AI Features That Actually Fit Your Product • myko.design

AI is everywhere right now.

For businesses, though, the interesting question isn’t necessarily whether you should “add AI” to your product. It’s where AI could actually make something better.

That might mean making a large amount of content easier to search, helping users find the right product or information, summarising something that would otherwise take a person several minutes to read, or automating a repetitive process that your team currently handles manually.

These aren’t necessarily AI products in themselves. They’re existing products and workflows made more useful with AI.

That’s the kind of work I can help with.

AI doesn’t always need to be the product

When people talk about AI, it’s easy to imagine building an entirely new application around it. But many businesses already have the product, the content, the data, and the workflows. The opportunity is often to improve something that already exists rather than start from scratch.

You might have a website with thousands of pages, an internal tool, a customer portal, a knowledge base, or a SaaS product with a large amount of information behind it. You might also have repetitive processes that involve reading, categorising, copying, summarising, or moving information between different systems.

AI can become another layer on top of those existing systems.

For example, instead of replacing a traditional search experience, you could make it understand natural language. Instead of asking employees to manually process every incoming request, you could use AI to classify and extract the relevant information before someone reviews it.

The best AI feature is often the one that makes an existing product significantly more useful without requiring users to learn an entirely new product.

1. AI-powered features

The first opportunity is using AI directly inside a product, where it becomes part of the user experience rather than something that exists separately from it.

One common example is search. Imagine a website with hundreds or thousands of articles, products, documents, or other resources. A traditional search experience usually relies heavily on matching the words someone types with the words that exist in the content.

An AI-powered search experience can take a different approach. Instead of requiring users to guess the exact terminology used by the website, they can describe what they’re looking for in more natural language.

The same principle can be applied to recommendations, chat interfaces, content discovery, and other product features.

AI search

AI can make large amounts of content easier to explore by allowing users to search using natural language and intent rather than relying entirely on exact keyword matches.

Recommendations

AI can help users discover relevant products, articles, resources, or other content based on what they’re interested in or trying to accomplish.

AI chat

A conversational interface can provide another way for users to interact with a product, service, or body of information, particularly when there isn’t a single obvious page or navigation path they should follow.

Working with your own content

Perhaps the most useful part is giving AI access to information that belongs to your business. That could be documentation, product information, articles, internal knowledge, or other structured and unstructured data.

The AI model itself isn’t necessarily the most important part of this.

The value comes from connecting it to the right information and building a useful experience around it.

AI becomes more interesting when it knows your content

A general-purpose AI model already knows a huge amount about the world, but it doesn’t automatically know about your company.

It doesn’t know your internal documentation, your product catalogue, your processes, your services, or the specific information you’ve accumulated over years of running your business.

That’s where AI features can become particularly useful.

Instead of asking a generic question such as:

“What is the best way to improve website performance?“

you could build an experience that answers a much more specific question using your own information:

“Which of our services would be most suitable for a company migrating from WordPress to a modern frontend?”

The second question requires context that a generic AI model won’t necessarily have. The product therefore needs to connect the AI with the relevant information and provide that context at the right time.

This is one of the areas where AI development becomes more closely connected to traditional product engineering. The challenge isn’t simply getting an answer from an AI model. It’s deciding what information the model should have access to, how that information should be retrieved, and how the result should be presented to the user.

2. AI automation

Not every useful AI feature needs to be visible to customers.

Some of the most practical applications happen behind the scenes, where AI can help with workflows that currently require someone to process information manually.

Businesses often have large amounts of unstructured information coming in through emails, forms, documents, support requests, or other systems. Someone then has to read that information, understand what it means, categorise it, extract the important details, and decide what should happen next.

That can be a good opportunity for automation.

For example, an AI-powered workflow could analyse an incoming enquiry, identify what the customer is asking about, extract important information, assign it to the appropriate category, and generate a short summary for the person who needs to handle it.

The goal isn’t necessarily to remove people from the process.

Instead, AI can handle some of the repetitive work around the decision-making process, allowing people to spend more time on the parts that actually require their judgement.

Common examples include:

Classifying incoming requests
Extracting information from documents
Summarising long messages
Categorising support tickets
Turning unstructured text into structured data
Identifying important information in emails
Generating internal summaries
Processing large amounts of content

If a team is spending hours every week doing essentially the same information-processing task, it’s worth asking whether part of that process could be automated.

3. Connecting AI to the tools you already use

AI becomes even more useful when it can work with the systems your business already depends on.

That could include your CRM, CMS, helpdesk, project management system, internal database, APIs, analytics platform, or other SaaS applications.

For example, an automated workflow could receive an incoming request, extract the relevant information, classify it, look up additional information from an existing system, create or update a record, and notify the appropriate person.

The AI is only one part of that process. The rest is integration.

That’s why building useful AI features often requires more than knowing how to call an AI API. You also need to understand how the feature fits into an existing application, how data moves between systems, how users interact with the result, and what should happen when something goes wrong.

This is where frontend and product engineering become particularly important.

AI might provide the intelligence, but the surrounding product still needs to work like a product.

