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Introducing Afiniti Orchestrator, Intelligence, and Agents: the first platform that optimizes every contact center interaction to positively influence whether customers stay, spend, or come back for more

MAY 28, 2026, WASHINGTON, D.C. — Afiniti today launched three new products on its unified AI platform that will make it easier than ever for enterprise contact centers to optimally manage support issues and protect customer lifetime value. New Afiniti Agents, Afiniti Orchestrator, and Afiniti Intelligence now join Afiniti Pairing under one architecture, giving contact centers a single system accountable for whether customers stay, spend more, and come back.

The Contact Center Is a Retention Battlefield. Most Brands Aren’t Set Up to Win.

Customer acquisition has never been more expensive. The brands that win the next decade will win on retention and loyalty built through everyday interactions. The contact center is where that battle is fought, and for most brands, it is where value is quietly lost.

The problem is structural. Workforce management handles staffing, routing engines manage queues, CRM systems store customer history, and IVRs control the front door, but no single system determines whether the right customer reaches the right person, through the right channel, at the right moment. In a 300-agent contact center, that gap between insight and action can cost up to $35,000 per hour.

“Every business leader understands the cost of a broken contact center experience: a misrouted call, a frustrated customer, or a high-value relationship lost because the right intervention never happened,” said Jerome Kapelus, CEO of Afiniti. “Afiniti was built to close that gap by helping enterprises make smarter decisions to turn the contact center into a growth driver.”

One Platform. Four Products. A Flywheel That Never Stops.

While most vendors focus on one part of the contact center, Afiniti is designed to optimize the entire customer experience and business outcomes.

Afiniti Intelligence connects data across the enterprise to create a unified view of performance, showing where customer value is being lost and where incremental value is being left on the table. It dynamically simulates changes and predicts who is at risk for churn. When risk or opportunity is identified, Afiniti Orchestrator acts in real time to coordinate routing, staffing, and channel decisions across the existing technology stack to drive the right opportunities to the right people and outcomes.

Within each interaction, Afiniti Agents help resolve complex customer needs and seamlessly escalate to human agents when necessary, preserving full context during handoff. Afiniti Pairing then connects customers to the specific agent most likely to deliver the best outcome using behavioral science backed by $2.5 billion in control-group-validated results.

Every interaction feeds back into the platform, continuously improving decision-making over time.

About Afiniti

Afiniti is the AI decisioning layer for enterprise contact centers. For more than two decades, the company has helped the world’s largest brands turn customer interactions into measurable business outcomes, deploying on top of any existing stack without disruption. The proof is in the results: $2.5 billion in verified incremental value created for customers, with every dollar validated against a live control group.

Media Contact
Fabiola Camacho
fabiola@rogerthatcomms.com 

As AI becomes increasingly embedded in complex, high‑stakes business decisions, trust is the primary currency of the digital economy.

Organizations today are not only expected to demonstrate that their AI systems perform effectively, but also to provide clarity into how those systems operate, how decisions are informed, how risks are managed, and how outcomes are evaluated over time. Without this visibility, even well-performing AI can be difficult to assess, govern, and confidently use at scale.

Transparency is no longer a “nice‑to‑have” corporate value; it is an operational requirement for building, deploying, and managing AI responsibly. It enables organizations to better understand system behavior, support oversight, and align AI-driven outcomes with real-world expectations as conditions evolve.

What Transparency Means in Practice

Transparency in AI is about providing meaningful visibility into how systems are designed, how they function, and how outcomes are generated, so stakeholders can understand and assess them within their intended context.

Transparency helps clarify not only what a system is doing, but also the conditions under which performance may vary, the factors that influence outputs, and the boundaries within which the system is expected to operate.

At Afiniti, our approach to transparency focuses on making this information practical, role-appropriate, and useful for oversight. This includes:

This visibility is balanced with a rigorous approach to privacy and security. The goal is to make system behavior and outcomes understandable while protecting sensitive data, proprietary information, and the integrity of the systems themselves. 

Transparency as a Foundation for Traceability and Accountability

Transparency plays a critical role in enabling accountability within AI systems. When stakeholders have visibility into how systems operate and how outcomes are generated, it becomes easier to understand where responsibility sits, how decisions are supported, and how systems can be evaluated over time.

This is particularly important in environments where AI contributes to decision-making processes. Transparency helps distinguish where AI provides input or decision support, and where responsibility remains with human operators, ensuring accountability is defined and actionable in practice.

At Afiniti, transparency is designed to support this level of oversight by providing the structures needed for evaluation, governance, and intervention where appropriate. This includes maintaining logs, records, and documentation that support internal review and, where appropriate, external assessment; enabling stakeholders to evaluate system impact against operational, ethical, and performance expectations; and establishing clear pathways for monitoring, escalation, and corrective action when outcomes require further review.

