The Finance Technology Decision Most Companies Never Discuss: How Tuxera Built an AI-Ready Data Foundation

Reporting, planning and AI all depend on the same thing: trusted data. Yet most companies invest in each one separately and never decide what data foundation sits underneath. In Staria’s webinar "The Finance Technology Decision Most Companies Never Discuss", Mikko Kurki, Head of Commercial Excellence at Staria, and Jonas Juslin, CFO of Tuxera, explain why that decision matters most, and share how Tuxera built one source of truth for finance, sales and management.

The webinar was held on 29 September 2026. Tuxera, a global software company headquartered in Finland, went from data scattered across NetSuite, Salesforce, an HR system and countless Excel files to one governed foundation, live within about six to seven months of starting their review.

Key takeaways

• Decide the data foundation before you choose the tools, because reporting, planning and AI all depend on it.

• AI does not fix fragmented data. It fills the gaps with answers that sound right but may be wrong.

• When comparing options, price the whole stack: add-ons, a data warehouse, integrations and upkeep, not just the feature list.

Watch the full webinar recording and get the AI-ready finance foundation scorecard.

Why do reporting, planning and AI end up as separate decisions?

Finance is under pressure from every direction. Leadership expects better forecasts, the business expects faster answers, and AI promises new productivity. Each expectation is reasonable, but they all land on the same team.

Most companies respond to each one separately. Reporting becomes a project with its own tool and budget, planning becomes another, and AI becomes a pilot on the side. Each ends up with its own copy of the data. As Mikko points out, nobody is doing anything wrong: needs arrive at different times, software is sold in categories, and ownership is split between finance, IT and the business. The result is that nobody owns the foundation. The data layer underneath falls between projects, so it is never actually decided.

Can’t AI just fix fragmented finance data?

No. To give a reliable answer, AI needs access to all the relevant data, the same definitions in every system, numbers it can trace back to their source and clear rules on who may see what. In most companies, it finds the opposite: data split across systems, customers and products that don’t match, and no clear owner of the numbers.

AI doesn’t stop at those gaps. It fills them with answers that sound right and may be wrong, it hallucinates. In Mikko’s words, a confident wrong number is worse for finance than a slow right one. AI doesn’t replace the foundation; it depends on it.

What makes a data foundation trusted and AI-ready?

A trusted data foundation sits between your source systems, such as NetSuite and Salesforce, and the things the business wants: reporting, planning and AI-powered decisions. According to Mikko, connecting the systems isn’t enough. The foundation needs three things:

• Master data management: one agreed version of every customer, product and legal entity, so nobody has to match them by hand.

• Curated, governed data: shared definitions, a clear owner for every key number and figures you can trace back to their source.

• Security built in: access rights defined once, so they apply the same way to people, reports and AI tools. An AI assistant should only see what the person asking is allowed to see.

How Tuxera built one source of truth for finance and the business

Tuxera’s financials lived in NetSuite, sales and customer data in Salesforce and HR data in Hailey. Excel held it all together, with several versions of the same file on SharePoint and laptops. Finance pulled the numbers together by hand and was never quite sure which file was the latest.

Jonas describes three costs. The first was confusion: depending on who asked and who answered, the same question could get different answers. The second was speed: understanding what had changed in sales, and why, could take several days. The third was inefficiency: forecasts and budgets ran in separate Excel files, entirely driven by finance, which made finance a bottleneck. In Jonas’ words, the reports weren’t the problem; the data behind them was.

Tuxera’s answer was one governed foundation built on Naviloq, a data platform delivered by Staria’s BI and planning team. Data from NetSuite and Salesforce, along with relevant HR data and the remaining Excel inputs, is now brought together in one data warehouse with saved versions, reporting and dashboards, forecasting and budgeting, and shared definitions and access rights. Finance, sales and management all work from the same numbers.

For Jonas, that is what AI-ready means: not buying an AI feature, but having trusted, governed data in one place.

What changed for Tuxera?

Jonas says the biggest gain isn’t the days saved, but quality: function owners now see their own numbers and get better answers.

The biggest surprise for Jonas was who benefited most: the sales team, which now runs almost all its analytics in Naviloq.

How did Tuxera choose its finance technology approach?

Tuxera spent about four months comparing four approaches, watching demos and interviewing users of each.

Tuxera chose Naviloq because it combined a data warehouse, reporting and planning in one, and let them go live without a separate IT project for data management. When asked why they didn’t build on a dedicated data platform such as Snowflake or Databricks, Jonas pointed to speed and cost: for Tuxera’s current needs, a packaged solution was quicker and more cost-efficient, although a dedicated platform could become relevant as the company grows.

The whole journey took about six to seven months: four months reviewing the options and two to three months implementing. Most of the time went into choosing well.

Lessons from evaluating and implementing

Jonas shared seven lessons for other finance leaders:

1. Start from your questions, not the tools. The variance question showed Tuxera the real scope.

2. Price the whole stack. Count add-ons, the data warehouse, integrations and upkeep, not just one tool.

3. Speed has value. Tuxera skipped a six-month IT data warehouse project.

4. Talk to users, not just vendors. References showed how each tool works day to day.

5. Plan time for data cleanup. Moving data to one place exposes quality issues in source systems, especially the CRM.

6. Finance owns the definitions; the business builds its views. Teams build their own reports, including with AI, which drives adoption, as long as everything runs on the one source of truth.

7. Choose the partner as carefully as the product. Much works out of the box, but your processes still need customisation, and the partner’s know-how shapes timeline, outcome and cost.