Having worked with companies across healthcare, retail, telecommunications, and regulated industries to implement artificial intelligence at scale, I've observed a concerning trend: Organizations rushing to deploy comprehensive AI solutions without considering the strategic advantages of a diversified approach. While the promise of end-to-end AI automation is compelling, the most successful contact centers I've encountered are adopting a multimodal AI strategy by combining different AI technologies strategically rather than betting everything on a single solution.
Just as financial advisors recommend diversified investment portfolios, smart contact center leaders are discovering that different AI capabilities serve different purposes. Simple rule-based automation handles routine tasks efficiently and cost-effectively, while sophisticated AI tackles complex problem-solving scenarios that require current technology functionalities like real-time reasoning and decision-making.
In my experience working with companies processing millions of interactions, this portfolio approach delivers the following three critical advantages:
- First, it provides operational resilience; when one AI system encounters limitations or latency, others can maintain service continuity.
- Second, it reduces costs by avoiding over-engineering routine interactions with complex, expensive AI when simpler solutions are sufficient. For straightforward tasks, you can use lighter models and reserve more advanced models only when deeper reasoning is required.
- Third, it enables gradual implementation, reducing the operational risk that comes with wholesale system replacements.
The most sophisticated implementations I've encountered use AI that can reason through problems in real time, making autonomous decisions based on conversational context rather than only following predetermined scripts. However, these advanced capabilities are deployed strategically for interactions that truly require them, while simpler automation handles the majority of routine inquiries through chat or voice.
Voice Requires a Different Strategy
Enterprise experience has made one thing clear: voice interactions demand a different architectural approach. Unlike chat, where short delays or structured inputs are tolerated, voice requires real-time comprehension and response, with the ability to manage nuance, emotion, and conversational flow naturally. Customers expect faster resolution times and more human-like exchanges; standards that traditional chatbot frameworks simply cannot meet.
Organizations that attempt to stretch text-based AI solutions into voice often run into performance gaps. Processing delays acceptable in chat become conversation-stoppers in voice. Structured dialogue trees that work in text collapse under the free-form nature of human speech.
Stronger results come when companies build on their existing contact center platforms rather than attempting wholesale replacements. By layering purpose-built voice AI capabilities onto proven infrastructure, organizations can accelerate transformation without disruption, delivering natural, real-time experiences that meet customer expectations today while laying a scalable foundation for tomorrow.
The Enterprise Reality Check
What separates demo-ready AI from enterprise-ready AI becomes apparent at scale. In regulated industries like healthcare and financial services, accuracy rates below 99 percent create more problems than they solve. A single mishandled interaction can trigger compliance issues, damage customer relationships, or create liability concerns that far outweigh any efficiency gains.
During one healthcare implementation, we discovered that the organization's chatbot performed adequately for general inquiries but completely failed when handling appointment scheduling, insurance verification, and clinical triage--the interactions that mattered to patients and generated revenue for the practice. The solution wasn't to abandon AI but to implement a multimodal, hybrid approach that deployed appropriate AI capabilities for each interaction type.
Similarly, a major retailer found that their customer service AI handled product questions effectively but struggled during peak seasons like Black Friday and Christmas when customers needed complex order modifications, returns processing, and technical support with tight deadlines. By implementing a strategic mix of AI capabilities--simple automation for routine inquiries, sophisticated reasoning AI for complex problems, and seamless escalation to human agents when needed-- it achieved both efficiency gains and improved customer satisfaction scores.
Building Your Evaluation Framework
When assessing multimodal AI strategies, I recommend focusing on the following four critical capabilities:
- First, evaluate real-time decision-making ability; can the AI reason through problems autonomously, or does it simply follow decision trees?
- Second, assess platform integration flexibility; how well does the solution enhance your existing contact center investments?
- Third, examine compliance readiness, particularly if you operate in regulated industries where accuracy and auditability are non-negotiable.
- Finally, consider enterprise scalability; solutions that work for thousands of interactions often break when handling millions.
The most effective implementations also maintain graceful degradation, when sophisticated AI encounters limitations, the system seamlessly transitions to simpler automation or human agents without disrupting the customer experience. This requires careful orchestration of different AI capabilities, but the operational resilience it provides is invaluable.
As you evaluate your AI strategy, resist the temptation to seek a single solution for all customer service challenges. Instead, consider how different AI capabilities can work together to create a more robust, efficient, and customer-friendly operation. Start with clear identification of your interaction types and complexity levels, then match appropriate AI capabilities to each category.
The future of customer service isn't about choosing between human and AI; it's about orchestrating the right combination of capabilities to deliver exceptional experiences at scale.
Marie Angselius-Schönbeck is chief impact officer of teneo.ai, where she leads go-to-market strategy for agentic artificial intelligence solutions. She has extensive experience helping companies transform their contact center operations through intelligent automation.