What AI means for the software you already have

AI is reshaping how software is built, used and maintained but most organisations aren’t starting from scratch. They’re working with what they already have: legacy platforms, off-the-shelf SaaS, or custom tools that still perform core business functions.

So the question isn’t should we use AI. It’s: how do we apply AI to the systems already in place?

Here’s a breakdown of your main pathways.

1. Replatforming: Set the groundwork for AI

If your current system is no longer fit for purpose, hard to change, expensive to maintain, incompatible with APIs, then it may be time to replatform.

This isn’t about chasing the latest trend. It’s about building a modern, modular architecture where AI can be introduced safely and incrementally.

Replatforming to low-code environments like OutSystems can significantly reduce delivery timelines while giving you the flexibility to add AI components later. These platforms also have prebuilt connectors for popular AI models, audit logging features, and alignment with security standards like ISO27001 and Essential 8.

Key considerations:

• Avoid monolithic rebuilds and focus on replatforming business-critical workflows first
• Introduce automation only once data quality and permissions are under control
• Re-use core business logic wherever possible to reduce risk.

This approach suits departments that already have transformation funding allocated or a pressing need to modernise due to compliance, end-of-life technology, or performance issues.

2. Layering AI onto existing software

You don’t always need to replace the software you have to benefit from AI. In many cases, AI can be introduced as a service layer that augments existing systems. This can provide immediate impact while avoiding major disruption.

Examples include:

• Using document AI to extract data from forms, PDFs, or reports
• Introducing large language models (LLMs) to summarise long case files or meeting transcripts
• Adding predictive analytics to existing dashboards for better forecasting
• Using generative AI to draft standard communications or reports based on structured data
• Automating testing or bug triage in legacy codebases.

This approach works best when your current systems support APIs or file exports and there’s a clear, well-bounded task that AI can perform. In regulated environments, it’s critical to ensure models are applied within guardrails and this includes model explainability, auditability, and access controls.

Don’t overlook change management. Adding AI to a process affects how people work. A clear operational model, training, and escalation path are essential for adoption.

3. Assessing the SaaS you already use

If you’re using enterprise SaaS like Microsoft 365, Salesforce, or ServiceNow, chances are AI is already available, whether or not it’s been activated. This means some of the value is already in your hands and it’s just a matter of turning it on in a controlled, responsible way.

Before enabling AI features, consider:

• What data is being ingested into the AI engine, and where is it stored?
• Are permissions and roles clearly defined to prevent data leakage?
• Will outputs be reviewed by a human before being used in production or customer-facing contexts?
• Is the model trained on your data, or shared tenant data?

In many cases, business units are already experimenting with generative AI tools in isolation. As an application owner or CIO, you have a role to play in coordinating this experimentation, guiding responsible use, and avoiding duplication.

4. When to do nothing (yet)

Sometimes the right decision is to wait.

If your system is tightly integrated, business-critical, and heavily customised, retrofitting AI might pose more risk than it’s worth - at least in the short term.

Instead, take this time to:

• Document current-state workflows and dependencies
• Clean and consolidate your data sources
• Run controlled pilots in non-critical areas
• Start small with automation that has clear governance boundaries
• Build internal AI capability including technical, operational, and ethical.

Use this time to establish the foundations so that when you do move, you move with purpose.

AI is not a single product or solution, it’s a set of capabilities. Applying those capabilities in a way that respects your architecture, risk profile, and compliance needs is where the real work begins.

At Kiandra, we work with CIOs and application leaders to navigate exactly. Whether you're modernising core systems, connecting fragmented tools, or applying AI selectively to gain efficiency, we help you make smart decisions about how to move forward.

If you’re not sure where AI fits in your current stack, start by identifying friction. AI is most effective when it removes repetitive work, speeds up decisions, or improves accuracy. Not everything needs to change.

Use this checklist to assess if your existing systems are ready for AI integration, need enhancement, or require replatforming.