By embedding transparency into governance frameworks, AI systems can be better understood, evaluated, and managed, reinforcing their role as tools that support human decision-making rather than replace it. 

Transparent Design: Moving Beyond the Black Box

Transparency begins well before a system is deployed. For AI to be understood and trusted, visibility needs to be built into how systems are designed, not added later.

At its core, transparent design is about clearly defining how a system is intended to function, what assumptions shape its behavior, and where its boundaries lie. This helps ensure that AI systems are not treated as “black boxes,” but as tools that can be understood, evaluated, and used within appropriate context.

At Afiniti, this approach is reflected in how we develop and document our AI systems, with transparency practices tailored to each system’s role, operational context, and potential impact. Key elements include:

By embedding these considerations into system design, transparency becomes a built-in characteristic rather than a retrospective addition.  

Making AI Behavior and Outcomes Understandable

Advanced AI systems can involve significant complexity, but the way they behave, and the impact they create, still needs to be understandable for the people responsible for using and overseeing them.

Transparency and explainability are closely connected, but they are not the same. Transparency focuses on understanding the broader design, purpose, and boundaries of an AI system, while explainability relates more specifically to the factors influencing individual recommendations or outcomes.

At Afiniti, our approach combines both perspectives to help provide a more complete understanding of how AI systems operate in practice. This includes evaluating the key factors that influence outputs, assessing how changes in inputs may affect outcomes under different conditions, and providing visibility into how systems balance considerations such as performance, responsiveness, efficiency, and fairness.

Transparency also means clearly communicating known limitations and identifying situations where additional review, context, or human judgment may be required.

Continuous Performance Monitoring and Measurement

Transparency does not end once an AI system is deployed. As systems evolve and operating conditions change, ongoing monitoring becomes essential to understanding how AI performs over time and whether outcomes continue to align with expectations.

At Afiniti, transparency is closely tied to continuous measurement and evaluation. Our approach includes monitoring for changes in system behavior, identifying potential performance drift or emerging risks, and conducting ongoing reviews to help ensure alignment with operational objectives, governance expectations, and system design considerations.

In practice, transparency is demonstrated through objective, observable performance comparison mechanisms. For example, within certain AI‑driven optimization contexts, Afiniti employs a patented benchmarking approach in which the AI system cycles on and off in short, controlled intervals. By measuring outcomes during these “off” periods and comparing them to periods when the system is active, customers can evaluate the incremental impact delivered by the AI, such as improvements in revenue or other key business metrics.

This approach allows customers to assess performance using their own operational data, creating a transparent, measurable, and defensible view of AI value that supports internal governance, auditability, and trust.

When issues are identified, transparency supports timely investigation, remediation, and organizational learning, strengthening systems over time. 

Transparency in Practice: Supporting Customers and Building Trust

Transparency is not a one-time disclosure; it is an ongoing responsibility that continues throughout the AI lifecycle.

As AI systems evolve, organizations need continued visibility into how these systems operate, how outcomes are evaluated, and how governance expectations adapt alongside new technologies, use cases, and operational environments. Maintaining this level of transparency helps support more informed oversight, clearer accountability, and greater confidence in how AI is used in practice.

At Afiniti, transparency is embedded across the lifecycle through ongoing documentation, explainability practices, performance evaluation, and governance processes designed to support long-term visibility into system behavior and impact. This approach helps organizations better understand how AI systems function within their own environments, evaluate outcomes in context, and maintain appropriate oversight as conditions change over time. 

In the next post in our Responsible AI Corner series, we’ll explore Data Protection, and how Afiniti approaches privacy, security, and responsible data practices across the AI lifecycle to help support trustworthy and resilient AI systems. 

When AI supports real people and decisions, fairness matters.  

As AI becomes more embedded in how organizations operate, it’s important to consider how outcomes are shaped, how different factors may influence results, and how potential risks, including unintended bias, are identified and mitigated. 

At Afiniti, we integrate fairness considerations across the AI lifecycle, from design through deployment and ongoing operation within the scope of a system’s intended use, current technical capabilities, and applicable legal and contractual requirements. Because these systems operate within complex environments, we also work closely with our clients to evaluate how they are applied in practice – helping align fairness-related assessments with specific use cases, client-defined objectives, and evolving industry expectations.

What Fairness Means in Practice

Fairness in AI starts with intention and discipline. 

AI systems learn from data, interact with human workflows, and can influence outcomes in different ways depending on context. That’s why fairness must be addressed across the entire AI lifecycle, from early design decisions through deployment and ongoing operation, within the boundaries of applicable legal, technical, and operational capabilities.  

