The Asymmetric Agentic Capability Divide

Customer communication is undergoing a massive transformation. Businesses are rapidly adopting AI-driven agentic capabilities to optimize costs and scale interactions. But this change is largely one-sided: companies expect customers to interact personally with these agents - while many customers simply don’t want to. This raises an important question: what if the future of communication is not about making machines sound more human, but about giving customers their own agents? 

Traditional customer service has always been expensive. Every hotline call costs businesses real money - studies estimate between $7–20 per call depending on complexity (source: Liveagent). To cut costs, organizations are now leveraging large foundation models (LFMs). These systems can not only process text but also generate natural-sounding voices with emotional nuance, reducing the need for costly human call agents. 

What started with simple rule-based menus has evolved into AI-powered “agents” capable of intent recognition, slot filling, and human-like dialogue. From customer service hotlines to lead generation, fraud prevention, and even brand marketing, AI agents are being deployed at scale to replace or augment humans. 

Modern Customer Engagement Platforms (CEPs) use predictive models to select the most effective communication channel -voice, chat, or email - based on customer behavior. The voice agents we now encounter are not just rule-based systems but hybrids that combine LLM-driven natural language processing with contextual emotional expression. 

Their purpose? To replicate human service agents as closely as possible, while keeping operational costs drastically lower. A human agent can be 10–20x more expensive than a virtual one, and businesses are eager to push this substitution further. 

But here lies the problem: replicating the “human feel” doesn’t guarantee a better customer experience. 

From the customer’s point of view, these interactions rarely bring joy—whether with a human agent, a rule-based bot, or a sophisticated LLM-driven system. The reality is simple: customers don’t seek conversations, they seek results. 

• I want to book a doctor’s appointment. 
• I want to fix my wife’s broken phone. 
• I want a refund processed quickly. 

That’s it. Many customers don’t care if the system has empathy, a pleasant voice, or mimics natural pauses. What matters is speed, accuracy, and resolution. 

The irony is that even with human agents, the service often feels rule-based and scripted, constrained by processes businesses have predefined. Which begs the question: if the human element has already been reduced to a scripted interaction, why not empower customers with their own agents to handle this for them? 

Today, there is an asymmetry in agentic capabilities: 

• Businesses deploy armies of AI-driven agents to engage customers. 
• Customers lack equally powerful agents to manage these interactions on their behalf. 

The next technological era will shift toward symmetry where customers also have personal AI agents that handle communication for them, among many other tasks. Imagine a world where your AI negotiates directly with a company’s AI, using optimized machine-to-machine protocols to resolve your issues in seconds. 

This is where companies like Apple (Siri) and Google (Assistant) may step in, since they already own the “last mile” of customer interaction. But startups have been slow to prioritize consumer-side agents, focusing instead on business applications. 

For businesses, this evolution means that customer access will become harder. As personal AI agents shield us from unsolicited communication, traditional marketing and outbound calls will lose effectiveness. Recommendation systems trained on historical customer journeys will also struggle. 

The focus will shift toward indirect influence: 

• Delivering exceptional product experiences that encourage repeat purchases. 
• Building frictionless resolution systems that reinforce trust. 
• Avoiding an “agent vs. agent arms race” where customers feel adversarial toward brands. 

Businesses that fail to anticipate this shift risk alienating customers and driving them to adopt counter-agents faster. 

If you’re considering AI-driven agents to cut service costs, pause and rethink. Instead of focusing solely on cost reduction, design communication flows that are: 

• Valuable: Deliver outcomes customers truly need. 
• Simple: Minimize friction and wasted time. 
• Adaptive: Use AI not only to simulate empathy but to optimize outcomes in real time, like adjusting refunds, offers, or resolutions based on long-term value, not rigid rules. 

Ultimately, customer communication should not be a divide, it should be a unification of goals. Businesses and customers both win when agents are designed to resolve problems effectively, not just cheaply. 

The asymmetric agentic capability divide is one of the defining challenges of the next digital era. At Tallence AG, we believe the future lies not in making machines appear more human, but in creating symmetrical ecosystems where both businesses and customers benefit equally from agentic capabilities. One obvious fact: TelCo´s are naturally positioned to integrate services into communication channels. A big opportunity would be to deliver that customer-side agent via unified communication platforms such as Thor from Tallence AG. 

The question isn’t whether this future will arrive, it’s whether businesses will be ready when it does.