How AI agents will redefine banking customer experience: A strategic guide for CX leaders

Banking faces a defining moment. BCG research reveals that artificial intelligence could unlock more than $370 billion in annual profit potential by 2030, yet most banks struggle with cost-to-income ratios exceeding 60%—nearly double that of digital-native competitors. Meanwhile, 73% of customers cite trust as their primary retention driver, but only 42% feel truly understood by their bank.

AI agents will redefine banking customer experience not because they automate more tasks, but because they allow banks to scale trust, empathy, and responsiveness across every interaction. Unlike traditional chatbots that follow scripted flows, these autonomous systems can pursue goals, break down complex problems, and adapt to novel situations within compliance boundaries.

This guide explores seven strategic transformation opportunities, provides an implementation roadmap for CX and IT leaders, and demonstrates how human-centred AI can drive both customer loyalty and measurable business growth. We'll examine practical frameworks for deploying AI agents across voice, chat, and email channels whilst maintaining the empathy and oversight that regulated banking environments require.

What AI agents mean for banking customer experience

The banking industry often conflates different types of AI-powered systems, creating confusion for leaders evaluating transformation strategies. Understanding these distinctions is crucial for making informed deployment decisions.

Defining agentic AI in banking context

Agentic AI in banking: Autonomous systems that can pursue goals, break down complex tasks, and adapt to novel situations within compliance boundaries—enabling end-to-end resolution of customer issues without human intervention.

Traditional chatbots respond to predefined intents using decision trees and pattern matching. They're effective for simple queries like balance checks but escalate when customers deviate from expected scripts. Copilots augment human agents with real-time suggestions and context, improving productivity without replacing human judgement. Workflow automation handles structured backend processes but cannot make decisions on exceptions.

AI agents represent a fundamental shift. They can understand a customer's intent, retrieve relevant account information, assess risk factors, and determine appropriate actions—all whilst maintaining conversation context and emotional awareness. For example, when a customer reports a disputed transaction, an AI agent can analyse transaction patterns, compare against historical data, assess fraud probability, and either resolve the dispute autonomously or escalate with full context to a human specialist.

The shift from reactive to proactive service

Traditional banking operates reactively: customers contact the bank when problems arise. AI agents enable a proactive service model where banks anticipate needs and reach out with solutions. This transformation is particularly powerful in building the trust and understanding that customers increasingly demand.

Graia's approach to agentic AI emphasises technology that feels human rather than robotic automation. By combining emotional intelligence with advanced decision-making capabilities, banks can deliver personalised experiences that strengthen relationships whilst improving operational efficiency.

Why banking CX transformation matters now

The convergence of rising customer expectations, competitive pressure, and operational constraints creates an urgent imperative for transformation.

Customer expectation evolution

Digital-native customers expect 24/7 availability, instant resolution, and personalised service across all channels. Research from Harvard Business Review and Capgemini shows that whilst 73% of bank customers cite trust as their primary retention driver, only 42% feel understood by their bank. This empathy gap represents both a challenge and an opportunity.

Banking customer experience expectations now mirror those set by technology leaders like Amazon and Netflix. Customers want proactive notifications about account activity, contextual recommendations based on spending patterns, and seamless experiences whether they're using mobile apps, calling support, or visiting branches. When these expectations aren't met, customers increasingly switch to digital-first competitors that offer more intuitive experiences.

Digital-first competitor pressure and operational strain

Traditional banks face a structural disadvantage. Their cost-to-income ratios typically exceed 60%, compared to approximately 35% for well-run digital banks. This disparity stems from legacy system complexity, regulatory overhead, and channel fragmentation that creates inefficiencies.

AI in financial services has become a competitive necessity rather than an innovation experiment. Digital-native banks leverage conversational banking interfaces to reduce friction, whilst traditional institutions struggle with siloed systems that prevent seamless customer journeys. The result is a widening gap in customer satisfaction and operational efficiency.

Legacy system constraints limit agility and personalisation capabilities. When customers interact across multiple channels, context is often lost, forcing them to repeat information and creating frustration. This channel complexity multiplies operational costs whilst degrading the customer experience.

Regulatory and compliance considerations

Banking compliance requirements, rather than hindering AI adoption, can actually enable more consistent and auditable customer interactions when properly implemented. The EU AI Act requires disclosure when customers interact with AI systems, especially for decisions affecting account status. OCC guidance mandates governance, testing, and monitoring for AI systems in banking operations.