At Afiniti, our approach to fairness focuses on:  

While AI systems inherently involve residual risk, we apply ongoing monitoring and human oversight designed to support risk awareness and informed decision-making, rather than guaranteespecific outcomes. Our approach is grounded in governance practices and system design considerations that help ensure fairness is actively considered throughout the lifecycle. 

Why Fairness Matters

Afiniti’s AI is used across a wide range of industries and  real-world environments, where it supports decision-making, shapes interactions, and influences outcomes at scale.

Because of this, fairness plays an important role in how organizations build confidence in AI-driven insights, ensure systems behave consistently within defined parameters, and maintain trust with customers, partners, and stakeholders. 

At the same time, fairness is not one-size-fits-all. Expectations can vary depending on the use case, industry, and deployment context. What matters in one environment may not apply in another, which is why fairness must be evaluated within the realities of how AI is actually used. 

At Afiniti, we work closely with our clients to understand these nuances – including their objectives, operational environments, and regulatory considerations. We provide tools, documentation, and technical support to help make fairness assessments more practical, relevant, and adaptable as expectations and standards continue to evolve.

How Fairness Is Applied Across the AI Lifecycle

Fairness cannot be accounted for through a single control or checkpoint. It is supported through a combination of governance, technical practices, and ongoing review, applied throughout the AI lifecycle and in coordination with client-side controls and responsibilities.

Data: Intentional Use and Governance

Fairness begins by defining the scope and data relevance. At Afiniti, we take an intentional approach to data governance, focusing on using data for defined and legitimate purposes, and limiting use to what is relevant and necessary. These decisions are guided by practices that prioritize data minimization, relevance, and an awareness of potential impacts on individuals, consistent with applicable data protection requirements. Because data configurations often reflect specific client environments, we work closely with our clients to align on governance expectations and provide visibility into how data contributes to system behavior. 

Design: Building Systems with Fairness in Mind

Fairness is also considered at the design stage. We proactively work to identify and reduce potential sources of unintended bias where such risks are reasonably foreseeable and technically assessable. Our systems are designed and evaluated in an effort to mitigate material disparities in performance, where relevant and contextually appropriate, based on the use case, available data, and lawful evaluation methods. 

Testing: Validation and Bias Evaluation

As systems are developed, validation and testing approaches are applied to better understand how models behave. These evaluations go beyond overall accuracy and may include assessing performance across relevant segments, based on deployment context, using lawful, context-appropriate data and evaluation methods. The goal is to surface potential unintended patterns or disparities, not to guarantee uniform outcomes, but to support a more informed and contextual understanding of system performance. 

Monitoring: Ongoing Review of System Behavior

Fairness is not static. We maintain ongoing monitoring of model outputs and system behavior to observe changes over time, identify outcomes that may warrant closer review, and support timely adjustments where appropriate, consistent with system design, contractual scope, and governance requirements. These processes are carried out in alignment with system design, contractual scope, and governance requirements. 

Collaboration: Working with Clients in Context

Because AI systems operate within complex, real-world environments, collaboration with our clients is an essential part of how fairness is evaluated. Client insight helps contextualize outcomes, align assessments with specific use cases, and inform how fairness-related risks are interpreted and managed, recognizing that deployment decisions and use case objectives are defined by clients within their own governance frameworks. Where relevant, we provide supporting materials and insights to help clients assess potential impacts and determine any additional mitigation or governance steps within their own frameworks.

Oversight: The Role of Human Judgment

Human oversight remains a critical component throughout the AI lifecycle. AI systems do not operate in isolation, and informed judgment plays an important role in reviewing outputs, interpreting results, and guiding decisions about how systems are used in practice. This shared responsibility supports a more thoughtful and context-aware approach to managing fairness-related considerations.

Fairness, Transparency, and Ongoing Responsibility

Managing fairness requires a clear understanding of how AI behaves in practice. At Afiniti, fairness is fundamentally linked to appropriate levels of transparency and explainability, helping provide the visibility needed to interpret outcomes, assess system behavior, and identify areas that may require closer attention.  

By supporting interpretable outputs and maintaining open collaboration with our clients, we aim to enable more informed insight into how AI systems operate, so potential issues can be surfaced and addressed within the appropriate context. Fairness is not a static goal. As technologies, data sources, business needs, and global expectations evolve, fairness considerations must be revisited and refined. 

At Afiniti, fairness is addressed as an ongoing responsibility embedded across our product suite, supported by continuous collaboration with our clients and informed by evolving regulatory, technical, and ethical standards. This approach is designed to help organizations better understand how AI systems behave, manage potential risks, and make more informed decisions in real-world environments. 

