Finance is shifting from hindsight to foresight—from reporting on the past to actively shaping what comes next. That means spending less time compiling data and more time applying it to guide decisions, influence strategy, and drive results.
This transition doesn’t happen automatically. It requires new tools, new structures, and a new mindset. CFOs are leading the charge, pushing finance teams to operate more like strategic partners and less like support centers. But to get there, they need a clear vision, a modern finance tech stack, and the right mix of technical fluency and business acumen across the team.
We’re outlining three key trends driving that shift and what it takes to respond: from embedding AI into everyday operations to retraining teams for cross-functional work. Each trend points to a broader goal—building a finance function that can move faster, think ahead, and lead with confidence.
The future of finance: The 3 top trends to watch
Finance leaders are responding to new pressures across the business, including shorter planning cycles, higher performance expectations, and increasingly complex systems. The work is changing, and so are the skills, the tools, and the role finance plays in shaping strategy.
The following three trends reflect that shift. From AI adoption to cross-functional talent and evolving system ownership, each one shows where finance teams are heading.
1. Digital transformation is accelerating
Digital transformation used to be a milestone that organizations planned for, executed, and completed. That’s no longer the case. Today, it’s a continuous process. As new technologies like AI and cloud-native systems develop, they introduce new opportunities and new disruptions. What worked last year might already be slowing your teams down.
“Transformation is not going to stop… A few years ago, it was cloud technology.
Now we have things like AI that come in and disrupt that.”Nick Scott | Managing Director of Advisory & Transformation, SC&H
The cycle keeps moving. Organizations need to stay clear on their goals and flexible in their approach to make steady progress.
But steady progress doesn’t mean winging it. Successful transformation efforts share several key characteristics: a clear vision, a committed finance leader, and team-wide alignment on what’s changing and why. “You and your team need to be able to articulate what you are transforming to and why,” said Tom Hood, Managing Director of Advisory & Transformation at SC&H, in the same Gartner panel. “And your team needs to understand that and be bought into it.”
At the core of that transformation? Your finance tech stack.
The finance tech stack includes everything from ERP and source systems to EPM, data solutions, and the presentation layer. True digital transformation doesn’t just modernize one layer in isolation. You can’t rip out your ERP and call it a day. Instead, you need to take a holistic approach—considering how each layer connects and contributes to your future goals.
This is where the divide is forming. Some companies have already modernized legacy systems, integrated AI, and aligned their tech stacks around real-time decision-making. Others are still managing critical processes in silos on outdated platforms.
Wherever you fall, the pressure to modernize is increasing, and the companies that think holistically about their tech stack will be best positioned to keep pace.
2. Teams are incorporating AI into financial operations
AI is everywhere. It’s improving operations, accelerating processes, and giving early adopters in every industry a competitive edge. For finance teams, the question is no longer if they’ll adopt it, but how to use it in ways that drive meaningful results.
In finance, AI is already being used to:
- Automate routine tasks like reconciliations and variance analysis
- Improve accuracy and speed in forecasting and budgeting
- Flag anomalies in transactions for fraud detection
- Generate rolling forecasts using predictive models
- Identify cost-saving opportunities through spend analysis
- Support real-time scenario modeling to guide strategic decisions
Maurice Claggett, CPA and Senior Manager of Solution Engineering EPM at Oracle, has seen firsthand how finance professionals are adopting embedded AI tools.
“Financial analysts, controllers, and professionals working outside IT are learning how to leverage AI technologies already integrated into existing solutions,” he said. “Recently, one of our clients was able to turn on a new AI functionality within Oracle’s planning and forecasting tools and get this new initiative up and running in less than 60 days.”
This kind of built-in functionality means finance teams don’t have to wait on a centralized data science team to move forward. With the right EPM solution, a unified data model, properly validated inputs, and the right use cases, finance can move quickly and make real progress.
