are a type of machine learning model, trained on massive datasets. Tools like ChatGPT fall into this category; they’re great at generating human-sounding language, summarising content, and answering questions.
• AI agents are more like digital co-workers. They use LLMs (or other models) to perform tasks on your behalf, like replying to customer messages, sorting through emails, or triaging support tickets. They're not just answering a question; they’re acting with intent.

Each of these technologies serves a slightly different purpose. What matters is how you use them to enhance your product, not whether you’ve got the latest acronym in your pitch deck.

We get asked this a lot: “Isn’t this just automation?”

Not quite.

• Traditional automation follows strict rules. “If this, then that.” It’s fast, reliable, and great for predictable tasks. Think: sending a confirmation email, updating a CRM record, generating a PDF.
• AI-based decisions, on the other hand, deal in probabilities. Instead of hardcoded rules, an AI model will predict the most likely outcome based on patterns in data. It’s how a chatbot guesses the right answer. Or how image recognition spots a dog in a photo without being explicitly told what a dog looks like.

That makes AI incredibly useful for messy, nuanced tasks, like natural language, visual data, or behaviour prediction, where traditional logic falls flat.

But it also means AI can be a bit... unpredictable. It needs testing, guardrails, and a clear role in your product.

You don’t always need to build AI from scratch. In fact, most apps start by plugging into off-the-shelf models, like OpenAI’s GPT or Google’s Vision API, to add smart features quickly and cost-effectively.

These models are already trained on huge datasets, so you can tap into them via API and get going within hours. They’re ideal for common use cases like search, summarisation, customer support, and content tagging, and they power apps used by 92% of the Fortune 500.

If your needs are more specific (like analysing legal contracts, or processing medical notes), custom-trained models or fine-tuning an existing model might be the better fit. They’re more work and more expensive, but they give you tighter control, better accuracy, and improved privacy.

Sometimes, a hybrid approach is best. Off-the-shelf for speed, with a bit of custom logic layered in for performance.

Curious about the trade-offs? We’ve broken down the pros and cons in this guide.

You don’t need to become an AI expert. You just need to understand the basics well enough to ask the right questions, challenge the hype, and steer your product in the right direction.

And if you need help figuring out which model, API, or agent fits your product? That’s where we come in. We’re your AI partner, just like we’re your app partner. You bring the vision, we’ll help make it smarter.

There’s a lot of noise out there. Some of it’s overly optimistic, some of it’s outright misleading. So before we go any further, let’s clear the air.

Here are some of the most common AI misconceptions we hear and what’s actually true when you’re building AI into an app.

Maybe not.

Custom models sound exciting, private, powerful, tailored to your business. But they’re also expensive, complex, and take serious time to build. In most cases, you can get 80–90% of the value by plugging into a well-trained, off-the-shelf model and layering in a bit of logic or context.

Custom only makes sense if:

• You’ve got niche data that general models can’t handle
• You need strict control over tone, accuracy or behaviour
• Privacy is a dealbreaker

If that’s not you, a pre-trained model with some smart tailoring will do just fine.

Not always.

AI’s job isn’t to replace humans, it’s to support them. The best AI features don’t remove people from the process; they help them move faster and make better decisions.

You might still need a person to review, approve, or refine the result. That’s not a failure, it’s a smarter workflow.

As we covered in our blog, AI is perfect for repeatable, high-volume tasks like document processing or triaging support requests, but it’s not great at edge cases, emotional nuance, or high-stakes decisions where a human touch is still essential.

Only sometimes.

Yes, some tools are easy to get started with, especially off-the-shelf models from OpenAI or Google. But making AI actually useful in your product takes thought. You need:

• The right data
• Guardrails to avoid errors or misuse
• A user journey that makes the AI feel seamless, not shoved in

The tech might be ready to go. But getting real value from it? That’s all in the implementation.

A healthcare company saved 15,000 hours a month using AI to process medical documents, with 99.5% accuracy.

It worked because the task was repeatable, well-structured, and backed by a human-in-the-loop to spot anomalies. That’s where AI shines.

When AI is used well, it becomes a quiet powerhouse inside your app. The kind of tool that removes friction, handles the grunt work, and elevates the user experience in ways your customers might not even notice… until it’s missing.

Let’s break it down by theme and take a look at where we’ve seen AI perform at its best.

Forget clunky keyword matching. AI can help users surface exactly what they’re looking for, even when they don’t know how to ask for it.