Disclaimer: This post reflects Afiniti’s general approach to Responsible AI as of April 2026. It is provided for informational purposes only and does not constitute a legal guarantee, a contractual commitment, a warranty of specific system performance, or a representation of compliance for any particular deployment.

AI can drive powerful business outcomes – but only if you understand how it works. 

At Afiniti, explainability is a core part of how we build solutions designed to support transparency and informed insight that organizations can trust and confidently act on. In this second installment of our Responsible AI series, we focus on explainability – and why it’s essential for turning AI from a “black box” into a strategic advantage.

What Do We Mean by Explainability?

Explainability is about making AI understandable. 

It means providing visibility that supports customer monitoring and mitigation efforts, such as: 

In short, AI shouldn’t just produce outcomes – it should provide insight into how those outcomes are generated. 

At Afiniti, we design our AI to be observable, explainable, and grounded in evidence, so customers can see not only what is happening, but how.  

Why Explainability Matters

Building Trust Through Transparency

AI delivers its greatest value when people trust it. 

Without visibility, even high-performing models can feel like “black boxes,” making it difficult for organizations to adopt and scale them. Explainability removes that uncertainty by making AI decision processes more interpretable – helping teams understand how predictions are formed and how outcomes are achieved. 

At Afiniti, we build this transparency directly into how we deliver AI. We translate complex model behavior into meaningful human-readable insights so both technical and business teams can understand how decisions are made and what drives performance. This helps position our AI as powerful as well as a solution teams can confidently rely on. 

Strengthening Accountability and Governance 

Explainability is also critical for AI accountability and governance. 

When decisions are linked to data, logic, and measurable outcomes, organizations can: 

This creates a stronger foundation for responsible AI – where decisions are not only effective but also supported by documentation and aligned with governance expectations. 

That’s why Afiniti grounds its AI in measurable, evidence-based performance. Our approach is based on transparent benchmarking and continuous evaluation, giving customers structured performance insights, consistent evaluation approaches, and materials that support customer validation. This means that AI performance isn’t something you have to trust – it’s something you can understand and meaningfully evaluate.

Supporting Fairness and Bias Detection

Explainability makes it possible to understand how models behave across different conditions, including: 

Explainability also plays a critical role in how we approach fairness. By providing insight into model behavior, we enable ongoing monitoring of how outcomes are generated, where bias may emerge, and how mitigation approaches can be evaluated and adapted over time. This continuous visibility helps support alignment with both operational realities and evolving expectations. 

Enabling Better Business Decisions

Explainability doesn’t just support governance – it improves business outcomes. 

When leaders understand how AI works, they can make better decisions about how to deploy, optimize, and scale it. Instead of treating AI as a fixed tool, they can use it strategically – aligning it with business goals and continuously improving performance. 

And importantly, explainability is not static. We work closely with our customers through regular reviews, model walkthroughs, and collaborative evaluation cycles – creating an ongoing dialogue around performance, transparency, and governance. This ensures that explainability remains practical, relevant, and actionable as business needs to evolve. 

Explainability Is Essential to Responsible AI

As organizations scale AI across increasingly complex environments, trust becomes a competitive advantage. 

Explainability is what enables that trust – by making AI transparent, measurable, and aligned with governance needs. It helps organizations understand how decisions are made, evaluate performance with confidence, and maintain meaningful oversight as AI becomes more embedded in day-to-day operations. 

At Afiniti, this isn’t an add-on – it’s core to how we build and deliver AI. Our commitment to explainability ensures our systems are not only high performing, but also supported by responsible AI practices, and aligned with our customers’ goals and expectations. 

In the next post in our Responsible AI series, we’ll explore Fairness – and how we design AI systems to promote equitable outcomes, reduce unintended bias, and support more responsible decision-making. 

AI is everywhere in the contact center. 

It influences who customers speak to.
How decisions are made.
Which interactions drive revenue.
Which experiences build loyalty. 

But as AI becomes more powerful, something else becomes more important: 

Trust. 

Not marketing trust.
Operational trust. 

Enterprises want proof. They want clarity. They want to understand how AI works, how it is governed, and how risk is managed over time.

At Afiniti, we define a new category: Outcome Orchestration – unifying contact center systems, data, AI, and human interactions to drive measurable business results. When AI is orchestrating decisions across the entire ecosystem – influencing routing, workflows, revenue, retention, and customer experience simultaneously – governance cannot be an afterthought. 

The broader the impact, the greater the responsibility. 

That’s why we’re launching Afiniti’s Responsible AI series. Over the coming months, we’ll break down the six principles that guide how we design, deploy, and govern AI.

