Finance is entering a new era. As artificial intelligence (AI) becomes increasingly embedded within enterprise systems, finance functions are evolving from reactive, process-driven operations into predictive, insight-led business partners. In Episode 16 of the Reshaping Finance, “The Intelligent Finance Stack: How AI Is Transforming Enterprise Systems,” I spoke with Lukas Deutsch, chief operating officer at SAP, about how AI is reshaping finance, from transactional processes and forecasting to planning, governance, and the skills finance professionals will need for the future.
From reactive finance to predictive decision-making
For decades, finance teams have been responsible for critical processes such as financial close, reporting, forecasting, and planning. However, many of these activities remain heavily manual, requiring significant time and effort to collect, validate, and analyse data.
According to Deutsch, this traditional model is rapidly changing.
“We are now shifting from a reactive, manual past into a future that's more predictive, with agents helping us advise the business on making the right decisions quicker,” Deutsch said.
Rather than spending valuable time gathering information and reconciling data, finance professionals are increasingly able to focus on interpreting insights, supporting decision-making, and providing strategic guidance to the wider business.
At the heart of this transformation are intelligent AI agents that can automate workflows, analyse data in real time, and surface recommendations that help organisations respond more quickly to changing business conditions.
Why AI must be embedded into enterprise systems
One of the key themes of our discussion is that AI delivers the greatest value when it is built directly into core enterprise systems rather than added as a standalone layer.
While external AI tools can provide useful capabilities, they often lack access to the full business context required for reliable financial decision-making. Finance processes rely on interconnected data from across the organisation, including operations, procurement, supply chains, sales, and human resources.
“The golden path should be building on top of your existing enterprise application landscape and then embedding AI natively into it, instead of bolting [it] on, because it is important for agents to have the full process context available in real time,” Deutsch explained.
When AI is embedded within enterprise systems, it can access real-time data, understand process dependencies, and operate within existing governance frameworks. This allows organisations to generate more accurate insights while maintaining control, compliance, and auditability.
Unlocking efficiency in core finance processes
The most immediate opportunities for AI adoption are often found in high-volume transactional processes.
Activities such as financial close, accounts receivable management, and shared service operations typically involve repetitive tasks that consume significant resources. AI can automate many of these processes while improving speed and accuracy.
“We are able to accelerate the financial close, reducing the closing cycle time by up to five days, and in some cases almost run this in a fully automated way,” Deutsch said.
Beyond efficiency gains, AI can also provide contextual information that helps teams identify issues faster, resolve exceptions more effectively, and reduce manual intervention across finance operations.
As organisations continue to face pressure to do more with fewer resources, these productivity gains are becoming increasingly valuable.
Forecasting in a constantly changing world
Traditional forecasting models often struggle to keep pace with today's fast-moving business environment. Economic uncertainty, geopolitical events, supply chain disruptions, and shifting customer demand can all quickly undermine assumptions that were considered valid only weeks earlier.
AI offers a powerful solution by combining data from multiple sources and applying predictive models that continuously monitor changing conditions.
This enables finance teams to move beyond static forecasts and develop more responsive planning processes that adapt in near real time.
“The second [a traditional] plan is in the system, it's already outdated because the world is moving forward,” Deutsch said.
By integrating both internal operational data and external market signals, AI can help organisations identify emerging risks and opportunities earlier, allowing leaders to make better-informed decisions.
The shift to continuous planning
One of the most significant changes discussed in the episode is the move away from annual planning cycles towards continuous performance management.
Historically, organisations would spend months building annual plans, only to see those assumptions become outdated shortly after completion. In today's dynamic business environment, many organisations are replacing fixed planning cycles with rolling forecasts that are updated more frequently.
AI makes this approach significantly more practical.
Looking ahead, intelligent agents may take on an even greater role by automatically updating plans, identifying deviations, assessing risks, and proposing mitigation actions.
This creates the possibility of finance functions that are constantly evaluating performance and helping leaders understand trade-offs around pricing, costs, investments, and growth opportunities without requiring extensive manual modelling.
Building trust through governance and transparency
Despite the excitement surrounding AI, our discussion makes clear that trust remains the foundation of successful adoption.
Finance teams operate in highly regulated environments where decisions must be explainable, traceable, and auditable. Black-box outputs are unlikely to gain acceptance, particularly when they influence financial reporting or regulatory compliance.
“Trust is non-negotiable in finance,” Deutsch said. “You need to provide transparency on the action. It can’t be just a black box; it needs to be traceable and understandable for the auditor.”
Organisations must therefore establish strong governance frameworks that define how AI models are used, monitored, and validated. Clear visibility into how recommendations are generated will be essential for maintaining confidence among finance teams, auditors, regulators, and business leaders.
The human element remains essential
Although AI is becoming increasingly capable, our conversation reinforced that technology will not replace professional judgement.
Finance professionals remain responsible for reviewing outputs, challenging assumptions, and making final decisions, particularly in areas involving external reporting, compliance, and strategic planning.
“It’s similar to onboarding a new employee,” Deutsch said. “You wouldn’t trust that person to already know it all from day one; there might be errors. And that’s what we do every day; we provide feedback and refine the process.”
This human-in-the-loop approach ensures that AI continues to learn and improve while maintaining appropriate oversight and accountability.
Preparing finance teams for the future
As routine activities become increasingly automated, the role of finance professionals will continue to evolve.
Future finance leaders will need strong AI literacy, an understanding of how models work, and the ability to critically evaluate AI-generated outputs. Equally important will be communication and storytelling skills that enable professionals to translate complex insights into clear business recommendations.
Success will also require closer collaboration across functions, stronger business partnering capabilities, and a willingness to embrace new ways of working.
For organisations beginning their AI journey, the message is clear: Technology alone is not enough. Achieving meaningful transformation requires clear leadership, high-quality data, integrated systems, and effective change management.
Those organisations that invest in these foundations today will be best positioned to build the intelligent finance functions of tomorrow.
Hear our full conversation by listening to Episode 16 of the
Other relevant resources
Future-Ready Finance: Technology, Productivity, and Skills Survey Report
Future-Ready Finance: Productivity at the Human–Technology Crossroads
Partnering with AI: A Strategic AI Adoption Blueprint for Finance and Accounting Excellence