AI has quickly become an integral component of modern contact centers, playing a central role in interpreting customer intent and guiding real-time decisions.

Virtual agents and agent-assist tools, along with analytics, quality management, and workforce optimization, now rely heavily on AI to support both customers and agents.

As organizations deploy more AI-driven capabilities, they face a foundational challenge: providing AI systems with accurate and secure context across environments that continue to become more complex.

For without the right context, even advanced AI models can produce unreliable outputs or responses that fail to align with business rules or regulatory requirements.

This challenge has driven growing interest in an emerging concept known as the Model Context Protocol (MCP). MCP is not a product or a single technology, but an approach to standardizing how AI models access and use contextual information from enterprise systems.

While adoption is still in the early stages, MCP has important implications for how contact centers scale AI in a responsible and effective way.

Why Context Is a Growing Challenge

Context has always been fundamental to effective customer service. Human agents rely on customers’ histories, previous interactions, policies, and real-time signals to resolve issues efficiently and accurately.

But as AI takes on a greater role in customer interactions, those same requirements apply but at a far greater speed and scale.

To work efficiently, AI-powered contact center tools may need access to:

• Customer profiles and interaction histories.
• Real-time conversation states.
• Knowledge bases and policy documents.
• Journey data across multiple channels.
• Operational rules and compliance constraints.

In many organizations, this information is distributed across separate systems. As AI deployments grow, context is often handled through custom integrations, hardcoded prompts, or workflow-specific logic.

While these approaches may work for isolated use cases, they become difficult to scale. Because AI systems are only as effective as the context they receive, this limitation is driving interest in more standardized, flexible approaches to context management.

What Is MCP?

MCP is an emerging protocol and architectural pattern designed to standardize how AI models request, receive, and use contextual information from enterprise systems.

Rather than embedding context directly into every AI application or integration, MCP introduces a consistent way for models to access approved context when needed. It helps decouple AI models from the systems that store data, allowing both to evolve independently.

MCP offers a promising framework…by standardizing how AI systems access and use information.

Large language models (LLMs) operate on historical training data and have no intrinsic access to real-time facts. For example, you can ask an LLM to predict the weather in New York City, but it won’t know the current conditions unless it is explicitly provided with live, authoritative context.

MCP addresses this limitation by providing a standardized way for AI models to request and receive approved, real-time context from external systems.

Rather than relying on static prompts or assumptions, MCP enables models to incorporate current state information such as live data, operational signals, or policy updates: at the moment a decision or response is generated.

MCP is about creating a common language between AI models and enterprise data sources. It enables organizations to manage context as a governed asset, rather than duplicating logic across tools, workflows, and integrations.

How MCP Works

While implementations may vary, MCP generally follows a straightforward model for connecting context providers with AI requests.

(Context providers are enterprise systems, such as customer databases, CRM platforms, knowledge systems, or operational tools, that control what information can be shared and under what conditions.)

AI models or applications submit context requests specifying the required information, the scope of access, and any security or compliance constraints.

Approved context is then delivered in a structured, consistent format, ensuring models receive only the information they are permitted to access. This separation enables AI systems to request current context dynamically, without requiring tight integration with every backend system.

How MCP is Different

Many contact centers already use AI today, so it is useful to understand how MCP differs from existing approaches.

Traditional AI deployments often rely on direct integrations between AI tools and data sources, which can become brittle and costly to maintain as environments grow. MCP helps reduce integration complexity by decoupling AI models from systems.

Some AI tools embed context directly into prompts or workflows. While effective for narrow use cases, this approach can be difficult to govern and update. MCP treats context as a managed, reusable resource rather than static input.

Workflow and orchestration tools help manage processes, but they often lack standardized methods for exchanging context across multiple AI models.

MCP focuses specifically on how context is requested, delivered, and governed across tools, aiming to address context management as a high-priority concern rather than an afterthought.

What MCP Means for Operations

For contact centers, MCP has significant implications. By ensuring AI systems receive consistent, approved context, it helps reduce errors and improve response relevance across self-service and assisted channels.

As customers move between voice, chat, messaging, and digital channels, maintaining context becomes more difficult. MCP supports smoother transitions by enabling access to shared context across interactions.

MCP also allows organizations to experiment with different AI models and tools without re-engineering context handling each time, which supports innovation while reducing technical debt.

By centralizing how context is accessed, MCP helps enforce policies related to privacy, security, and compliance, which are critical concerns for regulated industries. For contact centers, context is the difference between automation that feels helpful and automation that erodes trust.

MCP is still evolving, and industry alignment and best practices are developing, which may create uncertainty for early adopters. Context must be accurate and governed, and poor data quality or unclear ownership can undermine MCP’s effectiveness.

Contact center environments often require real-time responses. MCP implementations must be designed to deliver context quickly and reliably. Adopting MCP may require rethinking existing architectures and collaboration across IT, customer experience (CX), data, and security teams.

These challenges highlight the importance of a measured, incremental approach.

Next Steps for Contact Center Leaders

For organizations evaluating MCP, here are several practical steps that can help prepare for adoption.

• Teams should begin by assessing how context is currently managed across existing AI tools, identifying areas where inconsistent or fragmented context limits performance and reliability.
• Engaging vendors and partners in discussions about their approach to context governance can also provide insight into future compatibility and integration readiness.
• At the same time, organizations should monitor industry developments around MCP and related standards to stay aligned with emerging best practices.

While MCP may not be an immediate requirement for every organization, its underlying principles are becoming increasingly relevant as AI usage continues to expand.

Looking Ahead

As contact centers continue to evolve into highly orchestrated, AI-enabled environments, managing context effectively will become a critical differentiator. MCP offers a promising framework for addressing this challenge by standardizing how AI systems access and use information.

For an industry built on understanding customer intent and delivering meaningful interactions, getting context right is no longer a nice-to-have. It is foundational to scaling AI responsibly in the contact center.

One of the constants of the human experience is coping with disasters. And as their nature changes (which is also a constant), so must our responses to them.

Contact centers sit at the nexus of business continuity and disaster response (BCDR). They must simultaneously inform customers what is happening and provide the latest recommendations for actions when an event threatens and strikes: while safeguarding their staff and operations.

To find out and understand more about the evolving disaster dangers – and to learn how contact centers can best plan and respond to them – we had a virtual conversation with Lou Corbeil, General Manager, Platform Operations, NiCE.

Q. What are, and please rank, the top five disaster threats facing contact centers? Have these changed over the past 12 months? Do you expect them to change over the next 12 months?

The biggest risks to contact centers have continued to evolve. Today, the top five threats can be ranked as follows:

1. Cyberattacks are more sophisticated and relentless, making strong security and compliance frameworks essential.
2. Platform outages remain a top concern, and multi-site redundancy and high availability are table stakes for continuity.
3. Severe weather and climate-driven disasters are hitting more regions, which has elevated the importance of geo-redundant deployments.
4. Workforce disruption, whether illness, evacuation, or staffing shortages, has become more unpredictable.
5. Finally, vendor and telecom failures remain systemic risks, underscored by the need for transparent incident reporting.

