For years, companies – including many in the business process outsourcing (BPO) space – have relied on nearshore and offshore call center staffing to cut costs, deal with U.S. staffing shortages, and offer services around the clock.

However, a series of Federal Communications Commission (FCC) Notices of Proposed Rulemaking (NPRMs) is putting this model at risk.

These proposed regulations cover:

• Call branding (FCC 25-76), including – and notably – from outside of the U.S.
• Call center onshoring (FCC 26-16).

Both have a common thread: foreign traffic, near/offshore agents, and call centers that route U.S. campaigns through international providers, all of which are now squarely in the FCC’s sights.

But these proposed changes could impose a real operational and compliance burden on not only customer service, but also on marketing campaigns and debt collection outreach efforts (see BOX 1).

The new rules, should they go into effect, likely will affect how companies and their BPO partners operate and comply with regulations.

Consequently, companies relying on near/offshore call center staffing should be ready to make necessary adjustments to avoid compliance exposure.

Call Branding

The call branding FNPRM (the F stands for further), released October 29, 2025, would require incoming/outgoing calls from outside the U.S. to transmit a visible foreign-origin indicator to devices.

But this rule, if implemented, could cripple offshore call centers with near-zero answer rates for debt collection, financial services, healthcare, and lead generation.

…these proposed changes could impose a real operational and compliance burden…

Industry comments submitted expressed concerns over issues such as accuracy identifying the calling party, and man-in-the-middle (MITM) call hijacking.

In response, the FCC did soften some of the proposed provisions between the circulated draft of the FNPRM and the version it adopted, such as by making the labeling and spoofing rules clearer.

Call Center Onshoring

The FCC NPRM on call center onshoring and English-language proficiency (26-16), published in the Federal Register on April 23, 2026, extends the call branding FNPRM’s foreign-call themes with onshoring requirements, English proficiency standards, and gateway-provider accountability.

This NPRM floats strict limits on offshore telecom call center work. A March 26, 2026 Law360 article, “FCC Floats Cap For Offshore Telecom Call Center Work” notes the agency wants to cap foreign-staffed calls when callers struggle with U.S. English or local context. The FCC sees these limits as a national priority tied to scam prevention and service quality.

Chairman Brendan Carr’s statement, released when this NPRM was adopted on March 26, 2026, frames it bluntly: offshore centers mean “confusing service, delayed support, and even security risks,” with scammers learning the ropes before launching their own fraud operations.

Marketing efforts could also face heavy exposure here too: especially with lead-generation and appointment-setting campaigns. Many campaigns run through Philippines or Indian contact centers, where agents call U.S. numbers from places like Manila or Bangalore.

Debt collection sees similar risks. Offshore teams often struggle with understanding specific state laws or handling disputes properly, leaving customers frustrated and issues unresolved.

Onshoring NPRM Details

Specifically, the onshoring NPRM:

• Proposes percentage limits on the number of offshore customer service calls allowed for U.S. telecom carriers with oversight extending to their BPO vendors and contractors.
• Mandates English proficiency certification for agents handling American consumers, drawing from benchmarks like TOEFL or TOEIC to check language skills, cover tone, idioms, and cultural fit.
• Requires start-of-call disclosures for calls routed overseas, alerting customers of their option to easily switch to a U.S.-based agent.
• Suggests strict rules barring offshore handling of sensitive info like passwords, Social Security numbers, and payments.
• Seeks comment on whether these protections should extend to emails, texts, and online chats.

Several of these proposed requirements echo the Keep Call Centers in America Act (S.2495) introduced in 2025. That bill requires location disclosure at call start and free transfers to U.S. agents.

…inbound follow-ups…would trigger disclosures like “This call is from (country). Do you prefer U.S.?”

Providers of telecom, CMRS (wireless carriers), interconnected VoIP, cable, and DBS (satellite) services – plus affiliates – also fall under the scope of this NPRM, with extensions eyed for internet access service.

• Proposed caps may limit foreign calls to 30% of volume (as an example), pushing onshoring but without completely banning foreign calls.
• Monthly, quarterly or annual reports would track foreign percentages, transfer rates, and complaints, while broadband labels disclose U.S. call usage at service signup.
• To deter scams, bonds or fees are suggested to target gateway providers carrying risky foreign traffic, layering onto Robocall Mitigation Database (RMD) filings and tracebacks.

Implications for Outbound Marketing

Marketing campaigns lean heavily on offshore scale for dialing and qualification. Should the call center onshoring NPRM go into effect in its current form, BPOs serving telemarketers must now track volumes and certify compliance, risking contract losses if caps hit.

Not only that, inbound follow-ups i.e., opt-outs and billing queries, would trigger disclosures like “This call is from (country). Do you prefer U.S.?” This adds Federal Trade Commission (FTC) Telemarketing Sales Rule (TSR) and FCC Telephone Consumer Protection Act (TCPA) layers as poor clarity fuels do not call (DNC) complaints.

U.S. wages are, from our sources, two to three times higher than offshore rates and training costs pile on top: so expenses increase. While nearshoring to Mexico cuts some pain, the English proficiency rules will still apply.

The upside is better trust: telecom already sits low in American Customer Satisfaction Index (ACSI) rankings due to language gaps. Companies mixing outbound work with service calls should re-evaluate vendor lineups, leaning toward hybrid U.S. – offshore models.

Nuances for Debt Collection Outreach

Collections face the same pressures as marketing, but the Fair Debt Collection Practices Act (FDCPA) and Reg F make it tighter. Offshore agents chasing accounts often fumble with notices or disputes, drawing Consumer Financial Protection Bureau (CFPB) scrutiny alongside FCC heat.

Sensitive financial data must stay U.S.-only, aligning with Department of Justice (DOJ) rules on foreign adversaries. Meanwhile, gateway bonds (posted by gateway providers, surrendered upon exceeding a “threshold of reported unlawful robocalls”) raise costs for blended dialing.

Quick U.S. transfers cut deception claims but squeeze capacity. As a result, profit margins suffer short-term, but debt resolution rates get better in the long term.

Broader Contact Center and BPO Effects

Inbound support, billing, and technical help get hit with disclosures first. Routing must handle transfers without delays to cut down on the cultural nuances/gaps that drive escalations through the roof.

BPOs – 70% of companies offshore “something” – now face reshoring pressure, bringing jobs back but at the price of jacked-up operating costs.

The NPRM allows smaller/rural providers extra time to build U.S. capacity (staffing, facilities) before the call ceilings are enforced. Meanwhile, training in legitimate foreign call centers (to learn scripts, consumer lingo), minimizes opportunity for fraudsters.

Compliance and GRC Shifts

Governance, Risk & Compliance (GRC) teams should get into the habit of building certification logs and audit data flows as a general rule. Teams should also align consent management systems and outbound dialing workflows with the TSR and TCPA consent requirements and both federal and state DNC rules; there could also be dialing system changes (see BOX 2).

Additionally, compliance teams should become familiar with foreign adversary reporting requirements, increasing accountability.

The FCC’s tone has been set. On April 2, 2026 the agency hit a gateway provider with a proposed $4.5 million fine for routing scam traffic from unregistered foreign sources, echoing the Foreign Robocall Eliminate Act (H.R.6152) push for gateway accountability.

More Regulations Forthcoming

The FCC is not slowing down. The agency issued a release April 30, 2026 on the Know Your Customer (KYC) FNPRM (26-27). It proposes per-call penalties for originating providers that fail/skip customer verification, scaling fines to call volume and harm caused.

The proposed regulation would increase pressure on carriers to stop blocking or mislabeling valid calls. It pushes new Caller ID rules that could make contact centers spend more on compliance, exposing more foreign call traffic to labeling, blocking, and verification requirements.

..audit your foreign call reliance now…Outbound work stays solid if you build compliance-first hybrid models.

CTIA’s April 13, 2026, Ex Parte to the FCC staff underscores industry pushback. The wireless carriers highlight existing KYC and know-your-business (KYB) tools (e.g., branded calling ID) and urge flexibility to avoid stifling legitimate calls while chasing scammers.

Also, the FCC approved a FNPRM with enhanced know-your-upstream-provider (KYUP) STIR/SHAKEN requirements for voice providers. [It aims] to combat illegal robocalls, deter number spoofing, provide better information for call blocking and call labeling decisioning, and close implementation loopholes.

Industry Path Forward

The bottom line from these proposed rules is that they are designed to bring more customer service work back to the U.S. and improve accountability and service quality.

The final rules are still subject to change, and comments filed after Federal Register publication will help shape the FCC’s ultimate approach.

So, audit your foreign call reliance now. Model call volume ceilings and run pilot tests. Outbound work stays solid if you build compliance-first hybrid models.

Email sits at the center of customer service in Canada. Renewal notices, account updates, service alerts, appointments, and payment reminders all depend on it.

Email also drives broader customer interaction across contact centers, including calls, chat sessions, web self-service, and in-person visits and appointments.

When email fails, everything slows down. Contact centers feel it first because they rely on steady communication to resolve issues and keep customers informed.

Over the last few years, there has been a clear shift. Canadian organizations are paying closer attention to the privacy and governance structures behind the tools they use.

In contrast, many U.S. organizations have historically prioritized scale, cost efficiency, and feature expansion, with privacy and governance often addressed after deployment.

As a result of organizations on both sides of the border moving apart, a growing compliance gap has opened between Canadian expectations and how many U.S.-owned email platforms operate. This gap also applies to other communication channels such as voice, chat, and customer data platforms.

That gap shapes vendor selection, trust, data handling, and inbox performance. It has become a common theme in procurement conversations across government, public institutions, and regulated sectors.

Data Residency Versus Data Control

The root issue is simple: data residency is not the same as data control.

Data residency refers to where data is physically stored. Data control refers to which legal jurisdiction has authority over that data, including the ability to access or compel disclosure.

If a platform is governed by U.S. law, Canadian data can be subject to it even if the servers sit in Canada. But many Canadian public bodies will not accept that risk. More private Canadian organizations are now reaching the same conclusion. They want predictable rules and local oversight.

This reality influences how leaders think about the systems that support their email communications. It also affects how they evaluate systems that support voice, chat, and broader customer interaction channels.

Why Canadians Expect Canadian Control

Canada’s privacy framework puts consent, accountability, and transparency at the center of how personal information is handled. Organizations must explain why they collect data, how long it will stay in the system, how it is used, and who can access it.

