Introduction
AI in accounting refers to the integration of artificial intelligence into financial processes, fundamentally reshaping how accounting professionals handle everything from data entry to strategic forecasting. As generative AI and machine learning continue to evolve, the accounting industry stands at a pivotal moment, with traditional accounting methods giving way to intelligent, automated systems that promise unprecedented efficiency and insight.
The adoption of AI in accounting is accelerating rapidly. Recent studies show that many accounting firms are already integrating AI into their practices, and the market for accounting AI solutions is projected to grow exponentially over the next five years. This transformation isn’t just about keeping pace with technology trends; it is also about surviving and thriving in an increasingly competitive landscape where firms use advanced AI tools to deliver faster, more accurate services to clients.
This comprehensive guide explores how AI is transforming the accounting profession, from fundamental automation capabilities to sophisticated implementations on cloud ERP platforms like NetSuite. We’ll examine the benefits of AI in accounting, implementation strategies, real-world use cases, and why cloud-native systems are becoming essential infrastructure for accounting firms that want to leverage AI effectively.
How AI Transforms Core Accounting Functions
The rise of AI is fundamentally changing how accounting work gets done. Where accounting professionals once spent hours on manual data entry and reconciliation, AI systems now handle these tasks in minutes with greater accuracy.
Automating Data Entry and Invoice Processing
Modern AI can automate the most time-consuming aspects of accounting tasks. Optical character recognition (OCR) combined with machine learning allows AI to analyse invoices, purchase orders, and receipts, extracting relevant data and routing it to the appropriate accounts without human intervention. This technology, used in accounting firms, has reduced processing time by up to 80% in some implementations.
Intelligent Reconciliation and Exception Handling
AI can be used to match transactions across multiple systems, identifying discrepancies and flagging exceptions for review. Unlike rule-based automation, AI learns from historical patterns and can handle variations in data formats, vendor names, and transaction descriptions. This helps accounting teams focus on genuine anomalies rather than spending time on routine matching exercises.
Automated Compliance, Tax Calculations, and Reporting
Complex accounting requirements, including regulatory compliance and tax calculations, are areas where AI excels. AI algorithms can process vast regulatory frameworks, automatically applying the correct treatment to transactions and ensuring compliance across multiple jurisdictions. As AI continues to mature, these systems are becoming increasingly sophisticated at interpreting regulatory changes and adapting calculations accordingly.
Predictive Analytics for Cash Flow and Forecasting
Perhaps the most transformative capability is AI’s ability to analyse historical accounting data to generate accurate forecasts. By analysing receivables, payables, seasonal trends, and external economic indicators, AI provides finance professionals with forward-looking insights that inform strategic decision-making. This represents a shift from accounting as purely historical record-keeping to accounting as a strategic, predictive function.
Business Benefits of AI in Accounting
The integration of AI into accounting delivers measurable business value across multiple dimensions, fundamentally changing the future of the accounting profession.
Increased Efficiency and Reduced Manual Errors
AI technologies eliminate the repetitive manual work that has characterised traditional accounting for generations. By automating routine accounting processes, firms report efficiency gains of 40%-70% in specific workflows. More importantly, AI reduces the human error inherent in manual data entry, with error rates dropping from 3%-5% to less than 0.5% in many implementations.
Cost Savings and Improved Resource Allocation
When accounting firms use AI to handle routine tasks, they free up skilled professionals to focus on higher-value activities like analysis, advisory services, and client relationships. Smaller accounting firms particularly benefit from this leverage, as AI enables them to compete more effectively without increasing headcount in proportion. The adoption of AI in accounting typically delivers ROI within 12-18 months through reduced labour costs and improved throughput.
Faster Close Cycles and Real-Time Financial Insights
AI can significantly speed up month-end close processes. What once took 10-15 days can now be accomplished in 3-5 days as AI handles reconciliations, prepares standard journal entries, and generates reports. Beyond faster closes, modern accounting powered by AI provides real-time visibility into financial positions, enabling more agile decision-making.
Improved Auditability and Internal Controls
AI systems create comprehensive audit trails that document every decision and data transformation. This transparency actually strengthens internal controls compared to manual processes, where documentation may be inconsistent. AI helps accounting firms demonstrate compliance and provides auditors with detailed evidence trails that make audits more efficient and thorough.
