What is AI integration into applications?
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Definition of AI integration into applications
AI systems such as natural language processing and machine learning algorithms can be integrated into existing applications to add functionality and improve their performance over time. Examples of AI features that can be integrated into applications are:
facial recognition image processing
speech processing
personalised content
Executive view
If your company is developing an application there are numerous development costs to consider that are associated with security features, personalisation and data collection. Often AI solutions exist to support these requirements, which are effective and improve your product over time. This leads to improved performance of your product, and greater customer satisfaction, as well as better, more efficient data collection.
AI integration into applications helps businesses:
save on development costs by introducing machine learning features.
create more secure and profitable products that effectively meet end-users' needs.
Business function leader view
AI integration into applications helps teams build effective products with enhanced security features and intelligent, personalised content. These products are generally more profitable than applications without AI features because they attract a larger user base and introduce up-selling and retention opportunities.
This is worth considering if:
- you are developing an application.
- your team lacks data science skills and experience in-house.
KPIs worth measuring for this are:
increased sign-ups to your application
increased revenue from up-selling via intelligent features (e.g. product/upgrade recommendations)
improved retention rate
Technical view
Developing intelligent features for your application leads to better usage feedback for you and a more efficient and personalised experience for the end-user. AI integration into applications helps deliver:
actionable feedback
automation
increased security
reduced development load
Worth investigating if you encounter:
difficulty or lack of time and resources for developing security, recommendation and automation features for your product.
a lack of insight into how your product is being used.
low customer retention.
Key criteria to consider are:
Does a solution for your automation and security needs already exist for integration?
Do you have the resources available to monitor feedback from AI integrations?
Are you able to store and process data from intelligent features securely?
Would AI features enhance your product?
A real example: mill certificate processing as AI integration
A concrete example of what “AI integration into an application” looks like in practice, rather than in the abstract, is GoSmarter’s own mill certificate pipeline.
From a user’s point of view, uploading a mill certificate to the platform just works: the document is read, the data appears against the right stock item, and the certificate is linked by heat number — no AI vocabulary anywhere in the experience. Under the hood, that’s an event-driven pipeline stitching together two distinct AI services with the rest of the application:
- A user uploads a certificate through the core application, which stores the file and publishes a message onto an internal event bus.
- A classification step (built on Azure Document Intelligence) reads the first page and works out which supplier’s certificate format this is.
- An extraction step (built on Azure Content Understanding) reads every page and pulls out the structured fields — heat number, chemistry, mechanical properties — according to a schema.
- The result is written back into the core application’s database and linked to the relevant inventory record, and the user sees the finished, structured data.
This is a useful pattern to notice: the AI capability isn’t the product, it’s one stage in a pipeline of otherwise ordinary application logic (file storage, message routing, database persistence, a UI). Integrating AI into an application well usually means exactly this — treating the AI call as a service with a defined input and output, wired into the surrounding system with proper error handling, rather than bolting a chatbot widget onto the side of an existing product and calling it “AI-powered.”