But speed only matters if the data is sound. “There are certainly AI tools that improve day-to-day workflows—collaboration, communication, meeting summarization,” said Mike McGee, Vice President of FP&A at VaynerX. “But when it comes to deploying AI for this organization’s specific processes and data, preparation is crucial. If AI-driven forecasting or modeling is applied to unverified datasets, it can produce errors and so-called ‘hallucinations.’”
That’s why AI deployment requires clean data, coordinated systems, and the judgment of skilled professionals who understand both the technology and the business.
Tools and talent matter—but so does how teams approach change. The most successful teams are always ideating, always thinking about what’s next, and never settled into their current plans or systems. Whether it’s piloting small projects or testing predictive models within existing platforms, these teams are already seeing strong returns—and laying the groundwork for bigger gains.
When implemented responsibly, AI gives finance teams the power to act faster, think bigger, and contribute more strategically. But success depends on the right mix of tools, training, and vision.
“You need to be disciplined in your finance transformation, pairing it with upskilling your team, so you can keep moving forward and maintain each step you reach. That also means you can’t just suddenly adopt AI and expect it to work if the rest of your finance tech stack isn’t set up properly.”
Tom Hood | Managing Director, Advisory & Transformation, SC&H
3. Finance teams are upskilling to improve in areas where AI can’t help
As AI takes over routine finance tasks—such as reconciliations, forecasting, and expense planning—it’s reshaping what finance teams need to know. Understanding the technical basics is still critical, but that alone isn’t enough for a finance professional. The most valuable skill sets today are the ones AI can’t replicate: communication, strategic thinking, adaptability, and leadership.
AI adoption and automation have exposed a widening skills gap within many finance organizations. As automation accelerates workflows, finance leaders are expected to interpret data, model outcomes, and collaborate cross-functionally to inform business strategy. But those responsibilities require a new mix of soft and technical skills—skills that haven’t historically been prioritized in accounting or FP&A roles.
“Your finance team needs to do more than just debits and credits. They need to understand the business. They need to understand the planning processes, scaling upwards and also scaling across different business units,” said Claggett.
“That means talking with other departments like IT, marketing, and leadership, and connecting the pieces… organizations are able to reallocate their people from operational tasks to more strategic efforts on the revenue side, like predictive modeling.”
The technical demands on finance teams are increasing.
“Back in 2010, an FP&A professional probably needed strong skills in EPM, Excel, and a bit of business intelligence,” said McGee. “But now, someone with just those skills alone wouldn’t survive in today’s world… Everything now is interconnected, often through microservices, so each tool relies on the others. To stay competitive and succeed, you need to have some level of experience with all of them.”
That interconnectedness is driving more organizations to retrain and retool their existing teams, blending finance expertise with system fluency and business acumen. The goal is to build the human skills that make AI effective, like collaboration, strategic thinking, and adaptability.
As Claggett explained, the key is creating the conditions for continuous learning and adaptation. “Before we even dive into upskilling for AI, it’s important to focus on upskilling toward a growth mindset,” he said. “What really matters is your willingness to learn and adapt… If you don’t create a culture that encourages humility, you won’t foster the desire to grow, as people will feel they have to come in as experts. But the truth is, expertise evolves—what you’re an expert in today might change entirely tomorrow.”
Whether through formal training in data analytics and AI applications or by developing techno-functional leads who can work fluidly across teams, the most effective finance functions are actively investing in their people. They’re training for flexibility and giving teams the space to learn, iterate, and lead.
How to stay ahead in light of these trends
Forward-thinking CFOs are already adjusting their organizational structures, investing in people, and rethinking their finance technology stacks. Here are three concrete ways to stay ahead of the curve.
1. Build a solid data foundation before implementing AI
Before finance teams can see meaningful gains from AI, they need to address one of the most fundamental issues: data. That means building a strong, consistent enterprise data model that supports automation, analytics, and decision-making at scale.
Connect your systems ASAP
Instead of relying on manual exports between systems, organizations are creating connected ecosystems where financial data flows seamlessly from ERP systems to EPM platforms and supporting data solutions. This setup allows data to be accessible at the transactional level—granular enough for deep analysis but organized enough for planning, forecasting, and variance analysis. That structure starts with basics like:
A unified chart of accounts
A consistent framework that ensures data can be compared and analyzed across entities.