That gap shapes vendor selection, trust, data handling, and inbox performance.

Several laws (and a proposed law) reinforce these expectations:

• The Personal Information Protection and Electronic Documents Act (PIPEDA), which sets private-sector standards across Canada.
• Bill C-27, which aimed to overhaul Canada’s federal private-sector privacy framework, did not pass before the last parliamentary session ended but is widely expected to return in a revised form.
• If that happens, it would strengthen individual rights, increase accountability obligations, and introduce stricter governance expectations, further raising the compliance bar for Canadian organizations and the platforms they rely on.
• Quebec’s Law 25, which requires explicit consent, privacy impact assessments, and strict retention rules.
• Canada’s Anti-Spam Legislation (CASL), which governs consent, opt-outs, and digital communication practices.
• Provincial sector regulations, such as British Columbia’s and Nova Scotia’s public sector data-hosting rules and Ontario’s strict health privacy requirements, add further requirements in the health, education, and the public sectors.

Canadian organizations operate under these rules every day. They expect the tools they use, especially email platforms, to match those standards. But when a platform is owned outside Canada, alignment becomes difficult. Jurisdiction follows the company, not the physical server.

This is why U.S. ownership is now treated by Canadians as a core risk.

How U.S. Laws Widen the Compliance Gap

U.S. privacy laws rely on different assumptions. The Patriot Act and Cloud Act give broad access rights to U.S. authorities, including rights over data stored in other countries – regardless of the channel used to collect or process that data – if the provider is American-owned.

On the other side, General Data Protection Regulation (GDPR) in the European Union member states and Law 25 in Quebec require strict consent rules, data minimization, and predictable governance. Many Canadian organizations expect similar protection.

This difference creates friction during vendor evaluations and onboarding. It also adds pressure on contact centers that must show full compliance to their clients and regulators.

This is not theoretical; it affects daily operations.

The compliance gap shows up in several tangible ways.

1. Vendor reviews take longer. Data ownership and foreign access exposure must be evaluated.
2. Authentication expectations rise. Sender Policy Frameworks (SPF), DomainKeys Identified Mail (DKIM), and Domain-based Message Authentication, Reporting, and Conformance (DMARC) must be configured correctly to prevent spoofing and protect customers.
3. Consent handling must match CASL. Weak record-keeping or unclear unsubscribe paths are not acceptable.
4. Procurement cycles expand. Public institutions need documentation for governance, impact assessments, and incident response.
5. Inbox placement becomes part of compliance. Gmail, Yahoo, and AI-driven filters use trust signals to determine visibility.

Email is still a communication channel, but it is also a compliance environment. Decisions behind the scenes have direct operational impact.

Why Responsible Decisions Shape Outcomes

Email platforms face steady pressure to increase volume. Allowing high-risk senders may bring short-term gains, but it creates problems later. It harms domain reputation, increases complaint rates, and pushes teams into reactive support.

Strict controls can feel limiting at first, yet they protect the infrastructure over time. They reduce operational strain and create more predictable outcomes for users who depend on stable delivery. Responsible governance often pays off long after the decision is made.

Political Volatility Increases Stability Demands

Shifts in U.S. political direction raise questions about how privacy will be treated in the future. This is increasing demand in Canada for data control under Canadian jurisdiction and reinforcing expectations that Canadian data should be governed by Canadian laws.

…leaders who approach privacy as operational infrastructure, not as a checkbox, protect their teams and their customers. They are also more likely to earn long-term customer trust and loyalty…

Canadian organizations want stability. They prefer systems governed by Canadian law because it removes uncertainties they cannot control. This preference becomes more visible each time contracts come up for renewal.

What U.S. Platforms Can Do

The future will reward organizations that treat privacy as infrastructure. U.S. platforms that want to support Canadian organizations have several practical options:

• Provide clear documentation on data governance and ownership.
• Explain how foreign access laws apply.
• Support CASL-compliant consent and unsubscribe workflows.
• Enforce SPF, DKIM, and DMARC by default.
• Offer retention controls and audit logs.
• Provide tools or templates for privacy impact assessments.
• Allow configuration paths that match Law 25 and PIPEDA expectations.

Canada’s privacy rules, and other rules (see BOX), are becoming more consistent and more demanding. Misalignment between platform governance and Canadian regulatory expectations creates operational risk, compliance exposure, and potential loss of customer trust.

Those who delay the work in alignment often face avoidable issues in delivery, service reliability, and long-term trust.

But leaders who approach privacy as operational infrastructure, not as a checkbox, protect their teams and their customers. They are also more likely to earn long-term customer trust and loyalty in a market where privacy expectations continue to rise.

We’re witnessing something unprecedented in customer support (CS) and customer experience (CX) Operations: a widening gap between what organizations say they want and what they can actually deliver.

Our recently published research, surveying these functions’ decision-makers across multiple industries between October and November 2025, reveals a market approaching an inflection point.

The data tells a story of strategic clarity colliding with operational reality, and the consequences of this collision will reshape how we think about building versus partnering for CX excellence. It points to gaps in AI implementation, and also in measurement, support, and global capability.

The Worrying Numbers

Let me share the statistics that stopped me in my tracks: 75.7% of organizations prioritize AI and automation investments in the next 12 months. That’s three-quarters of the market moving in the same direction (also see FIGURE 1).

But here’s where it gets interesting. 48.6% simultaneously struggle with agentic AI implementation, and 64.9% face limited budgets and resources.

Read those numbers again. Nearly half the organizations racing toward AI lack the capability to implement it. Two-thirds are trying to do so with inadequate resources. The result is an AI implementation gap.

This isn’t a technology problem. Instead, it’s an execution crisis.

I’ve spent years building organizations that deliver CX excellence at scale. What this data reveals is that AI investment is increasingly being mistaken for AI progress. Organizations are committing to strategies their operating models are structurally unable to deliver.

Understanding the AI Gap

Our research identifies two distinct cohorts emerging: AI-Capable organizations (51.4%) and AI-Aspirational organizations (48.6%). The difference between these groups isn’t budget size or strategic vision; it’s execution infrastructure.

AI-Capable organizations have moved beyond AI ambition into repeatable deployment. They still face complexity, but they have:

• Technical infrastructure and data foundations that support automation at scale (clean data, integrated knowledge, and operational analytics).
• Internal AI expertise and delivery capacity (dedicated owners, implementation resources, and governance to ship and iterate).
• Change management and adoption frameworks that drive agent and customer uptake (training, playbooks, and performance reinforcement).
• Integration capabilities across the existing technology stack so AI can operate end-to-end (CRM, ticketing, WFM, QA, and reporting).

These organizations tend to treat AI as an operating model upgrade: measuring time-to-value, redesigning workflows, and building feedback loops to improve outcomes over time.

AI-Aspirational organizations demonstrate consensus on AI’s strategic importance. They’ve allocated a budget. They’ve made commitments. But they lack:

• The technical infrastructure and data foundations necessary for implementation; 35.1% struggle with digitalization and advanced analytics.
• Internal expertise and implementation resources.
• Change management and adoption frameworks.
• Integration capabilities with existing technology stacks.

These organizations face a critical decision: build internal capabilities (requiring 18-24 months and significant investment) or access capabilities through strategic partnerships. Given that nearly 65% already face resource constraints, the math on internal builds becomes increasingly challenging.

Organizations are committing to strategies their operating models are structurally unable to deliver.

Taken together, these two cohorts define the AI implementation gap: nearly everyone agrees AI is the next lever for efficiency and experience. But only about half have the infrastructure, talent, and operating discipline to turn spend into measurable impact.

For AI-Aspirational teams, the risk is obvious; AI becomes a line item without ROI. For AI-Capable teams, the risk is different: the bar rises quickly, and sustaining investment depends on proving value continuously.

The Measurement Gap

Our survey reveals a fundamental shift in how organizations conceptualize CS and CX Operations value. Revenue impact and customer retention are now tied as the top CX priority, each at 29.7%, while traditional metrics like CSAT (16.2%) and NPS (10.8%) are being deprioritized.

This represents CS and CX Operations function maturation. Organizations increasingly view support as revenue drivers, retention mechanisms, and as growth catalysts: not just as cost centers.

The problem? 35.1% have difficulty measuring impact on business results, and 24.3% struggle to justify investments to C-suite executives.

Organizations have achieved strategic clarity; they understand revenue metrics matter but they lack the frameworks, data infrastructure, and analytical capabilities to measure effectively.

This measurement gap undermines investment justification efforts and resource acquisition, creating a self-reinforcing constraint cycle.

CS and CX Operations leaders who are unable to demonstrate revenue impacts will see their budget allocations decline by 15%-20% according to our projections.

This reduction in financing, but also in prestige and internal organization respect, will accelerate the talent exodus, which I will discuss next; 21.6% already perceive their departments’ functions as cost centers despite this strategic shift.

The Support Gap

The data on organizational support reveals a leadership crisis in CS and CX Operations functions. Only 18.9% of CS and CX leaders feel highly supported by their organizations, while 56.8% struggle to secure resources. The average organizational support score is just 3.7 out of 5.

Despite the rhetorical elevation of CX’s importance, leaders report insufficient resources. 64.9% face organizational resource constraints, measurement and credibility challenges, and limited strategic integration.

This tells us that most organizations operate in environments that expect high performance but provide low institutional support for their employees.

This support gap creates critical mid/senior-level management retention risks, which, in turn, could result in lowered CX and higher frontline, including contact center agent performance, burnout, and turnover.

CS and CX Operations leaders who are unable to demonstrate revenue impacts will see their budget allocations decline by 15%-20% according to our projections.

Simply put, high-pressure environments with inadequate support drive burnout. Organizations risk losing CX leadership talent to:

• Competitors offering better support and resources.
• Consulting firms seeking practitioners with real-world experience.
• Technology vendors building customer success functions.

Our research identifies four organizational maturity segments: Champions (18.9% highly supported), Progressives (37.8% well supported), Traditionalists (29.7% moderately supported), and Laggards (13.5% poorly supported).

There is an upside in our data. The concentration of organizations in the middle segments suggests significant opportunity for differentiation through CX investment.

The Global Capability Gap

Our research reveals persistent operational deficits that have real competitive consequences. 32.4% of organizations lack 24/7 coverage and 18.9% lack multilingual support capabilities, even as businesses expand digital channels and global customer reach.

The alignment between these capability gaps and outsourcing priorities is striking: 32.4% cite 24/7 coverage as an important outsourcing factor and 29.7% cite multilingual support.