While not a disaster, the possibility of government mandates quickly implemented around data residency and sovereignty may require rapid rerouting of operations.

Looking ahead, the core risk categories are unlikely to change, but greater emphasis will be placed on cyber resilience and AI governance.

Q. Are your clients experiencing increases in disaster-caused/related contacts and interactions with worried customers (e.g., outages, flight cancellations/delays)?

When issues occur – like travel disruptions and extreme weather – contact centers see sharp spikes in customer interactions, and the capacity to absorb those surges is now considered an expectation. Industries like travel and utilities are especially exposed.

In response to these events, we consistently see that proactive incident communications can significantly reduce customer anxiety and help manage volume.

Q. Has the rise in automation, lifted by new AI tools, shifted more of the BCDR focus to digital customer service? So that when disasters occur, agent-provided service is shut down, the agents are evacuated, and the contacts are shifted to AI agents, IVR/online self-serve, etc.?

Automation and AI play a key role in maintaining operations, particularly during disruptions, seamlessly handling queries if staff aren’t reachable.

As organizations adopt more AI-driven self-services, continuity planning increasingly includes digital channels as a first line of response.

“…we consistently see that proactive incident communications can significantly reduce customer anxiety and help manage volume.” —Lou Corbeil

Coordinated AI assistants, combined with user-driven platforms, increasingly support emergency response plans. Strong observability and governance of automated systems are critical to ensuring consistent access during crises.

Q. Conversely, with automation already handling a large share of contacts, and with those answered by agents being high-value, emotionally charged, and/or urgent, should contact centers place a greater emphasis on business continuity by keeping their staff available, safely?

As automation takes on the more routine interactions, it raises the stakes for keeping staff available and protected to handle those that require human engagement.

Hybrid operations and secure remote access are critical to ensuring staff can remain available safely. Staff safeguards – like emergency plans, secure devices, and key personnel lists – must be treated with the same priority as technical backup systems.

Staying operational involves aligning digital resilience alongside human safety and availability.

Q. Are contact center organizations doing enough to ensure effective BCDR? If not, why not? Where are the common deficient areas? Or is it a case of putting together a strong business case, along with educational and training programs (including drills) for staff?

Most organizations have plans in place, but gaps remain. End-to-end testing tends to be minimal, vendor dependency management is incomplete, AI governance is still maturing: and remote agent protections differ significantly from one site to another.

Resiliency guidance emphasizes the need for regular drills and architectural safeguards, while governance frameworks highlight compliance expectations.

Still, many organizations underinvest in overflow capacity or site redundancy, exposing operations to spotty disruptions or telecom failures. Strengthening business cases and integrating practice routines can help address these shortcomings.

Q. Following up on your answers to the preceding question, remote agents have long been a proven BCDR strategy. But are these agents adequately protected?

Home networks, electrical stability, and device safety differ, introducing gaps in consistent operations. While protected logins and uniform device rules set starting points, remote agents should be treated as critical infrastructure.

Alternate connections, encrypted hardware, or clear communication protocols remain necessary. Operational resilience must extend beyond software to include those providing support remotely.

Q. What are your recommendations for contact centers to ensure safe, reliable customer contact operations?

The path forward involves building layered safeguards:

• Organizations should map customer journeys to potential failure modes and match recovery goals with the right levels of resiliency. They also should orchestrate AI and human failover, ensuring routine inquiries are absorbed digitally while urgent cases reach trained agents.
• Cyber resilience must be hardened through robust controls, audits, and monitoring.
• Remote agents should be equipped with protected devices and backup connectivity, and joint practice exercises with vendors should simulate high demand and partial outages.

Business continuity becomes possible when technology, governance, and human safety are integrated.

No matter the industry, just about every brand is trying to navigate a real-world dilemma. That is, to meet (and exceed) rising customer expectations while also managing to cut costs and boost revenues.

This is a constant balancing act. Your customers demand speed, empathy, and consistency, but delivering that at scale often feels like a complex puzzle.

In this high-stakes environment, savvy brands are realizing that their contact center, once viewed as merely a necessary expense, is actually a strategic sales gold mine.

The key to unlocking this potential? It’s harnessing contact center intelligence (CCI).

…CCI is a dynamic, living ecosystem built on AI, real-time analytics, and intelligent automation.

CCI isn’t just about bolting on a few new tools. Instead, it’s a fundamental shift in how we collect, measure, manage, and act on customer data points.

At its heart, CCI is a strategic imperative that fuses human expertise with AI to drive better outcomes for both customers and employees. It’s about making sure your service is not only smarter but also more genuinely human.

What Intelligence Really Means

What passed for “intelligence” in the contact center just a few years ago was pretty basic and very artificial.

Then, intelligence usually meant reviewing historical reports that only looked backward at lagging metrics, such as AHT, queue length, and/or call abandonment rates.

But what was important to one manager would be different for the next: and correlation with actual agent productivity was not possible. While those numbers still matter, they’re not enough to navigate the complexity of today’s customer journey.

Today, CCI is a dynamic, living ecosystem built on AI, real-time analytics, and intelligent automation. It’s designed to understand the “why” behind the numbers.

How does it do this? By fusing two types of data:

1. Structured data. Neat, organized information like the codes an agent selects after a call.
2. Unstructured data. This is the messy, rich data that makes up most human communication, like call transcripts and knowledge of diverse types.

The system applies AI to this combined data stream to surface real-time, contextual insights.

By applying CCI you no longer have to wait for IT to build a custom dashboard. By creating a simple, natural language prompt like “Why did our service volume spike last Tuesday?” you can instantly uncover performance trends, summarize team-level activity, or explain queue underperformance.

CCI Tools at Work

This ability to instantly turn raw data into actionable knowledge is a genuine game-changer. Here’s how intelligent tools empower both staff and leadership.

• For agents. AI tools like agent co-pilots or agent assist listen to live voice or chat interactions, retrieve context-aware answers, recommend next-best actions, predict and suggest wrap-up codes, and even summarize the conversations.
• This functionality dramatically decreases cognitive load, reduces manual effort, and ensures that agents focus on complex, empathetic problem-solving.
• For leaders. Tools like real-time performance dashboards, AI-powered quality assurance (QA), and predictive analytics provide a clear, instant views of operations.
• These tools enable targeted coaching, better forecasting, smarter staffing, and more empowered leadership teams. They can also be used to focus attention on outliers in reporting or operations.

Making Customer Service a Revenue Driver

Brands that successfully leverage CCI can see measurable and often rapid return on investment (ROI). This isn’t abstract; it hits the bottom line.

