AI Based Call Center: Transforming Customer Service with Intelligent Automation in 2025

Understanding the AI Revolution in Call Centers

In recent times, there has been extensive discussion about AI based call center technology (also referred to as intelligent contact center or automated customer service platform) where traditional voice-based customer service operations are being transformed through artificial intelligence capabilities that dramatically improve both efficiency and experience quality. The purpose of an AI based call center is to enhance customer interactions by implementing sophisticated automation that handles routine inquiries independently while providing human agents with intelligent assistance for complex situations, creating a hybrid approach that optimizes both cost structure and service quality.

The Evolution from Traditional to AI-Powered Call Centers

The landscape of customer service has undergone remarkable transformation with the emergence of AI based call center solutions. Traditional call centers relied entirely on human agents following rigid scripts, creating operations that were simultaneously expensive to maintain and often frustrating for customers due to long wait times, inconsistent service quality, and limited availability. According to McKinsey’s research on contact center transformation, organizations implementing comprehensive AI enhancements typically reduce operational costs by 25-40% while simultaneously improving customer satisfaction scores by 15-20%.

This dramatic improvement results from several fundamental advantages of AI based call center implementations. AI systems eliminate wait times by handling multiple inquiries simultaneously without capacity constraints. They maintain perfect consistency across interactions, eliminating the quality variations inevitable with human-only approaches. Perhaps most significantly, they operate continuously without staffing limitations, enabling 24/7 availability without the premium costs associated with overnight or weekend staffing in traditional models.

Core Technologies Powering Modern AI Call Centers

The technological foundation of an AI based call center consists of several sophisticated components working in concert to create intelligent customer experiences. Natural language processing (NLP) capabilities enable systems to understand caller intent regardless of exact phrasing, extracting meaning from conversational language rather than requiring specific keywords or structured inputs. This capability creates more natural interactions compared to traditional IVR systems that force callers into rigid decision trees.

Speech recognition represents another crucial technology for AI based call center implementations, converting spoken language to text with increasingly impressive accuracy. Modern systems achieve recognition rates exceeding 95% across diverse accents and speaking styles, enabling reliable transcription even in challenging acoustic environments. This capability creates the foundation for both real-time agent assistance and fully automated conversation handling that would be impossible without accurate speech-to-text conversion.

Text-to-speech capabilities complete the conversation technology stack, generating natural-sounding voice responses that engage callers effectively. Unlike the robotic voices characteristic of earlier systems, modern AI based call center platforms utilize neural voice synthesis that creates remarkably human-like speech with appropriate pacing, intonation, and emotional qualities. This advancement transforms automated interactions from obviously artificial experiences to conversations that many callers cannot distinguish from human agents in typical business contexts.

The Business Case for AI Call Center Implementation

Organizations implementing AI based call center solutions typically pursue several business objectives simultaneously. Operational efficiency naturally represents a primary motivation, with most implementations reducing per-interaction costs by 30-60% compared to fully human-staffed approaches. This substantial saving results from automated handling of routine inquiries that previously consumed agent time without adding significant value through human judgment or empathy.

Beyond direct cost reduction, AI based call center implementations deliver significant improvements in customer experience quality. First-contact resolution rates typically increase 15-25% as AI systems provide agents with relevant information exactly when needed, eliminating the need for callbacks or transfers that frustrate customers and increase overall handling costs. Average handle time typically decreases 25-35% through elimination of manual lookups, automated documentation, and streamlined authentication processes.

Perhaps most significantly, AI based call center implementations enable consistent service quality across all interactions regardless of volume fluctuations, time of day, or agent experience levels. This consistency represents a meaningful competitive advantage in industries where service quality directly impacts customer retention and lifetime value. As Harvard Business Review’s analysis of customer experience notes, “Consistency across touchpoints represents the single most important factor in customer perception of service quality, outweighing even occasional service excellence if accompanied by inconsistent experiences.”

Different Implementation Models for AI Call Centers

Organizations typically implement AI based call center solutions through one of several models, each offering distinct advantages for particular business contexts. The augmented agent approach represents the most common starting point, with AI systems providing real-time guidance, information retrieval, and documentation assistance while human agents maintain primary conversation control. This model improves efficiency without dramatic operational changes, making it particularly suitable for organizations prioritizing smooth transition with minimal disruption.