For organizations that prioritize revenue impact and customer retention, these capability gaps directly undermine strategic objectives.

But organizations that recognize these deficits, and understand external partnerships, may be able to address them more effectively than those that are building them internally.

Digital channels create 24/7 access expectations while geographic expansion requires multilingual support. Competitors with these capabilities capture demand while organizations without them experience revenue leakage.

The In-House Paradox

Perhaps the most revealing finding from our CS and CX Operations survey is what I call the “In-House Paradox.”

75.7% of organizations maintain fully in-house support teams, yet the average satisfaction with current setups is just 7.0 out of 10. Only 40.5% rate their model as high performing (scores of 9-10).

Think about what this means: organizations are tolerating mediocre performance rather than pursuing alternative operational models.

A 7.0 satisfaction score signals that most in-house teams are delivering “good enough,” not excellence. In a competitive landscape where CX increasingly determines market winners and losers, “good enough” is a strategic liability.

Why do organizations cling to in-house models? Several factors drive this paradox.

• There’s concern about brand representation and customer relationship ownership.
• There are previous experiences with legacy BPO models and the call center nightmares of decades past.
• Influenced by the legacy model’s most visible failure modes: cost-driven staffing, inconsistent training, script dependency, fragmented systems, and “ticket ping-pong” that made customers repeat themselves and stretched simple issues into multi-contact journeys.
• And there’s a belief that CS and CX Operations represent core competitive differentiation requiring internal ownership.

The last point deserves scrutiny. If customer support is truly a competitive differentiator, why accept mediocre outcomes? Organizations are clinging to in-house models not because they work well, but because switching feels riskier than tolerating underperformance. That calculus is about to change.

The Path Forward

The data reveals no single path to CX excellence. Success depends on honest capability assessment, strategic prioritization, and a willingness to access external expertise where internal builds prove impractical.

Organizations that embrace hybrid models, combining internal strategic ownership with external capability access, will achieve superior outcomes compared to purely internal or fully outsourced approaches.

The key is moving beyond the In-House Paradox: the preference for internal control despite mediocre outcomes.

For CS and CX leaders, the imperative is clear.

First, audit your AI implementation capability honestly. If you’re in the 48.6% struggling with agentic AI implementation, acknowledge that reality rather than hope your way through it.

Second, quantify your measurement gaps. If you can’t demonstrate revenue impact, you can’t justify investment, and you can’t break the resource constraint cycle.

Third, evaluate hybrid models seriously. The BPO industry has evolved dramatically from the offshore call center nightmares of the past. Modern partnerships can feel like extensions of internal teams while delivering capabilities that would take years to build.

Fourth, advocate for organizational support elevation. The current support deficit (3.7/5 average) undermines execution and creates talent retention risks. CS and CX Operations leaders must make the case for strategic integration, not just operational resources.

The 2026 Inflection Point

We project that sometime this year (2026), 70% of organizations maintaining fully in-house CS and CX Operations will face critical scaling challenges due to AI capability gaps and resource constraints, forcing strategic decisions about build-versus-partner models.

Organizations that fail to resolve the AI implementation gap will also experience 25%-30% higher customer acquisition costs compared to competitors with mature AI-enabled operations.

Success depends on honest capability assessment, strategic prioritization, and a willingness to access external expertise…

The convergence of AI capability requirements, resource constraints, and evolving success metrics creates both risk and opportunity.

Organizations must resolve the fundamental tension between AI ambition and implementation capability, closing that gap, while simultaneously addressing operational gaps in measurement, support, and global capabilities and their frameworks.

The organizations that bridge these gaps, whether through internal builds, strategic partnerships, or hybrid models, will transform CS and CX Operations from operational necessities to competitive advantages.

But those that don’t will find themselves on the wrong side of an increasingly unforgiving market.

The data is clear. The clock is ticking. The question for every CX Operations leader is simple: which cohort will you be in?

The enterprise customer experience (CX) software world is changing. A quiet but intense battle has been unfolding for decades in the enterprise CX/contact center industry. At its core lies a fundamental question: Who owns the agent desktop?

For years, telephony vendors—and later contact center-as-a-service (CCaaS) providers—argued that the center of the agent experience is the voice interaction itself.

According to this perspective, every other digital channel should ultimately converge around the moments when a customer speaks directly with an agent.

If a customer emails a company and later calls about the same issue, the experience is clearly poor if the call reaches a different agent who cannot see the email context or who provides a conflicting answer.

CRM vendors, however, have long taken a different stance. Their arguments have been that the real problem isn’t voice or channels: it’s data fragmentation.

In large enterprises, customer information often exists in dozens of systems. Records are duplicated, incomplete, or outdated. Consequently, agents spend large portions of their conversations searching for the right information across multiple tools.

From this perspective, the key to efficiency is not optimizing voice interactions, but creating a single source of truth: a clean, centralized customer record that eliminates the friction caused by fragmented data.

Today’s Landscape

Fast forward to 2026, and the landscape looks very different.

If software was once said to be “eating the world,” today AI is increasingly eating the software world itself.

Companies are starting to question why they should pay millions in software-as-a-service (SaaS) seat subscriptions for functionality that can increasingly be assembled or automated using AI-driven development tools.

Recent moves by major vendors illustrate how much the boundaries between systems are blurring. For example, Salesforce recently introduced Agentforce Contact Center, a platform that effectively merges CRM and contact center functionality.

When a CRM vendor of its prominence starts expanding directly into CCaaS territory, it is a clear signal that the traditional divisions between systems are breaking down. At the same time, the priorities of CX organizations themselves are changing, as I will discuss next.

The Shift from Inbound to Outbound

Beyond the technological shifts occurring in the vendor landscape, there is another major transformation underway: the move from inbound reactive service to proactive engagement.

This shift goes by many names. Some describe it as moving from inbound to outbound. Others refer to it as proactive service, lifecycle engagement, or customer health management.

Regardless of the terminology, the core idea is the same. Instead of waiting for customers to contact you when they have a problem, businesses increasingly need to reach out to customers first.

That outreach might take the form of a phone call. It might also be a text message, a reminder, or a proactive in-app service notification. But the principle is the same. Businesses must take initiative if they want to maintain strong customer relationships.

This is not happening simply because organizations have suddenly decided it’s the right thing to do. It’s happening because market dynamics and customer expectations demand it.

Competing Against Customers’ Experiences

One of the biggest misconceptions among CX leaders is misunderstanding who they are actually competing with. Yes, if you sell cereal, jewelry, insurance, or enterprise software, your direct competitors are other companies selling those same products.

But when it comes to CX, the comparison set is far broader.

Customers don’t compare their experiences with your company solely against your industry peers. Instead, they compare them against the best experiences they have ever had anywhere.

• The convenience of overnight shipping.
• The concierge who handled everything perfectly at a boutique hotel.
• The legendary returns policy of Zappos.
• The hyper-personalized advice of a great coach or consultant.

Those experiences shape expectations across every industry.

For large organizations operating at scale—with complex processes, legacy systems, and layers of bureaucracy—competing with those experiences can feel almost impossible.

But one simple action can go a long way, and that’s proactively reaching out to customers:

• A quick check-in about a subscription.
• A call informing a customer about a new package that might better suit their needs.
• A notification that their account balance is running low.

These gestures demonstrate that the business is paying attention and is actively invested in the relationship.

Proactive engagement is not a new concept. Over the past 15 years, digital-first CX platforms have introduced many tools to automate these interactions through email, (social) messaging, and in-app notifications.

…the move from inbound reactive service to proactive engagement…because market dynamics and customer expectations demand it.

Yet despite all these digital channels, the phone remains a powerful medium. At least for the foreseeable future, many situations will still require a human conversation. And of course, nearly everyone has a phone with them wherever they go.

The challenge is that most CX organizations are not well equipped to run outbound engagement programs. To do so effectively, they may need to borrow lessons from an industry that has long specialized in this area: telemarketing.

Reading the Telemarketing Playbook

The term “telemarketing” often carries negative connotations. For many people, it evokes images of spam calls or fraudulent schemes.

Yet the reality is more nuanced. Many legitimate businesses rely on outbound calling because their customers actively want those conversations.

Millions of U.S. consumers, for example, are researching major purchases every day: new roofs, mortgage refinancing, insurance policies, solar installations, and other significant financial decisions.

These consumers often don’t have time to call multiple businesses themselves and wait in IVR queues. Instead, they welcome outreach from companies offering relevant solutions.

Customers often appreciate learning about solutions they were not aware of or discovering products that better fit their situations.

The companies that succeed in these high-velocity outbound sales environments have developed sophisticated operational capabilities. These are capabilities that modern CX teams can learn from, as I will discuss later.

The Operational Challenges of Outbound

However, running an outbound contact center comes with several persistent challenges.

1. Number Flagging

The first challenge is simply reaching customers in the first place.

Contact rates—the percentage of outgoing calls that lead to a live conversation—can range anywhere from less than 1% to roughly 15%, based on our two decades of experience.

Consumers are busy. They are in meetings, commuting, picking up their kids, or otherwise unable to answer the phone (law enforcement really doesn’t like drivers picking up their phones either).

On top of that, major carriers such as AT&T, Verizon, and T-Mobile have implemented aggressive spam detection systems. Many outbound calls are automatically labeled as “Spam Likely” or “Scam Likely.”

While these systems successfully block many fraudulent calls, they also impact legitimate businesses trying to reach their customers. When a call is flagged this way, very few recipients will answer. The result is a dramatically reduced contact rate.

2. Idle Agents

Low contact rates create another operational challenge: idle agents.

Imagine an agent manually dialing phone numbers one at a time. With the very low contact rates noted previously, they spend most of their time listening to ringing tones or voicemail greetings.

Few things destroy productivity faster than agents waiting for calls to connect with a human.

3. Compliance Complexity

Outbound calling and texting are also subject to extensive regulatory oversight:

• The primary governing laws and regulations in the U.S. are the FCC’s Telemarketing Consumer Protection Act (TCPA) and the FTC’s Telemarketing Sales Rule (TSR).
• Many states impose their own–and often stricter–calling restrictions, including permitted call windows, allowable calls per day, and additional consent requirements.
• Outbound teams must also scrub their calling lists against the National Do Not Call (DNC) Registry as well as various state registries.