These requirements create opportunities for banks that deploy AI agents responsibly. Proper auditability, decision traceability, and bias monitoring can improve compliance outcomes whilst delivering superior customer experiences. The key is building these capabilities into the foundation of AI systems rather than treating them as afterthoughts.

7 ways AI agents will redefine banking customer experience

1. Deliver 24/7 personalised service without quality compromise

Customer expectations for instant responses don't follow business hours. Research shows 67% of banking customers expect immediate responses regardless of when they contact their bank, yet traditional staffing models can't economically provide round-the-clock coverage without sacrificing service quality.

AI agents maintain service quality standards whilst providing always-on availability. They can handle account emergencies, fraud alerts, and travel notifications at any hour, using the same empathy and context awareness that characterises daytime interactions. Unlike basic chatbots that feel robotic during off-hours, well-designed AI agents adapt their communication style to match the urgency and emotional context of each situation.

Implementation requires careful attention to personalised banking experiences. AI agents should access customer history, preferences, and risk profiles to provide contextually appropriate responses. For high-value customers accustomed to relationship banking, the AI agent might proactively offer additional services or escalate to human specialists when appropriate.

Graia's human-centred approach ensures that 24/7 availability doesn't come at the expense of empathy. AI voice agents and AI text agents can recognise emotional cues and adjust their responses accordingly, maintaining the trust and understanding that customers expect from their financial institutions.

2. Reduce customer effort through proactive engagement

Customer Effort Score improvements of 30-40% are achievable when AI agents maintain context across channels and anticipate customer needs. Rather than waiting for customers to contact the bank with problems, proactive customer engagement allows institutions to surface solutions before issues escalate.

AI agents can monitor account patterns and trigger personalised outreach when they detect potential problems or opportunities. For example, if spending patterns suggest a customer might benefit from a higher credit limit or different account type, the AI agent can initiate a conversation with relevant options. When fraud indicators appear, proactive alerts with clear next steps reduce anxiety and resolution time.

The key is designing proactive engagement that builds relationships rather than feeling intrusive. Notification frameworks should consider customer preferences, communication history, and the sensitivity of different message types. Payment reminders, for instance, can be delivered with empathy controls that adjust tone based on the customer's payment history and current account status.

Banking self-service capabilities expand significantly when AI agents can guide customers through complex processes proactively. Instead of leaving customers to navigate complicated forms or procedures alone, AI agents can offer step-by-step assistance, anticipate common questions, and provide reassurance throughout multi-step interactions.

3. Transform sensitive interactions with empathetic automation

Banking interactions often involve emotionally charged situations: fraud disputes, financial hardship discussions, loan denials, or inheritance handling. Research indicates that 71% of banking customers abandon chatbot interactions during money-related stress due to lack of empathy, yet these sensitive moments are precisely when customers need the most support.

AI agents can handle emotionally sensitive situations with appropriate tone and escalation protocols. They're trained to recognise distress signals in customer communications and respond with empathy whilst maintaining professional boundaries. For fraud detection scenarios, AI agents can acknowledge the customer's concern, explain the security measures being taken, and provide clear timelines for resolution.

Collections processes benefit particularly from empathetic automation. Traditional collections approaches often damage customer relationships, but AI agents can detect financial hardship indicators and adjust their approach accordingly. When customers indicate difficulty making payments, the AI agent can offer payment plan options, connect them with financial counselling resources, or escalate to human specialists trained in sensitive financial conversations.

The implementation framework requires careful emotional intelligence integration. AI agents need training on recognising emotional context, appropriate response patterns for different situations, and clear escalation triggers when human intervention is necessary. This approach transforms traditionally transactional processes into relationship-building opportunities.

4. Scale relationship banking across all customer segments

Banks using AI for relationship insights report 22-35% higher NPS gains compared to institutions focused solely on transactional efficiency. Traditionally, personalised advisory support was reserved for high-net-worth customers, but AI agents enable banks to scale relationship banking across all segments.

AI agents can recognise life events, provide financial wellness guidance, and offer product recommendations tailored to individual circumstances. When a customer's spending patterns suggest they're planning a major purchase, the AI agent can proactively discuss financing options. For customers approaching retirement, relevant investment and planning resources can be surfaced during routine interactions.

