Contact Center AI: Myths vs. Facts and What to Expect Next
We've heard for years that contact center AI will transform customer service. But between the transformative ideas of the future and what CX frontline teams are experiencing every day, there is a reality that no one is talking about.
This report examines the current state of contact center AI, describes how to get your staff and procedures ready for the next three to five years, and offers a framework for wise, calculated AI investments.
An AI Reality Check
While there is no denying AI's long-term potential to provide faster service, more intelligent workflows, and lower costs, the short-term reality tells a different story.
AI Customer Acceptance Is Still Low Due to Ineffective Solutions
Customer expectations are frequently not met by AI-powered self-service. This is because most frontline AI service solutions are inexpensive RAG (Retrieval-Augmented Generation) systems that are generating conversational-style responses but ultimately just regurgitating information from a knowledgebase based on key words that the customer communicates.
The reality is that customers typically only get in touch with a contact center once they’ve already exhausted all online resources and scoured the FAQ section of a company’s website. At this point, customers need more than a standard FAQ retrieval but instead, that’s exactly what they get, often sending them into a frustrating continuous loop of repeating the same question that doesn’t get answered.
Much better AI systems exist, but…
Agentic AI Still Costs More Than Offshore Staffing
Higher-quality agentic AI implementations are still more expensive than traditional offshore outsourcing, even with the promise of better efficiency. The total cost of ownership of these solutions is increased by the need for massive amounts of processing power, licensing fees, data integration, custom development, and the internal resources needed to oversee AI projects.
Agentic AI Still Hallucinates
Even when companies are willing to accept the high costs associated with agentic AI, they must also be willing to tolerate the potential for it to make mistakes. AI “hallucinations” still occur and the technology, while improving, is still imperfect. Particularly in highly regulated industries such as banking and healthcare there is no margin for error.
Regional Accents Are Beyond the Reach of Automation
AI solutions frequently fall short of language expectations for contact centers in Canada. The majority of auto translation platforms use Parisian French which fails to capture the unique idioms, accent, and cultural nuances of Quebecois French, leading to a subpar and less connected customer experience. While AI automation can streamline many aspects of customer service, it cannot fully capture the nuance of regional accents, dialects, and speech patterns that shape authentic human communication. Different countries, regions, and even communities within the same country can have different native accents. Accents carry identity, warmth, and trust. AI automation may approximate speech, but it cannot fully replicate the cultural authenticity that comes from a human voice rooted in place.
People Continue to Be the Differentiator Despite Tech Advancements
End-to-end AI contact center solutions are already beginning to revolutionize the industry by helping live agents with call summaries, offering knowledge base prompts, and suggesting ways to resolve issues more quickly. However, when every company has access to the same technology, competitive advantage disappears. Instead of relying on technology for differentiation, companies will set themselves apart by how well their employees use the tools.
More Government Regulation on AI May Be Coming
While it’s easy to imagine human contact center agents reserved for only the most premium or luxury brands, government intervention may demand that every contact center offer the option of human agents (or at least be required to disclose when human agents are not being used). The “Keep Call Centers in America Act of 2025,” introduced in August 2025 by Senators Ruben Gallego and Jim Justice, aims to protect U.S. call center jobs and give consumers more transparency when interacting with customer service. The bipartisan bill would mandate that center workers disclose their location and whether AI is in use and allows customers to request a human representative. The bill seeks to address job displacement from both AI automation, safeguard American consumers’ data, and maintain service quality, earning support from the Communications Workers of America.
AI FOMO Leads to Poor Decision Making
AI-related FOMO can result in hasty choices and expensive mistakes.
According to Gartner, 40% of agentic AI projects will be abandoned by the end of 2027 because of growing expenses, a lack of clear business value, or insufficient risk controls.
According to an MIT Sloan Management Review, projects that are rushed because of AI hype can cost up to 3.5 times as much as those that are carefully planned.
According to a PwC study, 42% of workers oppose adoption when AI is introduced poorly or is irrelevant.
Adopting AI without a strategic plan not only wastes money but also causes conflict within the company, lowers morale, and postpones the changes that will have the biggest impact on service. Because of this, a recent US Census survey showed that companies are on average dropping their investment in AI this year by half a percentage point. This is the first decline in AI adoption since 2022.
