The AI implementation plateau is holding businesses back

Dvir Hoffman at CommBox explains why many deployments of artificial intelligence systems fail

 

AI in customer service is advancing fast, but for many companies, the excitement of deployment fades into frustration when results fall short. Hidden challenges in scaling, integration, and security often stall progress, leaving business leaders uncertain about the next steps.

 

Our research shows that only 45% of deployments meet targets in the first year, and over 30% face security and compliance setbacks. This is what I call the ‘implementation plateau’. 

 

 

Why AI deployments stall

Our recent survey revealed that, despite 90% of brands planning to integrate AI across their service function, only 15% use the technology extensively. Businesses often start by deploying AI for simple, repetitive tasks, which can yield quick wins in productivity and efficiency.

 

However, scaling AI beyond these initial applications to scheduling, account management, and purchasing presents significant challenges, often leading to frustrating customer experiences, such as chatbots that struggle to resolve inquiries effectively. In today’s AI era, customer inquiries go far beyond simple Q&A to completing actions.

 

Without the right approach, productivity plateaus, and AI delivers diminishing returns rather than driving meaningful improvements. 

 

Several factors contribute to these limitations, particularly when tackling more complex tasks. Security concerns topped the list of barriers to fully adopting AI (32%), as evolving compliance requirements and data privacy issues add further friction to the process. Similarly, a Salesforce survey found that 84% of CIOs believe AI will be significant to their businesses, yet implementing proper security measures and data quality is often cited as a hurdle.

 

The barrier here is usually a lack of advanced automation and integration capabilities to perform the required back-end business tasks securely, such as authentication or sensitive data integration. If you can’t securely authenticate who you’re speaking to through a bot, how can you expect to automate most of the inquiries you receive and ramp up the level of automation you can provide? 

 

As mentioned, integration challenges can also cause progress to plateau quickly. To unlock AI’s full potential, it must function seamlessly across various channels, something many businesses underestimate. Legacy systems, data silos, and incompatible technologies create friction, turning a simple deployment into a more complex, time-consuming process.

 

In customer service, effective AI agents rely on real-time access to data across multiple systems and channels to provide accurate, context-aware responses. Without this connectivity, AI struggles to pull relevant customer history, transaction details, or past interactions, leading to fragmented experiences and unresolved inquiries.

 

 

Pushing past the AI ceiling

Breaking through the barriers in the early phases of adoption can push your productive output into the next stage. The key is to rethink customer service automation, not by rushing into full automation but by fostering human-AI collaboration.

 

Businesses shouldn’t have to choose between human agents and automation; instead, they should focus on implementing AI that can fully resolve complex inquiries while ensuring human agents step in where they provide the most value. 

 

Building strong security frameworks from the start is a long-term but essential strategy to ensure compliance with evolving regulations. A secure foundation not only protects data but also enhances AI’s ability to handle complex, authenticated interactions with accuracy.

 

Additionally, seamless integration across systems is critical, breaking down data silos and leveraging APIs ensures AI can access real-time information, leading to more efficient and context-aware customer interactions.

 

Focusing on scalable, secure, and well-integrated solutions enables businesses to break free from the ‘implementation plateau’ and move toward continuous innovation. Beyond efficiency gains, this shift unlocks the potential for AI-driven personalisation, more sophisticated natural language processing (NLP), and adaptive systems that evolve with customer needs.

 

Organisations prioritising seamless data integration and security from the outset will be better positioned to leverage AI not just as an automation tool but as a strategic asset, one that enhances customer engagement, streamlines operations, and drives long-term competitive advantage.

 

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Dvir Hoffman is CEO of CommBox

 

Main image courtesy of iStockPhoto.com and Sean Anthony Eddy