AI features don’t have to be complicated

One of the easiest ways to approach AI incorrectly is to start with the technology rather than the problem.

It’s tempting to begin with questions about which model to use, which API to integrate, or what the latest AI platform can do. Those questions matter eventually, but they’re usually not the best place to start.

A better starting point is to look at where users or employees are experiencing friction.

Maybe customers can’t find the information they’re looking for. Perhaps your support team repeatedly answers the same questions. Maybe employees spend hours processing similar requests, or important information is trapped inside documents that are difficult to search.

Those are the problems worth investigating.

Once the problem is clear, AI becomes one of the possible tools for solving it.

Sometimes the answer will be an AI-powered interface. Sometimes it will be an automated workflow running in the background. And sometimes the best solution will be a more traditional piece of software.

The goal isn’t to use AI everywhere. It’s to use it where it provides a meaningful advantage.

Start with the workflow, not the model

Before deciding which AI technology to use, it’s useful to understand the workflow you’re trying to improve.

What information comes into the system? What needs to happen to that information? What should the user or another system receive at the end?

It’s also important to understand where the AI fits into the process. Does it belong directly in the interface, should it run in the background, or does it need to work between several existing systems?

And perhaps most importantly, what happens when the AI gets something wrong?

AI isn’t deterministic in the same way as traditional software. A useful AI feature therefore needs sensible boundaries, validation, fallbacks, and an appropriate level of human oversight.

That doesn’t mean AI can’t be trusted. It means the system should be designed with the technology’s limitations in mind.

AI still needs good product development

An AI feature still needs to be a good product feature.

It needs a clear interface, useful feedback, sensible loading and error states, and a way of handling unexpected input. It needs to work across devices and fit naturally into the rest of the product.

This is where my frontend and design background becomes particularly useful.

An AI API can generate an answer, but it doesn’t design the experience around that answer. Someone still needs to decide how users interact with the feature, what information they see, what they can do next, and how the AI fits into the existing product.

For example, a chatbot might technically work, but that doesn’t necessarily make it a good way of helping someone find information. A search interface, guided workflow, or contextual assistant might provide a much better experience depending on the problem.

The technology is only part of the feature. The experience around it matters just as much.

What I can help build

I work with businesses that already have a product, website, workflow, or idea where AI could provide a useful improvement.

AI-powered features

Best for: adding useful AI to a product

AI can become part of an existing customer-facing experience rather than a separate tool.

AI chat
Natural-language search
Recommendations
AI-assisted interfaces
Working with your own content or data
Content and knowledge discovery

AI automation

Best for: repetitive work that should happen automatically

AI can handle parts of information-heavy processes that currently require repetitive manual work.

Classification
Information extraction
Summarisation
Content processing
AI-assisted decision workflows
Automated data transformation

Automated workflows & integrations

Best for: connecting AI to the systems you already use

The AI doesn’t have to live in isolation. It can become part of the workflows and tools your business already relies on.

APIs
CMS platforms
Databases
Internal tools
SaaS applications
Business workflows
Notifications and downstream actions

The goal isn’t to add AI because it’s fashionable.

It’s to find a part of your product or workflow where AI can make something genuinely better.

You might not need an AI product

You don’t necessarily need to rebuild your entire product around AI. In many cases, the better opportunity is to take something you already have and make one part of it significantly more useful.

That might be a better search experience, a smarter recommendation system, an assistant for a specific task, or an automated step in an internal workflow.

Small improvements can have a surprisingly large impact when they’re applied to something people use every day.

This is also one of the reasons I prefer approaching AI work from the existing product rather than starting with the technology. If the problem can be solved with a relatively small feature or workflow improvement, there may be no reason to introduce unnecessary complexity.

AI should solve a problem, not create another one

The technology is changing quickly. New models, APIs, and AI products appear constantly, but the underlying product questions haven’t really changed.

What problem are we solving? Who does it help? Why is AI useful here? What happens when it makes a mistake? How does it fit into the existing experience?

Those questions are ultimately more important than which particular AI model happens to be popular today.

That’s why I approach AI work from the product and engineering side. I look at what already exists, where the friction is, and where AI could realistically improve the experience.

Sometimes that means building an AI-powered feature. Sometimes it means automating a manual process. And sometimes the best answer is that AI isn’t needed at all.

From AI idea to working feature

If you have an idea for using AI in your product, you don’t necessarily need a complete technical specification before starting.

We can look at the problem you’re trying to solve, understand your existing product, data, and workflows, and identify where AI could provide real value.

From there, the opportunity might become a small product feature, an internal tool, an automated workflow, or a deeper integration with your existing systems.

The important part is getting from an interesting AI idea to something that actually improves the way people use your product or run your business.

AI is the technology. The useful part is what you build with it.

Thinking about adding AI to your product?

If you have an AI idea, an existing workflow that feels unnecessarily manual, or a product that could benefit from smarter search, recommendations, or content interaction, I can help turn it into a practical feature.

Let’s get in touch on LinkedIn →, or contact me via form link below, and I will get in back to you, as soon as possible