The front-end automation model represents another common AI based call center implementation approach. In this model, AI systems handle initial caller greeting, authentication, and basic information gathering before transferring to appropriately skilled human agents for the main conversation. This approach typically reduces average handle time 20-30% by eliminating routine preliminary activities, allowing agents to focus exclusively on portions of interactions requiring human judgment or empathy.

For organizations ready for more comprehensive transformation, fully automated handling represents the most powerful AI based call center implementation model. In this approach, AI systems manage entire conversations for specific interaction types like appointment scheduling, account inquiries, or simple transactions. Human agents become involved only for exceptions or particularly complex situations, creating dramatic efficiency improvements while maintaining escalation options when automation reaches its limitations.

Key Features of Advanced AI Call Center Platforms

When evaluating AI based call center platforms, several key features differentiate sophisticated solutions from basic offerings. Omnichannel capabilities represent an increasingly essential requirement, enabling consistent conversation management across voice calls, chat, messaging applications, and email. This unified approach maintains context as customers move between channels, eliminating the frustrating need to repeat information while enabling more natural conversation flows that align with modern communication preferences.

Intent recognition capabilities represent another crucial consideration for AI based call center implementations. Advanced systems identify caller purpose with remarkable accuracy even when expressed in conversational language, eliminating the need for explicit menu selections or specific trigger phrases. This natural interaction style significantly improves customer experience compared to traditional IVR systems that force callers through rigid decision trees before addressing their actual needs.

Analytics capabilities round out the essential features for AI based call center platforms. Comprehensive solutions provide visibility into conversation patterns, quality indicators, and business outcomes across all interactions rather than the limited sampling typical in traditional quality assurance approaches. This complete visibility enables data-driven optimization that continuously improves both automated and human-assisted interactions based on actual conversation outcomes rather than assumptions or limited observations.

Real-World Applications of AI in Call Centers

The application of AI based call center technology spans diverse industries and use cases, demonstrating remarkable flexibility in addressing specific business requirements. Customer service naturally represents the most common implementation, with AI handling tier-one support inquiries, processing simple transactions, and providing status updates without human intervention. This automation of routine inquiries allows human agents to focus on complex issues that truly require human judgment, creativity, and empathy.

Sales operations represent another productive application for AI based call center solutions. AI-powered qualification calls identify promising prospects and gather preliminary information before transferring to skilled sales representatives, dramatically improving efficiency compared to having valuable salespeople handle initial screening conversations. For outbound campaigns, AI systems can navigate connection challenges like voicemail, gatekeepers, and initial objections, ensuring human salespeople engage only with qualified, available prospects.

Appointment scheduling and management represent particularly successful applications for AI based call center technology. AI systems excel at handling the complex back-and-forth conversation required to find suitable appointment times, send confirmations, deliver reminders, and process rescheduling or cancellation requests. Healthcare providers, field services companies, financial advisors, and other appointment-based businesses find exceptional value in this application, simultaneously reducing costs while improving both staff utilization and customer convenience.

Case Studies: Successful AI Call Center Implementations

Examining real-world implementations provides valuable insight into the potential of AI based call center solutions. A national healthcare provider implemented AI-powered appointment scheduling and reminders, reducing no-show rates by 35% while decreasing scheduling staff requirements by 60%. The system now handles over 5,000 calls daily with an 87% completion rate, transferring only the most complex scheduling scenarios to human staff. This implementation paid for itself within four months through reduced staffing costs and improved appointment utilization.

A financial services firm deployed an AI based call center for account servicing and transaction processing, achieving 92% customer satisfaction while reducing call center costs by 40%. The AI system authenticates customers through voice biometrics, provides account balances and transaction history, processes transfers and payments, and handles password resets—all without human intervention for most interactions. This implementation simultaneously improved service availability (expanding from business hours to 24/7) while reducing operational costs, creating both customer experience and financial benefits.

A telecommunications provider implemented an AI based call center for technical support, resulting in a 52% increase in first-call resolution and a 37% reduction in average handle time. The system diagnoses common issues, guides customers through basic troubleshooting, and ensures human agents have complete diagnostic information before joining conversations that require advanced assistance. This implementation transformed customer perception from industry-lagging to industry-leading in independent satisfaction surveys, demonstrating the strategic impact possible through thoughtful AI implementation.

