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.
22 CONTACT CENTER PIPELINE
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.
AS LANGUAGE EXPECTATIONS RISE, ORGANIZA- TIONS STILL NEED A SUSTAINABLE SUPPLY OF TALENT...
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.
CONTACT CENTER SKILLS
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.
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.
April Cantwell, Ph. D., is Director of People Science at Harver, where she helps organizations turn hiring data into better decisions and better outcomes. For more than 20 years, she has worked at the intersection of applied research and real-world talent strategy, specializing in assessment design, workforce analytics, and practical, evidence-based hiring.