• Customer loyalty (CSAT and NPS). When customers receive faster, more accurate, and more empathetic service (because the agents are supported by AI), they are happier. This often translates directly to customer satisfaction (CSAT) and the Net Promoter Score (NPS) increases.
• Operational efficiency (FCR). Real-time assistance and automation tools boost efficiency metrics. FCR, which is solving the customer’s issue on the very first try, goes up: which is a big win for both the customers and the brand.
• Using topics to look for phrases where the agent asks the clients if there is anything else they can help with, or if they answered all the customers’ questions, is ideal for finding calls to avoid subsequent calls.
• Cost reduction and productivity. By automating repetitive tasks and shortening AHT, brands can improve agent productivity without sacrificing quality, which in turn helps reduce overall operating costs.
• Operational agility. Intelligence provides a real-time pulse on your business. This allows your team to shift staffing, adapt call scripts, and refine processes in near real time, ensuring that you can quickly respond to anything from a major product launch to an unexpected service outage.

Building the Right Foundation

To truly realize their contact center’s potential, brands need to look beyond the technology itself and focus on the bedrock of their operations. This requires three key investments: clean data, flexible architecture, and a culture of continuous learning.

Don’t just chase the newest tool; embrace a design-first, platform-agnostic approach. This means designing the ideal customer and agent experience first, and then choosing the technology that best fits that vision.

This enables a customized intelligence ecosystem that aligns precisely with your specific goals and customer needs.

The AI will only become more capable in the years ahead, but the human element will remain the deciding factor. The brands that succeed will be those that realize CCI and AI are not about replacing human agents, but about elevating them.

With the right blend of automation and empathy, the contact center truly transcends its traditional service role and becomes a powerful, true driver of brand loyalty and business growth.

The headlines over the past 12 months appear to give the impression that remote work/work-from-home (WFH) is dying as employers seek to gain more or regain control over their workforces by compelling them to return-to-office (RTO), raising concerns like security (see BOX 1 on security).

At the same time, there have also been reports of pushback to RTO by employees. WFH freed them from arguably arduous, expensive, and sometimes dangerous commutes, and from struggling to find and access affordable childcare. And such freedoms have made it hard (and costly) to go back.

But contact centers have long been early WFH adopters to provide flexibility, business continuity disaster recovery (BCDR), and to recruit/obtain the services of and retain workers who live outside of daily commuting distance. Business cases that appear to still hold today.

So, what is really happening with WFH, including in the contact center? Is it finished as a practice? Or are organizations realizing that it has a place in the workplace?

To find out, we had a virtual conversation (from our home offices, naturally) with Jeremy Hyde, president, WFH Alliance, which serves as a mutual, sharing resource for contact centers seeking information and guidance on remote working.

Q. What is the state of remote working/WFH in the contact center? Are you seeing it increase, decrease, or stay the same as last year?

Despite the headlines, contact centers aren’t experiencing a mass RTO. Nationally, remote work has dipped slightly in average days per week, but it has largely stabilized.

Much of the national RTO narrative is overstated, and the data we’ve seen (see CHART 1) suggests we’ve settled into a new normal of roughly two remote days per week across the U.S. workforce.

For contact centers, that number is typically higher, and most teams that adopted remote or hybrid models (see BOX 2 on hybrid work) have continued using them because the structure works well for staffing, performance, and flexibility.

So, RTO is happening in pockets. But not on the scale that public narratives suggest (see CHART 2). This can create the perception that remote work is shrinking when it’s actually holding steady.

It’s also worth noting that demand for remote roles remains extremely high. Research suggests, drawing from other studies, that about twice as many job seekers want fully remote positions versus what employers have or have offered.

Q. How is the use of WFH by contact centers compared to that in other departments? Is there a convergence or divergence of trends like RTO?

Many of the concerns that drive RTO elsewhere, such as visibility into work or productivity monitoring, are less relevant because contact centers already rely on clear performance metrics, recorded interactions, and structured workflows.

As a result, WFH adoption remains strong in the contact center. Oftentimes, even if an organization has an RTO mandate, the contact center is excluded from the requirement due to effective management, geographical locations of agents, and space constraints.

Q. Has there been any pushback to RTO in contact centers?

Yes, there is pushback to RTO in contact centers, and it typically shows up first in hiring. Many job seekers filter for remote-only roles and never consider on-site positions, which makes it significantly harder to attract talent when teams are required to RTO.

Attrition can increase as well. But the more immediate challenge is the shrinking candidate pool.

Q. Let’s discuss BCDR. Is the severe weather that has also been in the headlines prompting organizations to look again at WFH? In how they use it, noting that remote agents are also affected by these events? Or are they resorting to AI-driven self-service instead of WFH as a BCDR strategy?

BCDR is a very strong operational reason to offer hybrid or fully remote work. Using an airline example, a snowstorm at a Midwestern hub creates a spike in delays and cancellations.

At the same time, agents who must commute may be unable or unwilling to drive to the office. Staffing drops at the exact moment customer needs spike.

“…even if an organization has an RTO mandate, the contact center is excluded from the requirement…” —Jeremy Hyde

Remote teams perform much better in these situations. Recently, my team has seen unplanned absences fall by roughly half during critical events compared to normal days. We have also seen staffing increase by about 40% as unscheduled agents log in to help, even if only for short periods of time.

Home-based employees can still experience disruptions, but spreading your workforce across multiple regions significantly improves resilience. For many organizations, remote work models have become a core part of BCDR planning.

Q. Have there been any changes in how contact centers manage remote agents, e.g., use of AI tools and any other technologies? New best practices, like for recruiting, coaching, training, team cohesion, and for issues like isolation and wellness?

A lot has changed in how contact centers manage remote teams:

• Flexibility has shifted from a perk to an expectation, and employees place a much higher value on leaders who listen, act on feedback, and support their development.
• Remote work has also surfaced challenges, such as isolation and loneliness, which require leaders to be more intentional about communication and connection.

We’re seeing that the direct leader is the most impactful factor on employee satisfaction and engagement, and more leaders are leaning into the human side of leading even as technology and AI dominate so many conversations.

Technology has evolved alongside these leadership expectations. The first wave of AI adoption in contact centers has focused on tools that support agents rather than replace them.

This includes tools such as real-time agent assist, automated quality monitoring, sentiment analysis, and training simulations that help new hires ramp more quickly.

The goal many organizations are moving toward is a true human plus AI partnership, where technology enhances the agent’s work rather than overshadowing it.

Q. Are you seeing any new or renewed interest in models of WFH or hybrid working, e.g., geo-sourcing, satellite offices? Please discuss what they are and their benefits and challenges.

Remote hiring has opened the door to entirely new workforce models in contact centers:

• Some organizations simply cast a wide net and hire the best candidates anywhere in the country.
• Others are expanding globally through a mix of direct hiring, traditional BPO partners, and Employer of Record arrangements.

A more targeted approach is also emerging. Instead of hiring everywhere, some teams identify regions where there is a strong employer–candidate fit.