Implementation Considerations for AI Call Centers

Organizations pursuing AI based call center implementations should approach the project with careful planning addressing both technical and organizational considerations. Data integration represents a common technical challenge, as effective AI requires access to various information sources including customer records, product information, transaction history, and knowledge bases. Successful implementations typically address this challenge through API-based integration layers that provide the AI with comprehensive data access without requiring major modifications to existing systems.

Conversation design represents another crucial implementation consideration for AI based call center projects. Effective design balances efficiency with natural interaction patterns, guiding callers toward successful outcomes without feeling overly scripted or rigid. This specialized expertise combines linguistic understanding, psychology, and user experience design to create conversations that satisfy both business objectives and caller expectations. As Nielsen Norman Group’s research on voice interaction design emphasizes, “The most successful voice interfaces mirror natural human conversation patterns while subtly guiding users toward successful outcomes.”

Change management deserves particular attention when implementing AI based call center solutions. Human agents often initially view AI with skepticism or concern about job security, potentially creating resistance that undermines implementation success. Effective approaches emphasize how AI handles routine aspects of calls while enabling human agents to focus on more rewarding, complex interactions requiring their unique skills. This positioning highlights how AI transforms agent roles toward higher-value activities rather than simply eliminating positions, creating more accurate expectations while reducing resistance.

Measuring ROI from AI Call Center Implementation

Organizations implementing AI based call center solutions naturally want to understand the return on their investment. Comprehensive ROI analysis should include both direct cost impacts and broader business benefits that might not immediately appear on the balance sheet. Direct cost impacts typically include reduced staffing requirements through improved efficiency, lower training costs as AI guidance reduces ramp-up time, and decreased telecommunications expenses through shorter call durations.

Beyond direct cost reduction, AI based call center implementations typically deliver significant business benefits through improved customer experience. Higher first-contact resolution rates reduce customer effort and improve satisfaction, typically translating to measurable improvements in retention, cross-selling success, and positive word-of-mouth. The comprehensive conversation analytics provided by AI systems also enable continuous improvement through data-driven insights previously unavailable with traditional quality monitoring approaches.

According to research from Boston Consulting Group on AI in customer operations, organizations implementing comprehensive AI solutions typically achieve ROI within 9-12 months, with ongoing benefits that increase over time as the AI continuously learns and improves based on interaction data. This favorable financial profile makes AI based call center implementation worthwhile even for organizations with relatively tight budget constraints, particularly when implementation follows a phased approach that delivers incremental benefits while managing project scope.

Best Practices for Agent Training in AI Call Centers

Successfully implementing an AI based call center requires appropriate training for the human agents who will work alongside automated systems. Effective training programs emphasize the collaborative relationship between human and artificial intelligence, highlighting how each contributes distinct strengths to create superior customer experiences. Agents should understand that the AI handles routine aspects of conversations and provides information access, allowing them to focus on uniquely human skills like empathy, problem-solving, and relationship building.

Technical training for AI based call center implementations should cover how to interpret AI suggestions, when to override automated responses, and how to provide feedback that improves system performance over time. Agents should understand that their feedback plays a crucial role in continuous improvement, creating a virtuous cycle where human input enhances AI performance, which in turn makes human agents more effective. This collaborative improvement model represents one of the most powerful aspects of well-implemented AI call center solutions.

Ongoing coaching represents another crucial element for successful AI based call center deployments. Regular review of interaction analytics identifies both individual and team-wide improvement opportunities, enabling targeted coaching that addresses specific development needs. The comprehensive data provided by AI systems makes this coaching more effective by identifying precise patterns rather than relying on anecdotal observations or limited sampling of calls, transforming quality assurance from subjective evaluation to data-driven development.

AI Call Center Security and Compliance Considerations

Security and compliance represent crucial considerations for AI based call center implementations, particularly for organizations in regulated industries handling sensitive customer information. Voice biometrics provides a powerful authentication mechanism that improves security while reducing friction in customer interactions. Unlike traditional knowledge-based authentication (passwords, PINs, security questions), voice biometrics is both more secure and more convenient for customers, creating rare alignment between enhanced security and improved experience.

Data protection must address both transmission security and storage policies for conversation recordings, transcripts, and the derived insights used for system improvement. Leading AI based call center implementations employ enterprise-grade encryption, role-based access controls, and comprehensive audit logging to ensure appropriate information handling throughout the AI ecosystem. These protections satisfy both regulatory requirements and customer expectations for careful management of sensitive personal and financial information.