• Semantic search: Understands the intent behind a query. “Dog-friendly hotel near a beach” returns relevant results, not just pages with those keywords.
• Auto-tagging: Automatically applies relevant labels to content (images, documents, videos) to make filtering faster and more accurate.
• Personalised sorting: Ranks and recommends content based on user behaviour or stated preferences.

Seen in action: travel platforms, ecommerce apps, knowledge bases, internal tools.

AI is powering a new wave of customer support: faster, friendlier, and far more scalable.

• Conversational chatbots: Handle FAQs, password resets, and common issues in natural language.
• Helpdesk agents: Triage tickets, route to the right team, and summarise interactions for human staff.
• Sentiment analysis: Detect user frustration and escalate at the right moment, before things boil over.

Seen in action: banking apps, healthcare portals, onboarding flows, field service tools.

AI can process unstructured content faster than any human and make it usable in your product.

• Image recognition: Label photos, blur sensitive data, categorise content at scale.
• Transcription and summarisation: Turn voice notes or long documents into actionable summaries.
• Language translation: Make your app accessible to new audiences, instantly.

Seen in action: social platforms, training tools, logistics software, retail marketplaces.

AI isn’t just user-facing; it’s also working hard behind the scenes to help businesses run smoother.

• Pattern recognition: Spot trends, anomalies or risks buried in data.
• Document handling: Extract, validate, and process data from forms, invoices or medical records.
• Predictive insights: Forecast stock, detect fraud, or anticipate user churn before it happens.

Seen in action: healthcare systems, back-office portals, supply chain platforms, finance apps.

Most people think of AI as reactive. You ask, it answers. But AI agents flip the script. They’re not just there to respond, they’re built to act.

Think of a standard AI model like a smart brain in a jar. It can understand and respond, but it needs you to steer the conversation.

Now imagine giving that brain a body, a goal, and a set of tools.

That’s an AI agent.

It doesn’t just sit and wait; it works proactively, using APIs, accessing data, making decisions, and coordinating across systems to complete complex tasks. It's less like a chatbot and more like a digital team member.

• In field service apps: Automatically assign engineers based on skills, location, and job urgency, while adjusting routes in real time based on weather or traffic. It can also escalate incidents or chase missing data without human prompting.
• In health or wellbeing apps: Guide users through check-ins, book appointments, adjust reminders based on wearable data, and flag urgent issues to clinicians.
• In ecommerce: Act like a personal shopper. Track price drops, recommend items based on behaviour, and even handle returns, no support queue in sight.

Why it matters: AI agents don’t just enhance the experience. They own it.

They cut down admin, boost retention, and help users get what they need, fast.

And because they remember context, they get smarter the longer they run.

Seen in action: banking assistants (like Erica at Bank of America), government admin tools, productivity platforms, and an increasing number of customer-facing apps.

AI isn’t a silver bullet, but when used smartly, it can do some serious heavy lifting.

Repetitive tasks: If it’s manual, repetitive, and rules-based, AI is a natural fit. Think claims processing, admin follow-ups, form filling, or chasing incomplete reports. The more often it happens, the more time you’ll save.

Pattern recognition at scale: AI is excellent at spotting trends across huge datasets, whether that’s identifying anomalies in user behaviour, scanning documents for key insights, or detecting early signs of fraud.

Enhancing (not replacing) human effort: Used right, AI doesn’t sideline your team; it supercharges them. It takes care of the repetitive stuff so your people can focus on higher-value thinking: the strategic, the empathetic, the complex.

Edge cases with nuance: AI can fumble the ‘one in a thousand’ situations, especially those that rely on human judgment, cultural context, or emotional intelligence. You still need people for that.

Poor data inputs: Garbage in, garbage out. If your data is messy, incomplete, or biased, the AI’s output will be too. Strong foundations matter.

Replacing foundational UX or strategy: AI isn’t a shortcut for clear thinking. If your user journey doesn’t make sense, no AI model can fix that. It’s a booster, not a blueprint.

AI workflows work best when they’re built into a well-designed system, backed by quality data, and focused on specific, measurable outcomes. They’re not magic, but they can feel like it when they’re done right.

Not every app needs to build AI from scratch, and in most cases, it really shouldn’t.

Think OpenAI for natural language, Google Vision for image recognition, or Amazon Comprehend for document analysis. These tools are fast, powerful, and constantly improving behind the scenes.

They come with pre-trained models, easy-to-integrate APIs, and, crucially, someone else footing the bill for ongoing R&D.