Another complication is phone number reassignment for calls and texts. Over 35 million phone numbers change owners every year, according to FCC data that we’ve received. The FCC requires organizations to check their lists against the federal reassigned numbers database.

For companies trying to proactively engage customers while remaining compliant, the complexity can be daunting.

4. Fragmented Data

Finally, many outbound operations suffer from the same problem that has long plagued enterprise CX: fragmented data systems.

Agents often need to navigate multiple applications while speaking with a customer: CRM systems, billing tools, marketing platforms, and internal knowledge bases. In some environments, agents literally work across multiple monitors just to gather the information required for the conversations.

But when agents are focused on navigating systems instead of listening to the customers, the quality of the interaction inevitably suffers.

How Best-in-Class Outbound Teams Operate

Organizations that run large outbound operations successfully have developed solutions to these challenges.

1. Intelligent Number Management

At first glance, placing outbound calls might seem straightforward: simply dial a number and wait for someone to answer.

In practice, it is far more complicated.

Carriers rely on constantly evolving algorithms to determine whether numbers should be flagged as spam. These algorithms are essentially black boxes; no one outside the carriers fully understands the exact logic.

To maintain high contact rates, outbound teams must carefully manage large pools of phone numbers across different geographic areas. They must continuously monitor which ones remain “healthy” and which have been flagged.

This requires sophisticated number management systems that dynamically assign healthy numbers and rest unhealthy numbers (pausing the usage of flagged numbers) for a period of time to maintain deliverability.

The past practice of frequently replacing flagged numbers with newly bought numbers is counterproductive and might even put your business at risk.

2. Predictive Dialing at Scale

Because contact rates are so low, outbound operations rely heavily on predictive dialing technology.

Predictive dialers automatically call multiple phone numbers simultaneously and connect calls to available agents only when a human answers. This ensures that agents spend most of their time speaking with customers rather than waiting for calls to connect.

While this can be relatively easy to implement with small teams of 10 or 20 agents, it becomes significantly more complex when managing hundreds of agents and many campaigns.

While specialized outbound sales call centers operating at scale may have access to state-of-the-art technologies, the reality is that the aforementioned CX contact centers looking to shift to outbound are running on CCaaS platforms that were simply not built for outbound.

These platforms offer preview dialing, power dialing, and, in some cases, even predictive dialing functionality that simply lacks the power to ensure performance.

3. Campaign Workflow Automation

Successful outbound engagement requires more than a single call attempt. Reaching a customer often requires a structured sequence of outreach attempts across multiple channels.

This is why outbound teams think in terms of campaigns rather than individual calls. A campaign is a coordinated series of actions designed to achieve a specific objective. Outbound engagement campaigns involve a sequence of calls, messages, and follow-ups designed to reach the desired outcomes.

Modern outbound platforms provide automation tools that manage these workflows, ensuring that each contact follows the appropriate engagement sequence.

4. Built-In Compliance Guardrails

Given the regulatory complexity surrounding outbound communication, compliance cannot be an afterthought.

Organizations must implement systems that automatically enforce compliance rules: such as call time restrictions, consent management, and DNC list scrubbing.

Because the TCPA allows private lawsuits, violations often result in class-action litigation. Beyond financial penalties, the reputational damage to a company’s brand can be significant. Robust compliance guardrails, therefore, become essential for any organization operating at scale.

The Human Challenge

There is also a more subtle challenge in the shift toward proactive engagement: people.

Many contact center agents have spent years focused purely on customer service. Their role has traditionally been to solve problems, answer questions, and assist customers.

As organizations move toward outbound engagement and revenue generation, these agents are increasingly asked to adopt a more commercial mindset.

This can be uncomfortable. The image of a friendly service representative helping a customer solve a problem feels positive and familiar. The image of a pushy salesperson can feel far less appealing.

But the reality is more nuanced. At its core, selling is simply helping customers solve problems by connecting them with products or services that meet their needs.

When done correctly, proactive engagement and thoughtful sales conversations can actually improve the CX. Customers often appreciate learning about solutions they were not aware of or discovering products that better fit their situations.

To help agents with this transformation, contact center leaders should invest in two areas.

First, there will be agents for which this sales mindset will come more naturally than others. Leverage these resources as examples and coaches. Agents will learn better from peers they work with daily and respect.

Secondly, I would recommend investing in the many AI-driven QA and coaching technologies available. These AI tools allow for previously unseen levels of coverage and real-time feedback loops.

The role of the customer service agent is evolving. And so are the expectations placed on CX organizations.

The future of customer CX will increasingly require organizations to blend service, proactive engagement, and revenue generation in ways that were previously separated.

And that future will almost certainly involve picking up the phone.

Every customer has felt it. You log into a portal, try to find an answer, and get caught in a maze of automated menus, chatbot responses, and dead-end links. After a few failed attempts, the only solution is to call customer service. By the time a human answers, patience is thin and frustration is high.

That cycle of rising effort and falling satisfaction – resulting in customer friction – is now a defining feature of modern customer experience (CX). The 2025 Liferay Digital Self-Service Report reveals how deep this frustration runs.

• 68% of consumers have abandoned a digital task.
• 73% have skipped a purchase because the process was too annoying.
• 82% said they feel they are doing work that company employees once handled.

These numbers demonstrate that customers are working harder than they should to get basic things done.

This shift carries consequences that extend beyond individual interactions. When people feel they are performing labor for a brand, they lose trust. When their frustration reaches the contact center, it affects both customer relationships and the wellbeing of frontline employees.

This article explores the hidden costs of customer effort and the human toll it takes on service teams. But it also examines the opportunity organizations have to restore balance through better design, smarter orchestration, and renewed empathy.

The Cost of Effort

Customer self-service was built on the simple idea that you can give people the tools to solve their own problems quickly and conveniently.

The promise was efficiency for both sides. But over time, it has eroded. The Liferay survey found that 64% of respondents feel frustrated during digital self-service experiences, and 39% feel exhausted afterward (see CHART 1).

When customers sense that a company’s systems are designed for internal convenience rather than user success, patience disappears.

The result is dissatisfaction and emotional fatigue. A digital form that rejects an address, a chatbot that loops the same questions, or a portal that times out before completion sends the message that the customer’s time is less valuable than the company’s.

Each of these moments has a measurable impact.

• Every restart or re-entry increases abandonment rates.
• Every failed self-service attempt pushes volume to the contact center, often at higher intensity.

According to the report, 91% of users have had to restart a digital task after an error. That number represents lost time, lost trust, and increased strain across the organization.

When Customers Become Unpaid Staff

One of the most striking findings in the Liferay report is that 82% of respondents feel they are performing tasks that employees once handled. This sentiment signals a major disconnect in how companies view self-service.

Customers now routinely fill out data forms that replicate what agents once entered. They search for solutions that once came from support staff. They even manage workflows, such as submitting, verifying, and tracking claims or orders, which used to occur behind the scenes.

These activities are framed by companies as empowerment. But they often feel like unpaid labor.

When that labor fails to deliver results, frustration builds quickly. For contact center agents, this has direct consequences. Agents report facing more emotional customers than ever, many of whom are already fatigued by digital systems before they even make a call.

Automation should simplify, not shift complexity onto the users.

In CX strategy, this is known as the effort paradox. As organizations invest in automation, customers feel less supported. They encounter more steps, more screens, and less empathy.

This tension undermines brand loyalty, placing revenues at risk. It also puts additional emotional pressure on the employees who serve as the final human touchpoint, which I am going to discuss in the next section.

The Agent Experience Connection

The consequence of this effort paradox is most directly felt by the employees who are tasked with being the final point of human contact.

Contact center agents often absorb the emotional residue of poor design decisions. So, when self-service tools fail, they become the safety net for every broken digital interaction.

By the time customers reach the agents, they are already drained. These employees then spend time apologizing for systems they did not create, shouldering frustration they cannot resolve alone.

This dynamic has a cost. Emotional exhaustion is one of the leading contributors to agent attrition. Training new staff is expensive, and turnover interrupts service consistency.

More importantly, constant exposure to customer frustration erodes morale. When employees feel powerless to fix root causes, engagement declines.

But improving digital experience is both a customer and employee benefit.

• Every minute spent simplifying a process or clarifying instructions upstream reduces stress downstream.
• The more intuitive a system becomes, the fewer customers call in anger, and the more agents can focus on creating value instead of defusing tension.

I am going to explore this further on, but first let’s look at why customers experience the issues that lead to customer friction. Namely their inability to realize value from their purchases caused by difficulties in their buying and usage experiences.

Friction by Design

Many of the problems customers face, causing customer friction, are not random errors but are the result of decisions made in isolation.

• Teams optimize processes for efficiency without considering the full journey.
• A marketing department might add extra verification to reduce errors.
• A compliance team might introduce a new confirmation step.

Each change makes sense locally. But they compound friction globally.

Journey orchestration provides a path forward. In marketing, this approach maps each stage of engagement to understand how people move between systems. Applied to CX, it helps identify where confusion or duplication occurs.

• An account recovery process requires multiple password resets across platforms.
• A form designed for internal clarity demands excessive detail from customers.

These insights reveal where effort accumulates. And where small changes can have a big impact.

Designing with empathy means evaluating every step through the customer’s eyes.

• Does the interface anticipate what they need next?
• Does it remember progress if they pause?
• Are instructions clear and consistent across devices?

Questions like these translate abstract user experience (UX) goals into tangible improvements.

Making Self-Service Work as Intended

When designed well, self-service can still be transformative. Customers value speed, independence, and the ability to complete tasks without waiting for support.

The key is balance. Automation should simplify, not shift complexity onto the users.

Several principles help achieve this balance:

1. Clarity. Every interaction should have a clear purpose and an expected outcome. Ambiguity drives frustration faster than delay.
2. Context. Systems should recognize who the customer is and where they are in their journey. Prefilled forms, personalized help options, and consistent data across channels reduce repetition.
3. Connection. When digital tools fail, escalation to human assistance should be seamless. Contact information and live chat should appear within reach, not hidden behind multiple layers.
4. Feedback. Users need visible confirmation that their effort produces results. Progress indicators, status updates, and follow-up messages create confidence that the process is working.

These principles apply equally to customer-facing and employee-facing systems. The design lessons that make portals intuitive for customers also improve the technology experience for agents. When internal tools are as thoughtful as external ones, service quality rises across the board.

Redefining What Support Means

The future of CX depends on how companies define support. Automation and self-service will continue to expand, but their success will depend on empathy. Customers do not want to work harder. They want to feel capable and respected.

