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Overcoming the Top 6 Generative AI Risks Holding Back Business
Artificial intelligence (AI) has shifted from being a futuristic concept to a present-day necessity for businesses looking to enhance customer experience (CX). While AI’s potential to streamline operations, reduce costs, and personalize interactions is clear, enterprises are facing a host of challenges when it comes to successful deployment—particularly with generative AI (GenAI).
In this post, we’ll explore the top six obstacles to generative AI implementation in today’s customer experience landscape and offer practical solutions to overcome them.
1. Worries About Impact on Customer Interactions
Businesses are concerned that AI will depersonalize their customer interactions, creating a disconnect between brands and consumers. We recently conducted a CX survey on the generative AI landscape and found that the number one obstacle facing 71% of companies is worries about the impact of AI on customer interactions. There is a significant fear of damaging brand reputation and losing customer trust. For AI to succeed in CX, transparency is essential. Clear indicators that customers are engaging with an AI system, coupled with the option to speak to human representatives, can mitigate this concern. Additionally, ongoing feedback loops will help ensure that AI systems remain aligned with customer needs.
2. Skepticism Around Commercial Readiness
Despite growing investments, many businesses are still unsure whether AI is truly ready for commercial use, especially in CX operations. According to our research, less than 40% of respondents feel confident that AI technology is ready, and 33% are not confident it will be a major disruptor to customer experience. This hesitation stems from past experiences with overhyped technologies that didn’t deliver. Overcoming this requires starting small—deploying AI in specific pilot projects where results can be measured, optimized, and scaled incrementally. Addressing AI’s limitations, such as accuracy in complex customer inquiries, is critical to building confidence.
3. Data Integration and Lack of GenAI-Ready Knowledge Bases
One of the biggest hurdles is the lack of AI-ready knowledge bases (KBs) and integrated data systems. AI systems must pull from unified, accurate datasets to function effectively. Many companies struggle to integrate data from various CX tools, leading to poor performance in answering customer queries. Without clean, organized, and relevant data, AI systems can’t fulfill their potential, frustrating customers with inaccurate or irrelevant responses. Organizations must invest in unifying their data systems and improving data governance to maximize the value of AI.
4. Security, Privacy, and Governance Concerns
AI systems are inherently risky when it comes to data privacy and security. With personal customer information at stake, a full 50% of respondents in our CX survey indicated security concerns as a key factor behind their hesitation, while another 37% are specifically worried about governance within a complex regulatory landscape, particularly with diverse regional privacy laws across the US, Europe (GDPR), and Asia. Establishing robust governance frameworks and complying with region-specific laws is crucial to avoid legal pitfalls while maintaining trust with customers.
5. Lack of AI Expertise and High Implementation Costs
The shortage of AI experts remains a significant barrier, compounded by the high costs of implementation. AI deployments often require substantial upfront investments in infrastructure, talent, and system alignment across both technical and business teams. For many organizations, the lack of internal expertise means that they struggle to even get AI off the ground, let alone optimize it for scale. Exploring phased approaches and forming partnerships with experienced AI vendors can help ease these burdens while delivering value incrementally.
6. Too Many AI Choices and Lack of Enterprise-Grade Solutions
The sheer number of AI tools, suppliers, and solutions available today can overwhelm companies looking to implement GenAI in CX. Each vendor offers different capabilities, making it difficult for organizations to select the right partner for their unique needs. This uncertainty often leads to indecision, delaying AI deployment and diminishing the competitive advantage AI could offer. In fact, roughly 32% of survey respondents indicated that it was a lack of knowledge or indecision about generative AI suppliers that was holding them back. Compounding this issue is the lack of proven, enterprise-grade AI applications that are scalable, reliable, and customizable enough to meet the diverse needs of large companies.
How Concentrix and iX Helloâ„¢ Can Help
Despite the many challenges, the potential rewards of AI in CX are transformative—improved efficiency, faster response times, and deeper customer insights. But how can businesses overcome the obstacles mentioned? Concentrix offers a path forward through our iX Hello solution.
iX Hello is a customizable, no-code GenAI-powered virtual assistant designed for rapid deployment across enterprises. Within days, companies can implement AI solutions that integrate seamlessly with their existing data, providing virtual assistants for tasks ranging from data analysis to internal self-service. By addressing the problem of too many AI choices, iX Hello offers a simplified, scalable solution that aligns directly with a business’s specific needs—without the need for extensive AI expertise. Additionally, it allows businesses to offset high initial investments through phased implementation, helping to reduce upfront costs.
Ultimately, iX Hello provides the speed, flexibility, and security needed to thrive in today’s AI-powered landscape. It bridges the gap between AI’s potential and the realities of successful AI implementation, allowing businesses to enhance their CX operations without the common pitfalls that have stymied AI adoption across industries.
Guillermo Zuniga
Senior Director, AI Solutions