Common Use Cases and Examples
Examples of AI in accounting span the full spectrum of accounting functions, from transactional processing to strategic analysis.
Accounts Payable Automation and OCR
AI-powered AP systems are among the most mature applications of AI. These systems use OCR to capture invoice data, match it to purchase orders, route approvals through workflow, and schedule payments—all with minimal human intervention. The impact of AI on AP processing has been dramatic, with some organisations processing 10 times more invoices with the same staff.
Accounts Receivable Collections and Credit Scoring
AI can analyse customer payment patterns, predict which invoices are at risk of late payment, and prioritise collection efforts accordingly. AI algorithms also assess credit risk more accurately by analysing broader datasets than traditional scoring methods, helping firms make better decisions about extending credit and managing working capital.
Automated Journal Entry Suggestions
AI learns from historical journal entries and can generate suggestions for recurring entries, accruals, and allocations. Accounting professionals review and approve these suggestions, but the AI handles the initial preparation, significantly reducing the time spent on routine bookkeeping tasks.
Continuous Monitoring and Anomaly Detection for Fraud Prevention
AI is particularly useful for identifying unusual patterns that may indicate errors or fraud. By continuously monitoring transactions and comparing them to established baselines, AI can flag anomalies for investigation. This capability represents a fundamental shift from periodic audit sampling to continuous, comprehensive monitoring of all accounting workflows.
Implementation Roadmap for AI in Accounting
Successfully implementing AI requires careful planning and a phased approach. The accounting sector has learned that rushed implementations often fail, while methodical rollouts deliver sustainable value.
Assess Readiness: Data Quality, Processes, and Skill Gaps
Before adopting AI, accounting firms must evaluate their current state. This assessment should examine data quality (AI requires clean, structured data), process maturity (chaotic processes won’t improve with AI), and team capabilities (staff need basic data literacy to work alongside AI). Understanding these gaps is essential for realistic planning.
Define Priorities: High-Value Processes to Automate First
Not all accounting tasks benefit equally from AI. The best candidates for initial AI use are high-volume, rule-based processes with clear quality metrics—like invoice processing or bank reconciliations. Starting with these “quick wins” builds organisational confidence and generates funding for broader transformation.
Choose AI Tools and Vendors: Capabilities, Integration, Governance
The market offers numerous AI software solutions, ranging from point solutions focused on specific accounting functions to comprehensive platforms with embedded AI. Evaluation criteria should include integration capabilities with existing accounting systems, explainability of AI decisions (critical for audit), vendor stability, and data governance features.
Pilot Projects, Measure KPIs, Scale Incrementally
Begin with controlled pilots that test AI in production-like conditions. Measure specific KPIs like processing time, error rates, and user satisfaction. Use these results to refine the approach before scaling. This iterative methodology reduces risk and allows the organisation to learn and adapt.
Training, Change Management, and Governance
The human element is often the biggest challenge in AI adoption. Accounting teams may fear that AI threatens their jobs or feel overwhelmed by new technology. Effective change management addresses these concerns transparently, emphasising how AI augments rather than replaces human expertise. Establish governance frameworks that define who approves AI decisions, how models are monitored, and how the organisation ensures ongoing compliance.
Risks and Challenges
While the benefits of AI in accounting are substantial, the accounting profession must also navigate significant risks and challenges.
Data Privacy, Security, and Regulatory Compliance
AI systems process sensitive financial data, making security paramount. Accounting firms using AI must ensure that solutions comply with data protection regulations, maintain appropriate access controls, and protect against data breaches. The regulatory landscape for AI is also evolving, with new requirements emerging for transparency and accountability.
Bias, Model Explainability, and Auditability
AI models can perpetuate or amplify biases present in training data. For accounting applications—particularly those involving credit decisions or fraud detection—bias can have serious consequences. Additionally, many AI technologies operate as “black boxes,” making it difficult to explain why specific decisions were made. This opacity conflicts with accounting’s need for transparency and audit trails. Firms must prioritise AI tools that offer explainability features.
Integration Complexity with Legacy Systems
Many accounting firms operate on legacy accounting software that wasn’t designed for AI integration. Creating effective interfaces between old systems and new AI capabilities requires significant technical effort. Poor integration leads to data silos, manual workarounds, and ultimately, failure to realise AI’s potential.