Organizations that invest in usability testing, accessibility standards, and cross-functional coordination are already seeing dividends.

• Their contact centers handle fewer high-stress calls.
• Their agents report higher satisfaction.
• Their customers show greater loyalty because the experience feels effortless from start to finish.

This success hinges on a shared responsibility. CX has always been a shared effort across Marketing, IT, and Service teams. The same collaboration that fuels great campaigns must now extend to the systems customers use every day. Eliminating friction requires shared ownership of design, data, and process.

When departments align around the customer journey, each decision becomes more intentional.

• Marketing ensures messaging is consistent.
• IT ensures performance and security.
• Contact centers ensure empathy and clarity.

Together, these teams can create experiences that respect customer time and reduce emotional strain on agents.

In one sense, the question “Should your customers work for you?” is rhetorical. They already do, through every login, click, and submission.

The challenge is ensuring that their effort feels meaningful rather than mandatory. When companies take responsibility for simplifying that work, they strengthen every relationship that follows.

Self-service technology will always have a role to play. The goal is not to eliminate it but to make it feel natural. When people complete a task smoothly, they leave with confidence in both themselves and the brand. That is the foundation of trust, and trust is the currency of loyalty.

The promise of digital experience has never been to make customers work harder. It has always been to make life easier. The organizations that remember that simple truth will continue to earn loyalty in a world where convenience defines value.

For decades, contact centers have lived with a frustrating paradox: despite better training, routing, and knowledge systems, first contact resolution (FCR) has barely improved. In many organizations, it has even declined.

Customers describe their issues in detail, agents follow every troubleshooting step, and yet something is still missing. Customers call back, escalations rise, and support costs quietly grow.

The reason is simple. Most AI and support tools only understand one dimension of the customer’s problem, while real-world issues are multidimensional.

To illustrate, a customer may:

• Describe an error, but the real clue is hidden in a screenshot.
• Read out a code, but the root cause becomes obvious only when the agent sees a device’s blinking pattern.
• Insist a feature is not working, but the screenshot reveals a configuration mismatch.

Voice alone is no longer enough. Text alone is no longer enough. To solve modern problems on the first attempt, contact centers need AI that can understand what customers say, what they show, and what they experience.

By merging visual and contextual clues with language understanding, multimodal AI eliminates the ambiguity that causes repeat contacts.

This is where multimodal AI becomes the missing critical pathway in modern FCR, supplying it with fresh oxygen. It gives agents the same visual and contextual cues customers rely on when they experience issues.

Traditional AI Has Hit Its Limits

Most contact centers have already invested in AI. Speech analytics, sentiment detection, auto-summaries, and knowledge recommendations are common.

But these systems nearly all share the same hidden limitation: they rely almost entirely on text. Speech becomes text. Chats remain text. Ticket notes become text.

But customers do not experience problems in text. They experience problems in screens, devices, apps, error lights, misconfigurations, environmental factors, and network behavior.

A customer can spend 10 minutes describing what a single picture could clarify instantly. This is why so many calls end with the same sentence: “I just need more information.”

This line is the death of FCR. It is where customers fall through the cracks. Not because agents are unskilled, but because their tools are not designed to capture the true context of the issues.

What is Multimodal AI And Why It Helps

Multimodal AI is a new class of systems that can understand and combine multiple types of input at once. In the contact center, this includes voice, chat, images, screenshots, video, device logs, telemetry, and contextual signals.

…the true pinnacle of successful customer service and support, enabled by agentic AI, is when the customer doesn’t need to reach out at all.

Instead of forcing the customer to translate visual information into words, multimodal AI can see the issue directly. Examples in a contact center workflow include:

• A customer uploads a photo of a router, and AI identifies the model, status lights, and likely errors.
• The customer shows an app screen, and AI recognizes missing permissions or misconfigurations.
• AI reviews a short video and detects unusual device sounds or operating patterns.
• The customer verbally explains the issue, and AI correlates the voice description with visuals and logs.

By merging visual and contextual clues with language understanding, multimodal AI eliminates the ambiguity that causes repeat contacts. And, as I will discuss later in this article, it also sets the stage for the next major evolution in customer service: agentic AI systems.

How Multimodal AI Revives FCR

Multimodal AI breathes new life into FCR by breaking the expensive cycle of incomplete information. When an agent relies solely on a verbal description, they often troubleshoot the symptom(s) described by the customer rather than the root cause visible only to the eye.

This creates a cascade of failure; the agent applies the wrong fix based on a guess, the issue persists, the customer calls back, and the cost per resolution doubles.

Multimodal AI mitigates this efficiency loss through three foundational mechanisms.

1. Eliminates guesswork from troubleshooting. Most repeat contacts occur because the initial call lacked the right context.

If an agent cannot see the problem, they are forced to rely on the customer’s interpretation. If that interpretation is wrong, the troubleshooting is wrong.

For example, a visual clue that is impossible to describe clearly – like a specific artifact on a screen or a frayed cable – becomes immediately recognizable when AI processes an image or video.

This precise diagnosis ensures the correct fix is applied the first time, preventing the “bounce back” effect that drives up support costs in:

• Hardware troubleshooting.
• Application configuration.
• Connectivity issues.
• Device setup workflows.
• Subscription or account mismatches.

Industry analyses of multimodal visual support deployments indicate reductions in repeat contacts, with reported FCR improvements averaging around 22% in select workflows.

2. Gives AI and human agents full pictures before they act. Agents perform complex work while juggling multiple tools and rapidly changing customer explanations.

• What AI agents do: Analyze visuals and logs, extract key signals, summarize findings in plain language, and detect known patterns to recommend likely fixes.
• What human agents do: Interpret the findings, apply judgment, empathy, and decision-making, confirm the path forward, and manage the customer relationship.

This partnership shortens resolution time and increases accuracy.

3. Reduces unnecessary technician dispatches. Many field visits, or “truck rolls,” happen because the contact center did not have enough information to confidently confirm the root cause remotely.

This is one of the highest costs a service business can incur. Direct operational costs range from $200 to $500. But the Technology & Services Industry Association (TSIA) reports that the true cost when factoring in vehicle depreciation, labor burden, and opportunity costs can exceed $1,000 per incident.

Multimodal AI strengthens that decision point. Here’s how.

• AI agents: Analyzes visual evidence, matches error states to known failures, validates environment or configuration issues, and predicts whether a field visit is truly required.
• Human agents: Makes the final determination, communicates next steps to the customer, and manages expectations.

Across industries, organizations using multimodal visual AI report an average of 19% reductions in avoidable technician dispatches, driven by more accurate remote diagnosis and better dispatch decisioning.

The SEE-SAY-SOLVE Framework

In this article I want to introduce the SEE–SAY–SOLVE methodology (see FIGURE 1), an original operational model for applying multimodal AI in contact center environments to improve resolution accuracy and FCR across enterprise contact centers.

• Phase 1 (SEE) visual ingestion: The AI ingests telemetry and visual inputs.
• Phase 2 (SAY) multimodal contextualization: The system interprets visual signals into structured text.
• Phase 3 (SOLVE) execution: The human agent uses these insights to guide the customer, supported by AI-recommended actions.

This model preserves human leadership and judgment while providing AI-powered clarity.

Telemetry-Based Proactive Support

While the interactions above describe a customer reaching out to us, the true pinnacle of successful customer service and support, enabled by agentic AI, is when the customer doesn’t need to reach out at all.

By integrating device and system telemetry into the SEE-SAY-SOLVE methodology, we can move from reactive ticket-handling to proactive problem-solving that connects machine detection directly to human resolution.

• SEE (The silent signal): Instead of waiting for a customer to report a “slow laptop” or “network error,” AI agents continuously monitor telemetry streams (e.g., CPU temperatures, application crash logs, battery health cycles).
• The AI “sees” the anomaly the moment it deviates from the baseline, often identifying that a hard drive is failing days before the user loses data.
• SAY (pre-emptive outreach): Once the signal is caught, the AI triggers a proactive communication flow.

If the issue is critical, the AI prepares a warm handoff summary, translating raw telemetry codes into plain English context. It doesn’t just say “Error 404”; it tells the system, “The user’s primary application has failed three times in the last hour.”

• SOLVE (The empowered handoff): This is where the connection to the human agent becomes vital. When the customer engages – perhaps via a callback triggered by the AI – they are never asked, “What seems to be the problem?”
• That’s because the human agent already has the diagnostic data on their screen. They can immediately say, “I see your device flagged a memory failure this morning. I’ve already ordered the part for you.”

This seamless thread from the silent telemetry signal to the AI’s alert and finally to the human agent’s resolution of the issue transforms support from a cost center into a trust-building engine.

For the first time, we have AI that can understand customers the way humans do: through sight, sound, and context.

Industry analysis confirms the value of this shift. McKinsey reports that AI-driven proactive engagement and communication can reduce cost-to-serve by 20% to 30% while simultaneously boosting revenue by 5% to 8%.

Where Multimodal AI Fits In

Multimodal capability integrates smoothly with existing layers in the contact center.

• Self-service and virtual assistants: AI handles intake, visual understanding, and simple resolutions.
• Agent assist: AI provides real-time context and guidance to agents.
• Post-call summaries: AI documents both visual and verbal root causes.
• Proactive support with telemetry: As described earlier, AI detects early warning signs, interprets signals, and gives agents the ability to resolve issues before customers experience failure. Thus moving operations from reactive to preventative.

Four Practical Steps to Start

1. Pinpoint high repeat contact categories: Focus on issues where agents repeatedly ask what the customer is seeing.
2. Enable image or screenshot intake: This forms the foundation for multimodal understanding.
3. Train agents on AI-generated insights: Agents remain the decision-makers. AI enhances clarity.
4. Start with one journey: A single high-impact workflow builds momentum and proves value.

The Future: Agentic Multimodal Systems

Agentic systems, with multimodal AI, are the next level of service and support and FCR. These do not just perceive and summarize problems. They can take safe, reversible actions that shorten time to resolution and reduce operational load.

This shift is accelerating rapidly; Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024.

Here’s how to clearly differentiate the roles played by AI and by people.

What future AI agents will do:

• Interpret visuals, logs, and telemetry automatically.
• Run guided diagnostics without human intervention.
• Validate device states or configurations.
• Trigger safe workflows such as resets or permission checks.
• Resolve simple issues independently.
• Prepare full context packages for human agents on complex problems.

