Artificial Intelligence is no longer a futuristic concept for finance—it has become a transformative force reshaping how CFOs and finance teams operate.

 

From automating repetitive tasks to providing predictive insights, AI is enabling faster decision-making, stronger controls, and more strategic engagement across the business.

 

In this Q&A with Pierre Montersino, a global Chief Technology Officer, Camino Search transformation practice consultant, Jack Parkhouse explores a deep dive into the practical applications, tools, and governance considerations for AI in finance, exploring how CFOs can embed intelligence into everyday processes—from accounts payable and reconciliations to forecasting, risk management, and ESG reporting.

 

Beyond efficiency gains, AI is helping finance teams elevate their role as strategic partners, freeing capacity for analysis, planning, and business partnering while ensuring accuracy, auditability, and compliance.

 

As the finance function evolves in 2026 and beyond, this discussion illuminates the pathways for embedding AI into the operating model, creating high-performing, resilient, and forward-looking finance organisations.

 

For any finance leader, the insights here offer both a roadmap and inspiration for harnessing AI not just as a tool, but as a core capability.

 

 

Jack Parkhouse (JP): Starting off, how can AI add value to financial systems within a business?

Artificial Intelligence can add significant value to finance and financial systems by improving efficiency, accuracy, control and decision making.

In 2026 the conversation shifted from “automation” to “automation plus intelligence”, where finance teams use AI to both execute work and to explain what is happening, why it is happening and what to do next.

A second shift is the move from isolated pilots to embedded capabilities inside the tools finance already relies on (ERP, EPM, data platforms, Excel and collaboration suites).

•       Automating repetitive work - Invoice capture, coding, reconciliations, journal support, cash application and management reporting drafts can be automated, releasing capacity for higher value work.

•       Faster close and continuous accounting - anomaly detection and control monitoring can flag issues, reducing end of month surprises and supporting a more continuous close.

•      Better forecasting and scenario planning - Predictive models and Gen AI supported scenario narratives help FP&A move from historical reporting to forward looking insights and rapid what if analysis.

•     Improved accuracy and consistency - AI can identify outliers, duplicates, unusual postings and policy exceptions at scale, improving the quality of reporting packs and board materials.

•       Risk and fraud management - AI can surface suspicious patterns in payments, expenses, vendor set up and revenue leakage, strengthening preventative controls.

•    Decision support in the flow of work - The big trend is finance copilots embedded in Excel, email and collaboration tools, pulling ERP connected data and turning it into explainable insights and actions. (Microsoft)

•     Narrative reporting at speed - Gen AI can draft commentary for month end packs, variance explanations and investor style narratives, with finance retaining ownership and review.

•       Working capital optimisation - better prediction of cash conversion, customer payment behaviour, supplier terms compliance and early warning on margin erosion.

•       Better stakeholder self-service - Controlled ask finance experiences allow business leaders to interrogate numbers safely and reduce ad hoc analysis requests.

 

 

(JP): What AI tools are currently available on the market for CFOs?

The market has matured in two clear directions:

A.     Role based copilots and agents from major enterprise platforms, designed to connect to systems of record and support core finance workflows. Microsoft has made its Finance solution in Microsoft 365 Copilot available, focusing on areas like collections, variance analysis and reconciliation inside familiar tools such as Excel and Outlook. (Microsoft)

SAP is also expanding Joule with finance focused capabilities and agent led automation, aiming to reduce time on transactional tasks and improve resilience. (SAP)

B.    Specialist point solutions that target a single finance problem, often delivering rapid ROI. The current trend is consolidation: vendors are packaging finance specific assistants into broader enterprise Copilot offers to accelerate adoption and reduce licensing complexity. (The Verge)

Common categories include:

•       Close and reconciliation - Tools that detect anomalies, automate matching and manage close workflows.

•       AP automation and spend compliance - Invoice capture, coding, duplicate detection, PO matching, policy checking and supplier risk signals.

•       FP&A and integrated planning Driver-based forecasting, rolling forecasts, scenario generation and narrative support.

•       Audit and assurance analytics - Sampling, evidence capture and anomaly detection across journals and transactional datasets.

•       Treasury and liquidity - Cash forecasting, bank connectivity analytics and alerting for exposures and covenant signals, with vendors publishing playbooks and use cases aimed at treasurers and CFOs. (gtreasury.com)

CFOs frequently evaluate include close and reconciliation platforms, AP automation solutions, integrated planning tools, audit analytics and treasury forecasting. The right answer is a coherent architecture and operating model, with clear ownership of data, controls and outcomes.