Skill Shortages and Change Resistance
The accounting landscape faces a shortage of professionals who combine accounting knowledge with data science skills. This gap makes it challenging to implement and manage AI effectively. Additionally, change resistance—particularly among senior staff comfortable with traditional accounting methods—can undermine adoption efforts. Addressing these human factors is as important as solving technical challenges.
Introducing Cloud ERPs: The Next Layer for AI in Accounting
While standalone AI tools deliver value, integrating AI with cloud ERP systems represents a more transformative approach that addresses many implementation challenges while unlocking greater capabilities.
Why Cloud ERPs Matter for AI in Accounting
Cloud ERPs provide the centralised, structured data that AI requires to function effectively. Unlike fragmented on-premise systems where data resides in multiple disconnected databases, cloud ERPs maintain a single source of truth for all financial transactions. This unified data foundation is essential for training accurate AI models and generating reliable insights.
Modern cloud-based architectures also expose robust APIs that facilitate AI integration. Rather than complex point-to-point integrations typical of legacy systems, cloud ERPs allow AI tools to access data and trigger actions through standardised interfaces, dramatically reducing integration complexity.
How Cloud-Based Architectures Enable Real-Time AI Insights
The real-time nature of cloud ERPs transforms how AI can be used in accounting. As transactions occur, AI can immediately analyse them for anomalies, update forecasts, and trigger workflows—delivering insights when they’re most actionable rather than days or weeks later during batch processing.
Benefits Over On-Premise: Scalability, Faster Upgrades, Vendor-Managed Security
Cloud ERPs offer compelling advantages for organisations implementing AI. They scale effortlessly as data volumes grow, eliminating the need for capacity planning. Vendors handle security patching and compliance updates, reducing the burden on internal IT teams. Perhaps most importantly, cloud ERPs receive continuous upgrades that incorporate the latest AI capabilities, ensuring the organisation benefits from ongoing innovation without disruptive reimplementations.
How Cloud ERPs Accelerate AI Use Cases
Cloud ERP platforms create a virtuous cycle in which better infrastructure enables more sophisticated AI applications, which, in turn, deliver greater value.
Unified Master Data and Transaction History as AI Training Assets
AI can generate accurate predictions only when trained on comprehensive, high-quality data. Cloud ERPs provide years of transactional history, master data on customers and vendors, and contextual information that enriches AI models. This breadth of data allows AI to identify patterns and correlations that would be impossible to detect in fragmented systems.
Native Automation Capabilities and Embedded AI Modules
Leading cloud ERPs increasingly include native AI capabilities as standard features. These embedded AI modules benefit from deep integration with the underlying accounting functions, offering capabilities such as intelligent payment term recommendations, automated variance analysis, and smart report generation, without requiring external tools or complex integrations.
Marketplace Integrations and Low-Code Connectors
Cloud ERP ecosystems typically include marketplaces where third-party AI solutions are pre-integrated and certified. This dramatically reduces implementation risk and time. Low-code integration tools allow accounting teams to connect AI services and create custom workflows without extensive programming, democratising AI adoption.
Examples: Automated Reconciliations, Continuous Auditing, Smart Reporting
On cloud ERP platforms, AI powers use cases that would be prohibitively complex on legacy systems. Automated reconciliations match transactions across dozens of bank accounts and payment processors in real-time. Continuous auditing AI monitors every transaction as it posts, flagging issues immediately. Smart reporting AI learns which reports executives actually use and proactively prepares them with relevant commentary.
Best Practices for Integrating AI with Cloud ERPs
Successful integration of AI into accounting on cloud ERP platforms requires following proven design principles.
Establish a Single Source of Truth and Data Governance
Even with a cloud ERP, organisations must establish clear data governance policies. Define authoritative data sources, data quality standards, and ownership responsibilities. AI’s accuracy depends entirely on data quality, making governance non-negotiable for successful AI in your accounting systems.
Use APIs and Middleware for Loosely Coupled Integrations
Rather than creating tightly coupled point-to-point connections, use the cloud ERP’s APIs and consider middleware platforms that manage data flow. This approach creates flexibility—you can swap AI tools or upgrade the ERP without breaking integrations. Loosely coupled architectures also simplify troubleshooting and maintenance.
