SuiteConnect Chicago: NetSuite AI and the Future of Finance

Introduction:

Last week, I attended SuiteConnect Chicago, one of NetSuite’s regional conferences focused on product innovation, finance transformation, and the future of the finance office.

The big theme this year was AI.

The event mixed big-picture features with practical steps for CFOs to incorporate AI to help controllers, CFOs, and finance teams.

That was the most useful part of the event for me. It was about making strong finance teams more effective. The question everyone is trying to answer is:

How can AI and NetSuite make your finance function add more value to the organization?

Read on to hear what I learned at the event.

Real-world examples from the COO at DM Merchandising

One of the more interesting customer stories came from DM Merchandising.

What stood out was not just the technology. It was the operational discipline behind it.

They talked about shortening the close from roughly 30 days to 5 days, improving purchasing visibility, and better understanding of costs at the SKU level. One comment that stuck with me was the idea that good systems force you to get your data right. Once your data is right, then you are in the game.

AI is not a shortcut around bad data. In most cases, it makes the quality of your underlying data even more important.

Treat AI like a new hire

One of the better lines I heard at the event was:

Treat AI like a new hire out of college.

That framing works because it sets the right expectations.

No one expects a new hire to transform the whole company on day one. You train them, give feedback, correct mistakes, and help them learn what good looks like. Over time, they become much more valuable.

AI is similar.

The companies getting the most out of it are not expecting perfection immediately. They are starting with focused use cases, being patient, and improving the results over time.

Several practical examples came up during the event:

• Preparing debt financing documents
• Reviewing long pitch decks and pulling out questions or inconsistencies
• Analyzing contracts and answering questions about key terms
• Summarizing operational or financial information for internal discussions

That feels much more realistic than the broader headlines. AI is not magic, but it can save meaningful time on first-pass FP&A analysis and repetitive financial planning work. The practical takeaway:

Start small, fail fast, and iterate to see the best results.

What NetSuite is building

NetSuite is clearly pushing AI deeper into the product.

A few of the capabilities discussed stood out.

Intelligent Close and exception management

One session focused on AI and the close process. The vision they discussed was a faster, more automated close, working towards a zero-day close over time.

The most practical piece was exception management.

Instead of asking the accounting team to review everything, the system can identify transactions or balances that appear unusual and direct attention to them first. Examples included:

• Missing transactions
• Unusual fluctuations in balances
• Anomalies that may require investigation

There was also discussion of a sensitivity or materiality setting, which is important. To me, this is the real opportunity. AI does not eliminate review. It helps narrow the review to what actually matters.

Flux analysis

Flux analysis was another interesting topic. That is already a familiar process for many accounting and finance teams, especially for management reporting, MD&A reporting in SEC filings, or budget-vs-actual analysis.

Using AI to accelerate flux analysis is a no-brainer in my opinion. The system can highlight what changed, where the outliers are, and what deserves follow-up.

AI connectors and SuiteAgents

There was also discussion of AI connectors and SuiteAgents, which point toward a future in which finance teams can use AI more directly within their workflows.

The details are still evolving, but the direction is clear. AI agents are coming to native NetSuite. These types of agents already exist in some 3rd-party products, such as Cauzzy AI (link).

That is worth watching, especially as companies think about how AI will fit into their broader finance systems over the next few years.

The private equity perspective was practical, too

The private equity roundtable had a similar tone.

The advice was not to wait for the perfect strategy. It was to start learning, pilot something, and build experience inside the business.

The speakers cited an MIT study saying that 95% of enterprise-level AI initiatives were failing. Rather than stay on the sidelines, they emphasized the importance of finding the right use case. That 5% win rate can be the differentiator between you and your competitors.

A few themes came up to help your organization identify and execute AI initiatives:

• Create internal AI super users
• Evaluate the buy versus build decision carefully for AI products
• Define metrics to evaluate whether an AI initiative is actually working
• Use AI to support time-consuming tasks like document review and analysis

That is a useful lens for finance leaders. AI should not be adopted because it is interesting. It should be adopted because it improves a process, reduces effort, or helps the business move faster.

The hidden requirement: historical data

The most important takeaway for me came from a session on AI and the close.

The speaker mentioned that NetSuite’s AI models may need about 18 months of historical data before they really start working well. This is similar to how the demand planning module works.

That has major implications for NetSuite implementations.

Many companies still go live with opening balances, limited history, and summary-level prior-period data. That approach can work from a pure accounting standpoint. But if AI depends on historical patterns, then a thin data set limits what the system can do.

In other words, if you do not migrate meaningful transaction history, your AI capabilities may be limited until NetSuite accumulates enough data on its own.

That is a much bigger strategic issue than most companies realize during implementation.

Why this matters for the data migration strategy

For years, discussions of historical data during ERP implementations have usually focused on reporting, audit support, or convenience.

AI adds a new reason to care.

Detailed transaction history can help support:

• pattern recognition
• stronger management reporting
• quicker adoption of AI-driven features

That does not mean every company should load every transaction from the past ten years. In many cases, a practical middle ground is best.

For many organizations, 18 to 24 months of detailed transactional history plus summarized older balances is probably the right mix. It gives the system enough context to be useful without overcomplicating the migration.

Final takeaway

The biggest lesson from SuiteConnect Chicago was simple:

AI in finance is becoming practical, but it will only be as useful as the data behind it.

The companies that benefit fastest will not just be the ones that turn on new features. They will be the ones who have enough clean, relevant history in NetSuite for those features to work.

For controllers, that means better exception review and more efficient closes.

For finance executives, that means faster insights and a stronger return on the NetSuite investment.

And for companies in the middle of an implementation, it means the data migration strategy matters more than ever.