Use them when:

• You need to get to market quickly
• Your use case is already well-supported (like summarising text, analysing photos, or automating chat)
• You want scalable performance without managing infrastructure

These services can seriously level up your app with minimal effort, and they’re built to handle massive scale.

There are rare cases where third-party tools don’t cut it, usually when:

• You’re working with highly specialised data (like legal, medical, or proprietary)
• You need to bake in domain-specific context or logic
• You have unique security or compliance needs

But let’s be real: custom models are expensive to train, expensive to run, and require ongoing maintenance. Unless you’ve got a very niche use case and a serious budget, they’re often overkill.

• Performance: Third-party tools are trained on huge datasets and benchmarked at scale. Custom models might beat them on accuracy, but only if you’ve got the data and expertise to back it up.
• Scalability: Third-party APIs are built for global traffic. Building that in-house? Costly and complex.
• Cost: You’ll pay per API call for plug-in services, but you’ll save massively on infrastructure, dev time, and iteration.

The smart approach:

Start with third-party services. Prove the value. If you outgrow them (and most don’t), then look at custom. Not every app needs to reinvent the wheel; sometimes, you just need to pick the right engine.

Let’s demystify the jargon: you don’t need a PhD in machine learning to understand the different types of AI models. What you do need is a clear idea of what problem you’re solving and the right tool for the job.

• Rule-based systems: Not technically AI, but still smart. These follow clear if-this-then-that logic and are great for simple workflows, think booking confirmations or eligibility checks.
• Supervised learning: Trained on labelled data (like “this is a cat, this is a dog”) to make predictions. Used in fraud detection, spam filters, and personalisation algorithms.
• Unsupervised learning: Finds hidden patterns in unlabelled data. Think customer segmentation or clustering behaviours, ideal when you don’t know what you’re looking for (yet).
• Generative models: These are the headline grabbers. Tools like ChatGPT, Midjourney or DALL·E that create text, images, or code from prompts. Powerful, but not always the right fit for every use case.

It comes down to the job you need it to do.

Let’s say you’re building a travel app:

• Want to group travellers based on their habits? Unsupervised learning.
• Need a chatbot that can handle FAQs? Generative model.
• Looking to flag potentially fraudulent bookings? Supervised learning.
• Just need a quick yes/no logic flow? Rule-based might be all you need.

No two apps are the same, and that’s exactly why we don’t shoehorn in a one-size-fits-all AI setup.

At The Distance, we don’t lead with shiny tech. We start by mapping the pain point, then select the right AI model (or sometimes, no AI at all) to get the job done efficiently and responsibly. That might mean layering third-party APIs with your own business logic, or integrating different model types into a single workflow.

Sometimes the smartest solution isn’t the most complicated, it’s just the one that works.

You don’t need perfect data or a PhD to start using AI effectively. What you do need is a clear understanding of what you want to achieve and a strong digital foundation to support it.

The best AI features don’t feel tacked on; they’re baked into the user experience with purpose. So before jumping into models and tools, it’s worth asking: is your app solving a real-world problem? Is there a particular feature or process that feels clunky, repetitive, or ripe for improvement? That’s often where AI shines.

Once you've spotted an opportunity, the next step is to look at your data. Don’t worry if it’s messy or incomplete, we’ll help you figure out what’s usable, where the gaps are, and how to structure things so that AI can actually do its job. Many teams already have more than enough data sitting quietly in customer interactions, support tickets, usage logs, or internal documents. It’s just about unlocking it in the right way.

Rolling out AI doesn’t have to be dramatic, either. In fact, we recommend starting small. A simple use case, like automating admin tasks or making search more intelligent, can be the perfect test bed. From there, we test, iterate, and scale based on what’s working. It’s about building momentum, not chasing buzzwords.

AI isn’t just a trend - it needs strategy, context, and craftsmanship to deliver real results.

At The Distance, we embed ourselves in your team, turning complex AI ideas into practical product features. Whether you’re automating for the first time or scaling intelligent tools, we help you make smart, sustainable decisions.

Our approach is the same as with app development: user-first, outcome-driven, and seamless in execution. From backend workflows to on-screen interactions, we integrate AI without sacrificing performance or usability, making it work where it matters.

You don’t need to chase every shiny new AI headline. You just need the right tools, applied to the right problems, in the right way.

Whether you're just starting to explore AI or you're ready to evolve your app into something smarter, more efficient, and more scalable, we’re here to help you get there.