These capabilities will allow entire categories of low-complexity contacts to become fully autonomous, improving speed and reducing cost.

What human agents will focus on:

• Complex, emotionally sensitive, or high-consequence issues.
• Situations with multiple variables or unclear signals.
• Customer reassurance, negotiation, and expectation setting.
• Oversight and approval for agentic workflows.
• Relationship building and brand experience.
• Final decision-making in ambiguous cases.

This division of labor results in a high-efficiency model. Where simple, mid-complexity issues are resolved autonomously by AI, and complex interactions are solved through AI plus human collaboration, not by human effort alone.

Agentic multimodal systems represent a natural continuation of the SEE-SAY-SOLVE model. Once AI can see and explain, the next logical step is allowing it to take carefully defined actions.

This is not speculation. The earliest forms of these systems are already emerging across advanced support operations.

Conclusion

For years, contact centers have attempted to improve FCR through better routing, training, and knowledge systems.

But the real barrier was not agent capability. Instead it was a lack of information and a lack of context.

Customers experience problems visually. Agents troubleshoot verbally. Traditional AI sits in the middle and fails to connect the two worlds.

Multimodal AI finally bridges this gap, providing that missing link. It replaces heuristic assumptions with deterministic data, gives agents the full picture instead of partial information, and enables resolution on the first attempt instead of repeated frustration.

And by doing so, multimodal AI becomes the foundation for the next frontier: agentic systems that autonomously resolve simple issues, while empowering agents to solve complex ones with more speed, more confidence, and more context than ever before.

For the first time, we have AI that can understand customers the way humans do: through sight, sound, and context. That is why multimodal AI is not just the future of FCR. It is the beginning of a fully agentic support ecosystem.

For many global brands, Latin America or LATAM is still treated as a single contact center market. It isn’t. The region spans dozens of countries with distinct languages, dialects, cultures, labor markets, regulatory environments, and levels of digital maturity.

Yet too often, many companies apply a one-size-fits-all operating model, assuming that what works in one country will translate seamlessly across the rest of the region.

Customer expectations have risen sharply across global markets. According to Salesforce, 80% of customers say the experience a company provides is just as important as its products or services, and nearly nine in 10 say strong customer service increases their likelihood to remain loyal.

For global and local decision-makers about outsourcing in Latin America, that means service quality and consistency matter as much as – if not more than – geography or cost.

BPO organizations like us are now approaching this challenge through a “total experience” lens: recognizing that customer experience (CX), employee experience (EX), partner experience, and operational execution are deeply interconnected. That gains in one area are difficult to sustain without alignment across the others.

Successful LATAM operations account for cultural differences by design.

After more than two decades building and operating contact center programs across Latin America, one lesson stands out clearly to me: success in the region depends on intentional, market-by-market design, not templates.

Understanding the Distinct Markets

In practice, successful contact center strategies in Latin America depend on understanding how national differences – from language and culture to labor dynamics and customer expectations – influence both service delivery and performance.

Recognizing and planning for these differences is one of the most important steps contact center leaders can take to deliver consistent, high-quality CXs across the region.

Brazil, for example, operates in a category of its own. Portuguese-language requirements, unique cultural norms, and local market dynamics make it fundamentally different from Spanish-speaking countries.

As a result, most U.S.-focused contact center operations in Latin America are concentrated in Spanish-speaking markets. But even within that group, there is significant variation.

And over the past decade the capability and readiness of the Spanish-speaking portion of the region has changed dramatically:

• English proficiency, digital connectivity, and operational maturity have all advanced considerably.
• Increased access to education, global media, international travel, and reliable broadband has expanded the talent pool and raised the ceiling on what LATAM-based teams can support.

Countries like Colombia and Chile illustrate how these differences translate into real-world operating decisions:

• Colombia offers scale, a large and increasingly bilingual workforce, and proximity to North American time zones, making it well-suited for larger programs.
• Chile, while smaller, delivers strong quality, stability, and cultural alignment, which can be especially attractive for small- to mid-sized operations that value consistency and long-term retention.

Beyond these two markets, other LATAM countries play more specialized roles:

• Mexico has a large domestic contact center market, though it is typically better suited for serving local demand than U.S.-focused nearshore programs.
• Argentina offers strong human capital and a capable workforce, but higher volatility and labor complexity often require a more deliberate program design.
• Panama and Costa Rica deliver high quality and geographic proximity to the U.S., but at higher costs and with limited scalability, positioning them best for niche or premium use cases.

Emerging markets such as Peru are gaining attention as wage dynamics shift across the region, offering growing English-language capability and increasing competitiveness for certain program types. Other markets, including Ecuador, remain more constrained due to labor economics and limited English-language availability.

But rather than viewing these differences as constraints, successful organizations treat them as design inputs. The opportunity lies in understanding where each market excels and aligning location strategy accordingly.

From a total experience perspective, this alignment matters not only for customers, but also for agents. Thus ensuring that teams are placed in environments where language, culture, and working conditions support long-term engagement and performance.

Where Contact Center Strategies Break Down

Organizations that succeed in Latin America tend to share a common approach: they treat transparency and precision as non-negotiable from day one.

Rather than relying on informal processes, leading BPO providers mirror North American expectations for accuracy, directness, and proactive communication across every aspect of service delivery: from reporting and escalation to issue resolution and performance management.

This level of operational discipline has become standard practice among tier one providers, and it is what separates commodity outsourcing from true strategic partnerships.

Successful LATAM operations account for cultural differences by design. Leading providers establish structured communication protocols from the outset: including North American-style reporting cadences, clear escalation frameworks, and full performance transparency.

When these guardrails are in place – which mature BPO partners now provide as standard – cultural differences become advantages rather than obstacles, bringing greater empathy, relationship focus, and customer warmth to service interactions.

Transparency matters more than perfection. Problems will happen in any operation. What customers cannot tolerate is feeling misled, underinformed, or surprised. Trust is especially fragile when teams are geographically distant — once broken, it is extremely difficult to restore.

Recent research underscores how high the stakes have become. According to the Edelman Trust Barometer, trust is now the most important factor shaping long-term relationships between organizations and the people they serve, with transparency, competence, and reliability cited as core drivers of confidence.

In distributed contact center models, where every interaction represents the brand, trust is built – or lost – one conversation at a time.

Choosing the Right Operating Model, Case by Case

The maturity of Latin America’s contact center ecosystem means buyers today have real choice and the data to make confident decisions. In-house, outsourced, and remote models can all be highly effective when aligned to specific program requirements and regional strengths.

Need scale and round-the-clock coverage? Markets like Colombia offer deep talent pools and operational flexibility. Prioritizing quality, stability, and retention for high-value interactions? Chile’s consistency excels.

Organizations that match program needs to regional strengths consistently outperform one-size-fits-all approaches, and experienced BPO partners provide this market intelligence as part of the engagement, not as a custom consulting exercise.

Remote and hybrid work has expanded the available talent pool across LATAM. Flexibility allows organizations to attract higher-quality candidates, accommodate different life circumstances, and reduce unnecessary attrition. At the same time, not every employee wants to work remotely, and not every program should.

The strongest models are adaptive. They evolve as customer expectations change, talent markets tighten or loosen, and business priorities shift.

Bringing AI Into a Market-by-Market Strategy

The same forces that have strengthened Latin America as a contact center destination (e.g., expanding talent pools, improving digital infrastructure, and rising customer expectations) are also accelerating the adoption of AI across the region.

Recent surveys indicate that a growing share of LATAM businesses are already using AI to support operations, particularly in customer analytics and front-office experiences.

At the same time, the call center AI market in Latin America is expanding rapidly, signaling that AI is moving beyond experimentation and becoming a core component of modern service design.

For BPO providers and enterprise leaders alike, the opportunity is not to deploy AI as a one-size-fits-all “LATAM” solution, but to apply it in ways that align with the strengths and realities of individual markets.

As economic and workforce conditions shift from country to country, AI can also help organizations respond more quickly. This allows leaders to rebalance capacity, adjust staffing models, and maintain service levels without disrupting CX or EX.

In higher-volume hubs such as Colombia, for example, AI agents and intelligent virtual assistants can handle routine inquiries in Spanish and English, allowing live agents to focus on more complex, value-driven interactions.

The same forces that have strengthened Latin America as a contact center destination… are also accelerating the adoption of AI across the region.

In markets where quality, stability, and long-term relationships are the primary differentiators, such as Chile, AI copilots can support agents by surfacing relevant context, recommending next-best -actions, and reinforcing consistent tone and messaging: without diminishing the human connection customers value.

Analytics is where this market-by-market approach becomes especially powerful. Across Latin America, narrowly focused AI systems are already being used to analyze large volumes of interaction data, identify behavioral patterns, and forecast demand with relatively low implementation complexity and clear returns.

When applied at the country or site level, these insights allow leaders to fine-tune training, staffing models, and channel strategies to local preferences: prioritizing messaging apps in mobile-first markets, for example. Or investing more heavily in voice where cultural nuance plays a central role in building trust.

In this way, AI does not replace the need for intentional, market-specific design in Latin America. Instead, it equips contact center leaders with the tools to execute that design with greater precision, transparency, and speed: reinforcing both customer outcomes and employee effectiveness.

The Experience Alignment Most Companies Overlook

One principle underpins all successful LATAM contact center strategies: CX cannot improve if EX is neglected. A true total experience approach recognizes that how agents are trained, supported, and empowered directly shapes how customers experience the brand.

Agents who feel respected, supported, fairly compensated, and empowered bring a fundamentally different level of commitment to their work. They take ownership, protect the brand, and fight to resolve customer issues rather than simply closing tickets.

This matters even more when supporting brands that are physically and culturally distant from the region. Agents must feel connected to the customer’s business: not just to their own job. That connection does not happen accidentally. It is built through leadership involvement, trust, transparency, and consistent communication.

When organizations align how customers are treated with how employees are treated, performance follows.

Rethinking Success in LATAM

Latin America is no longer an emerging option for contact center operations. It is a mature, capable, and competitive region, but only for organizations willing to approach it thoughtfully.

One principle underpins all successful LATAM contact center strategies: CX cannot improve if EX is neglected.

Success requires abandoning assumptions, investing in cultural understanding, and designing operating models that respect both customers and employees. Companies that do this well don’t just reduce costs: they build trust, resilience, and long-term advantage.

The future of contact center excellence in Latin America belongs to leaders who recognize that the region’s diversity is not a complication to manage, but a strength to design around.