For example, my team focuses on part-time employees in specific small-town Midwest markets near the airports we serve. These employees can use flight benefits, have strong schedule flexibility, and often stay longer because the role aligns well with their lifestyles.

The possibilities with remote and hybrid models are nearly endless, but many organizations still limit themselves by managing remote work the same way they managed on-site teams.

Q. What are your recommendations for contact centers seeking to retain, expand, and make the best use of WFH staff?

My recommendation is to design your remote model intentionally rather than simply replicating old on-site practices.

Start with strong frontline leadership, because the direct supervisors have the greatest impact on retention, engagement, and overall performance. Equip leaders to communicate frequently, act on feedback, and create meaningful connections so remote employees don’t feel isolated.

Second, embrace flexibility where possible. Remote work is a major retention driver, and small amounts of schedule flexibility can open up highly reliable talent pools you might otherwise miss.

“Remote work has also surfaced challenges…which require leaders to be more intentional about communication and connection.”

As mentioned earlier, there are twice as many candidates who want remote jobs as there are remote jobs, so if you offer a good one, people will want to stick around.

Finally, rethink your hiring strategy. Remote work allows you to tap into new geographic markets, tailor roles to different talent segments, and access stronger fits for your business.

Teams that approach staffing creatively get the most out of their WFH model, improve retention, and build resilience into their operations.

Remote agents have become the standard operating model for contact centers across North America. During the COVID-19 pandemic, remote work was the only viable option for many contact center and BPO leaders, requiring organizations to pivot quickly to support their customers’ demands.

Now, nearly 70% of organizations operate under a hybrid model, according to a report by ICMI reviewing industry trends, reinforcing that remote work is no longer an exception but an expectation.

But the experience of contact centers with remote work is that it cannot be used as a quick fix as it poses its own challenges. And there are many advantages to having agents in-office that are often difficult to duplicate remotely.

Therefore, as I will discuss in this article, contact centers that want to have successful remote agent programs must make a serious and long-term commitment to them.

Remote Work Benefits

With the ongoing demand for flexible work, candidates increasingly request work-from-home (WFH) positions and, in some cases, decline in-office–only roles. As more North American contact centers offer remote options, this model has become a key factor in attracting new talent.

From an operational standpoint, contact centers are responsible for managing volume and responding quickly to changes in customer demand. Remote operating models can reduce dependency on physical capacity, allowing organizations to scale without being constrained by in-office space or location.

Reliance on a single physical contact center can also introduce operational risk. Recent events, including the pandemic, highlighted how quickly in-office models can be disrupted when remote work is not already established.

…the experience of contact centers with remote work is that it cannot be used as a quick fix as it poses its own challenges.

In regions prone to extreme weather or potential infrastructure interruptions, remote teams can provide a practical contingency plan, allowing for operations to remain functional even through challenging and unpredictable disruptions.

Remote staffing further allows organizations to extend coverage beyond their immediate geographic reach. Contact centers that lack the physical capacity to support multiple time zones can leverage remote agents without the added burden of establishing additional locations.

Remote work can also provide financial and operational advantages particularly for small- to mid-sized contact centers.

Those with limited office space, or those operating as fully remote organizations, often rely on remote agents to support customer service operations. In some cases, remote agents may also provide their own hardware and equipment, reducing upfront infrastructure costs.

Remote Work’s Resources Strain

But remote work can strain contact center resources. Supporting remote agents requires significant operational effort to ensure policies, compliance standards, and quality expectations are consistently maintained without face-to-face access to the agent.

Even in organizations with well-established processes, maintaining strict adherence to procedures can be more difficult at a distance.

Here are a few key points to consider:

1. Work Environment

Agents must be set up in quiet, secure, and non-distracting environments, conditions that are not always guaranteed outside of a traditional contact center floor.

Traditionally, in-office contact centers have multiple resources in place to ensure quiet and productive working conditions, such as white noise machines, sound barriers, multiple queue screens, dedicated workstations, and building soundproofing. Managers also control access to the floors.

2. Supervisor Access

In-office contact center management allows supervisors to provide real-time, on-floor support, with direct line of sight visibility into agent challenges, distractions, and concerns as they occur.

This immediate oversight enables teams to address issues collectively and make timely adjustments to processes when patterns emerge.

In remote environments, this same hands-on support is not consistently available. With a heavier reliance on communication tools such as Microsoft Teams, Slack, and Google Chat, tone and sentiment in agent–management interactions can be more difficult to interpret.

3. Adherence to Procedures

Even in organizations with well-established processes, maintaining strict adherence to procedures can be more difficult at a distance. Remote agents do not have access to the advantages of collaborative QA sessions, real-time feedback, and in-person coaching typically provided in physical settings.

Even though contact center platforms give users more insight into agent activity and time management, operational issues may still arise because surface-level metrics might not accurately represent agent performance or behaviour.

4. Agent Availability

Agent availability in certain contact center platforms is dependent on manually setting statuses like “break,” “lunch,” or “away.” Visibility is limited, even in routine or unintentional cases where a remote agent forgets to update their status.

In contrast, in physical in-office contact centers, supervisors can observe agents’ presences and activities, whereas in remote environments, this level of awareness is challenging to maintain.

Enabling Remote Work Effectively

Enabling remote contact center operations requires more than deploying preferred technologies and software. It also depends on the regular application of defined expectations, leadership supervision, and transparent procedures that facilitate the execution of accountable remote agent work.

Clear expectations…help remote agents understand not only what is required of them but also how their performance is measured.

The realities of WFH, such as agent responsiveness, timeliness, and adherence to established workflows that support customer demand, must be reflected in processes and standard operating procedures that go beyond those created for in-office settings.

Consider the following points:

1. In most cases, remote programs require agents to formally acknowledge WFH policies that outline conduct, security, and performance expectations. Increased visibility through management tools can support both agent preparedness and managerial oversight. But these tools are most effective when paired with clear expectations and consistent follow-through.
2. Providing feedback to remote agents often requires a higher cadence than for in-office teams. As I noted earlier, in the office, agents benefit from proximity to management and frequent real-time support, allowing questions to be answered quickly and coaching to occur informally throughout the day. But in remote environments, those informal interactions are largely absent, increasing reliance on structured coaching and regular QA feedback. More frequent touchpoints help reinforce expectations, support skill development, and prevent performance drift. QA feedback should be delivered consistently, with sufficient time for agents to reflect and respond, rather than feeling constrained by tight deadlines.
3. Access to management is also critical in remote settings, particularly when agents are handling active customer interactions. Without the ability to quickly ask questions in person, escalation paths must be clear, responsive, and consistent with in-office support models. These, along with responsive leadership, reduce hesitation and enable agents to resolve issues efficiently.

Ongoing conversations around performance and the emotional realities of working remotely are essential to sustaining remote agent engagement.