Consistent naming conventions
Labeling standards that keep data clean, trackable, and report-ready.
Alignment between financial and operational systems
Smooth integration across platforms helps turn financial inputs into actionable plans.
These elements help teams manage data quality from the moment a transaction is logged to the point it is included in a report.
Getting the structure right doesn’t always require a complete ERP reimplementation. Teams can often pilot a reimagined chart of accounts by combining data lake capabilities with EPM tools, improving reporting outcomes without a heavy lift.
Don’t overlook data governance
Governance is equally critical. To manage master data and maintain its quality, finance leaders are assigning specific roles:
Data Custodians
These team members are responsible for maintaining integrity and enforcing governance protocols.
Business Owners
These are subject matter experts for assigned data elements. They approve changes and provide context.
Documenting responsibilities—and making that documentation accessible—is key to long-term success. Clear ownership, defined processes, and strong internal coordination help finance teams create a foundation that AI can actually build on.
Claggett said it clearly: “When it comes to transformation, it starts with this foundational level of unifying data. You need to have good data for analytics and other operations to work properly, so you can find that data across your metadata, target channels, and structures.”
With well-governed, clearly structured data, finance teams can shift their focus to higher-level work—using AI for scenario modeling, performance insights, and proactive decision-making.
“Transformation looks different for every organization. Either way, you need a solid foundation — and that starts with your data model. If your data isn’t clean and accurate, you’ll run into problems downstream. Address those issues early and get ahead of the bottlenecks, so you can keep moving forward.”
Nick Scott | Managing Director, Advisory & Transformation, SC&H Group
2. Make better use of EPM
Most finance teams stop at the surface, using EPM for budgeting and reporting. But the full value comes when it’s used as a strategic platform. EPM can connect data, support scenario modeling, and turn planning into a continuous, enterprise-wide effort.
Leading finance teams are already tapping into deeper EPM functions like driver-based planning, continuous forecasting, and built-in AI features for anomaly detection and predictive insights. But these capabilities only deliver results when the system is owned and managed with intention, by people who know how to evolve it.
Hilton’s transformation is a prime example.
Hilton’s transformation journey began in 2012 with the implementation of Oracle EPM to support global close and consolidation and consolidated budgeting and forecasting. This setup provided the platform to streamline reporting at the corporate level.
With support from SC&H, Hilton expanded their Oracle EPM footprint to operational hotel level planning for over 700 owned properties around the world. They used this opportunity to standardize their planning process and create an integrated dynamic driver based model.
This enabled Hilton to reduce their planning cycle and increase their forecast accuracy and decision making across the entire organization. Additionally, deploying a flexible, driver based model that was consistent across regions and properties, Hilton was able to quickly gather insights at the lowest level to make decisions, from procurement to staffing.
By evolving their EPM usage from a static input tool to a united dynamic planning platform, Hilton created a more connected, agile, and strategic finance function. If you’re ready to follow their lead, next are the functionalities to focus on to start maximizing your EPM.
Innovate faster with EPM-driven upskilling
To enable finance to fully own and evolve their EPM platforms, organizations must build cross-functional skillsets within the finance team. These include:
Agile Process Ownership
- Iterative improvements to the planning cycle based on business feedback
- Real-time updates to models and calculations as business needs shift
- Ownership of rolling forecasts and continuous planning initiatives
EPM Design & Architecture
- Scenario modeling and driver-based planning frameworks
- Integration design: ERP, HCM, CRM, and external data sources
- Report building and analysis using native EPM reporting tools
- Metadata design/updates to meet new reporting requirements
Data & Integration Fluency
- Understanding of data pipelines: staging, transformation, mapping
- Use of automation tools (e.g., Data Management)
- Hands-on ability to configure and troubleshoot integrations
Advanced Analytics & Reporting
- Integration with BI platforms (e.g., Power BI, Tableau, Oracle Analytics)
- Application of AI/ML insights in forecasting, outlier detection, and trend analysis
Upskilling means building confidence and fluency with your existing tools. It equips finance teams to respond quickly, plan strategically, and improve decision-making across the business. When finance owns the EPM platform from end to end, transformation moves faster and delivers greater impact.