Those principles are: 

Each plays a critical role in building safe, trustworthy AI. But we’re starting with the principle that makes all the others possible: Accountability. 

Why Accountability Comes First: Responsible AI by Design

Accountability is the cornerstone of Afiniti’s approach to Responsible AI. It is not simply one principle among many – it is the principle that allows others to work. 

Transparency only matters if someone stands behind it.
Fairness only holds if it is actively maintained.
Privacy and compliance can only be sustained when there is clear ownership.

Accountability turns Responsible AI from aspiration into practice. 

At Afiniti, that accountability begins early – at the design stage – not after a system is deployed. It shapes architecture decisions, model development, data handling, validation, deployment, and long-term monitoring. Human oversight is built into both development and operations. 

In practical terms, that means: 

Accountability is not layered on. It underpins how our AI is built, validated, and managed from the start.

Accountability Across Teams — and Over Time

Responsible AI requires many stakeholders.

It doesn’t sit only in engineering.
Or legal.
Or compliance. 

Afiniti assigns accountability to defined owners, with engineering, data science, product, privacy, legal, and compliance teams each playing defined roles in development, risk management, and oversight. That structure reflects reality: AI systems are complex. Their impact spans technology, operations, regulation, and customer experience. 

Afiniti’s cross–functional governance structure keeps oversight continuous and multidimensional. 

And accountability doesn’t stop once a model is deployed. 

AI systems evolve. Data changes. Regulations move. Performance shifts. 

That’s why governance continues after launch. 

We maintain: 

There are formal escalation paths. Clear processes. Defined responsibilities. This is part of what it means to be the measurable AI company – not just proving performance but maintaining control and oversight over time. 

Accountability Makes the Other Principles Real

Principles like transparency, fairness, data protection, and compliance only matter if they can be tested and sustained. 

For us, accountability is what makes that happen. 

It ensures transparency is backed by evidence.
It ensures fairness is actively reviewed.
It ensures compliance is continuously maintained – not assumed. 

One clear example is Afiniti’s on/off benchmarking methodology. 

Rather than relying on model projections, we measure performance through controlled comparisons –running AI-enabled decisions against control groups to isolate incremental impact. This on/off cycle allows customers to see exactly what the system is contributing.

It is a disciplined, repeatable way to validate outcomes. 

The same rigor applies to fairness monitoring, data governance, and regulatory alignment. Each is structured. Each is reviewed. Each has defined ownership.

Responsible AI is not sustained by intention. 

It is sustained by proof.

Responsible AI as a Standard — and a Differentiator

AI is under more scrutiny than ever. Enterprises are no longer satisfied with performance claims alone — they expect clarity, proof, and governance that can stand up to review. 

That is where an accountability-first approach becomes differentiating.

AI should be understandable, transparent, and aligned to outcomes that matter. Measurable results require measurable responsibility. 

Afiniti’s accountability-first approach is a differentiator because it pairs high performance with disciplined governance – proving impact while maintaining control. 

This article is the first in our Responsible AI series. Next, we’ll explore Explainability – how complex AI systems can remain understandable and why clarity is essential to lasting trust. 

As AI adoption accelerates, many enterprises are discovering a widening gap between promised innovation and measurable results. Fragmented decisions, opaque performance, and unclear cause–effect relationships have become common — especially inside complex, mission-critical environments like the contact center. 

After 20 years of operating AI in production, Afiniti is defining a new category to address this gap: Outcome Orchestration.

Defining Outcome Orchestration 

Outcome Orchestration deploys AI to unify and steer contact center data, intelligence, and decisioning across people, systems, and workflows — holding performance accountable to real business baselines. 

Rather than replacing existing platforms, Afiniti operates as an intelligence layer within complex environments, orchestrating decisions that consistently drive outcomes. This approach reflects a foundational belief: AI only matters if it measurably improves outcomes in production. 

Proven in Production 

At the core of Outcome Orchestration is Afiniti Pairing, Afiniti’s patented AI technology that dynamically matches customers with the agents most likely to achieve a desired outcome. 

Afiniti Pairing has delivered more than $2.5 billion in measurable value across enterprise contact centers of all sizes and platforms. In 2025, Afiniti achieved 100% client retention, reinforcing a model built on long-term performance rather than experimentation. 

A Foundation for Responsible Expansion 

In 2026, Afiniti will extend Outcome Orchestration beyond pairing to address broader contact center decisioning needs; including agent experiences, routing decisions, and intelligence. These capabilities are being introduced deliberately, informed by real operational challenges observed across Afiniti’s customer base. 

What remains constant is Afiniti’s commitment to AI that earns trust, by integrating into real environments, delivering measurable outcomes, and proving its value over time. 

