AI Consulting

It depends on where leadership currently sits. If there is no shared understanding of what AI can realistically deliver, or no agreed position on where it should be applied, that is the right starting point, not a use case or a vendor evaluation. Moving into delivery before that alignment exists tends to produce pilots that do not progress.

If leadership is already aligned and the question is which use cases to prioritise, we can start there. We will identify quickly if anything upstream needs to be addressed before committing to build or procure.

Before any build or procurement decision is made, we define what success looks like in measurable terms as part of an AI consulting engagement, whether that is time saved, decisions improved, cost reduced, or risk managed. We then assess whether the use case, the data, and the organisational context can actually support that outcome.

If the numbers do not support the investment at this stage, we identify that before anything is committed. That is where most of the waste in AI programs occurs.

AI consulting helps organisations determine where artificial intelligence can deliver value, and how to introduce it in a way that is practical, controlled, and aligned to the business.

Most organisations do not need to start with a tool or a vendor. They need clarity on where AI fits within their existing systems, data, and operating model, and whether the conditions are in place to support it.

AI consulting is typically most valuable when:

The objective is to establish a clear position before any commitment is made, and to ensure that any investment is grounded in outcomes that can be measured.

We assess three things: whether a vendor solution genuinely fits your use case and operating context, whether your data is ready to support what you are trying to do, and whether the total cost of each path including integration, change management, and ongoing maintenance is justified by the return.

We have working knowledge of the major AI platforms and can advise on where they perform well in practice. When a vendor solution is the right fit, we will say so. When it is not, we will identify that too and help you determine whether building or engaging a specialist partner is the better path.

Depending on what the use case requires, we work alongside your team directly or engage a trusted specialist partner with the relevant capability. Solutions are built in your environment with your data, across LLMs, retrieval-augmented generation systems, AI agents, and automation workflows.

The approach is the same either way: solutions are designed to reach production rather than sit in a demonstration environment, and capability transfers to your team throughout so what gets built is owned and maintainable by your organisation.

At a minimum: clear accountability for AI decisions, a documented position on acceptable use, controls around data quality and privacy, and a process for reviewing AI outputs where they affect material decisions. Australian regulatory expectations are developing, and the EU AI Act is already shaping how organisations with international exposure approach this.

Governance is significantly easier to establish from the start than to retrofit once AI is already embedded in operations. We help organisations build the right frameworks at whatever stage they are at and can support boards in understanding what questions to ask of management.

For most mid-to-large organisations, the highest-value AI opportunities sit inside or adjacent to existing enterprise systems, in the data they already hold, the processes they already run, and the decisions made repeatedly at scale. Treating AI as a separate initiative from those systems tends to produce fragmented results.

SMC brings experience across 750+ ERP and enterprise transformation programs. That context shapes how we identify AI opportunities that are practical given your current technology landscape, not just theoretically attractive.

Yes, and this is one of the more common ways organisations engage us. We review what is in place, assess whether it is delivering against the original business case, and identify where governance, data quality, or delivery discipline may need to be strengthened.

We have no stake in what has already been decided, which means the review is genuinely independent. If the program is on track, we will confirm that. If it needs adjustment, we will be direct about what needs to change and why.

Whether you are at the strategy and roadmap stage or already reviewing AI already in place, a short conversation is usually the clearest way to work out where SMC can add the most value.