Consistency is Key

Organizations that succeed with remote contact center models tend to focus less on control and more on consistency.

Clear expectations, reinforced and mutually agreed on from the start, help remote agents understand not only what is required of them but also how their performance is measured.

When standards are documented, communicated early, and applied, remote teams are better positioned to operate with confidence through accountability.

Ultimately, remote work is most effective when treated as a long-term operating model rather than a temporary fix.

Organizations that invest in clear processes, engaged leadership, and ongoing support are better equipped to maintain performance, quality, and agent engagement in distributed environments. They are also better positioned to adapt to change over time.

Contact centers are among the most infrastructure-intensive operations in any organization, with applications including voice and chat channels, AI assistants, analytics engines, and workforce management (WFM) systems.

But the complexity and cost of supporting modern customer experiences (CXs) with these tools and their supporting platforms can quickly spiral out of control.

Many contact centers embraced the cloud in pursuit of flexibility, rapid scalability, and innovation. Yet without a clear strategy, cloud adoption can lead to surprise costs, inefficient workloads, and disappointing returns on investment (ROI).

Rather than abandon the cloud, contact center professionals must find ways to unlock its potential. Doing so requires aligning cloud strategy with CX outcomes, operational efficiency, and disciplined cost management.

When Flexibility Becomes Complexity

Cloud initially promises agility and scalability but hidden costs often emerge over time. Underutilized computing capacity and power, idle test environments, redundant storage, untagged services, and poorly optimized workloads can create significant waste.

Once cloud operations are disciplined and efficient, the technology becomes a lever for CX.

For contact centers, the impacts are tangible.

• Unpredictable costs make provisioning for seasonal scaling or burst capacity difficult.
• Inefficient workloads can introduce latency in voice systems, slow routing decisions, or delay analytics.
• Lack of visibility limits managers’ ability to correlate spending with agent performance or key operational metrics.
• Disjointed governance encourages shadow projects, where teams spin up bots or transcription services outside of IT oversight.

To regain control, IT and contact center operations must partner closely. And in these two areas.

• It’s important to measure cloud investments that matter to CX, such as average handle time (AHT), resolution time, and/or first contact resolution (FCR), rather than raw computing power.
• Equally essential is a shared understanding across teams of what cloud success means – scalability, resilience, and cost efficiency – and the adoption of cloud-native designs that fully exploit the platform. Rather than simply lifting and shifting legacy workloads.

Practical Optimization

Before undertaking large-scale cloud transformations, contact centers can achieve immediate benefits by focusing on operational efficiency.

• Non-production environments for development, QA, demos, or training often run around the clock despite minimal usage. Scheduling downtime during the night or weekends can yield substantial savings.
• Many AI engines, routing servers, and analytics workloads are overprovisioned and can be scaled down during low-demand periods.
• It’s also important to eliminate unused assets like idle compute instances or stale storage volumes: which quietly contribute to rising costs.

While these optimizations address immediate waste, lasting transformation comes from modernization. Contact centers benefit from moving away from monolithic virtual-machine stacks toward microservices, containers, serverless functions, and managed cloud services.

As a CIO at a large, diversified conglomerate, we lift-and-shifted a sprawling set of digital experience platforms to the cloud. But costs quickly ran well over budget with underwhelming performance.

In response, we consolidated onto modern Kubernetes microservices, edge caching, and serverless components. These changes dramatically lowered spend and improved responsiveness.

By focusing on optimization, modernization, cost accountability, and alignment with CX outcomes, contact centers can transform the cloud…into a strategic asset.

This firsthand experience demonstrates how, without a clear cloud strategy and modern infrastructure, organizations can quickly face runaway costs and disappointing performance. And what they could be faced with doing, at a fair expense and downtime, to fix the problems after they occurred.

Modernized systems scale under unpredictable loads. This enables faster deployment of routing changes or AI features and increases resilience, so that a single service failure doesn’t disrupt the entire operation.

This is a win-win all around. Agents experience fewer outages, more responsive systems, and faster access to new capabilities. Leadership gains greater insight into the cost-to-value relationship of cloud investments. Customers, and by extension enterprises, gain by improved CX.

Governance and Cost Accountability

But optimizing workloads and modernizing infrastructure alone isn’t enough to prevent overspending. Contact centers need a governance framework that accounts for all stakeholders, including IT, CX operations, analytics teams, and business leadership.

A disciplined approach to cloud spending, often described as a FinOps mindset, blends finance, operations, and development to ensure shared accountability.

• Finance teams must understand metrics like agent cost-per-minute and correlate cloud spend to these operational outcomes.
• Have platform teams accountable for resource usage that are tied to agents or campaigns.
• Business leaders must own the ROI of new features such as predictive routing and AI capabilities.

Visibility and accountability are crucial. Here’s how they can be implemented.

• Tagging cloud resources so that costs can be attributed to teams, campaigns, or even individual agents helps leadership see exactly where money is going.
• Policies and guardrails can prevent uncontrolled provisioning, enforce usage limits, and manage high-cost workloads, like AI or transcription services.

A mature governance model empowers leaders to make informed tradeoffs between performance, capacity, and spend. This turns cloud management from a reactive burden into a proactive strategy for long-term success.

Driving Customer Experience Through Cloud

Once cloud operations are disciplined and efficient, the technology becomes a lever for CX.

• Cloud-based analytics can identify root causes and repeat contacts.
• Machine learning can predict wait times and dynamically shift agents across channels to meet demand.
• AI-assisted tools support agents, reduce errors, and shorten AHT.
• Real-time voice transcription and sentiment analysis provide immediate insight into customer interactions.
• Simultaneous workloads allow teams to forecast staffing and routing needs before launching campaigns.

Most importantly, every cloud initiative should be measured in terms of its impact on key contact center performance indicators, such as FCR, customer satisfaction (CSAT), and cost-per-contact.

By tying infrastructure investment to tangible business outcomes, contact centers can justify innovation spend and avoid projects that consume resources without improving CX.

The cloud also enables rapid experimentation, allowing teams to deploy seasonal bots, pilot predictive routing, or test new analytics features with minimal risk.

This flexibility must be paired with governance to ensure that experimental workloads don’t become uncontrolled cost drivers. Organizations that implement a culture of continuous evaluation and optimization sustain efficiency gains while simultaneously enhancing CX.

Cloud adoption doesn’t fail contact centers. Weak strategy, poor governance, and lack of operational discipline do.

By focusing on optimization, modernization, cost accountability, and alignment with CX outcomes, contact centers can transform the cloud from a potential cost burden into a strategic asset.

When every workload is tied to tangible outcomes, experiments are disciplined, and teams collaborate across IT, finance, and operations, the cloud becomes a platform for innovation, efficiency, and superior CX.

For contact center leaders, the opportunity is clear. The cloud can be a growth engine, but only when approached strategically and managed carefully. Those who embrace this mindset will be better equipped to deliver exceptional experiences, operational efficiency, and measurable business impacts.