Regulatory compliance capabilities vary significantly among AI based call center platforms, making careful evaluation essential for organizations with specific requirements. Healthcare organizations need solutions addressing HIPAA requirements for patient information, financial services firms require systems compliant with banking and privacy regulations, while organizations handling credit card information must ensure PCI DSS compliance. The most effective platforms include pre-built compliance modules for common regulatory frameworks, simplifying implementation while ensuring appropriate protection.

The Future Evolution of AI in Call Centers

Looking ahead, AI based call center technology continues evolving in several important directions. Emotional intelligence capabilities are advancing rapidly, with next-generation systems better recognizing and appropriately responding to customer emotions beyond basic sentiment analysis. These capabilities will enable more nuanced handling of complex situations, particularly those involving customer frustration or anxiety that require careful conversational approaches to reach successful resolutions.

Proactive engagement capabilities represent another frontier for AI based call center evolution. Future systems will increasingly identify opportunities for outreach before customers recognize issues themselves, enabling preventive service that addresses problems before they impact customer experience. These capabilities will transform call centers from reactive cost centers to proactive value creators that strengthen customer relationships through anticipatory service that demonstrates organizational commitment to customer success.

Multimodal capabilities represent yet another advancing frontier for AI based call center technology. Next-generation systems will seamlessly coordinate voice conversations with visual elements on mobile devices or computers, creating richer interaction experiences that leverage the strengths of both communication modes. These capabilities will prove particularly valuable for complex service scenarios like troubleshooting, configuration, or education where visual demonstration complements verbal explanation to create more effective customer support.

Comparing AI Call Center Platforms and Providers

The marketplace for AI based call center solutions includes numerous vendors with varying capabilities, making careful evaluation essential for implementation success. Established contact center providers like Genesys, NICE, and Avaya offer AI enhancements to their comprehensive platforms, providing tight integration with existing telephony infrastructure but sometimes lacking the conversational sophistication of purpose-built AI solutions. These platforms typically prove most suitable for organizations with substantial investment in traditional contact center technology seeking gradual enhancement rather than comprehensive transformation.

Cloud platform providers including Google, Microsoft, and Amazon have introduced AI contact center solutions that leverage their broader AI capabilities and cloud infrastructure. These offerings typically provide sophisticated natural language capabilities and seamless scalability but may require more integration work to connect with existing business systems. These solutions often appeal to organizations prioritizing cutting-edge AI capabilities over telephony features or those already standardized on these vendors’ cloud platforms for other business functions.

Specialized AI communication providers like Callin.io focus exclusively on conversation automation, often providing more advanced interaction capabilities with faster innovation cycles compared to broader platform providers. These focused solutions typically offer comprehensive API access for integration with existing business systems while providing purpose-built features specifically for intelligent customer conversations. Organizations prioritizing conversation quality and deployment speed often find these specialized platforms most effective for implementing truly transformative AI based call center capabilities.

Building vs. Buying AI Call Center Capabilities

Organizations implementing AI based call center solutions face a fundamental build-versus-buy decision that significantly impacts both implementation timelines and long-term results. Custom development approaches provide maximum flexibility but require substantial investment in specialized AI talent, training data, and infrastructure. This approach typically demands millions in investment and 12-24 months of development before delivering production-ready capabilities, making it suitable primarily for large enterprises with both substantial resources and truly unique requirements that commercial platforms cannot address.

For most organizations, purchasing established AI based call center platforms provides a more practical approach that delivers sophisticated capabilities without the substantial investment and timeline requirements of custom development. These platforms leverage development efforts and learning across many implementations, creating capabilities that would be prohibitively expensive for individual organizations to replicate independently. This approach dramatically accelerates time-to-value while reducing both financial and implementation risk compared to custom development alternatives.

Many organizations implement hybrid approaches that combine commercial AI based call center platforms with customization addressing specific business requirements. This balanced strategy leverages established platforms for core capabilities like speech recognition, natural language understanding, and conversation management while adding custom elements for unique business processes, specialized knowledge, or distinctive brand experiences. This approach typically provides an optimal balance between implementation speed and customization, delivering distinctive solutions without unnecessary development investment.

