The intelligent close

The potential for AI extends across the entire RTR process, creating opportunities to streamline activities that have traditionally required significant manual effort. As these capabilities mature, organizations can begin moving toward a more continuous, connected, and insight-driven approach to financial management.

Consider how AI and machine learning (ML) could help reshape key activities:

• Recording: AI-enabled journal entry automation can schedule and populate recurring entries, while ML models analyze historical spending patterns to estimate accruals and identify exceptions.
• Reconciliation: Predictive transaction matching can recommend clearing actions, while risk-based frameworks help shift lower-risk reconciliations outside the traditional close window.
• Intercompany: ML can identify unusual transaction patterns and potential settlement anomalies, enabling teams address issues earlier.
• Close: AI agents can monitor close activities, automate recurring entries, and escalate exceptions requiring human attention.
• Consolidation: Intelligent checks can identify inconsistencies in financial roll-ups and potential logic gaps, supporting more reliable, consolidated results.
• Reporting: AI-powered workflows can coordinate approvals and reporting activities, while intelligent dashboards provide financial insights and help identify emerging opportunities.

Together, these capabilities offer a glimpse into a future where controllership teams spend less time managing repetitive tasks and more time interpreting results, addressing risks, and supporting strategic business decisions.

In this reimagined future, it is important to note that human judgment is and will remain essential. Finance professionals will continue to play a critical role throughout this evolution when reviewing entries, interpreting policies, investigating variances, and providing oversight. With AI handling more routine activities, teams can devote greater attention to the work that benefits most from their expertise and delivers greater value.

Navigating the challenges of AI-driven controllership

Despite its potential, integrating AI into financial close processes does come with practical challenges.

Fragmented financial data can undermine confidence in AI-generated outputs, while legacy systems and governance gaps can often make it difficult to move promising initiatives beyond the pilot stage. AI also introduces new considerations for internal control over financial reporting (ICFR), which will require organizations to evaluate how existing control frameworks should evolve alongside emerging capabilities.

Operational pressures can add another layer of complexity. Many close activities remain dependent on a relatively small group of finance professionals, even as reporting timelines tighten and business requirements grow in this environment. Organizations must also consider how to demonstrate that AI investments can deliver measurable returns, particularly when implementation costs and competing priorities are also under scrutiny.

Addressing these challenges begins with understanding where the friction exists today. A phased approach that prioritizes high-value opportunities, establishes appropriate safeguards, and demonstrates tangible results can help organizations build confidence and sustain momentum.

An effective intelligent close depends on more than the capabilities of any single technology. Organizations also need the underlying processes, data structures, and operating environment to support it.

Six leading practices can help establish that foundation:

• Establish a standardized source of truth. Consistent data structures can reduce manual reconciliation and enable more reliable, on-demand financial information.
• Simplify the technology landscape. Scalable, integrated platforms can reduce complexity while supporting the adoption of emerging capabilities.
• Enable a seamless close process. Increasingly automated and continuous processes can reduce manual effort and improve efficiency.
• Strengthen process and data governance. Clear policies, accountability, and monitoring help maintain trust and control.
• Expand analytical capabilities. Flexible, on-demand analytics can improve visibility and support more informed business decisions.
• Create a platform for growth. Adaptable solutions can accommodate evolving business models, organizational changes, and mergers and acquisitions.

These practices can provide a framework for modernizing the close while also supporting broader finance transformation objectives. They also highlight the importance of aligning technology adoption decisions with how people work, how information flows, and how the organization expects to evolve.

As controllership explores AI solutions, determining the right investment approach is an important part of the transformation journey. Building capabilities internally, purchasing established solutions, or adopting hybrid models each offer advantages depending on the organization’s needs and strategic objectives.

• Building provides greater customization for complex or specialized use cases, which can allow organizations to tailor capabilities to unique business requirements.
• Buying can accelerate implementation for common, repeatable activities such as journal entry reviews and variance analysis. Established solutions may also provide preconfigured workflows, embedded controls, and ongoing innovation, helping organizations realize value sooner.
• Hybrid approaches combine packaged capabilities with customization, offering some flexibility while reducing the need to develop every component internally.

There isn’t one single solution that reigns above all others. The key is to weigh the benefits of each option and what the organization truly needs. Several factors should guide the decision, including architectural flexibility, data privacy, use-case complexity, user experience, industry-specific requirements, and total cost.

For standardized activities, established solutions may offer a more efficient path forward, particularly when implementation speed, security, and auditability are priorities. More specialized capabilities may warrant a tailored approach.

A good rule of thumb for a successful implementation: Invest in solutions that fit the organization’s needs today while providing the flexibility to adapt as those needs change.

A use case for building an agentic insights platform

Moving from experimentation to enterprise value requires a clear understanding of the business challenges AI is intended to help address.

Consider an example involving the development of an agentic insights platform for a large company. Faced with fragmented data, spreadsheet-heavy reporting, and legacy technology constraints, the organization focused on establishing a governed single source of truth and improving the access to financial information.

The initiative began with a targeted minimum viable product (MVP), supported by a prototype and a roadmap that prioritized user needs. This approach helped validate the experience, focus investment, and establish a foundation for future expansion.

The resulting platform demonstrated improvements with efficiency, effectiveness, and controls. Manual data-gathering activities that previously took days could be completed in minutes, while access to more real-time information supported timely decision-making. Consistent metrics and traceable information also helped reinforce governance.

This example illustrates the value of starting with a more defined business need, demonstrating results, and building out from there. Early successes can provide both the evidence of value and the organizational confidence needed to support expanded adoption.

As organizations advance their AI journeys, several lessons can help translate early progress into sustainable transformation. They include:

• Think big, start small. Establish a long-term vision while prioritizing manageable use cases with measurable outcomes.
• Design for scalability. Build capabilities that can evolve alongside business needs and expanding AI adoption.
• Put data at the center. Reliable, governed data remains fundamental to effective AI-enabled financial processes.
• Choose technology intentionally. Traditional automation, analytics, and ML may be better suited to certain activities than Generative AI.
• Meet users where they are. Integrate solutions into existing workflows to encourage adoption and minimize disruption.
• Keep humans in the loop. Maintain appropriate oversight, accountability, and professional judgment throughout AI-enabled processes.

The evolution toward an intelligent close creates opportunities for controllership teams to operate with greater efficiency, strengthen financial oversight, and deliver more timely insights to the business.

Ultimately, progress will depend on making deliberate choices about where AI can create value, establishing the right foundations, and bringing people along throughout the transformation. Controllers and finance leaders who approach these efforts with clear priorities and a commitment to continuous improvement will be better positioned to turn emerging AI capabilities into lasting results.

As AI continues to evolve, so too will the possibilities for financial close and reporting. For controllership leaders, there is an opportunity to help shape that evolution—by fundamentally rethinking what the close can deliver and creating a more agile, insight-driven finance function that meets today’s demands of today while preparing for what comes next.