The top priorities for finance strategy
For 85% of CFOs, the top priorities are optimizing financial performance and improving risk and compliance management. Joint runners up at 38% are accelerating digital transformation and finding and developing the talent and leadership needed to meet new work imperatives.
23% named increasing board and stakeholder engagement in their top 3—suggesting a minority of CFOs still lag behind in terms of the overall elevation of the role into a strategic advisory capacity.
What can we learn from this? CFOs are taking on a more important strategic role in enterprise leadership. But for many, the priority is still managing operational challenges. 54% have cited regulatory complexity, cybersecurity, fraud, and risk exposure—all operational concerns—as major external impediments to their ability to pursue their desired strategies. And internally, the pressure to reduce manual and repetitive workloads remains a primary driver for modernization for 53% of CFOs alongside optimizing overall performance. CFOs don’t just want to report on the numbers, they want to be able to proactively influence them.
CFOs are grappling with a maturity plateau
While most finance departments have managed to move beyond the most basic level of digital maturity, with 58% of respondents rating their finance department as “Established/Advanced” when it comes to management, reporting, risk, and compliance, only 8% consider themselves “Optimized/Leading”, where continuous, data-driven improvement backed by analytics and automation and predictive insights supports strategic objectives.
What does this mean in practice? Basic automated processes are in place in almost all finance organizations as we enter 2026. But while financial management, reporting, risk management and compliance are all integrated into business processes, there’s almost no formal governance structure. And the vast majority of teams still aren’t able to take advantage of the full power of the automation technology now available to them—whether that’s traditional process automation, AI-powered predictive and prescriptive analytics, or enterprise-wide integrations.
In fact, only 17% of CFOs reported that their systems are “fully integrated” in a way that allows all relevant departments and teams to operate with shared data, workflows, and control integrated across their SAP installations and other third-party systems. 50% say they’re “mostly” or only “partly” integrated in this way, with most using SAP as their system of record with siloed solutions operating on the edges of their estate.
54% of respondents rely primarily on S/4HANA while 29% rely on ECC 6.0, suggesting a significant portion of the market still has a long way to go towards the stated goal for over half of those who have migrated of “consolidating financial activities” and driving their finance transformation through technical migration.
This means there’s a huge opportunity for CFOs to meet their goals by embracing new technologies—not just in terms of automation, but by taking the opportunity created by the global “AI moment” to fully migrate and upgrade their core ERP systems in order to embrace their existing cloud and hybrid strategies, integrate their entire tech stack, and fully embrace advanced AI use cases like agent-to-agent automation, predictive analytics, and generative-AI driven real-time insight.
This is music to our ears at Serrala, because this reality is exactly what we’ve created our new Serrala Finance Platform to address. A single integrated command center for finance is both necessary to successfully deploy AI, something that can only really be made possible by the AI moment. It’s important that both organizations and their vendors are working to anticipate this.
It’s official: thanks to finance automation the annual budget is dead
While most (57%) are still using an annual rhythm for their financial planning, this is mostly out of habit rather than necessity. 43% have already switched to rolling or continuous forecasting and planning models. 71% now also use a driver-based approach, as opposed to a more traditional lagging indicator model of planning, linking their financial forecasts and liquidity planning approach to dynamic operational drivers that allow for better real-time scenario modelling.
For treasury teams, liquidity is now a strategic lever that creates greater degrees of freedom
Liquidity is the font of flexibility and strength in a high-interest market. Treasury leaders realize this and they’re adopting technology that helps them take greater control of their working capital with greater visibility. This is vital in strategic and tactical liquidity timing that drives overall company strategy.
The most common adoptions here (both at 83%) are AI for fraud and anomaly detection, and FX/IR hedging programs that proactively manage financial risk.
AI for narrative assistance and board packs and agentic AI for payment proposals and approvals follow at 67%, as does in-house banking and cash pooling functionality that assists large multi-entity companies in optimizing their internal liquidity to reduce reliance on external credit and banking partners.