Integrating AI Call Centers with Broader Customer Experience

Organizations achieve the greatest value from AI based call center implementations when they integrate these capabilities with broader customer experience strategies rather than treating them as isolated technology deployments. Journey mapping represents a powerful methodology for this integration, examining how customers move between different touchpoints—website, mobile app, email, social media, and voice interactions—to accomplish their objectives. This comprehensive view ensures AI call center capabilities complement other channels rather than creating disconnected experiences that frustrate customers.

Data integration enables this cohesive experience by ensuring AI based call center implementations access the same customer information as other channels, creating consistent personalization and eliminating the need to repeat information across touchpoints. Effective implementations connect AI systems with CRM platforms, marketing automation tools, e-commerce systems, and other customer data repositories to create a unified view of each customer relationship rather than channel-specific fragmentation.

Consistent conversation design across channels represents another crucial integration consideration for AI based call center implementations. Organizations should develop cohesive conversation approaches that maintain consistent personality, terminology, and interaction patterns whether customers engage through voice, chat, messaging, or email channels. This conversational consistency creates a recognizable brand voice that enhances customer familiarity and comfort across all interaction touchpoints rather than creating disjointed experiences that vary by channel.

AI Call Center Analytics and Continuous Improvement

The analytics capabilities of AI based call center implementations represent one of their most powerful advantages compared to traditional approaches. While conventional quality assurance typically reviews less than 2% of interactions through random sampling, AI systems automatically analyze 100% of conversations, creating unprecedented visibility into customer needs, common issues, agent performance, and improvement opportunities. This comprehensive analysis transforms quality management from subjective evaluation based on limited samples to data-driven optimization using complete information.

Sophisticated AI based call center analytics extend beyond basic operational metrics to include conversation quality indicators: sentiment trends, silence patterns, interruption frequency, and resolution efficiency. These measurements provide insight into customer experience quality that simple metrics like handle time or transfer rate cannot capture, enabling more nuanced optimization focused on interaction effectiveness rather than merely operational efficiency.

The most valuable AI based call center analytics connect conversation patterns with business outcomes like retention rates, conversion success, and customer lifetime value. These connections enable organizations to identify specific conversation approaches that drive meaningful business results, creating continuous improvement focused on strategic impact rather than merely operational metrics. This outcome-oriented optimization approach transforms call centers from cost centers to strategic assets that measurably enhance business performance through superior customer experience.

Managing the Human Element in AI Call Centers

While technology capabilities naturally receive substantial attention when implementing AI based call center solutions, the human element remains equally important for implementation success. Effective change management approaches should acknowledge and address the legitimate concerns agents often experience when AI systems join their work environment. Clear communication about how AI transforms roles rather than simply eliminating positions helps establish appropriate expectations while reducing resistance that might otherwise undermine implementation success.

Redefining agent performance metrics represents another important human consideration for AI based call center implementations. Traditional metrics developed for fully human-operated environments often become inappropriate when AI handles routine interactions while humans address complex situations. Revised approaches should emphasize quality indicators like resolution effectiveness and customer satisfaction rather than efficiency metrics like handle time that incentivize inappropriate behavior in AI-assisted environments.

Career development paths deserve particular attention when implementing AI based call center solutions. As AI capabilities handle increasing volumes of routine interactions, human roles naturally evolve toward more complex problem-solving, relationship building, and judgment-intensive activities. Organizations should develop both training programs and advancement paths reflecting this evolution, helping agents develop higher-value skills that complement AI capabilities rather than compete with them. This developmental approach transforms potential resistance into enthusiasm as agents recognize opportunities for more rewarding work rather than merely increased efficiency pressure.

Getting Started with AI Call Center Implementation

For organizations considering AI based call center implementation, a structured approach significantly improves success probability. The journey typically begins with thorough assessment of current operations, identifying specific challenges and opportunities where AI can deliver meaningful improvement. Common starting points include automating routine inquiries that consume substantial agent time without requiring human judgment, providing real-time guidance for complex product or policy information, or enhancing quality monitoring beyond the limited sampling possible with traditional approaches.

Proof-of-concept implementations represent a valuable next step, enabling organizations to validate AI performance in their specific environment before full-scale deployment. These limited implementations typically focus on particular use cases with clearly defined success metrics, allowing objective evaluation of both technological performance and business impact. Most organizations find that even limited proof-of-concept deployments deliver significant value while building internal expertise and refining implementation approaches for broader rollout.