The contact center is both the heart and the headache of customer engagement for most businesses. Managing tickets, tracking leads, scheduling agents, and monitoring performance can lead to juggling spreadsheets or paying for solutions that are too rigid or limited in their ability to adapt.

But as data volumes grow and customer expectations rise, spreadsheets can’t always keep up. They’re not designed for complex relationships between data, such as linking customers to cases or calls to outcomes. The result is inefficiency, duplication, and frustration on both sides of the conversation.

The issue is particularly acute for small-midsized businesses (SMBs). They have a great need for delivering excellent customer experiences (CXs) as part of their market differentiation, and for increasing productivity and controlling costs to grow their profitability.

This is where databases can be a powerful solution that can empower managers and non-technical employees to design complex workflows themselves, without the need for cost-prohibitive IT resources. But traditional databases have their own issues.

Spreadsheets Strain CX

Organizations that rely on legacy tools – like spreadsheets – often encounter digital bottlenecks, which limit visibility, speed, and scalability.

SMBs’ small contact centers experience these challenges every day:

• Supervisors manually consolidate call logs, quality metrics, and agent schedules across dozens of spreadsheets and forecast staffing needs without reliable data.
• Agents search customer histories that are scattered across numerous file structures and data repositories.

When different team members create spreadsheets based on their own formats and preferences, managers often spend significant time consolidating data. This can lead to manual errors and raise concerns about data accuracy.

When systems don’t talk to each other, it’s the customer who feels it first: through slower response times, missed follow-ups, and inconsistent service.

…as data volumes grow and customer expectations rise, spreadsheets can’t always keep up.

Small inefficiencies compound quickly. In an environment where a single poor interaction can damage loyalty, operational agility becomes a competitive differentiator.

The upshot is that spreadsheets are not databases. While they remain a useful starting point, relying on them as the sole system of record introduces significant risks: inefficiencies, errors, lack of scalability, and weak data security.

When used in this manner, then, spreadsheets quickly become limiting, inefficient, and prone to errors. You are asking it to do more than what they are designed for.

Enter Complex Databases

A database, in contrast, is an organized collection of structured information stored electronically for efficient access, management and retrieval. Databases can manage enormous volumes of information across sales, customer records, inventory, financial transactions, and even real-time web content.

Databases are the backbone of modern digital experiences. They power everything from online banking and eCommerce to logistics systems and social media platforms.

Today’s databases enable businesses to connect disparate data sources, enforce data integrity, and automate workflows. They allow teams to query information instantly, generate reports, and uncover insights that drive smarter decision-making.

But traditional database platforms have required complex coding – and the not inexpensive expertise to write it – to customize and adapt applications to specific needs. Coding also takes time: including testing to make sure it works. And SMBs have neither the financial resources nor the time for complex IT.

No/Low Code Alternative

No-code platforms and low-code tools offer a new path forward. They enable non-technical users—such as operations managers, team leads, or even frontline agents—to design and automate their own workflows without relying on an IT department or external developer.

• Low-code tools use graphical interfaces (aka drag-and-drop) to write customized applications, requiring minimal IT expertise.
• No-code platforms avoid having to write any code.

Imagine creating a lightweight customer relationship tracker or a performance dashboard just like designing a spreadsheet form for people to fill out: no SQL. This takes advantage of the fact that everyone is so used to spreadsheets, and there is no steep learning curve.

These tools function like familiar spreadsheets but act like databases, linking calls to customers, customers to tickets, and tickets to outcomes in a single centralized system.

That kind of visibility helps SMB contact centers move faster, spot trends earlier, and deliver a more consistent CX.

Empower the “Citizen Developer”

This trend, often referred to as the citizen developer movement, is transforming the way SMBs operate.

Gartner defines a citizen developer as “an employee who creates application capabilities for consumption by themselves or others, using tools that it does not actively forbid.”

In SMB contact centers, this can mean:

• A supervisor develops a shift scheduler to balance workloads automatically.
• A sales agent creates a follow-up tracker that syncs with email.
• A QA manager designs a scoring system that updates in real time.

Empowering staff closest to the work to create their own solutions means the business can move faster and more nimbly, freeing leadership to focus on strategy instead of troubleshooting.

This is known as “innovation at the edges”: enabling everyday employees to identify problems and develop the tools to address them.

For SMB contact centers, it’s not just about cutting costs; it’s about unleashing agility and insight from within the team. The best application is one that not only fits the team’s workflow perfectly but also stays nimble: able to take in team insights and adapt as the business landscape keeps changing.

Building a More Adaptable Business

No-code platforms show their power when businesses layer automation into their workflows:

• Like generating a follow-up email when a ticket’s status changes.
• Or alerting a manager when an SLA is about to be breached.

Such automated moments can have an outsized impact on response times, accuracy, and employee satisfaction.

Even data visualization—once the domain of analytics pros—becomes accessible. Managers can build dashboards showing call resolution rates or customer sentiment, updating live from the same central data source.

Instead of spending hours reconciling reports, managers can then focus on coaching agents and improving outcomes.

This approach creates a culture of problem-solving. Why? When employees feel empowered to design their own digital tools, they take greater ownership of the systems and data that shape customer interactions.

It’s a subtle shift—from users of technology to co-creators of it—that helps small teams punch well above their weight.

Tangible Benefits for Contact Centers

The advantages of adopting no-code tools are evident in environments where efficiency and responsiveness matter most:

• Cost savings: Build and maintain your own systems instead of outsourcing.
• Speed: Launch new workflows or reports in days, not months.
• Customization: Tailor solutions to the way your agents actually work.
• Collaboration: Maintain consistent and accessible customer data across teams.

Instead of wrangling disconnected spreadsheets, a contact center can create a single system for managing customer interactions—from initial inquiry to resolution—and easily evolve that system as needs change.

From Digitization to Differentiation

Digital transformation can feel out of reach for smaller organizations that lack full-time IT staff. But the new wave of low/no-code tools changes that equation.

The most successful organizations are those that distribute digital capabilities widely, rather than concentrating them solely in IT. For SMB contact centers, that distribution can mean better service, happier customers, and a workforce that continually refines its operations.

This approach creates a culture of problem-solving.

No-code doesn’t just modernize the contact center: it democratizes it. It empowers every team member to build, improve, and adapt, resulting in CXs that are more efficient and more human.

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. In this article, which I have divided into three parts, I focus on three skill areas where the hiring bar is rising fast.

I covered digital and AI literacy in Part 1 and emotional intelligence in Part 2. Language skills is examined here in Part 3, along with the conclusion for all three parts.

Language Expectations and Skills

Language expectations in contact centers have changed meaningfully over the last decade. Language proficiency is increasingly being treated as a core job skill rather than as a hiring preference.

In North American contact centers, this most often begins with English proficiency. But increasingly it extends to Spanish, Canadian French, and in some markets, additional languages such as Mandarin (Chinese) or Russian, depending on the customer base and delivery model.

But these rising expectations apply whether customer interactions are handled domestically or through global BPO partners serving U.S., Canadian, and other global customers.

This includes English and increasingly Spanish for domestic U.S. interactions, as well as additional languages when supporting customers across global delivery models.

Representatives may be expected to communicate not only with customers, but also with colleagues, supervisors, and partners across regions and languages.

Organizations are increasingly adopting CEFR (the Common European Framework of Reference for Languages) as a standardized way to define language proficiency.

In practical terms, a B1 CEFR level represents functional fluency with limitations, B2 represents confident professional fluency in most workplace situations, and C1 represents advanced fluency, including nuance, speed, and precision.

Language proficiency is increasingly being treated as a core job skill…

While no publicly available longitudinal dataset tracks CEFR thresholds in contact center hiring over time, my experience over the last 15 years suggests the bar has steadily risen.

Organizations are increasingly requesting higher CEFR minimums alongside additional expectations such as multilingual capability, regionally neutral speech, and stronger written proficiency.

Roles that once commonly accepted B1 or B1+ proficiency, now more frequently target B2, with many segments targeting or moving steadily toward targeting C1.

But is Proficiency Keeping Pace?

The challenge is that language supply may not be rising as quickly as language expectations.

Large-scale measurement suggests domestic U.S. and global English language proficiency has softened in recent years, even as employers increasingly standardize and raise language benchmarks.

Similar constraints appear in other languages, where supply is shaped by education systems, immigration patterns, and regional exposure rather than by employer demand alone.

This shift reflects genuine business needs. Contact centers have expanded beyond voice into chat, email, and social support (including with video), where language quality is more visible and less forgiving.

Candidates are expected to demonstrate clarity, precision, tone management, and brand alignment in real time, both in conversation and in writing. Poor communication creates friction, repeat contacts, escalation volume, and dissatisfaction.

However, the way language requirements are applied matters. When standards shift from functional clarity to subjective notions of polish or “accent neutrality,” organizations risk filtering out candidates who can do the work but do not conform to informal language norms.

Non-native speakers, immigrant populations, and candidates from global or non-traditional backgrounds may be disproportionately impacted: even when their job performances would meet or exceed expectations.

Overscreening Risks

Language measurement also introduces practical complications. CEFR is designed to describe general language proficiency across broad contexts, not the specific language demands of customer service work.

Many contact center interactions rely on a narrower set of vocabulary and grammatical structures than a generalized B2 or C1 benchmark might assume.

As a result, organizations can over-screen candidates for generalized proficiency, rather than focusing on whether candidates can perform the language tasks that matter most on the job:

• Listening accurately.
• Clarifying needs and confirming understanding.
• Explaining options clearly and concisely.
• Documenting interactions appropriately.
• De-escalating conflict effectively.

In practice, this can create a mismatch between what a job requires and what generalized proficiency testing measures.

When B2 or even C1 proficiency becomes a default standard for customer service roles, pass rates can drop sharply, even among candidates who are capable of delivering clear and effective customer interactions.

High-frequency support tasks rely on clarity, listening accuracy, tone control, and precise problem explanation. They rarely require advanced vocabulary or academic nuance.

The risk is subtle but significant: language standards can drift toward sophistication instead of clarity. When that happens, organizations are no longer screening for customer-ready communication. They are screening for linguistic polish.

Employers can end up filtering for “Shakespeare” when what the job requires is someone who can confidently say: “I apologize for the issue; let me fix it for you.”

The Staffing Pipeline Issue

There is also a broader staffing pipeline issue that is easy to overlook. Historically, contact centers served as an on-the-job language development engine across many languages.

