Adding AI to Existing Software: A Practical Guide for UK SMEs

Artificial intelligence does not always require a brand-new application. For many UK SMEs, the opportunity is already inside their existing software.

There may be valuable business data and workflows already existing within a CRM, customer portal, dashboard, or web application. With the addition of AI, that old system can be upgraded to a smarter one without requiring the business to break away from its current technology.

This is referred to as “AI integration” and is appealing because companies can seek AI value while preserving their tech stack. There is growing emphasis in the UK on the integration of models with existing business models and processes.

Why Add AI to Software You Already Have?

The best part is, of course, the context. You already know your customers, products, process, and internal workflow, and your software does too. AI can leverage that context to make particular activities quicker or smarter.

For instance, a CRM can provide a summary of interactions, pinpoint high-priority leads, or compose follow-up messages. An internal application might be built to categorize requests, to pull out document data, or to assist workers in locating documents.

Rather than prompting employees to open a separate AI tool, copy information in and out of it, AI can be embedded into the workflow.

Which AI Features Can Be Added?

AI isn't a single feature. The right capability depends on the business problem. Below are some of the use cases:

1. AI Assistants and Copilots

An AI assistant can be embedded within an existing app to enable users to search for information, summarise records, draft content, or answer questions.

2. Intelligent Document Processing

AI can extract and categorise data from invoices, forms or applications and pass the data into current processes.

3. Natural Language Search

Using AI-powered search, users can type in questions in natural language to get information that's relevant to the meaning of the question they posed rather than just the words they used.

4. Predictive Analytics

Historical data can be analysed using AI to identify patterns, and patterns can be used to support demand forecasting, customer retention, sales analysis, or risk identification.

How Can AI Integration Be Achieved?

In general, there are multiple layers in a typical AI integration. First, the current software must be able to integrate data, usually by means of an API or integration service.

Next, relevant business information needs to be discovered and prepared. Not all the fields in a database must be accessible to an AI model. The AI functionality is then integrated with the application with the right instructions, permissions, and context.

Lastly, the output should be returned to the business workflow. But an AI recommendation on its own, in a separate dashboard, might not be of much value; one that generates a task for the appropriate employee can be much more useful. Effective integration of AI is not just a matter of choosing a model; it's also a matter of designing your workflows.

Does Existing Software Need to Be Modern?

Not necessarily. One common misconception is that businesses need to replace their existing platform before adopting AI.

Modern integration techniques can integrate AI functionalities around an application, such as an older application, by using APIs, middleware, or an application layer on top of the application. The approach is actively encouraged by UK providers for SaaS platforms, internal tools and legacy systems.

However, an assessment is important. Before implementing AI, there may be issues with data quality that need to be addressed, as well as integration points and security concerns that require attention.

What Should UK SMEs Consider Before Adding AI?

• Data Quality

AI can only be as effective as the information it consumes. Multiple entries of customers, missing or inconsistent information can compromise the accuracy of AI-generated content. The cleaning of the relevant data should then be part of the project and not an add-on.

• Security and Permissions

AI should not have full access to all the data in your business system.

The permissions for access should be the same as those used by the application. Only the information users are permitted to access should be provided to them; sensitive information should be processed in accordance with security requirements. In recent studies, security concerns were identified as a risk that organisations could fail to consider when focusing on functionality and speed. Recent studies have pointed out security concerns as one risk that can be overlooked when organisations focus on functionality and speed.

• Human Oversight

There is no need to automate all AI decisions.

In cases such as customer communication, financial transactions, or any other workflow where it is crucial to have a human review the results of an AI, a business might want an employee to check this before taking action. It's not about the maximum amount of automation. It's a fitting form of automation.

• Performance and Cost

There is a risk of extra processing costs when using AI. Take into account how often you use it, the volume of data you need, and whether you require a specific product. A smaller model is sufficient for classification; a more capable model is suitable for complex reasoning.

• Begin with a High Value Use Case

The best approach to bring AI into the application is to not attempt to replace the entire application. Look for a process that is repetitive, time-consuming, and measurable. Don't expand without measuring the results. This makes expansion more of a business case.

Why KodeCube for AI-Powered Software Solutions?

At KodeCube, we focus on making AI useful to software businesses that we already depend on.

We don't think about AI in isolation, but how it can fit into your existing applications, data and workflow. This enables businesses to test out intelligent features without having to switch out their entire technology infrastructure. We are delivering enterprise-grade quality with cost-efficient delivery, accelerated development, and clear communication, to provide a way for UK SMEs to get on the path to AI adoption.

FAQ

1. Can KodeCube integrate AI into our existing software?

Yes. KodeCube can analyse your current application, integrations and workflows to discover realistic possibilities to introduce AI features without replacing the system.

2. Why choose KodeCube for AI software development?

KodeCube brings enterprise-grade software development and cost-efficient delivery, faster development, and transparent communication. We prioritise the integration of AI around measurable business requirements, allowing for the adoption of intelligent technology within the context of UK SMEs without unnecessary complexity.