Customer lifetime value increases significantly when AI agents support advisory interactions. Rather than simply processing transactions, they can identify opportunities to deepen relationships through relevant financial guidance. This might include budgeting assistance, savings goal tracking, or educational content matched to the customer's financial sophistication level.

The key is maintaining authenticity in these interactions. AI agents should provide genuine value rather than pushing products inappropriately. Deposit stickiness improves when customers feel their bank understands their goals and provides relevant support, not when they feel targeted by aggressive sales tactics.

5. Strengthen collections and servicing with human-centred approaches

Collections success rates improve 25-35% when AI agents detect financial hardship and adjust their approach accordingly. Traditional collections processes often damage customer relationships and fail to address underlying issues, but human-centred automation can transform these interactions into supportive conversations.

AI agents can conduct sensitive financial conversations with appropriate empathy and compliance oversight. They're trained to recognise hardship indicators, offer payment plan alternatives, and connect customers with resources that address root causes of financial difficulty. This approach improves loan retention whilst maintaining regulatory compliance.

The framework for sensitive conversation design includes multiple escalation protocols. When customers indicate severe financial stress, immediate escalation to human specialists ensures appropriate support. For routine payment reminders, AI agents can adjust their tone based on payment history and account status, maintaining professionalism whilst showing understanding of individual circumstances.

Customer support automation in collections contexts requires careful balance between efficiency and empathy. The goal is resolving payment issues whilst preserving long-term customer relationships. AI agents can offer multiple resolution paths, explain consequences clearly, and provide timeline certainty that reduces customer anxiety.

6. Enhance human agents with real-time intelligence

Agent productivity gains of 25-40% are achievable with AI copilot assistance, whilst CSAT improvements of 8-15 points reflect the enhanced quality of human interactions when agents have comprehensive context and intelligent suggestions.

AI agents work alongside human teams to provide context, suggest next-best actions, and surface relevant information during complex interactions. For dispute resolution, the AI copilot can analyse transaction patterns, retrieve similar cases, and recommend resolution approaches whilst the human agent focuses on empathy and relationship management.

Human and AI collaboration models vary based on interaction complexity and customer preferences. For high-value customers or sensitive situations, human agents lead the conversation whilst AI provides background intelligence. For routine inquiries that require human judgement, AI agents can handle initial triage and context gathering before seamless handoff.

The orchestration between AI and human agents should be invisible to customers. When escalation occurs, the human agent receives full conversation history, sentiment analysis, and suggested approaches. This eliminates the frustration of customers repeating information whilst ensuring continuity of service quality.

Contact centre transformation through AI copilots improves job satisfaction for human agents by reducing repetitive tasks and providing tools that help them deliver better outcomes. Rather than replacing agents, this approach elevates their role to focus on complex problem-solving and relationship building.

7. Convert every interaction into actionable customer insights

AI-driven insights enable proactive problem management, reducing complaint volumes by 12-25% through early identification of trends and systematic issue resolution. Every customer interaction becomes a source of intelligence for improving products, processes, and experiences.

AI agents capture and analyse interaction data to improve service and predict customer needs. They can identify emerging issues before they become widespread problems, spot opportunities for process improvements, and track the effectiveness of different resolution approaches. This continuous feedback loop drives systematic enhancement of banking CX metrics.

Banking operations benefit from predictive engagement capabilities that anticipate customer needs based on interaction patterns. When multiple customers ask similar questions about a new product feature, the AI system can trigger proactive communications to address confusion before it spreads. When transaction patterns suggest customers might benefit from different services, targeted outreach can be personalised and timed appropriately.

The analytics-driven improvement cycle includes trend identification, product gap analysis, customer sentiment tracking, and risk indicator detection. Unlike traditional surveys that capture feedback after problems occur, AI agents provide real-time insights that enable immediate course correction and continuous optimisation.

Implementation roadmap for banking leaders

Successful AI agent deployment requires careful orchestration across CX, IT, compliance, and operations teams. A phased approach reduces risk whilst building organisational confidence and capability.

Phase 1: Foundation and low-risk deployment (Months 1-6)

Stakeholder alignment across all functions is essential before deploying customer-facing AI. CX leaders focus on journey mapping and quality standards, IT teams address integration and security requirements, compliance officers establish governance frameworks, and operations managers define success metrics and monitoring procedures.