Future-Proofing Your Business in a World of AI
Despite the current, short-term limitations of contact center AI, there’s no escaping that it’s poised to become more and more sophisticated. We can expect a point in the near future when AI adoption is no longer optional. Here’s how to best prepare.
Beware of AI Buzzwords
A large portion of today's "AI-powered" marketing is simply rebranded process automation technology that has been in use for years. These tools are helpful, but they don't always provide the intelligence, learning, or adaptability that the term "AI" suggests. By rebranding existing products, such as chatbots, AI assistants, and robotic process automation (RPA) without significant agentic capabilities, many vendors are adding to the hype. Gartner has even coined a term for this practice, referring to it as "agent washing.”
“Only roughly 130 of the thousands of agentic AI vendors are legitimate.”
In light of this, it's critical to assess technologies based on their true capabilities rather than their marketing speak. To that end, ask questions such as these when speaking to technology vendors:
Does the software change in response to data?
Does the solution merely automate processes or does it produce new insights?
Is the technology bolted on or integrated into agent workflows?
Beyond hype, are there actual business results?
Resist the Temptation to Take a Reactive Approach to AI
A hasty, overly reactive approach to AI leads to employee burnout, wasteful spending, and high failure rates. However, many businesses continue to make these common errors:
Investing in AI tools without a well-defined use case, return on investment plan, or an anchor to real KPIs
Integrating AI platforms without coordinating them with current processes or treating AI as a side project separate from the overall business strategy
Skipping structured upskilling and adoption plans resulting in tools such as chatbots becoming underused toys
Neglecting the need for human safety nets
Ignoring hidden ownership costs including evaluation, maintenance, and security and privacy hygiene
The good news is that there is a structured path forward to avoid many of these pitfalls.
“Skipping structured upskilling and adoption plans result in tools such as chatbots becoming underuse toys.”
What Effective AI Adopters Do Differently
Businesses that truly benefit from AI adopt a completely different strategy. They use AI to supplement human agents rather than replace them. And they start small with clearly defined pilot programs.
Early in the process, effective adopters create cross-functional teams (CX, IT, compliance, and HR), incorporate change management and agent training, and strike a balance between the application of technology with human insight and feedback.
This methodical approach reduces the risk of failed initiatives while building buy in and quantifiable results.
Six-Step Framework: Developing Your Contact Center AI Business Case
Prior to starting any contact center AI project, consider this framework:
1. Make the Goals Clear
Clearly state your goals for AI. Is it faster service? Lower labor expenses? Better assistance from agents? Focus on results rather than features.
2. Determine Use Cases with High Impact
Prioritize use cases for which AI has the potential to yield the greatest results such as Tier 1 inquiry triage, post contact notes, or agent guidance with live recommendations.
3. Map Existing Workflows
Prior to introducing new tools, ensure you fully appreciate and understand how work is currently done. Unless a particular workflow is especially clunky and inefficient, AI solutions shouldn’t interrupt the flow, but rather blend in with it.
4. Provide a Financial Justification
Calculate the initiative's total cost (tools, integration, training, and support) and project the expected return on investment. Be realistic and cautious.
5. Evaluate Risks and Readiness
Analyze teamwork, data quality, compliance considerations, and technology infrastructure. Determine any potential areas of friction or resistance.
6. Start a Pilot and Expand
Begin with a controlled, limited pilot. Before growing, measure results, get input, and improve the model.
This roadmap guarantees that AI investments are well-considered, long-lasting, and in line with corporate objectives.
From Uncertainty to Readiness
AI adoption is a fundamental shift that requires clarity and can be difficult to navigate without the support of a trusted partner. Here, contact center outsourcers are essential as both strategic advisors and service providers. They assist in lowering the risk of AI adoption and accelerating tangible outcomes by helping clients with workforce development, change management, roadmap planning, and technology selection.
AI enhances the future contact center, but humans still drive it. It's time to prepare.
About the Author
Mike is a seasoned IT entrepreneur and technologist with over 20 years of experience. He joined NQX in 2016 to lead our Business Intelligence unit and in 2020 was promoted to his current position, overseeing NQX’s IT and network infrastructure, its support services in addition to its business intelligence capabilities. Mike holds a Bachelor in IT Management from the Université du Québec à Montréal.