Phased implementation planning helps organizations maximize AI based call center value while managing change effectively. Most successful implementations begin with specific, well-defined applications that offer substantial value while remaining manageable in scope. As these initial implementations demonstrate success and build organizational expertise, expansion proceeds to progressively more complex applications. This measured approach manages risk while accelerating realization of benefits through prioritization of high-value applications.

Voice-First vs. Omnichannel AI Call Center Approaches

Organizations implementing AI based call center solutions must decide whether to focus initially on voice interactions or pursue omnichannel capabilities encompassing voice, chat, messaging, and email channels simultaneously. Voice-first approaches offer several advantages: voice remains the preferred channel for complex or emotionally significant interactions, creating substantial impact on critical customer experiences. Voice also typically represents the highest-cost channel in traditional operations, creating more compelling efficiency opportunities compared to text-based interactions.

Conversely, omnichannel approaches to AI based call center implementation offer their own advantages. Text-based channels like chat and messaging typically present simpler technical challenges than voice, potentially enabling faster implementation and earlier results. Many customer journeys naturally span multiple channels, creating experience advantages for implementations that maintain context as customers move between channels rather than treating each interaction in isolation.

Most organizations find that phased approaches starting with either voice or digital channels before expanding to omnichannel capabilities provide the most effective implementation path for AI based call center solutions. This approach balances ambition with manageability, delivering meaningful results quickly while building toward comprehensive capabilities over time. The optimal starting point depends on specific organizational priorities, existing channel distribution, and customer preferences rather than representing a universal recommendation applicable to all implementation scenarios.

Ethical Considerations for AI Call Centers

As organizations deploy AI based call center solutions, they must carefully navigate ethical considerations around transparency, privacy, and societal impact. Transparency requires clear disclosure when customers are interacting with AI systems rather than human agents, respecting people’s right to know who—or what—they’re speaking with. Many jurisdictions are beginning to require such disclosure through regulation, but ethical implementation goes beyond minimal compliance to ensure customers have appropriate understanding of the interaction context.

Privacy considerations extend beyond regulatory compliance to include thoughtful policies on data usage, retention, and security. Organizations should establish clear guidelines for how conversation recordings and transcripts will be used, who will have access to them, how long they’ll be retained, and what security measures will protect this potentially sensitive information. As The World Economic Forum’s guidelines for responsible AI emphasize, “Organizations should establish governance frameworks that ensure AI systems respect user privacy and data rights beyond mere compliance with existing regulations.”

Bias prevention deserves particular attention when implementing AI based call center solutions. These systems learn from historical interaction data, potentially perpetuating or amplifying biases present in that historical record if not carefully managed. Effective approaches include diverse training data, explicit testing for demographic performance differences, and ongoing monitoring for potential disparate impact. These precautions ensure AI systems serve all customers equitably rather than delivering inconsistent experiences based on demographic characteristics.

Conclusion: The Strategic Value of AI Call Centers

AI based call center implementations represent far more than incremental operational improvements—they fundamentally transform customer service capabilities, economics, and strategic position. By combining the efficiency of automation with the judgment and empathy of carefully deployed human talent, these hybrid approaches create superior customer experiences while dramatically improving cost structures compared to traditional models. This combination of enhanced experience and reduced cost creates compelling competitive advantage in increasingly service-differentiated marketplaces.

The analytics capabilities inherent in AI based call center solutions provide another strategic dimension beyond direct operational benefits. The comprehensive conversation insights generated by these systems enable continuous improvement driven by data rather than assumptions, creating ever-increasing performance advantages compared to organizations relying on limited sampling and subjective evaluation. These analytics also provide unprecedented visibility into customer needs, emerging issues, and market trends, transforming service operations from cost centers to strategic intelligence assets.

For organizations ready to explore the transformative potential of intelligent automation in customer service, platforms like Callin.io offer accessible implementation paths that deliver substantial benefits without requiring massive upfront investment or specialized AI expertise. Whether you’re looking to enhance existing contact center operations or build entirely new customer service capabilities, the expanding ecosystem of AI call center solutions provides increasingly powerful options for creating distinctive customer experiences while dramatically improving operational economics.