The ROI on all of these technologies is provable, and SAPinsider goes so far as to say that CFOs who aren’t investing in integrated treasury platforms are leaving “millions of dollars of value” on the table—and losing speed advantages to boot.
Many CFOs still struggle with the close
53% of our respondents say their month-end close takes 4-7 days, with 42% taking 8 days or more. That means that an eye-watering 95% of finance teams still can’t accomplish one of their most significant ongoing responsibilities without a significant delay.
And these delays aren’t just significant because of their length, they’re significant because of their cost and the strategic liability they create for the enterprise. If you can’t close and present final data until you’re a third of the way through the following month, none of the information your leadership teams are dealing with provides the kind of real-time visibility and control of working capital intelligence needed to steer the ship, you’re basing decisions on guesswork.
As many Serrala customers already know, the close doesn’t have to be an uphill struggle. It’s possible to find and consolidate most of the data collection and reporting needed for the process with existing automation technologies. And when we layer AI on top of that, it’s possible to graduate from a close that takes just one or two days to a model of “continuous closing”. Where data is confirmed and validated for every transaction as soon as it occurs, and it’s possible to monitor the status of all pending outbound and inbound payments in real time.
The 5% of respondents currently closing in 1-3 days have a decisive advantage here. If their data is in at the end of the month, they can close on day 1 or 2 of the new month and validate all data by day 3. The rest of the month becomes free time to analyze, to plan, and to unlock value for the rest of the organization through strategic partnership initiatives.
The real story of AI adoption and capability in finance—“it’s nuanced”
69% of CFOs say they’re already using AI in their finance workflows. That’s not surprising.
What might be more surprising given the hype is that the most common use cases are extremely specific and often very traditional: forecasting (42%) and anomaly detection (37%), with decision support and generative reporting both lagging at 26% and 21% respectively.
From our point of view at Serrala, this isn’t too shocking: new technologies always get brought on board to handle low hanging fruit, and speeding up the month end is the lowest hanging fruit of all.
For more advanced use cases like task and workflow orchestration, organizations are only just beginning to gear up evaluations and pilot projects. But among those who are leveraging it, AI is bringing 100% faster cycle times and reductions to manual effort in certain workstreams of 80% or more. This translates to weeks of extra analytical capacity each month, and in turn to better scenario planning, variance analysis, and improved business partnering.
Most importantly, this isn’t just a headcount reduction strategy. It’s actually a talent retention lever, with skilled employees now able to spend their time on more rewarding work with visibly higher value.
What’s stopping AI adoption?
The same thing that stops everything else: organizational inertia. 70% cited complexity of business and operational change as their top barrier. This reveals an interesting picture of the tech-finance dialogue at the moment: the main obstacle to tech adoption are actually change management challenges rather than the capacity of technology or the willingness and ability of the organization to sponsor it.
How can CFOs take their organization to the next level?
The report advises exactly what we’d advise to any of our customers:
If you’re just getting started with automation, fix the foundation first and work on smaller, self-contained areas for automation pilot projects. That means getting your data game cleaned up to leverage AI later. Fortunately, data cleaning is a task AI is well equipped to help with, making this an ideal pilot project.
For more established organizations, the next step is exploring agentic AI and determining high-value use cases where proof of concepts can show benefits quickly. Forecasting and anomaly detection work well here as it’s easy to measure both accuracy and cycle times to show impact.
Organizations at this level also need to consider their strategic governance to ensure the organization can manage AI-related security and privacy risks.
For those at the cutting edge, the path is obvious: start the shift from “automated” to “autonomous”. Start piloting the use of AI for workflow orchestration and leverage your capacity for driver-based planning to accelerate the business.
Want to know more?
You can check out the SAPinsider report for yourself here.
And if you want to know more about how to prepare your organization for its journey to autonomous finance, check out our guide here.