Contact centers must deliver more than they ever have before. Interactions are more complex, customers are less patient, products are more configurable, and channels now span voice, chat (including video), email, and social platforms.

In response, organizations have steadily raised the bar for what “good” talent looks like. But there is a fundamental constraint the industry cannot technology its way around; the labor pool is not expanding at the same pace as skill expectations.

And when organizations respond by tightening requirements and screening harder, they may narrow the pool in ways that do not reliably improve performance: and ultimately leave too few candidates to fill open roles.

If the contact center industry had a recurring storyline, it would be this: the next technology wave is always expected to solve the talent problem.

The current version centers on AI. In McKinsey’s words, “Many are being inundated with solutions from AI vendors amid predictions that calls requiring human agent support will virtually disappear in the next few years.”

But the reality inside contact centers remains more complicated, because, as McKinsey also points out, “humans remain valued for their ability to handle complex and emotionally nuanced interactions, too.”

Automation and Talent

There has always been a conversation, a dialectic, between automation and people in the workplace, one that is now being highlighted with the practicality of AI. But in practice, automation does not (and never has) eliminate the need for talent. Instead, it changes what talent is needed.

As AI absorbs routine tasks, the interactions left for humans become more complex, emotionally charged, and consequential. That shift is raising the skills bar, even as labor markets continue to constrain supply.

At the same time, hiring challenges persist. Time to fill remains high. Attrition remains stubborn. And despite new tools, innovative technologies, and louder promises, contact centers are no closer to solving their talent problem than they were years ago.

Avoiding the Destructive Spiral

This tension raises an uncomfortable question. If expectations continue to rise, but the labor pool does not expand in parallel, what are the unintended consequences? And who is most likely to be excluded along the way?

The answer is not keeping the bar where it is or lowering it. The problem is narrowing the pool of candidates needed to jump over it through requirements and proxies that do not improve performance.

Such practices can disproportionately screen out capable candidates, shrinking the workforce pipeline until there are too few people to staff open positions. Which can then result in long queues, angry customers, and frustrated agents: risking their turnover that together may lead to a destructive spiral.

…the interactions left for humans become more complex, emotionally charged, and consequential.

In this article, which I have further divided into three parts, I focus on three skill areas where the hiring bar is rising fast: digital and AI fluency (Part 1 here), emotional intelligence (Part 2), and finally language skills (Part 3), which will have my conclusion of these points.

For each, I outline the unintended consequences of how organizations are currently screening for these skills. I then suggest a more sustainable approach: measuring skills directly and prioritizing readiness and ramp potential, rather than relying on background-based proxies that narrow the talent pool.

Importantly, this approach does not require complex technology or purchasing new tools. Instead, it requires clearer definitions of job skills and more structured and consistent evaluations.

Contact centers have always been skills-based jobs. Even in “entry-level” roles, success has depended on communication ability, problem solving, and emotional control under pressure. What has changed is the level and breadth of those expectations.

As routine work is absorbed by automation and as service interactions become more complex, organizations continue to raise the skills bar, often without fully accounting for how those higher requirements narrow the available talent pool.

Increasing Digital/AI Literacy Requirements

Digital skill expectations in contact centers have expanded rapidly in a brief period of time, initially driven by moving large contact center populations home in response to the COVID-19 pandemic.

To enable agents to work remotely, hiring and workforce readiness guidance emphasized the practical ability to function independently at home: reliable internet, appropriate hardware, headset quality, secure connectivity, and baseline troubleshooting skills without onsite IT support.

That focus has not disappeared, but the definition of digital literacy has changed. Today, digital literacy is not simply the ability to use a computer and follow a process. In many modern contact center environments, it now includes these abilities:

• Navigating multiple systems and knowledge tools in parallel.
• Interpreting policy and account information in real time.
• Documenting accurately while maintaining customer rapport.
• Completing authentication steps and compliance scripts correctly.
• Switching between tools quickly under time pressure.

Omnichannel service further raises the bar:

• Writing becomes part of the role, not an occasional task. Agents must produce clear, appropriately toned written responses in chats and emails, while also working within structured workflows and meeting quality expectations.
• Video-enabled customer interactions introduce some additional skill demands, but, as I will explore in depth in a separate discussion (see BOX), not in the way they are often assumed.

AI adoption adds yet another layer. As AI copilots, automated knowledge systems, and AI-generated call summaries become more common, contact center talent is increasingly expected to demonstrate AI literacy as well.

This does not mean understanding how models work. Instead, it means knowing how to use AI-enabled systems effectively, including these abilities:

• Asking strong questions and using query tools correctly.
• Recognizing when an AI suggestion is incomplete, incorrect, or misapplied.
• Validating outputs against policy and customer context.
• Overriding automated guidance using human judgment when needed.

Clicking “accept” is easy. Knowing when to pause and verify is the skill.

A quieter risk is how organizations translate this shift into pre-hire requirements. When “AI literacy” becomes a hiring filter, employers often rely on resume-based proxies such as prior exposure to AI tools or “GenAI experience” listed in job history. But tool exposure does not equal safe and effective use.

In AI-enabled workflows, the representative becomes not only a customer advocate, but also a real-time quality control layer for automated support.

Consider two candidates:

• One candidate has worked in an AI-enabled environment and lists AI tool experience on their resume, but they relied on the system passively.
• The other candidate has no formal AI tooling experience, but they demonstrate strong learning agility, process discipline, and the ability to detect errors and apply judgment under ambiguity.

In a typical hiring process, the second candidate is filtered out early: even though those verification and judgment skills are the true predictors of performance in AI-enabled workflows.

But the unintended consequence is a narrower applicant pool shaped by access and prior opportunity, rather than readiness to succeed.

The scenarios requiring candidates with effective AI literacy are not theoretical:

• An AI-generated call summary may sound polished, but it omits key details required for downstream resolution or compliance.
• In other cases, an AI knowledge suggestion might point to the wrong policy or apply the right policy to the wrong customer context, requiring the representative to recognize the mismatch quickly.

In both situations, the agent’s job is no longer just following guidance. Instead, it is actively validating it.

This is why AI can raise, not lower, the skill requirements for many roles. In AI-enabled workflows, the representative becomes not only a customer advocate, but also a real-time quality control layer for automated support.

Many of these skills are developed through on-the-job exposure and coaching, particularly in environments that require navigating multiple systems, managing high interaction volume, and operating within tightly structured workflows.

Candidates who have not had access to those environments may possess the underlying capability to succeed but lack conventional signals of readiness. This can result in them unwisely being rejected.

The Flaws of Experience

As these requirements rise, hiring processes that rely heavily on resumes or prior job titles can unintentionally favor familiarity over potential. Resumes can indicate whether someone has been in these environments, but they rarely indicate whether someone was effective in them.