Many candidates entered the industry with functional proficiency and built job-relevant fluency through repetition, coaching, call exposure, quality monitoring, and daily immersion.

But as automation and AI absorb more routine interactions, the remaining human work becomes more complex and emotionally demanding.

The “entry-level” opportunities that once helped people build language skills on the job may shrink, even as expectations for accuracy, tone, and written clarity continue to rise.

The challenge is not setting language standards. It is ensuring those standards are job-related, measurable, and realistically attainable.

If AI takes the entry-level work, where does the next generation of contact center talent build the skills required to do the advanced work?

Organizations have responded to these pressures with the following two strategies.

1. AI-enabled translation, transcription, and language enhancement tools.

These technologies have improved rapidly and can support comprehension, documentation, and consistency. However, they are not yet reliable substitutes for real-time customer communication in complex or emotionally charged interactions.

Automated translation often struggles with nuance, cultural context, and escalation scenarios; it still requires agents to recognize when outputs are incomplete or incorrect.

In practice, these tools tend to augment language capability rather than replace it. They shift skill requirements toward verification and judgment rather than eliminating them.

2. Translation services or nearshore and offshore BPO models to access multilingual talent pools.

These strategies can expand language coverage, but they introduce their own constraints, including cost, latency, data security considerations, and variability in customer experience.

They also relocate the language challenge rather than removing it. Language proficiency still needs to be defined, measured, and managed within those delivery models.

Importantly, these two strategies are not currently leading organizations to lower language standards. In many cases, higher CEFR thresholds were introduced because existing proficiency levels did not deliver acceptable outcomes. Those thresholds remain in place because quality pressure has not disappeared.

In my practice, I’m seeing that technology and delivery model adjustments are being used to help organizations meet elevated expectations more consistently, not to justify relaxing them.

While some organizations are exploring whether better tooling may eventually allow more flexible language requirements, CEFR benchmarks often remain high or continue to rise in the near term.

What these responses share is that they help organizations manage rising language demand, but they do not resolve the underlying pipeline issue.

As language expectations rise, organizations still need a sustainable supply of talent who can listen accurately, communicate clearly, de-escalate effectively, and document interactions correctly.

Overreliance on technology or delivery model shifts risks masking capability gaps rather than addressing how language skills are built, measured, and developed over time.

Ensuring Language Standards: And Staffing

The challenge, then, is not whether to set language standards. It is whether those standards are job-related, measurable, and realistically attainable.

If automation absorbs the work that once supported language development, organizations must be deliberate about where and how the next generation of contact center talent will build the skills required to perform the more advanced interactions that remain.

Language proficiency is a legitimate and necessary requirement for modern contact center roles. Rising expectations around clarity, tone, and written communication reflect real business needs, not arbitrary preferences. The risk is not in setting high standards, but in how those standards are defined and applied.

When generalized proficiency benchmarks such as CEFR are used as blunt screening tools, organizations risk over-filtering for linguistic sophistication rather than customer-ready communication.

As language expectations rise, organizations still need a sustainable supply of talent…

Technology, translation services, and delivery model shifts may help organizations meet elevated expectations. But they do not eliminate the need for job-relevant language skills or solve the underlying measurement challenge.

The more sustainable path is to define language requirements in terms of the specific communication tasks the job demands and to measure those skills directly.

Without that precision, rising language standards risk narrowing the talent pipeline without delivering commensurate improvements in customer outcomes.

Conclusion

Contact centers are raising skill expectations across digital and AI literacy, emotional intelligence (EI), and language at a pace that labor markets are unlikely to match.

In many regions, the pool of candidates who meet these standards is not expanding fast enough to fill demand. Especially when requirements are screened through proxies such as prior experience, subjective language norms, or impressions of professionalism.

The predictable response is to screen harder. But that is a skills race contact centers cannot win.

If organizations respond by screening harder instead of building measurable pathways to readiness, the gap between rising skill demands and available talent will continue to widen.

The burden will fall on candidates who lack traditional signals of readiness, regardless of their ability to succeed. Hiring becomes slower, more expensive, and less sustainable. Over time, performance pressure increases while workforce stability declines.

The answer is not raising the bar. It is redefining it and measuring it well. Raise standards, not barriers.

The journey of a product from creation to customer is a complex dance of precision and timing. When the choreography is flawless, the result is a seamless experience. The customer receives their order on time, the product meets their expectations, and any interaction with your company is a positive one.

But when a single step is missed – a delay in shipping, an inventory error, a breakdown in communication – the entire performance can unravel.

Every time a customer calls about a late delivery, a damaged item, or a stock discrepancy, they are handing you a piece of data.

This chain reaction doesn’t stop at the warehouse door. It ripples outward, impacting everything from operational efficiency to brand reputation, with the contact center often feeling the most immediate shockwaves.

A well-oiled supply chain is the unsung hero of exceptional customer experience (CX). It’s the invisible framework that supports every promise your brand makes.

Conversely, a flawed supply chain is a primary driver of customer frustration and operational strain. Understanding this connection is the first step toward building a more resilient, responsive, and customer-centric business.

Here, we will explore the critical link between your supply chain and your customers’ happiness. We’ll examine where things typically go wrong, how to fix them, and most importantly, how to leverage customer feedback to create a cycle of continuous improvement.

Where the Chain Breaks

Supply chains are intricate systems with numerous potential points of failure. Identifying these vulnerabilities is crucial for proactive management. While every industry has its unique challenges, several common weak links appear across the board.

1. Inventory Inaccuracies

One of the most frequent culprits is a mismatch between digital records and physical stock. This can stem from manual data entry errors, poor system integration, or a lack of real-time visibility.

The consequences? Here’s an all-too-common example:

A customer orders an item that your system says is available, only to be told later that it’s out of stock. This immediately creates a negative experience and triggers a call or email to your contact center, increasing their workload with a problem that was entirely preventable.

2. Last-Mile Delivery Hiccups

The final leg of the journey – from the distribution center to the customer’s doorstep – is often the most fraught with peril. It’s also the part of the process most visible to the customer.

Delays caused by inefficient routing, carrier issues (that can include accidents, equipment problems, labor disputes, lack of crews, weather), lengthy Customs checks, or incorrect address data can turn an anticipated delivery into a source of anxiety.

When an order is late or lost, the customer doesn’t call the shipping carrier; they call you. Your contact center agents are then tasked with solving a logistics puzzle they have little control over.

3. Poor Supplier and Partner Communication

Your supply chain is only as strong as its weakest partner. A breakdown in communication with a supplier, a third-party logistics (3PL) provider, or a freight forwarder can bring operations to a halt.

A missed production deadline or a delayed raw material shipment isn’t just an internal problem. It directly translates to unfulfilled customer orders and a surge in “Where is my order?” (WISMO) inquiries, which are a significant and costly burden for contact centers.

Mitigating Supply Chain Disruptions

When a disruption occurs, the immediate goal is to resolve the customer’s issue. However, the long-term strategy must focus on prevention. A purely reactive approach is a recipe for escalating costs and eroding customer trust.

The first step is gaining end-to-end visibility. It’s impossible to manage what you cannot see.

Implementing technology that provides a single, real-time view of your entire supply chain – from procurement to final delivery – is no longer a luxury; it’s a necessity. This allows you to spot potential bottlenecks before they become critical failures.

Next, diversify and strengthen your partnerships. Relying on a single supplier or carrier creates significant risk.

Building a robust network of trusted partners provides flexibility and redundancy. When one route is disrupted, you can quickly pivot to another. Clear service level agreements (SLAs) and collaborative communication channels ensure all parties are aligned and accountable.

Finally, automate where it makes sense. Manual processes are prone to human error.

Automating tasks like data entry, inventory tracking, and order processing not only increases efficiency but also dramatically improves accuracy. This reduces the likelihood of stockouts and fulfillment errors, directly cutting down on the volume of problem-related inquiries reaching your contact center.

The Overlooked Goldmine: Your Contact Center Data

Many organizations view their contact center as a cost center: a necessary function for handling complaints.

This is a missed opportunity. Your contact center is one of the most valuable sources of business intelligence you have. It’s a real-time listening post that captures raw, unfiltered feedback directly from your customers about what is and isn’t working.

Every time a customer calls about a late delivery, a damaged item, or a stock discrepancy, they are handing you a piece of data.

…getting the supply chain right means recognizing that it’s not a back-office function, but a core component of your CX strategy.

When analyzed collectively, these individual data points reveal patterns. Are you seeing a spike in calls about damages from a specific shipping lane? Are customers in a particular region consistently reporting delays? This is not just customer feedback; it’s critical supply chain intelligence.

By integrating contact center data with your supply chain management systems, you can create a powerful feedback loop.

Agents can do more than just apologize for issues; they can categorize the root causes. Advanced analytics can then transform this categorized data into actionable insights for the logistics team, enabling them to address systemic problems rather than just individual symptoms.

Best Practices for a Resilient, Customer-First Supply Chain

Building an organization where the supply chain serves the customer requires a strategic and integrated approach. It’s about more than just moving shipments efficiently; it’s about aligning your entire operation around the end-user experience.

• Unify Your Technology Stack. Ensure your eCommerce platform, inventory management system, and CRM applications are seamlessly integrated. A unified view of data eliminates silos and provides a single source of truth for everyone: from the warehouse manager to the contact center agent.
• Empower Your Contact Center. Give your agents visibility into the supply chain. When they can see real-time order status, inventory levels, and shipping information, they can provide customers with accurate, immediate answers instead of escalating inquiries. This resolves issues faster and improves first contact resolution (FCR) rates.
• Establish a Formal Feedback Loop. Create a structured process for channeling insights from the contact center back to the supply chain and operations teams. Hold regular cross-departmental meetings to review trends in customer complaints and collaboratively develop solutions.
• Invest in Predictive Analytics. Move beyond tracking what happened and start predicting what will happen. Leveraging AI and machine learning can help you anticipate demand fluctuations, identify potential delivery risks, and optimize inventory levels to prevent both stockouts and overstocking.

Ultimately, getting the supply chain right means recognizing that it’s not a back-office function, but a core component of your CX strategy. Each step in the process is a domino that, if it falls, will inevitably impact the next, with the final one landing squarely on your customer.

By building a transparent, resilient, and data-driven supply chain, you are not just optimizing logistics; you are building a foundation for lasting customer loyalty and sustainable growth.