Read the full announcement: https://www.afiniti.com/afiniti-introduces-outcome-orchestration-defining-a-new-standard-for-enterprise-ai/

Washington, D.C. — January 27, 2026 — Afiniti, a leader and expert in driving AI-powered measurable outcomes for contact centers, today announced Outcome Orchestration, a new category of enterprise AI. Outcome Orchestration addresses contact center operators’ disappointment with the wide and persistent gap between today’s narrowly focused and bespoke AI products and the hard, measurable outcomes businesses truly need.  

The rapid adoption of AI tools in contact centers in the past three years has resulted in fragmented decisions that do not consider the entire estate, opaque and sometimes negative performance, and the lack of clarity related to the cause-effect of new products.  Outcome Orchestration was designed to overcome these exact challenges with a foundational belief that AI only matters if it consistently and measurably improves outcomes.

Defining Outcome Orchestration

Outcome Orchestration deploys AI products to unify and steer contact center data, intelligence, and decisioning across people, systems, and workflows toward specific business outcomes. Afiniti does not replace existing contact center infrastructure. Rather, it operates alongside existing tools and acts as an overarching intelligence layer within complex environments — orchestrating decisions to achieve business goals identified by contact center business owners and operators.

“If AI does not prove its impact in production, it does not matter,” said Jerome Kapelus, Chief Executive Officer of Afiniti.  “We empower contact center operators to predict change, dynamically adjust resources and priorities, and respond in real time to the uncertainty of daily operations”.

Proven in Production at Enterprise Scale 

Afiniti’s long time expertise and excellence in the contact center industry is already proven through Afiniti Pairing, the company’s patented AI technology that dynamically matches customers with the agents most likely to achieve a desired outcome. Pairing has delivered more than $2.5 billion in measurable value to clients, validated through continuous implementation in contact centers of all sizes and across various platforms. In 2025, Afiniti achieved 100 percent client retention, reinforcing a model that earns renewal by delivering results year after year. 

A Foundation for Responsible Expansion 

Afiniti enters its next phase of innovation and outcome-centric client solutions with a clearly defined category, a proven operating model, and a roadmap focused on responsible expansion. In 2026, Afiniti will extend Outcome Orchestration beyond pairing to address a broader set of enterprise decisioning needs across the contact center, including agent experiences, routing decisions, and intelligence. This expanded suite will solve real operational challenges observed across its customer base. 

About Afiniti

Afiniti unlocks hidden value in contact centers by applying AI to optimize decisions that drive higher revenue, improved retention, and increased customer lifetime value. Founded in 2006, Afiniti’s patented AI optimization technology determines which decisions within complex environments consistently lead to better business outcomes. Trusted by leading enterprises worldwide, Afiniti has generated more than $2.5 billion in measurable value. 

Learn more at www.afiniti.com 

Media Contact: info@afiniti.com 

Financial institutions are operating in one of the most dynamic periods the industry has seen in decades. New technologies are reshaping expectations, regulatory scrutiny continues to intensify, and customers demand more relevance, clarity, and support than ever before. Yet the central question remains unchanged: 

How can financial institutions deliver greater value and deeper trust in an increasingly complex world? 

A meaningful answer sits at the intersection of personalization, empathy, and responsible technology adoption. These principles run throughout the three-part series authored by David Kroner, which explores how institutions can modernize without losing the human foundations that define financial decision making.  

The themes of that series; perceived value, human connection, and thoughtful AI adoption reflect broader shifts that are already shaping the future of the industry. 

Personalization Has Become the Core of Customer Value 

Customers no longer evaluate financial products simply by their price or feature set. They evaluate them through the lens of personal relevance; how much the offering reflects their needs, lifestyle, and priorities. This dynamic is explored in Perceived Value in Financial Services: More Than Meets the Eye, where the concept of perceived value is reframed as something fundamentally individual. 

Traditional segmentation, once the industry’s go-to strategy, treated customers as averages. But averages rarely feel personal, and personal relevance is what drives loyalty. 

Today, AI and advanced analytics make it possible to tailor experiences at the individual level: 

This shift toward individual-level personalization will define the next competitive frontier. Institutions that can articulate value at the right moment, in the right channel, for the right person, will unlock brand affinity that broad segmentation could never achieve. 

Human Confidence Still Anchors High-Stakes Decisions 

Even as financial experiences become increasingly digital, high-stakes decisions remain deeply emotional. Mortgages, insurance coverage, long-term planning, or products tied to major life transitions all require more than precise calculations, they require reassurance. 

In Empathy Is the Real Currency in Financial Services, Kroner connects this reality to a personal moment of navigating a first home purchase. The insight is simple but often overlooked: 

Information creates understanding.
Empathy creates confidence. 