In other words, experience can be an imperfect proxy. It may screen out candidates with strong underlying capability who have not had the opportunities to show them, while screening in candidates whose prior exposure does not translate into strong performance.

…organizations should assess the specific skills that predict whether someone can become proficient quickly.

Skills-based testing is one evidence-based alternative that reduces reliance on resume-based proxies by replacing them with direct evidence. Rather than inferring capability from job history, organizations can evaluate job-relevant behaviors directly through simulations, work samples, and structured assessments.

Further, and importantly, and back to the discussion of AI skills, the goal should not be to confirm full mastery of AI-enabled workflows before day one. In most cases, that mastery develops inside the job.

Instead, organizations should assess the specific skills that predict whether someone can become proficient quickly. Namely learning agility, process discipline, attention to detail, and the ability to verify and apply information accurately in real time.

The hiring goal is not to find candidates who have already mastered the workflow. It is to identify candidates who have the prerequisite skills to master it quickly.

However, the result of relying on experience is not necessarily better selection, but narrower selection based on background rather than capability.

In practice, that often means favoring those who have had prior exposure to emerging tools over those who demonstrate the judgment required to use them well, particularly when organizations treat AI familiarity as proof of AI capability.

The question is: who does that leave out? Those candidates whose capability exceeds their resume.

When hiring systems rely heavily on experience thresholds, credential requirements, and subjective notions of readiness, they systematically disadvantage people who have the underlying skills to succeed but have not yet had access to the environments that signal those skills in familiar ways.

Here are two examples:

1. Candidates who learn quickly, apply information accurately, and perform well under pressure, but who lack the conventional markers that screening systems are designed to recognize. In practice, this often includes early-career candidates, people from non-traditional backgrounds, career switchers, and those whose prior roles did not carry the right former job titles, even when the work itself developed relevant capability.
2. Candidates who communicate differently in interviews or who do not conform to informal expectations of polish, despite being highly effective once expectations and workflows are clear.

The common thread is not lower ability but limited access to prior opportunity.

As skill expectations continue to rise faster than labor supply, excluding these groups is not just an equity concern. It is a capacity problem and organizations risk choking off the very pipeline they need to sustain performance over time.

The question is not whether standards should rise. They will. The real risk is mistaking exposure for capability and narrowing the talent pool based on signals that do not predict success.

The contact center industry is experiencing a fascinating contradiction in 2026. While enterprises race to deploy AI agents and automation at unprecedented scale, a quieter revolution is unfolding: human service is being repositioned as a premium offering, and it’s working.

Recent industry research reveals a compelling paradox: the vast majority of customers prefer AI chatbots over waiting in queue for simple tasks, yet they report significantly higher loyalty to companies that prioritize human service when it matters. This isn’t a contradiction; it’s a strategic opportunity that forward-thinking CCaaS vendors and their clients are already capitalizing on.

The Execution Crisis Behind the Hype

Before we explore the human service renaissance, we need to understand why it’s happening now. Despite widespread AI pilot programs, relatively few organizations have successfully deployed AI agents in production environments. According to Deloitte research, approximately 40% of agentic AI projects will fail, not because the technology doesn’t work, but because organizations are automating broken processes.

The pattern separating success from failure is clear: “Redesign, don’t automate.” Companies that succeed are those that reimagine their customer experience holistically, using AI where it excels (speed, consistency, scale) and human expertise where it matters most (complexity, empathy, relationship-building).

The Interaction Volume Challenge

Analysis by Sinch of 900 billion interactions across 200,000 businesses points to a significant scaling challenge: automated customer engagements are projected to increase dramatically in the coming years, and current infrastructure wasn’t built for this scale.

But here’s what the data doesn’t capture: as AI handles more routine interactions, the quality expectations for human interactions skyrocket. When a customer finally reaches a human agent, they’re no longer looking for basic information retrieval; they’re seeking resolution of complex issues, emotional support, or high-stakes decision guidance.

This is where “Outcome Certainty at Speed” becomes the new metric. Leading organizations are creating a new role: Resolution Specialists – agents who combine deep expertise, empathy, and AI-driven insights to deliver first-contact resolution on the issues automation can’t handle.

The Premium Service Model Emerges

In 2014, UK mobile operator EE tested “Priority Answer”- charging customers £0.50 to skip the queue. The backlash was swift and brutal. A decade later, the market has evolved, and premium human access is being repositioned not as queue-jumping, but as relationship-building.

Companies that succeed are those that reimagine their customer experience holistically, using AI where it excels (speed, consistency, scale) and human expertise where it matters most (complexity, empathy, relationship-building).

Today’s premium service model isn’t about paying to skip a line. It’s about guaranteed access to expert human support as a value-added tier within customer relationships. Industries pioneering this approach include:

• Financial Services: Private banking clients get direct access to dedicated relationship managers who never route them to AI
• Healthcare: Concierge medical practices offering immediate human triage
• Enterprise B2B: Platinum support tiers with named technical account managers
• Luxury Retail: Personal shoppers and stylists available on demand

The key difference from 2014? These aren’t friction removers, they’re relationship builders. The premium isn’t about avoiding AI; it’s about accessing human expertise when it matters most.

The Trust Equation

Consumer research reveals a complex relationship with personalization: while many want personalized experiences, significantly fewer believe the privacy trade-off is worth it, and trust in how companies handle personal information remains low. This creates a fascinating dynamic where human agents become trust proxies.

When customers interact with a human who demonstrates memory of past interactions, understanding of context, authority to make exceptions, and genuine problem-solving capability, they’re more willing to share information, provide feedback, and remain loyal. Human interaction becomes a differentiator in an AI-saturated landscape.

What This Means for CCaaS Strategy

For CCaaS vendors and the enterprises they serve, this trend demands a strategic reframe around three key pillars:

1. Tiered Experience Architecture

Instead of “AI-first” or “human-first,” successful implementations are building intelligent routing that recognizes customer lifetime value, issue complexity, emotional sentiment, interaction history, and channel preference. The goal isn’t automation for its own sake, it’s matching the right resource to the right interaction at the right time.

2. Agent Elevation, Not Replacement

According to BCG’s framework for AI implementation, successful deployments break down as: only 10% algorithms, 20% technology and data infrastructure, and a full 70% people and processes. This means the real work isn’t in the AI deployment, it’s in redesigning roles, workflows, and organizational structures. This means the real work isn’t in the AI deployment, it’s in redesigning roles, workflows, and organizational structures.

Forward-thinking contact centers are transforming agent roles entirely:

• AI handles repetitive queries
• Humans tackle high-value interactions
• Real-time AI assistance empowers agents during complex calls
• Knowledge management systems ensure consistency across channels

Solutions like AI Agent Assist, Conversational Analytics, and automated QA become strategic enablers rather than cost-cutting tools when deployed with this philosophy.