Use case prioritisation should follow a volume-versus-risk framework. High-volume, low-risk interactions like balance inquiries, transaction history requests, and simple password resets provide ideal starting points. These scenarios allow teams to establish monitoring procedures and build confidence without exposing the organisation to significant compliance or reputation risks.

Banking operations integration begins with API connections to core systems, enabling AI agents to access account information and transaction data securely. Pilot deployments should start with chat channels before expanding to voice interactions, as text-based conversations are easier to monitor and adjust during initial rollouts.

Graia's plug-and-play integration approach minimises disruption to existing banking systems whilst providing the flexibility to expand capabilities over time. The platform's modular architecture allows banks to start with basic interactions and gradually add more sophisticated features as teams gain experience.

Phase 2: Complex service scenarios (Months 7-12)

Mid-complexity use cases include account maintenance requests, document submissions, and simple dispute triage. These interactions require more sophisticated decision-making but remain within clear operational boundaries. AI agents can handle routine aspects whilst escalating exceptions that require human judgement.

Core banking automation expands to include payment scheduling, card management, and basic loan servicing inquiries. Integration with legacy systems typically requires middleware or API bridges, but the investment pays dividends through improved efficiency and customer satisfaction.

A/B testing becomes crucial during this phase, comparing AI-led versus human-led experiences for identical customer segments. Quality monitoring should track CSAT scores, first contact resolution rates, escalation patterns, and sentiment analysis to ensure AI interactions meet or exceed human benchmarks.

Omnichannel deployment enables seamless experiences across voice, chat, email, and mobile channels. Customers should be able to start conversations in one channel and continue in another without losing context or repeating information.

Phase 3: Proactive and predictive engagement (Months 13-18)

Advanced use cases include collections with empathy controls, KYC onboarding, and relationship banking support. These scenarios require sophisticated emotional intelligence and careful compliance oversight, but they offer the greatest potential for competitive differentiation.

AI-first retail bank capabilities emerge through proactive service deployment. Rather than simply responding to customer requests, AI agents can initiate conversations based on account activity, life events, or identified opportunities. This shift from reactive to proactive service represents a fundamental transformation in banking customer relationships.

Cross-channel orchestration ensures consistent experiences regardless of how customers choose to interact. AI agents maintain conversation history and context across all touchpoints, enabling truly seamless omnichannel support that meets modern customer expectations.

Revenue impact measurement becomes possible as AI agents contribute to retention rates, cross-sell success, and customer lifetime value improvements. These metrics demonstrate the strategic value of AI investment beyond simple cost reduction.

Governance and compliance in regulated environments

Banking compliance requirements create both challenges and opportunities for AI deployment. Proper governance frameworks enable consistent, auditable interactions whilst protecting customer interests and institutional reputation.

Auditability and decision traceability

EU AI Act requirements mandate disclosure when customers interact with AI systems, especially for decisions affecting account status or eligibility. Decision logging systems must capture input features, model inference processes, guardrail checks, and final outputs with complete reasoning trails.

Every AI-driven decision must be explainable in plain language. When a loan application receives initial screening, customers should understand the factors considered and have clear paths for human review. This transparency builds trust whilst meeting regulatory requirements for algorithmic decision-making in financial services.

Banking compliance benefits from native auditability features built into AI platforms rather than added as afterthoughts. Graia's enterprise-ready architecture includes comprehensive logging, real-time monitoring, and compliance-ready reporting that simplifies regulatory oversight and reduces audit burden.

Consent and disclosure requirements

Customer notification frameworks should balance transparency with user experience. Effective disclosure might state: "You're speaking with [Agent Name], powered by AI. If you prefer a human agent, just ask." This approach informs customers without creating unnecessary friction or anxiety.

Opt-out mechanisms ensure customers can always request human review without penalty. Appeals processes must exist for decisions affecting account status, eligibility, or charges. These safeguards protect customer rights whilst enabling AI agents to handle the majority of interactions efficiently.

Regulatory compliance in AI in financial services requires ongoing attention to evolving guidance from regulators. Banks should establish processes for monitoring regulatory developments and updating AI systems accordingly.