In practice, CFOs increasingly assemble a portfolio:

•       a core platform copilot to raise productivity across the function, plus.

•       a small number of point solutions where the value case is immediate (AP, close, treasury, or audit).

 

 

(JP): How can AI be used by CFOs to automate processes?

CFOs can leverage AI to automate end to end processes, not just individual tasks. The best results come when automation is anchored to measurable outcomes, not a technology feature list. The most effective approach is to redesign the workflow, then embed AI with clear controls and a human in the loop.

Common high impact opportunities include:

•       Invoice to pay - Automated invoice capture, coding suggestions, PO matching, exception routing and supplier query responses.

•       Record to report - Reconciliations, intercompany matching, journal support, close task orchestration and drafting management reporting narratives.

•       Order to cash - Credit scoring support, collections prioritisation, dispute categorisation, cash application and revenue leakage signals.

•       Expense and policy compliance - Automated checks for anomalies, duplicate claims, out of policy spend and travel and subsistence rules.

•       Tax and statutory reporting support - Classification support, evidence gathering and first pass drafting, with controls and sign off retained by finance.

•       Controls and assurance - Continuous controls monitoring, segregation of duties exception detection and evidence packs for audit and compliance reviews.

•       Contract and obligation intelligence - extracting commercial terms, renewal dates, service credits and pricing clauses to support accruals, revenue recognition judgement support and supplier management.

•       Customer and product profitability - Linking finance, operational and customer data to identify margin leakage and to test pricing and mix scenarios.

•       Planning and performance - Automated driver refresh, rolling forecast updates and variance to action recommendations that connect movements to decisions.

Another key 2026 trend is agentic automation: AI agents that can plan, execute and coordinate steps within guardrails, escalating to people when risk, ambiguity or materiality thresholds are breached. (Lloyds Banking Group)

 

 

(JP): How can AI be used to improve the quality of data within management reporting?

AI can significantly improve the quality and usefulness of management information, but only when paired with strong data fundamentals. The aim is to move from manual spreadsheet reconciliation to governed, traceable data products.

Practical ways AI supports MI quality include:

•       Automated data validation and reconciliation - Anomaly detection to surface unusual postings, missing mappings, or unexpected movements.

•       Master data and mapping assistance - Suggesting classifications and mappings (cost centre, product, customer) while enforcing controlled vocabularies.

•       Data completeness signals - Highlighting missing fields, late feeds, weak lineage, or low confidence estimates.

•       Metric definitions and a semantic layer - Using Gen AI to help users navigate what does this KPI mean and what drives the movement, while the underlying definitions remain controlled by finance.

•       Faster root cause analysis - Linking movements to drivers (volume, price, mix, FX, one offs) and proposing hypotheses for finance teams to verify.

•       Better commentary - Drafting variance explanations and business narratives, with finance review to ensure accuracy and tone.

The guiding principle is simple: AI should accelerate insight, not create a parallel set of numbers. CFOs should insist on reconciliation to the system of record and a clear lineage from source to board pack.

 

 

(JP): How can AI be used to create leaner, high performance finance functions?

AI is enabling a shift in the finance operating model, with finance leaders increasingly expected to act as strategy partners and to translate complex data into clear commercial choices. (Deloitte)

Key levers include:

•       Capacity release - Automating high volume processes and reducing rework caused by poor upstream data.

•       Finance as a strategic copilot - Using AI to shorten the cycle from question to answer for commercial leaders.

•       A new skills mix - More focus on data literacy, controls, storytelling and commercial partnering and less time spent building the pack.

•       Standardised processes and control by design - Codifying policies and controls so exceptions are handled consistently.

•       Process mining and optimisation - Using data to identify bottlenecks, root causes and automation opportunities, then redesigning the process.

•       Targeted use of shared services and centres of excellence - Separating transactional execution from value adding insight, while keeping governance central.

A practical CFO benchmark is whether AI is reducing close time, improving forecast quality and freeing senior finance partners to spend more time with the business.

 

 

(JP): How can AI automate a finance tech stack?

A modern finance tech stack is increasingly composable: ERP and EPM remain the systems of record, but AI layers are used to connect, interpret and orchestrate work across tools.

AI can automate the stack through:

•       Integration and orchestration - Using APIs, workflow platforms and agents to move work across ERP, procurement, banking and analytics tools.

•       Document intelligence - Extracting terms and obligations from contracts, invoices and unstructured documents to support revenue recognition, lease accounting and vendor management.