AI can support analysis, surface better options, and reduce administrative burden. But customers still want: 

This blend of human guidance and technological support will remain essential, especially as financial products grow more complex and more interconnected with customer data. 

Responsible AI Adoption Requires Clarity, Transparency, and Time 

Across the industry, leaders appreciate the transformative potential of AI. Yet many also move cautiously and for good reason. 

Financial services operate within strict regulatory frameworks. Institutions must demonstrate that decisions are; explainable, fair, compliant, auditable and free from unintended bias. 

In Adopting AI in Finance: Why Caution Is Natural, and Progress Is Possible, Kroner parallels this environment with another domain where caution is essential: personal health. Trust in new tools, whether financial or medical, requires an understanding of how they work, what risks they carry, and how they change established routines. 

Caution is not a barrier to innovation.
It is part of the process of adopting technology responsibly. 

Institutions embracing AI successfully tend to share common traits: 

This approach ensures AI strengthens trust rather than threatens it. 

The Next Decade Will Be Defined by Outcome-Based Transformation 

While many tools in financial services aim to automate or streamline processes, the greatest impact comes from technologies that meaningfully improve outcomes, both for customers and institutions. 

Outcome driven approaches, those that optimize decisions, reduce risk, and enhance customer experiences, require; transparent methodologies, rigorous modeling, strong governance, continuous monitoring and alignment with ethical standards 

Institutions that prioritize outcomes over hype will be the ones that modernize sustainably. The differentiator will not be how much AI an institution deploys, but how effectively its technology improves customer journeys, strengthens trust, and reflects responsible leadership. 

Trust Will Remain the Industry’s Primary Competitive Advantage 

The financial institutions that lead in the coming years will be those that: 

Trust is not created by technology alone.
Trust is created by thoughtful systems, transparent processes, and human connections strengthened, not replaced, by data and intelligence. 

The era ahead will reward institutions that treat trust as a design principle, not a byproduct. 

Explore the Full Series by David Kroner 

Customer experience no longer unfolds in a straight line. It moves across apps, websites, self-service flows, agentic AI, stores, and finally, when none of those paths succeed, the contact center. For many companies, this last stop is still treated as a cost center or a fail-safe. But for customers, it’s something much more consequential: 

It’s where their entire experience is decided. 

Not because the contact center handles the most interactions, it doesn’t.
But because it handles the ones that matter most. 

And in a landscape where expectations are rising, patience is shrinking, and digital systems rarely behave perfectly, the contact center has quietly become the defining arena for trust, loyalty, and long-term value. 

In practice, modern CX isn’t shaped by technology alone.
It’s shaped by a set of forces that influence how customers feel the moment a conversation begins and whether they believe the organization will stand behind the promises it makes. 

The Shift No One Talks About: Voice Isn’t Just a Channel. It’s the Trust Environment. 

Organizations have spent years investing in automation, self-service, and channel expansion. Yet despite these advancements, customers continue to reach for a human being when the stakes are high or the frustration is deep. 

What lands in the contact center today are the interactions that carry emotional weight: 

The voice channel has become the environment where digital failures surface, and where customer sentiment is either repaired or permanently damaged. 

This shift is explored in Why the Contact Center Still Matters in the Age of Digital CX, which reframes voice not as an outdated channel but as the place where trust is earned in real time. 

Customer Experience Is No Longer About the Call. It’s About the Journey That Arrived There. 

By the time a customer speaks to a person, the initial problem is only part of the story. 

The emotional context matters more: 

Did the website contradict itself? 

Did the app fail at checkout? 

Did automation loop them in circles? 

Did the customer already feel ignored? 

Two people can experience the same resolution but interpret it differently depending on what happened before the call. 

This is why customer experience cannot be evaluated solely through operational metrics. The emotional state entering the conversation is often the biggest determinant of the emotional state exiting it. 

That idea is examined further in Perception at the Core of Customer Experience in the Contact Center, which explains why perception, not process, drives the real outcome. 

Context Is the Missing Infrastructure in Most Contact Centers 

When customers move between channels, companies often lose visibility into those movements. A customer may try a self-service option, attempt a purchase, troubleshoot online, abandon the journey and then call. 

But unless the systems that captured those steps speak to each other in real time, the agent sees none of it. 

Customers then face the single most universal frustration in CX: 

“I just did that. Why don’t you know?” 

This leads to an invisible tax on both sides: 

This problem, and its implications, is explored in Data Persistence or Agent Persistence?, which argues that asking customers to start over is no longer acceptable in a world where technology should enable continuity, not undermine it. 

The Most Damaging CX Failure Isn’t a Defect. It’s Inconsistency. 