Security and identity verification

Multi-factor authentication and secure identity verification become more sophisticated with AI agents capable of analysing voice patterns, behavioural biometrics, and interaction context. These capabilities can improve security whilst reducing friction for legitimate customers.

Banking self-service security must balance convenience with protection. AI agents can recognise unusual request patterns or account access attempts and escalate appropriately without creating unnecessary barriers for routine interactions.

Integration with existing fraud detection and risk management systems ensures AI agents contribute to overall security posture rather than creating new vulnerabilities. Real-time risk assessment capabilities enable dynamic security measures that adapt to changing threat landscapes.

Measuring success: KPIs that matter

Comprehensive measurement frameworks track both customer experience improvements and operational efficiency gains. The most successful AI deployments balance cost reduction with quality enhancement and relationship building.

Customer-centric metrics

First Contact Resolution (FCR) rates of 70-80% are achievable with well-designed AI agents, representing 8-15 percentage point improvements over traditional approaches. This metric reflects the AI agent's ability to understand customer needs and provide complete solutions without escalation.

Customer Satisfaction (CSAT) scores show the clearest evidence of AI impact on experience quality. Banking averages of 3.8-4.0 out of 5 can improve by 5-12 points when AI agents are tuned for empathy and emotional intelligence. These gains reflect customers' appreciation for faster resolution combined with appropriate emotional support.

Net Promoter Score (NPS) improvements of 3-8 points demonstrate long-term loyalty impact. Customers who experience efficient, empathetic AI interactions are more likely to recommend their bank to others, indicating genuine satisfaction rather than mere tolerance.

Customer Effort Score (CES) reductions of 0.8-1.2 points on a 5-point scale reflect the reduced friction AI agents create through proactive service, context retention, and intelligent routing. These improvements translate directly into customer loyalty and retention.

Operational and business metrics

Containment rates of 75-85% are achievable with proper governance and quality safeguards. This metric balances efficiency with quality, ensuring AI agents handle appropriate interactions whilst escalating complex or sensitive situations to human specialists.

Cost per interaction reductions of 40-60% for AI-handled requests demonstrate clear operational benefits. However, these savings should be measured alongside quality metrics to ensure efficiency gains don't compromise customer experience.

Customer retention rate improvements of 2-5 percentage points reflect the relationship-building impact of proactive, personalised AI interactions. These gains are particularly valuable given the high cost of customer acquisition in competitive banking markets.

Banking CX metrics should include authentication success rates, complaint reduction trends, and cross-sell effectiveness to provide a comprehensive view of AI impact across all aspects of customer relationship management.

Common implementation mistakes to avoid

Learning from early adopters helps banks avoid costly missteps and accelerate successful AI agent deployment.

Automating wrong journeys first

Starting with high-risk, complex use cases before establishing governance creates unnecessary exposure to compliance and reputation risks. Risk management in AI agents in banking deployment requires careful journey selection based on volume, complexity, and regulatory sensitivity.

Beginning with high-volume, low-risk transactional inquiries builds confidence and capability before tackling more challenging interactions. This approach allows teams to refine monitoring procedures and establish quality standards without jeopardising customer relationships.

Ignoring emotional context and human factors

Treating AI agents as pure efficiency tools without empathy considerations creates robotic customer experiences that damage relationships. Human-centred design prevents the disconnected interactions that cause customers to abandon AI-powered channels.

Emotional intelligence and escalation protocols for sensitive conversations are essential for maintaining trust during difficult moments. AI agents should recognise distress signals and respond appropriately rather than following scripted responses that feel insensitive.

Poor human handoff design

Abrupt escalations without context transfer create customer frustration and defeat the purpose of AI assistance. Human and AI collaboration best practices require seamless orchestration with full conversation history and sentiment analysis.

Context preservation during escalation ensures customers don't repeat information or lose momentum toward resolution. Human agents should receive comprehensive briefings that enable them to continue conversations naturally and effectively.

Measuring efficiency without quality

Focusing solely on cost reduction and containment rates misses the relationship-building opportunities that drive long-term value. Balanced scorecards including CSAT, NPS, and relationship depth metrics ensure AI deployment strengthens rather than weakens customer connections.

Graia's approach emphasises top-line growth and customer loyalty rather than just operational efficiency. This perspective helps banks realise the full potential of AI investment through improved retention, cross-sell success, and customer lifetime value.

Frequently asked questions

What's the difference between AI agents and banking chatbots?