•       Intelligent reconciliation - Matching transactions across multiple sources (banks, payment processors, ERP subledgers) with confidence scoring and exception routing.

•       Automated reporting and narrative - Producing first draft packs, commentary and variance breakdowns, with controls for versioning and approval.

•       Control monitoring - Alerting on key risks (unusual journals, unusual vendor changes, payment pattern shifts) and generating evidence for audit.

•       Secure ask finance data capability - Retrieval of policy, procedures and historical pack content, with access controls and audit trails.

Importantly, CFOs are increasingly treating AI as part of the control environment. That means governance, monitoring and continuous improvement, not a one-off implementation.

 

 

(JP): What are the benefits of AI to a CFO?

The benefits have become clearer as adoption has matured:

•       Improved decision making - Faster access to explainable insights, more reliable scenarios and better understanding of drivers.

•       Increased efficiency - Lower manual effort in AP, close, reporting and MI production.

•       Reduced costs - Fewer hours spent on low value work, reduced external support for basic analysis and fewer error driven rework cycles.

•       Better risk management - Earlier detection of fraud and control breaches and more consistent policy enforcement.

•       Improved compliance and governance - Structured, auditable processes supported by modern AI governance standards such as ISO IEC 42001, which provides a management system approach to responsible AI use. (ISO)

•       Better third-party risk management - Clearer expectations of vendors on model transparency, data handling, monitoring and incident response, aligned to risk based regulatory approaches such as the EU AI Act. (Digital Strategy)

•       Better talent outcomes - Finance professionals spend more time on analysis and partnering, improving engagement and retention.

For many CFOs, the headline benefit is speed: faster close, faster insight and faster decision cycles, without compromising control.

 

 

(JP): How can AI support financial institutions and finance teams in meeting sustainability and ESG reporting requirements?

Sustainability reporting has moved from a nice to have to a core finance responsibility. IFRS S2 climate related disclosures are effective for annual reporting periods beginning on or after the 1^st January 2024 and are designed to be applied alongside IFRS S1. (IFRS)

In parallel, European sustainability reporting requirements continue to evolve, with initiatives to simplify and adjust scope and obligations. (Reuters)

For many organisations, the practical impact is more ESG data, more internal controls and more demand for traceable evidence across the value chain as CSRD phases expand scope over time. (CoreFiling website)

AI can help CFOs in practical ways:

•       Data collection and normalisation - Extracting ESG data from operational systems, supplier documents and sustainability frameworks and aligning it to reporting requirements.

•       Controls and assurance - Anomaly detection and lineage tracking to support auditability of ESG metrics, similar to financial controls.

•       Risk assessment - Modelling climate and transition risks and linking scenarios to financial planning and capital allocation.

•       Reporting production - Automating first draft disclosures, tables and commentary, with clear review and sign off by finance and sustainability stakeholders.

•       Supplier and value chain signals - Using AI to track supplier data completeness and to prioritise engagement where data quality is weak.

As ESG assurance expectations rise, finance leaders should treat ESG data with the same discipline as financial data: controlled definitions, traceable lineage and documented judgement.

 

 

 In conclusion

AI is no longer a future concept for finance. The competitive advantage now comes from how well CFOs industrialise it: embedding it into processes, controls and the operating model.

A practical 2026 CFO playbook looks like this:

•       Start with a value led use case portfolio (AP, close, forecasting, collections, treasury, controls), each with clear KPIs.

•       Invest in the foundations - Data quality, process standardisation and clear ownership of master data and metrics.

•       Build governance that matches the regulatory environment, including the EU AI Act requirements where applicable and internal control expectations. (Digital Strategy)

•       Adopt a human in the loop approach for material decisions and external reporting, with audit trails and approval.

•       Treat change management as a first class workstream: upskilling, role redesign and clear communication of what changes and what does not.

•       Measure outcomes relentlessly - Close days, forecast accuracy, working capital, policy compliance and capacity released for business partnering.

•       Put finance in control of the prompt to post chain - Define which decisions can be automated, which require review and which are prohibited, then instrument monitoring so finance can prove control.

•       Build a lightweight model risk management discipline for finance use cases - Model inventory, data provenance, testing, drift monitoring and periodic re validation, proportionate to materiality.

 

Companies that approach AI as a capability, not a tool, will increase productivity, make better decisions, strengthen controls and future proof the finance function.

 

 

For more information on transformation in the finance function, please speak to Jack Parkhouse (jack@caminosearch.com) or Harry Hedges (harryh@caminosearch.com) in our London transformation practice.

 

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