Modern journeys span channels, but most companies still manage channels as separate ecosystems. This leads to mismatches that feel like broken promises; the digital tool says one thing, the agent says another and the store says something entirely different 

Inconsistency erodes trust faster than inconvenience.
A customer can forgive a delay or an error.
They rarely forgive contradictory information. 

Consistency doesn’t require every channel to do everything.
It requires every channel to reflect the same reality. 

This challenge is dissected in The Other Half: Channel Consistency, which examines how misaligned policies, systems, and capabilities undermine even the strongest CX strategies. 

The Real Path to Fewer Calls Isn’t Automation. It’s Defect Elimination. 

Every CX leader today contends with the pressure to reduce call volume. But fewer calls are only a win when they come from fewer problems, not fewer pathways to help. 

Digital tools can create efficiencies, but they also create new points of failure; broken flows, unclear messaging, partial capabilities, dead ends and inconsistent rules. 

When these breakdowns happen, customers inevitably turn to human support. 

The question is not:
How do we reduce calls? 

The question is:
Why are customers calling at all? 

This principle is central to Reducing Calls or Reducing Defects?, which argues that true CX improvement comes from solving root causes, not creating new layers of digital insulation. 

So Where Does CX Go Next? 

Modern customer experience is no longer about adding more channels, more automation, or more features. It is about creating:

The contact center becomes the crucible where all of these forces converge.
It is where broken digital experiences are felt most acutely, and where organizations have a final opportunity to restore confidence. 

In an era defined by technological acceleration, the differentiator will not be how automated a journey becomes, but how human, coherent, and trustworthy the final moments of that journey feel. 

Explore the Full CX Power 5 Series by Jerry Adriano 

Washington, D.C. – December 3, 2025 – Afiniti, Inc., a global provider of artificial intelligence and customer experience optimization, today announced that its patented AI Pairing solution is now available on the NiCE CXexchange, following the company’s onboarding into the NiCE DEVone Ecosystem.

This strategic collaboration brings Afiniti’s outcome-driven AI technology directly to NiCE CXone Mpower, giving enterprises the ability to improve customer retention, sales conversion, and revenue growth – all without retraining agents or disrupting existing routing strategies.

Afiniti’s AI analyzes rich contextual and behavioral data in real time to match each customer with the best available agent for desired business outcomes. Seamlessly layered into CXone Mpower workflows, Afiniti amplifies NiCE’s native capabilities by adding a proven optimization engine that drives measurable improvements.

“Our collaboration with NiCE expands access to Afiniti’s real-time AI optimization through one of the industry’s most trusted CX platforms,” said Brendan McCarthy, Senior Vice President of Partnerships & Alliances, Afiniti. “Together, we’re helping enterprises unlock greater value from every customer interaction – whether that’s higher retention, improved efficiency, or revenue growth – while complementing and strengthening existing CXone Mpower deployments.”

“Afiniti brings a differentiated AI pairing capability that enriches the innovation available to customers through the CXexchange marketplace,” said Dan Belanger, President, NiCE Americas. “By integrating Afiniti’s proven optimization engine with CXone Mpower, we’re empowering enterprises to deliver smarter, more personalized customer journeys that maximize both satisfaction and business results.”

Through NiCE Seller Central and CXone Mpower’s global commercial teams, the partnership will also feature joint marketing initiatives, co-selling opportunities, and solution enablement – helping organizations across industries realize the compounded value of NiCE and Afiniti together.

Afiniti’s patented AI Pairing solution is now available on the NiCE CXexchange Marketplace. To view the listing, click here.

About NiCE 

NiCE (NASDAQ: NICE) is transforming the world with AI that puts people first. Our purpose-built AI-powered platforms automate engagements into proactive, safe, intelligent actions, empowering individuals and organizations to innovate and act, from interaction to resolution. Trusted by organizations throughout 150+ countries worldwide, NiCE’s platforms are widely adopted across industries connecting people, systems, and workflows to work smarter at scale, elevating performance across the organization, delivering proven measurable outcomes.

Corporate Media Contact
Christopher Irwin-Dudek, +1 201 561 4442, media@nice.com, ET

About Afiniti

Afiniti unlocks hidden value in your contact center to achieve higher revenue, better retention and increased lifetime value across the customer journey. Founded in 2006, Afiniti’s patented AI optimization technology accurately predicts how adjustments in an environment, like which agent a customer speaks to, can amount to consistently improved business outcomes. Trusted by global enterprises in telecommunications, financial services, healthcare, and more, Afiniti has generated more than $2.5 billion in incremental annual value worldwide. To learn more, visit www.afiniti.com.

Media Contact
info@afiniti.com