Common Challenges When Implementing AI In Support

AI models like ChatGPT and Claude are powerful, but they aren’t perfect. They can sometimes produce inaccurate, biased, or misleading answers due to issues related to data quality, training methods, prompt handling, context management, and system deployment. These problems arise from the complex interaction between model design, user input, and infrastructure. Here are the main factors that explain why incorrect outputs occur: 1. Model Training Limitations AI relies on the data it is trained on. Gaps, outdated information, or insufficient coverage of niche topics lead to shallow reasoning, overfitting to common patterns, and poor handling of rare scenarios. 2. Bias & Hallucination Issues Models can reflect social biases or create “hallucinations,” which are confident but false details. This leads to made-up facts, skewed statistics, or misleading narratives. 3. External Integration & Tooling Issues When AI connects to APIs, tools, or data pipelines, miscommunication, outdated integrations, or parsing errors can result in incorrect outputs or failed workflows. 4. Prompt Engineering Mistakes Ambiguous, vague, or overloaded prompts confuse the model. Without clear, refined instructions, outputs may drift off-task or omit key details. 5. Context Window Constraints AI has a limited memory span. Long inputs can cause it to forget earlier details, compress context poorly, or misinterpret references, resulting in incomplete responses. 6. Lack of Domain Adaptation General-purpose models struggle in specialized fields. Without fine-tuning, they provide generic insights, misuse terminology, or overlook expert-level knowledge. 7. Infrastructure & Deployment Challenges Performance relies on reliable infrastructure. Problems with GPU allocation, latency, scaling, or compliance can lower accuracy and system stability. Wrong outputs don’t mean AI is "broken." They show the challenge of balancing data quality, engineering, context management, and infrastructure. Tackling these issues makes AI systems stronger, more dependable, and ready for businesses. #LLM

For many companies, proving the ROI of AI is hard enough. But in customer experience? It's often a struggle because the benefits can be complex and difficult to measure. While AI can clearly improve efficiency, its most significant impacts, like increasing customer lifetime value, are harder to connect directly to a financial return. This is especially true for customer-facing applications like chatbots or personalized recommendation engines. The problem typically starts with how companies define success. They often focus on what's easiest to measure rather than what's most valuable. For example, companies might measure a chatbot's resolution rate but not whether that resolution drove additional spending or reduced churn. The real ROI in CX isn't just about saving money on call center agents; it's about increasing customer lifetime value. Let's take AI-driven personalization as an example. It can make a customer feel understood and valued, but how do you put a dollar amount on that feeling? The benefits are often intangible, like a stronger brand reputation or higher loyalty, which are important for long-term growth but don't show up on a quarterly balance sheet. Many organizations deploy an AI chatbot or a new recommendation engine just because the technology is available, not because they've identified a specific customer pain point to solve. This leads to disconnected, siloed projects that don't align with a clear business strategy, making it impossible to calculate a meaningful return. And when the "AI Strategy" isn't integrated into the "Business Strategy," the negative impact is higher given the scale. But even with a clear vision, bringing an AI-powered CX solution to life is riddled with practical challenges. What are those, you might ask? For starters, AI models for CX, like chatbots or sentiment analysis tools, rely heavily on high-quality, clean data. If your customer interaction data is fragmented across different systems, incomplete, or biased, the AI will produce flawed results. The initial work of integrating, cleaning, and structuring this data is a massive, time-consuming effort that often gets underestimated. Integration with legacy systems, like your CRM or support systems, is not designed to seamlessly integrate with new AI technology. Connecting an AI engine to these older systems can be a complex and expensive technical nightmare that drains budgets and delays projects. Finally, we have employees. Customer service agents may resist using AI tools for fear of being replaced. Without a clear plan for change management and a focus on how AI can augment their abilities, like providing real-time information or summarizing a customer's history, adoption will be low and the project will fail to deliver value. Find a problem. Get your data ducks in a row. Connect systems. Solve the problem with AI. And help your people along the journey. #customerexperience #ai #technology #innovation #changemanagement

Crossing the Chasm of AI pilots to enterprise wide rollouts. Most AI pilots in customer service look great in a controlled POC. But scaling to production across a global enterprise? That’s where the real challenge begins. It’s not about proving that the AI can work — it’s about making it 𝘄𝗼𝗿𝗸 𝗲𝘃𝗲𝗿𝘆 𝘁𝗶𝗺𝗲, 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲, 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗲𝘀𝘀𝘆 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 𝗼𝗳 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝗲𝗿𝘃𝗶𝗰𝗲. Here’s what that reality looks like: - 𝗜𝘁’𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘅. Enterprise environments have decades of legacy systems that the AI needs to interface with — to read, write, and act on information in real time. - 𝗜𝘁’𝘀 𝗼𝗻𝗴𝗼𝗶𝗻𝗴. This isn’t a one-and-done deployment. When AI handles tens of thousands of calls daily, you need continuous monitoring, quality assurance, and tight feedback loops to ensure it's learning, adapting, and performing safely. - 𝗜𝘁’𝘀 𝗻𝗼𝗶𝘀𝘆 — 𝗹𝗶𝘁𝗲𝗿𝗮𝗹𝗹𝘆. Customers aren’t calling from quiet environments. They’re in airports, cars, cafés, and crowded households. Your AI has to parse speech through background chaos while still delivering accurate, fast responses. - 𝗜𝘁’𝘀 𝗲𝗺𝗼𝘁𝗶𝗼𝗻𝗮𝗹. Many of these calls are escalatory — something’s gone wrong. Expectations for empathy and resolution are much higher than in casual interactions. It’s not enough to understand the words; the AI must understand the tone and respond accordingly. Getting from POC to production in customer service AI means solving for all of the above — not in theory, but in practice. At scale. Every day.

I've published a new note for Gartner clients on ways to minimize brand risks when implementing GenAI chatbots. As brands race to avail themselves of the latest AI capabilities, some marketers are not taking appropriate care to assess and limit potential brand risks. Those risks include providing customers with wrong or dangerous advice and reducing brand trust from customers who have reservations about AI. Last year, Gartner found that 58% of consumers agreed with the statement, "I would prefer to give my business to brands that do not use Generative AI in their messaging and communications." There is no doubt that consumers will grow more familiar with AI in the years to come, and that is likely to raise their trust and acceptance for some uses (but also increase their concern for others.) My note is available for Gartner for Marketing clients, but the summary is that CMOs and brand leaders must take a cautious approach when implementing GenAI chatbots to engage with customers. Recommended advice includes: - Deploy chatbots first to employees, who are better able to assess and test the accuracy of AI responses - Consider the purpose of the chatbot, tightly defining both the topics it should address, as well as the matters that must be escalated to employees or other channels - Carefully assess the content for the underlying knowledge library to which the chatbot has access to ensure it is current and accurate for every combination of products and customers. Gartner clients can read more here: https://lnkd.in/gww4xqeP