Design for Scalability and Monitoring of AI Models
AI models integrated with cloud ERPs should be designed with scalability in mind from the start. As transaction volumes grow, ensure the architecture can handle increased load. Equally important is implementing monitoring that tracks model performance, data drift, and prediction accuracy over time. AI models degrade without ongoing oversight.
Maintain Documentation, Model Versioning, and Audit Trails
Scalable accounting requires rigorous documentation of how AI models were developed, trained, and deployed. Maintain version control for models so you can roll back if issues emerge. Ensure a comprehensive audit trail document that records which AI version made which decisions and, based on what data—this is essential for both internal controls and external audits.
Measuring Success: KPIs for AI in Accounting on Cloud ERPs
Measuring the impact of AI requires establishing clear metrics before implementation and consistently tracking them.
Process Cycle Time Reductions
Track how long key processes take before and after AI implementation. Close-to-close time is a critical metric, but it also measures invoice processing time, reconciliation completion, and report generation speed. Reductions of 40%-60% are common with well-implemented AI.
Reduction in Manual Entries and Error Rates
Count how many journal entries, data inputs, and corrections are required manually. AI should dramatically reduce these, with many organisations seeing 70%+ reductions in manual interventions. Error rates should also decline significantly—track both error frequency and the time required to correct errors.
Cost Per Transaction and ROI Timelines
Calculate the fully loaded cost of processing invoices, payments, and other transactions. As AI handles more volume with the same resources, the cost per transaction should decline. Most organisations achieve positive ROI within 12-24 months, but establishing baseline metrics is essential for demonstrating value.
User Adoption and Satisfaction Metrics
Technology only delivers value if people use it. Track login frequency, feature utilisation, and user satisfaction scores. High adoption indicates successful change management, while low adoption suggests training or design issues that need to be addressed. Survey accounting teams regularly about how AI helps their work.
Transitioning from Legacy to Cloud ERP + AI
For organisations with legacy systems, transitioning to cloud ERP with AI capabilities requires careful orchestration.
Assessment and Migration Strategy
Begin with a comprehensive assessment of current systems, processes, data quality, and customisations. Define which capabilities must be preserved, which can be standardised, and where AI could provide new functionality. Develop a migration strategy that balances risk, disruption, and speed—between a “big bang” cutover and a phased transition.
Data Cleansing and Migration Sequencing
Data migration is typically the most challenging aspect of cloud ERP transitions. Existing accounting data may contain duplicates, inconsistencies, and obsolete records. Invest in data cleansing before migration—clean data is essential for both operational success and effective AI. Sequence migration thoughtfully, starting with master data before transactional history.
Pilot Phases and Co-Existence Strategies
Consider running the legacy and cloud systems in parallel during transition periods, allowing for validation of results and gradual confidence-building. Pilot with a business unit or subsidiary before full deployment. These approaches reduce risk and provide learning opportunities, though they do extend timelines and increase short-term costs.
Change Management and Upskilling Finance Teams
The transition represents a fundamental shift in how accounting involves technology. Invest heavily in training, ensuring accounting professionals understand not just how to use the new system, but how AI tech augments their expertise. Address fears about job security directly—emphasise how AI eliminates tedious work while creating opportunities for more strategic roles.
Why NetSuite is an Ideal Platform for AI in Accounting
Among cloud ERP platforms, NetSuite offers particularly strong capabilities for organisations looking to leverage AI in their accounting functions.
Cloud-Native Architecture with Unified Financial Data
NetSuite was built as a cloud-native platform from inception, avoiding the architectural compromises of systems migrated from on-premise origins. This native cloud design provides a truly unified financial data model, with general ledger, subsidiary ledgers, and operational data residing in a single database. This unified structure is ideal for AI, eliminating the data fragmentation that plagued traditional accounting systems.
Robust APIs and SuiteCloud Platform for Integrations
NetSuite’s SuiteCloud platform provides comprehensive APIs and integration tools that make connecting AI solutions straightforward. RESTlets, REST web services, and SuiteTalk offer flexible options for AI tools to access data and invoke actions. This integration flexibility means organisations can select best-of-breed AI solutions while maintaining tight coupling with their financial system.
Built-In Automation and Reporting Combined with Extensibility
NetSuite includes substantial native automation capabilities—custom workflows, SuiteFlow for visual workflow design, and SuiteScript for more complex logic. These tools allow organisations to implement significant automation before adding external AI. When AI is needed, the extensibility ensures seamless integration. SuiteAnalytics provides AI-ready datasets through saved searches and analytics workbooks.