3. The Resolution Specialist Model

Organizations are fundamentally redesigning contact center roles, moving from high-volume, low-complexity interactions to low-volume, high-complexity resolutions. This requires different recruiting, training, compensation, and career pathing, but the ROI is compelling. First-contact resolution on complex issues reduces customer effort, increases Net Promoter Scores, and builds genuine loyalty that automation alone cannot achieve.

The Post-Acquisition Experience Crisis

A pattern emerges repeatedly in failed CX transformations: companies over-invest in acquisition while neglecting onboarding, servicing, and renewal experiences. The result is silent churn. Customers don’t leave because of price or product features; they leave because of failed experiences after the sale.

The future of customer experience isn’t AI or human, it’s AI and human, strategically deployed.

When AI handles initial interactions beautifully but complex issues dead-end in frustration, the entire brand promise collapses. The solution isn’t more automation, it’s strategic human deployment at critical moments:

• Complex onboarding scenarios requiring judgment
• Escalated issues that need creative problem-solving
• Renewal conversations where relationship matters
• Win-back attempts for churned customers
• VIP and high-value customer touchpoints

The Strategic Questions to Ask Now

As contact center leaders evaluate their 2026 strategies, several critical questions emerge:

Which interactions would benefit from being human-only, and how do you position that as premium value rather than AI failure?

How do you measure success in a hybrid model? Automation rate alone is a dangerous metric that can optimize for efficiency while destroying customer relationships.

What does your agent career path look like when AI handles Tier 1 interactions? The best agents won’t stay in organizations where their expertise is undervalued.

How do you preserve institutional knowledge and relationship continuity when interactions fragment across AI and human channels?

Which vendors understand this paradox, and which are still selling pure automation plays that miss the strategic balance?

The Bottom Line

The future of customer experience isn’t AI or human, it’s AI and human, strategically deployed. The companies winning in 2026 understand that automation enables premium human service by freeing experts to focus on high-value interactions. Human touchpoints become differentiators in AI-saturated markets. Trust is built through relationships, not just efficiency. And outcome certainty at speed requires both technological capability and human judgment.

The question isn’t whether to automate, it’s how to automate in a way that makes your human service more valuable, not less.

Organizations that crack this code will discover something remarkable: their AI investments don’t replace human value, they amplify it. The contact centers that thrive in 2026 and beyond won’t be the most automated. They’ll be the ones that use automation to make their human expertise more accessible, more impactful, and more valuable than ever before.

In today’s volatile economy, contact centers face heightened pressure. Valuable customers — whose spending power is increasingly concentrated at the top — demand flawless experiences while organizations must control costs more tightly than ever.

Against this backdrop, hiring the right agents from the outset is no longer a “nice to have”; it’s a strategic necessity.

The True Financial Risk of a Bad Hire

When a contact center hires someone who doesn’t perform, the costs go far beyond their salary. Key contributors to this risk include:

• Recruitment and onboarding. Recruiting and training a new contact center agent can run between $4,000 and $7,000, according to research published by McKinsey & Company on contact center performance and workforce economics.
• Turnover rates. Annual agent turnover often falls in the 30%–45% range, according to industry benchmarking studies from SQM Group and Contact Babel.
• Replacement costs per agent. Replacing a single agent can cost $10,000–$20,000 when factoring in recruiting, training, and lost productivity, according to the Society for Human Resource Management (SHRM). Cost-of-turnover estimates frequently place replacement costs at 50%–200% of annual salary, depending on role and ramp-up time.
• Productivity ramp-up. New hires often operate at only 50%–60% efficiency in their first months, according to McKinsey. Workforce analyses have highlighted significant productivity gaps during ramp-up periods in contact centers, contributing thousands of dollars in indirect cost per agent.
• Hidden attrition costs. According to benchmarking research from Contact Center Pipeline, “The Hard, Hidden Costs of Attrition,” the total cost of attrition for a contact center agent can range from $10,882 to $22,691 per person depending on when they exit.

Putting it all together, a poor hiring decision can cost tens of thousands of dollars per agent: especially when considering training, lost productivity, and turnover. These cumulative cost models are consistent with workforce economics frameworks published by Deloitte and McKinsey & Company.

The Better Way to Predict Performance

Rather than relying solely on resumes or interviews, contact centers can leverage behavioral analytics grounded in a comprehensive job analysis.

…hiring the right agents from the outset is no longer a “nice to have”; it’s a strategic necessity.

The scientific foundation for this approach is well established in industrial-organizational psychology:

• Meta-analytic research by Frank L. Schmidt and John E. Hunter (published in Psychological Bulletin) demonstrated that general cognitive ability and structured assessments are among the strongest predictors of job performance.
• The principles of job analysis and validation are outlined in the Principles for the Validation and Use of Personnel Selection Procedures, published by the Society for Industrial and Organizational Psychology. Also, in the Standards for Educational and Psychological Testing, published by the American Psychological Association and the American Educational Research Association.

This involves:

1. Defining the competencies that matter most for a given role (e.g., cognitive ability, emotional regulation, multitasking, customer empathy, decision-making).
2. Developing or selecting assessments that reliably measure those competencies.
3. Validating the assessments by linking them to performance metrics such as handle time, first call resolution (FCR), quality scores, and turnover.

Because contact centers operate in many different subdomains, assessments should be tailored to specific roles.

This best practice is supported by competency modeling research from SHRM and validation guidance from the Equal Employment Opportunity Commission (EEOC) under the “Uniform Guidelines on Employee Selection Procedures.”

What Behavioral Analytics Buys You

Here’s what centers gain when they put predictive behavioral tools to work:

• Reduced turnover. Organizations using validated pre-employment assessments often report lower early-stage attrition, according to workforce analytics studies by Aberdeen Strategy & Research and Deloitte.
• Better performance consistency. Structured, validated selection systems reduce variability in performance outcomes: consistent with meta-analytic findings from Frank L. Schmidt.
• Greater ROI on training. Human capital ROI frameworks described by Josh Bersin (Deloitte) suggest that better selection improves downstream training yield.
• Stronger CX. Customer experience (CX) research from Forrester links employee performance and engagement to higher customer satisfaction and loyalty outcomes.

Strategic Recommendations

To implement effectively, contact center leaders should follow other best practices consistent with guidance from the EEOC (adverse impact and fairness) and SHRM (talent acquisition best practices).

Specifically:

1. Audit current hiring costs.
2. Conduct role-level job analyses.
3. Evaluate validated assessment vendors.
4. Integrate assessments early in the funnel.
5. Measure post-hire outcomes.
6. Ensure fairness via impact analysis and continuous monitoring.

Conclusion

In today’s economy, contact centers can no longer afford the guesswork of hiring. High turnover, steep training costs, and wide performance variability make bad hires a serious financial liability.

But by leveraging behavioral analytics rooted in thorough job analysis, organizations can better predict which candidates will thrive, thereby reducing costs, improving performance, and ultimately delivering a superior CX.