Stop Over-Complicating Innovation

Don’t rebuild. Extend.

When organisations start thinking seriously about AI, it can seem as though there are only two options: build something highly customised from the ground up, or replace core systems altogether. 

Both are expensive and, in many cases, unnecessary.

A more pragmatic path is to start with the AI capabilities already available within your existing software stack. Then, where needed, extend those systems using APIs, orchestration layers, or focused automation workflows that sit on top of your existing environment. You don’t always need a new model. Often, you need a better way to remove manual effort.

This is where many early wins are found: not in grand transformation programs, but in targeted use cases that improve speed, reduce effort and create measurable operational value.

Where To Find Early Wins

A useful early AI win doesn’t need to change your business model. It needs to remove friction in places where work is repetitive, manual, time-consuming or hard to scale.

The strongest opportunities are often human-in-the-loop workflows, where AI handles the routine elements, and people remain responsible for judgement, approval and exceptions.

If you need to demonstrate value in 90 days (especially to the Board), operational areas worth examining include:

1. FINANCE - Invoice Matching 

Accounts payable teams often spend significant time matching purchase orders to invoices, chasing discrepancies and resolving routine exceptions.

The opportunity here is not necessarily to buy another point solution. It may be to activate the automation features already available within your ERP, finance or logistics platform.

In this model, AI handles the routine matches and flags the exceptions. Your finance team focuses on where judgement is actually needed. As seen with a major logistics provider, this kind of platform-based automation can simplify invoicing, manage billing complexity and help teams scale volume without immediately scaling headcount.

2. CUSTOMER SERVICE - Routine Triage 

Many helpdesk and service teams are under pressure from high volumes of repetitive queries, but the first AI opportunity is not always a customer-facing chatbot.

A stronger starting point is often the work that happens behind the scenes: reviewing inbound requests, identifying what the customer needs, classifying the issue, and routing it to the right person or team.

Take the example of customer service ticket triage. In one scenario with a leading Australian retailer, each ticket previously took around 15 minutes to review, classify and forward to the right person. By applying focused AI automation, that process was reduced to less than a minute, including routing the ticket and generating actionable next steps for the responsible employee.

The key is to repeat this pattern. Start with one clear friction point, prove the value, then move to the next. Over time, the organisation builds practical AI capability from the bottom up, allowing value, confidence and experience to compound.

The human still remains in control where judgement, empathy or exception handling is required. The AI simply reduces the administrative load, helping the team respond faster and focus more of their time on the moments that matter most to the customer.

3. OPERATIONS - Compliance and Reporting 

In operational environments, a great deal of time can be lost to repetitive documentation, site reporting, and compliance administration.

This is where off-the-shelf computer vision, voice-to-text tools and workflow platforms can make a meaningful difference. AI can capture site activity, draft diary entries, or automate parts of safety reporting, while managers review and validate the output.

An industry-leading construction firm took this route by using established platforms such as Procore and OpenSpace to improve safety checks and site reporting. They didn’t need to build a bespoke model. They used tools that already had the required capability built in.

Earn The Right To Scale

Instead of asking for a major transformation budget based on distant potential, the better approach to management and boards is to identify a specific operational problem, apply focused automation, and show that the team can deliver a measurable outcome in a relatively short timeframe.

That matters for two reasons. First, it proves value. Second, it proves execution.

It also shifts the discussion from innovation theatre to operating performance. If a tool doesn’t make work faster, simpler, safer or more scalable, it’s worth asking why it’s there.

The 90-day Lens

Most organisations don’t need another broad internal experiment with generic AI tools. They need a clearer discipline for identifying where AI can solve a specific, manual and expensive problem - in 90 days.

That means starting with the workflow, not the hype.

Find the process that’s slow, repetitive and costly. Then look for the platform, feature set or vendor that has already solved that problem well. In many cases, the fastest route to value isn’t inventing something new. It’s applying existing capability with more focus.

This isn’t about becoming a different kind of company overnight. It’s about becoming a more effective one.

Why This Matters

For many organisations, moving beyond AI pilots requires more than access to technology. It requires disciplined thinking about where value can be created, what realistically can be delivered, and how to build early momentum without adding unnecessary complexity.

That’s where a more pragmatic approach matters. The goal is not to pursue AI for its own sake, but to identify where it can deliver tangible results and implement it in a way that is stable, useful and scalable over time.


Go to the next article in this series: Part 3 of The AI-Ready C-Suite Playbook

Need Help Identifying The Right AI Use Case?

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