Chatbots follow rule-based responses to predefined intents with limited ability to handle exceptions or novel situations. They're effective for simple, scripted interactions but require human handoff when customers deviate from expected patterns.

AI agents demonstrate autonomous decision-making within guardrails, capable of multi-step reasoning and adaptive behaviour. In banking contexts, agentic AI in banking can resolve end-to-end disputes by analysing transaction patterns, assessing risk factors, and determining appropriate resolutions without human intervention.

The key distinction lies in autonomy and adaptability. Whilst chatbots execute predefined workflows, AI agents can pursue goals, break down complex problems, and adapt their approach based on individual customer circumstances and conversation context.

How do AI agents maintain compliance in regulated interactions?

Built-in guardrails ensure AI agents operate within financial advice boundaries and regulatory requirements. Decision-making authority is carefully defined, with automatic escalation for situations requiring human oversight or regulatory review.

Banking compliance frameworks include audit trails capturing decision reasoning, compliance checks, and escalation triggers. These systems provide regulatory transparency whilst enabling efficient customer service for routine interactions.

Continuous monitoring tracks decision patterns by customer segment, flags potential bias incidents, and ensures consistent application of regulatory requirements across all AI interactions.

Which banking journeys should be prioritised for automation?

Phase 1 priorities include balance inquiries, transaction history, and password resets—high-volume, low-risk interactions that build confidence without significant compliance exposure.

Phase 2 expansion covers account maintenance, document requests, and simple disputes—medium complexity scenarios that demonstrate AI agents' problem-solving capabilities whilst maintaining clear operational boundaries.

Journey prioritisation in banking operations should consider volume, risk, customer impact, and regulatory sensitivity. The most successful deployments start conservatively and expand systematically as capabilities and confidence grow.

How do AI agents work alongside human teams?

Copilot mode provides real-time suggestions and context to human agents during complex interactions. AI systems analyse conversation history, retrieve relevant information, and recommend next-best actions whilst humans focus on relationship management and complex problem-solving.

Autonomous mode enables AI agents to handle routine interactions independently, escalating only when situations exceed defined parameters or require human judgement. Seamless handoffs preserve context and conversation continuity.

Human and AI collaboration models should be flexible, adapting to customer preferences, interaction complexity, and business requirements. The goal is leveraging each approach's strengths whilst maintaining consistent service quality.

What ROI should banks expect from AI agent deployment?

Operational improvements include 40-60% cost per interaction reductions for AI-handled requests, though these should be balanced against quality metrics to ensure customer satisfaction isn't compromised.

Customer experience gains manifest as 8-15 point CSAT improvements and 3-8 point NPS increases, reflecting customers' appreciation for faster, more empathetic service delivery.

Revenue impact emerges through 15-28% customer lifetime value increases driven by proactive engagement and advisory support. Timeline expectations should allow 18-24 months for full transformation, with measurable improvements visible within 6 months of initial deployment.

Conclusion and next steps

AI agents represent a fundamental shift in banking customer experience, enabling institutions to scale trust, empathy, and responsiveness across every interaction. The seven transformation opportunities we've explored—24/7 personalised service, proactive engagement, empathetic automation, scaled relationship banking, human-centred collections, intelligent collaboration, and actionable insights—demonstrate how technology can enhance rather than replace human connection.

Successful deployment requires balancing efficiency with empathy, automation with human oversight, and innovation with regulatory compliance. Banks that embrace human-centred AI agents will create competitive advantages through improved customer loyalty, operational efficiency, and relationship depth.

The strategic imperative is clear: financial institutions that don't adopt empathetic, enterprise-ready AI agents risk falling behind digital-first competitors who understand that technology should feel human. True business growth comes from building authentic customer connections, not just reducing operational costs.

Graia's AI-powered engagement platform combines decades of customer experience expertise with cutting-edge artificial intelligence to help banks create meaningful customer connections. Our solutions support seamless collaboration between human agents and AI across voice, chat, and email channels, with enterprise-grade security and compliance capabilities designed for regulated banking environments.

Request a demo to see how Graia's human-centred AI agents can transform your banking customer experience whilst maintaining the trust and empathy your customers expect. Discover how our platform's emotional intelligence and advanced decision-making capabilities can drive both customer loyalty and measurable business growth for your organisation.