In this three-part blog series, we’ll be demystifying the hype around Artificial Intelligence (AI), helping you see the wood for the trees on how it can benefit your business. We’ll guide you through the fundamentals, get you thinking about real-world applications and provide a roadmap for a smart AI strategy.
There’s a lot of excitement around what artificial intelligence can do for businesses, but there’s no point in jumping on the bandwagon and investing for the sake of it.
No matter which AI tool you choose, AI can be a big investment for your business – which means it requires careful consideration based on your goals and priorities. But first, we need to understand how different AI technologies lend themselves to different use cases.
In our last blog, we unpacked the key differences between various types of AI. Now, let’s dive a little deeper into the specifics of how these different technologies could apply to different situations, so you can make more informed, more impactful decisions.
How is AI being used in web and mobile app development?
- Ultra personalised user experience
By analysing user behaviour, AI-driven algorithms can create a more tailored user journey, for example through personalised recommendations and notifications.
Streaming platforms like Netflix have set the expectation for AI-driven recommendations, making personalisation a crucial aspect of modern user experiences. Leveraging AI is therefore key to creating user experiences that feel effortless and intuitive, thereby boosting engagement and retention.
- Advanced image recognition
Computer Vision (CV) technology is increasingly being used to enable apps to analyse and interpret visual data.
For example, GearedApp is proud to have partnered with Stamp Free – a startup using AI to eliminate the need for stamps and printed labels – by integrating an AI model capable of recognising handwriting. CV technology was therefore crucial for Stamp Free to streamline their solution, reducing errors and saving time for both businesses and customers.
- Automating routine tasks
Most of us would agree that we’d rather spend less time on repetitive, routine tasks and focus more energy on the areas that make a real impact. From automated data entry and AI scheduling assistants to chatbots handling customer enquiries, AI tools can help free up valuable time.
Automated app testing is another game-changer, helping to deliver faster and more reliable app releases without the manual heavy lift.
When to invest in traditional AI
Traditional AI encompasses rule-based systems and predictive models that rely on structured data and predefined rules or statistical patterns. It is best suited for tasks that require consistency, accuracy and structured data processing. It follows predefined rules and statistical models to make decisions based on historical patterns.
For example, you might consider using traditional AI for:
Data management and analysis
Traditional AI is great at handling structured data, automating data processing, identifying trends and generating insights. For that reason, businesses using AI for financial forecasting, inventory management or business intelligence primarily rely on traditional AI models.
Maintenance and monitoring
Industries like manufacturing and logistics often use traditional AI to monitor equipment performance. Traditional AI can help predict failures before they occur, helping businesses avoid costly downtime and optimise maintenance schedules.
Routine customer support
AI-powered chatbots that provide predefined responses to frequently asked questions are a great example of traditional AI. These bots efficiently handle repetitive queries, improving response times while freeing up human agents for complex inquiries.
Fraud Detection and Risk Assessment
Traditional AI is very effective in detecting fraudulent transactions in banking and e-commerce. By analysing vast amounts of historical data, AI can flag suspicious activities in real time and prevent financial losses.
The key takeaway here is that traditional AI is reliable, data-driven. This means it’s ideal if you’re looking to invest in streamlining structured and repetitive tasks that demand a high level of accuracy.
When to invest in generative AI
Unlike traditional AI, generative AI is designed to create new content rather than simply analysing existing data. It leverages advanced machine learning models, such as large language models (LLMs) and neural networks, to generate original text, images, code and more. This makes it ideal if you want to invest in elevating tasks requiring creativity, adaptability and dynamic content generation.
Here are some examples of where generative AI shines:
Content creation
Generative AI can help businesses by producing high-quality text, visuals and multimedia content at scale.
To give some examples, tools like ChatGPT can help with drafting marketing copy, blog posts product descriptions and brainstorming creative ideas.
Whereas when it comes to creating branding assets, ads and social media visuals, businesses can make use of AI-powered image generation tools such as DALL·E and Runway ML.
For video and audio, businesses can use AI to generate voiceovers, subtitles and even automated video editing.
Prototyping and rapid ideation
Designers and developers can use generative AI to quickly create UI/UX mockups, generate multiple design variations and explore different product concepts. This speeds up the creative process while reducing the manual effort involved in early-stage design.
Code generation and developer productivity
Generative AI is also transforming software development by assisting developers in writing, optimising and debugging code.
For example, GitHub Copilot is an AI-powered coding assistant that suggests code snippets, automates repetitive tasks and helps developers quickly prototype new features.
What’s more, AI-powered code generation tools can significantly boost efficiency, reducing development time and minimising errors by automatically suggesting improvements and refactoring existing code.
Dynamic User Interaction
Generative AI enhances user engagement by creating context-aware, personalised responses in conversational interfaces.
For instance, AI chatbots and virtual assistants can generate more natural and human-like conversations, improving customer experience.
When it comes to gaming, marketing and personalised learning experiences, AI can be used for its storytelling and interactive content generation capabilities.
A/B testing and content optimisation
Generative AI is increasingly being used for automated A/B testing, where it can generate and test multiple variations of content to determine what resonates best with an audience.
For example, marketing teams can use AI to automatically create multiple ad copy versions, product descriptions or website headlines to boost conversion rates. It does this by personalising content based on user preferences and behaviour, overall enhancing engagement.
Key things to consider when investing in generative Ai
All in all, generative AI is amazingly flexible and versatile, proving highly effective where creativity and language understanding are needed. But it’s important to keep in mind that it does have limitations, such as occasionally generating “hallucinations” – i.e. factually incorrect information!
Hallucinations can also result in biased, misleading or nonsensical outputs. These hallucinations happen because generative AI models predict outcomes based on patterns in their training data, rather than accessing factual databases or real-time information.
Beyond hallucinations, businesses also need to consider ethical implications such as data privacy, algorithmic bias and the environmental impact of training large AI models. Addressing these concerns is key to building trust and ensuring AI is adopted in a responsible way.
Eager to get going?
Now that we’ve laid out some of the key use cases for different types of AI, you’ve probably got some ideas brewing for how you could apply these solutions in your own business. But before you invest, the next – and arguably most crucial – step is to devise a thoughtful strategy based on your unique situation.
To find out more, keep an eye out for our next blog in this series, which unpacks how to make the most of AI solutions.