AI Is Changing ERP Support

10 trends our NetSuite experts are seeing

ERP support teams have a unique view of how organisations are using their systems in practice. They see where processes break down, which features are being overlooked and how new technology is changing customer expectations.

As AI and automation become more accessible, the nature of ERP support is beginning to shift. Customers can resolve more basic issues independently, but the problems reaching support teams are often more complex. At the same time, organisations are looking for greater strategic guidance, stronger integrations and more proactive ways to improve their systems.

To understand these changes, we spoke to our NetSuite support team about the trends they are seeing across customer environments and what finance and operational leaders should be considering as they prepare for 2027.

What changes have you noticed in the types of support requests customers are raising over the past 12–18 months?

The nature of requests has shifted from simple how-to queries toward more complex, in-depth issues. A big driver is AI: customers are increasingly attempting to solve problems themselves first, writing their own scripts, querying their databases, or automating tasks, before bringing the results to us when something doesn’t work. This means our team is spending more time diagnosing self-built solutions rather than answering basic questions, which naturally raises the complexity of the average ticket.

Are customers increasingly expecting their support partner to recommend improvements rather than simply respond when something goes wrong?

This isn’t a new expectation driven by AI: it’s always been part of good support. What’s changed is that AI can now often solve the immediate, basic fix itself, which shifts the value we add further up the chain: toward identifying why an issue keeps happening and improving the underlying workflow or configuration so it doesn’t recur. Our focus has always been on solving root causes rather than symptoms, and that’s become more visible as the ‘easy fixes’ get absorbed by AI tools.

What recurring issues do you see most often, and how frequently is the real cause related to process, configuration, or training rather than the software itself?

The vast majority of cases, informally, well over 90%, come down to configuration, training gaps, or process rather than genuine software faults. Common patterns include:

• Systems that are technically working correctly, but the customer doesn’t understand how they’re supposed to work, creating a perceived issue where none exists.
• Requested changes rather than defects.
• Knowledge gaps following staff turnover.
• Unexplained variances customers want investigated.

Genuine software errors are relatively rare by comparison. There isn’t one dominant recurring issue: it tends to move with the environment (e.g. a spike in reconciliation queries after an upgrade), rather than any one theme staying constant.

Where are customers still relying heavily on spreadsheets, manual reconciliations, or duplicate data entry despite having an ERP system?

Reporting is the clearest example. NetSuite’s native reporting can be limited for certain use cases, budget vs. actual vs. prior year comparisons being a common one, so customers fall back on tools like Power BI or build workarounds in Excel. Beyond specific reporting gaps, this often comes down to habit and investment: teams that have worked a certain way for a long time are reluctant to change, and full automation usually requires paying for custom workbooks or reports, which not every customer is willing to invest in.

What signs usually tell you that an organisation has outgrown a purely reactive ticket-based support model?

The clearest signals are volume-based: a high number of tickets overall, repeated tickets on the same issue, or a steady stream of improvement opportunities being raised. When these patterns appear, it suggests the customer would benefit from a more proactive model, such as scheduled site audits or system monitoring (as offered through Advanced Support), rather than only fixing issues as they’re reported.

Are customers making full use of the functionality already available within their ERP systems, or are valuable features often being overlooked?

Almost universally, no: there’s always more available functionality than customers are using. NetSuite’s ecosystem of apps, bundles, and integrations is broad, and few organisations exploit it fully. This connects closely to cost, customers are more likely to adopt underused features when doing so is bundled into an existing subscription or service (like time built into Advanced Support) rather than requiring a separate chargeable engagement. Ultimately, it comes down to whether the value of a feature to the customer outweighs the cost and effort of implementing it.

What integration challenges are becoming more common as organisations connect their ERP with more business systems?

We’d frame this more as an opportunity than a challenge. AI-driven components are making integrations more powerful, flexible, and automated than before. The main risk worth flagging is dependency: the more systems an ERP is connected to, the more the business relies on every one of those systems performing reliably. If one integration point goes down, it can have a knock-on effect across the wider operation. In practice, though, well-implemented integrations should save far more time than they cost through occasional disruption.

What are customers currently asking your team about AI, and are there any common misconceptions or unrealistic expectations?

Most immediate questions are practical, largely around permissions and what’s needed to connect AI tools to NetSuite. The clearest misconception is an assumption that AI-generated output is automatically correct. We’ve seen cases where customers use AI to build scripts or analyse workflows and treat the output as authoritative, without the expertise to validate it, which can introduce risk into their live NetSuite environment. AI is also far more reliable on generic questions than on anything specific to a customer’s own customised environment, where it lacks context and can produce confidently wrong answers.

When an organisation wants to introduce AI or greater automation, what weaknesses in its data, policies, or processes tend to become visible?

The most common weakness is less about data itself and more about the user: people underestimate how much skill is needed to prompt AI effectively and get it to a useful outcome. There’s often an assumption that AI will ‘just work,’ without guiding it toward the right context or end goal. Combined with this, AI has no inherent knowledge of a customer’s specific customisations, so incomplete context leads to unreliable answers, and the risk of hallucination remains constant, meaning outputs always need human validation rather than blind trust.

Looking ahead to 2027, what should finance and operational leaders prioritise now to keep their ERP environment efficient, secure, and ready for further automation?

Three priorities stand out:

• Data accuracy: automation and AI are only as good as the data behind them, so this remains foundational.
• Process simplification: reviewing workflows to remove unnecessary complexity before layering automation on top, rather than automating inefficiency.
• Balanced reliance on AI: using it to remove mundane, repetitive tasks (as we’ve done ourselves with manual email logging) and speed up reporting, while making sure staff retain the underlying knowledge to check and challenge AI output, rather than becoming dependent on it.

What this means for finance teams

The clearest takeaway is that AI is not reducing the need for expert ERP support. It is changing where that expertise delivers the most value.

As basic questions become easier to answer independently, support teams are increasingly being asked to diagnose more complex issues, validate customer-built solutions and identify the underlying causes of recurring problems. This makes deep knowledge of each organisation’s configuration, processes and wider system environment more important than ever. For finance and operational leaders, preparing for further automation means getting the foundations right first. Accurate data, simpler processes, reliable integrations and well-trained users will determine whether AI creates meaningful efficiency or introduces additional risk.

The goal is not simply to automate more. It is to build an ERP environment that is efficient, connected and capable of evolving with the organisation, while retaining the human expertise needed to question, validate and improve what the technology produces.

If your organisation is seeing repeated support issues, underused functionality or growing complexity across its ERP environment, it may be time to move beyond a purely reactive support model. Speak to Eureka Solutions about how a more proactive approach could help you get greater value from NetSuite.