3. Upskill your finance team
As automation takes over repetitive and rules-based tasks, finance leaders are now expected to model outcomes, interpret data, and collaborate across departments to shape strategy. But those responsibilities require a new mix of technical fluency, business acumen, and people skills.
To stay competitive, finance organizations are retraining and retooling their teams. More leaders are looking for cross-functional talent: employees who can bridge technical systems and real-world business needs.
Find the finance/tech ‘double majors’ on your existing team
That often means developing the skills of people already inside the organization. For example, a financial analyst who trains as an EPM administrator can translate strategic goals into scalable planning processes. A former accountant with hands-on ERP experience can spot inefficiencies in workflows and help build better structures from the ground up.
McGee described how his team evolved: “We’ve transitioned from being strictly part of finance to rolling up into the technology department as a business applications function.” Each member now brings dual fluency—pairing finance or business development experience with hands-on systems expertise. “Our analysts have upskilled in different ways, becoming administrators and developers of the tools they once used. Today, staying competitive means being a ‘double major’—combining practical experience in your field with technical skill in system administration, deployment, and adoption.”
The modern skillset needed to own the finance tech stack
There’s no shortage of upskilling advice out there. Most of it points to technical skills like Python, SQL, or data science. And while those are helpful, they’re not the priority. What matters more is whether your finance team knows how to make the most of your EPM platform, including how to configure it, evolve it, and expand its use across the business.
Today’s finance professionals need to move beyond transactional reporting and become strategic operators who understand how systems work, how processes connect, and how to turn data into action. That’s not just the job of the EPM administrator—it’s a core capability for anyone working in finance.
As Hood put it during the Gartner panel, “These EPM solutions also have AI embedded in them. So the more you can learn and expand that solution—and upskill your team to use and understand it—the more capable they’ll be to actually take advantage of these technologies.”
Here are the core skills that enable finance teams to do exactly that:
Finance-first Mindset
Deep understanding of budgeting, forecasting, and close cycles
System Fluency
Hands-on knowledge of EPM tools (e.g., Oracle Cloud EPM, OneStream, Workday Adaptive Planning)
Technical Adaptability
Comfort with scripting (Groovy, SQL), data integration, and APIs
Process Thinking
Awareness of how systems connect, including upstream/downstream dependencies across ERP, BI, and ops
Business Translator
Ability to turn business needs into system logic and configurations
Change Agent
Confident managing stakeholder expectations and leading adoption and change initiatives
These are the skill sets that will shape how finance teams contribute to strategy, manage transformation, and drive value from every layer of the tech stack.
How to apply these skills:
- Manage day-to-day operations of the EPM system, including user access, security, and scheduled processes
- Maintain metadata structures (accounts, entities, cost centers, etc.) and validate consistency across hierarchies
- Coordinate and execute data loads from ERP and other systems, ensuring data integrity and timeliness
- Monitor integrations and resolve ETL issues, working with IT where necessary
- Build and maintain data forms, reports, dashboards, and task lists to support planning, forecasting, and reporting
- Own the calendar and lifecycle of the forecast, budget, and close processes in the system
- Implement business rule enhancements, calculations, and logic updates to reflect new business requirements
- Provide first-line support for users, including issue resolution, training, and change communication
- Ensure compliance with governance standards, audit requirements, and data access policies
Getting ahead means building a team that can ask the right questions, pull in the right partners, and move the business forward.
Where modern finance teams are headed
CFOs are rethinking the role of finance. They’re investing in connected systems, embedding AI into everyday workflows, and developing cross-functional talent that can navigate both business strategy and technical execution. The focus is on adaptability—on building teams that can manage change. By combining modern tools with practical skill development, forward-looking finance leaders are turning transformation from a project into a capability.