Large Partner Ecosystem Offering AI Solutions and Connectors
The NetSuite ecosystem includes hundreds of partners offering pre-built AI integrations for accounts payable automation, revenue recognition, expense management, and forecasting. This marketplace accelerates implementation and reduces risk compared to building AI capabilities from scratch.
Continuous Upgrades and Vendor-Managed Security/Compliance
NetSuite delivers two major upgrades annually, automatically incorporating the latest AI capabilities and security patches. This continuous improvement model ensures organisations benefit from rapid innovation in AI without the disruption of major upgrade projects. Oracle manages security and compliance at scale, providing enterprise-grade protection that individual organisations would find difficult to match.
Positioning NetSuite as the Perfect System
For finance leaders evaluating platforms to support their AI in the accounting journey, NetSuite presents a compelling value proposition.
Executive Summary: NetSuite + AI Value Proposition for Finance Leaders
NetSuite provides the ideal foundation for modern accounting integrated with AI. By combining a unified financial data model, robust integration capabilities, and a rich partner ecosystem, NetSuite enables accounting firms and corporate finance teams to implement AI incrementally while maintaining full audit trails and controls. The platform’s cloud-native architecture ensures scalability, while continuous upgrades deliver ongoing innovation without disruptive reimplementation projects.
Key Features Supporting AI in Accounting
NetSuite’s architecture includes several capabilities that are particularly valuable for AI implementation:
Real-Time General Ledger and Consolidated Reporting
NetSuite maintains a real-time, multi-subsidiary general ledger that provides AI with immediate access to current financial positions across the entire organisation. This real-time data enables AI to generate current insights rather than working from stale batch extracts.
Custom Workflows, SuiteFlow, and SuiteScript Extensibility
Organisations can build sophisticated automations using NetSuite’s native tools, from no-code SuiteFlow visual workflows to SuiteScript for complex business logic. This extensibility allows AI to trigger actions and integrate seamlessly with existing processes.
Saved Searches and SuiteAnalytics for AI-Ready Datasets
NetSuite’s saved search functionality and SuiteAnalytics create structured datasets that AI can consume without complex ETL processes. Finance teams can define the data views that AI needs and maintain them as business requirements evolve.
Integration via RESTlets, REST Web Services, and SuiteTalk
NetSuite’s comprehensive API suite supports a wide range of integration patterns. AI solutions can query data, submit transactions, and trigger workflows using industry-standard protocols, minimising integration effort.
Typical AI-Driven Scenarios on NetSuite
Organisations implementing AI on NetSuite typically focus on several high-value scenarios:
Automated Invoice Processing with OCR and AP Workflows
AI tools scan incoming invoices, extract data, and automatically create bills in NetSuite. NetSuite’s approval workflows then route transactions based on approval hierarchies, with AI handling exception identification and routing.
Predictive Cash Forecasting Using Transactional History
By analysing NetSuite’s historical payment patterns, receivables aging, and planned expenses, AI can generate rolling cash forecasts that update daily. These forecasts help treasury teams optimise working capital and identify potential shortfalls early.
Automated Reconciliations and Exception Routing
AI reconciles bank statements with NetSuite’s cash records, automatically matching transactions and creating adjustment entries for differences. Unmatched items are routed to accounting staff via NetSuite workflows, enabling end-to-end automation with appropriate human oversight.
Embedded Analytics for Month-End Close Acceleration
AI monitors task progress closely, identifies bottlenecks, and alerts teams to issues that require attention. Some implementations use AI to prepare standard journal entries and variance explanations, significantly reducing close cycle time.
Implementation Approach with NetSuite
Successfully implementing AI on NetSuite follows a structured approach:
Define Scope and Outcomes Aligned to Finance KPIs
Start by identifying specific KPIs the organisation wants to improve—faster close, reduced costs, better forecasts. Define which accounting processes impact those KPIs and prioritise AI opportunities accordingly.
Leverage NetSuite Partners for AI Solution Selection and Integration
NetSuite’s partner ecosystem includes specialists who have implemented multiple AI solutions. Engage partners early to benefit from their experience and avoid common pitfalls.
Run Pilots in a Sandbox and Iterate Before Production
NetSuite’s sandbox capability lets you test AI integrations without impacting production data. Use sandboxes to validate functionality, train users, and refine configurations before going live.
Governance: Manage Roles, Permissions, and Model Audit Logs
Establish governance frameworks that define who can approve AI-generated transactions, how model decisions are logged, and procedures for investigating issues. NetSuite’s role-based security provides granular control over who can access AI features.
Migration and Integration Checklist
Organisations implementing AI on NetSuite should address several key areas:
Data Mapping and Master Data Consolidation
Map legacy chart of accounts, customer lists, and vendor data to NetSuite’s structure. Consolidate duplicates and standardise naming conventions—clean master data is essential for AI accuracy.
ETL Strategies and Real-Time Sync Considerations
Define how data flows between NetSuite and AI tools. Real-time integration delivers the most value but requires careful design. Batch integration may be appropriate for some scenarios, particularly during initial rollout.
Testing Plans and Cutover Strategies
Develop comprehensive test plans covering normal transactions, edge cases, and exception handling. Plan cutover timing to minimise disruption—many organisations choose month-end or quarter-end to align with natural accounting cycles.
Post-Go-Live Support and Continuous Improvement
Establish support structures for the first weeks after launch when questions and issues are most frequent. Plan regular reviews to assess performance and identify optimisation opportunities—AI implementation is a journey, not a one-time event.
Measuring ROI on NetSuite + AI
Demonstrating return on investment requires baseline metrics and realistic improvement targets:
Baseline Metrics and Target Improvements
Document current performance before implementation—invoice processing time, close duration, error rates, and headcount required. Set realistic targets based on industry benchmarks and vendor references. Conservative estimates suggest 30%-50% efficiency improvements are achievable in most scenarios.
Time-to-Value Expectations and Cost Considerations
Most NetSuite + AI implementations deliver measurable benefits within 3-6 months for focused use cases. Full ROI typically occurs within 12-24 months, depending on scope and complexity. Factor in software costs, implementation services, internal resources, and ongoing support when calculating the total cost of ownership.
Conclusion and Next Steps
The integration of AI into accounting represents one of the most significant transformations the accounting profession has experienced. From automating tedious manual tasks to generating predictive insights that inform strategic decisions, AI is reshaping the accounting landscape in fundamental ways.
Cloud ERPs like NetSuite provide the ideal platform for this transformation. By offering unified data, robust integration capabilities, and continuous innovation, cloud-based systems overcome the limitations that have constrained AI adoption on legacy platforms. Organisations that embrace this combination of AI and modern ERP infrastructure will gain significant competitive advantages through greater efficiency, improved accuracy, and superior insight.
Checklist for Finance Leaders Planning Adoption
As you consider your own AI journey, work through this checklist:
- Assess your current data quality and process maturity
- Identify 2-3 high-value processes where AI could deliver quick wins
- Evaluate whether your current accounting system supports AI integration effectively
- If on legacy systems, develop a business case for cloud ERP migration
- Research AI tools and vendors relevant to your priority use cases
- Engage stakeholders and address concerns about AI and job security
- Define success metrics and baseline your current performance
- Plan pilot projects with clear scope and success criteria
- Budget for implementation services, training, and change management
- Establish governance frameworks for AI oversight and audit
Evaluate Readiness, Pilot with a Cloud ERP Like NetSuite
The future of accounting is being written now by organisations that are implementing AI thoughtfully and systematically. For large enterprises seeking to transform their finance function, the time to act is now.
Start by evaluating your readiness for AI adoption. Assess your data, processes, and team capabilities honestly. If you’re on legacy systems, seriously consider transitioning to a cloud ERP platform like NetSuite that provides the infrastructure AI requires.
Begin with focused pilots that deliver value quickly and build organisational confidence. Learn from these implementations, refine your approach, and scale incrementally. The accounting profession is at an inflection point—those who adapt will thrive, while those who cling to traditional accounting methods will find themselves at an increasingly severe disadvantage.
AI has the potential not just to automate existing work, but also to fundamentally redefine what accounting involves and how accounting professionals add value. The technology is mature, the platforms are ready, and the business case is proven. The question is not whether to adopt AI, but how quickly you can implement it effectively. Your journey starts with the first step—evaluate your readiness and commit to exploring what AI can do for your organisation.
