In this post, we’ll discuss some of the challenges faced by those involved in AI chatbot implementation and the solutions. We have tagged each challenge as a business or technology issue.
Implementing a chatbot isn’t as simple as plugging it in and hitting “go.” Like a new employee, chatbots need training to know what they’re doing. Don’t let untrained chatbots break your clients’ trust and damage your reputation. So here are some common challenges people might face, and more importantly, how to solve them.
Challenge 1: Defining Business Objectives and KPIs (Business)
Before developing a chatbot for your organisation, you must know why you’re doing it! What are your objectives? What is it going to do for you? How will you measure success?
Without clear goals, your chatbot will take longer to meet its potential.
Solution: The goals for your chatbot may not be clear at first. If they are, we admire your certainty.
Now, looking at the most viewed pages on your website is a good start for understanding what users want to know. This is particularly helpful if you’ve got a vast knowledge base. A lot of information on a web page and dozens of links can be overwhelming for users.
Navigating these vast knowledge bases and guiding users might be the objective of your chatbot. Asking your chatbot a question is easier for your customers and your staff than scouring through pages of content. This is especially helpful when alerting users of updated information or an event.
You can book a free consultation with us here to learn more about what this technology can do for you. Japeto Chat is a platform created by Japeto. It helps you reach your goals and track your chatbot’s results. The dashboard shows performance indicators like total messages, total conversations, and the lengths. It also tracks messages per conversation and when they happen. Other key performance indicators included are top questions and link conversion rates.
Challenge 2: Underestimating an AI Project’s Complexity (Business)
When starting an AI project, it’s essential to understand it’s likely going to be a bit complicated. There are lots of things it can do so decisions to be made. So like building a house, creating a chatbot requires meticulous attention to detail. Due to its widespread presence in society, many tend to underestimate the intricacies of AI.
Solution: It is crucial to communicate effectively with stakeholders from the outset, manage expectations, and avoid over-promising.
Research indicates businesses overlook integration issues with existing technology, especially legacy systems. These systems may need more APIs, have outdated security standards, or use obsolete data formats, posing challenges for chatbot integration.
Solution: The good news is that middleware solutions exist to bridge the gap between old and modern technologies, offering a promising way forward.
Challenge 3: Data Quality and Relevance (Technology & Business)
AI assistants are your apprentice – they will only do their job well if given the right tools (in this case, the correct data). If your data is old, incomplete, or not relevant, your chatbot will give answers that are unhelpful or even wrong.
Solution: For a chatbot to work well, it must be trained on good data. Look at your websites frequently asked questions, customer support logs, and updates about new products or events.
To keep your chatbot from being behind on the latest news, ensure you’re regularly updating the data it learns from. A solid FAQ section is a great place for your chatbot to pull answers. Also, tap your customer support team’s trove of insights to train them on the most common queries.
Lastly, let’s not forget data relevance! Just because your chatbot knows the name of your CEO’s pet doesn’t mean it needs to be sharing that with customers. Keep it focused on useful and meaningful information for your business goals.
Quality is just as, if not more important than data quantity. Pruning all the unused stuff reduces model size and ensures speed, accuracy with less energy consumption.
Challenge 4: Security and Data Privacy (Technology & Business)
For chatbots, security isn’t just a nice-to-have—it’s a must. Because chatbots handle sensitive data, attackers often target them for cyberattacks.
Solution: First of all, strong encryption is non-negotiable. Data needs to be securely locked away so hackers can’t snoop.
Frequent security audits are key. You wouldn’t leave your house without checking that you locked the door, so don’t let your chatbots go without routine audits. Our regular checks help find weak spots before hackers can. This keeps sensitive data safe from prying eyes.
It’s also important to control who has access to this data, so multi-factor authentication (MFA) is used to add an extra layer of protection. Lastly, let’s not forget about GDPR. Ensure your chatbot only collects the information it needs and always gets consent from users.
In short, a chatbot without good security is like a door without a lock. Sooner or later, someone will walk in uninvited. So, let’s keep it secure, follow the best practices, and you’ll avoid any awkward “data breach” headlines.
Challenge 5: Environmental Concerns (Technology & Business)
While AI may be revolutionising industries, it’s also raising a few eyebrows when it comes to sustainability. As chatbots and large AI models grow in complexity, so does their energy consumption.
Training a model like Bigscience’s BLOOM in 2022 created a lot of carbon emissions. This 176 billion parameter language model produced nearly 24.7 tons of carbon. That is like driving 63,000 miles in a petrol car. It is also equal to charging 1,629,338 smartphones! So, while AI holds promise for improving efficiency, like many industries, AI needs to reduce energy consumption to mitigate its environmental impact.
Solution: Cleaner AI
Data centres housing AI systems have become increasingly efficient thanks to advances like direct-to-chip cooling and heat reuse programmes. Data centres use 1-1.5% of the world’s energy, and while this is a lot – it would’ve been a lot more without these new developments.
These technologies help ensure that the power your chatbot uses doesn’t go up in smoke – in fact, some of the heat generated by AI can be repurposed into energy, reducing waste. AI uses a lot of energy, which creates more heat. However, the higher the temperature, the more likely it is to convert back into power. Deep Green have been using heat produced in their data centres to heat swimming pools and communities homes.
By monitoring usage and choosing eco-friendly cloud services, we can reduce emissions. However, calculating the energy consumption of AI models remains a challenge, particularly during deployment – where most of the energy is consumed. While tools are emerging to help track these metrics, such as the Software Carbon Intensity specification and Green Algorithms project, they’re not yet widely adopted.
In short, while chatbots and AI are clever and capable, they’re still energy-hungry. The tech world is making strides, but more must be done before anyone can confidently say AI is truly green.
Challenge 6: Handling Growth and Scaling (Technology)
As your chatbot grows in popularity, it’s bound to get busier, and with great power (or usage) comes great responsibility. If unprepared for an influx of users, your chatbot could slow down, send incomplete messages, or crash. This challenge is more of a challenge for developers, so feel free to skip it.
Solution: Scale for Success
First up, cloud scaling. This allows your chatbot to automatically adjust its resources based on traffic demand. Like switching from a car to a bicycle during traffic and then back to a car once traffic clears – efficient and cost-effective.
Next up, load balancing, which spreads the workload across multiple servers. It’s the chatbot equivalent of sharing the heavy lifting – if one server gets a bit bogged down, the others step in to help out.
If you’re expecting rapid growth, microservices might be the answer. This clever approach breaks down your chatbot’s functions into smaller, more manageable services that can be scaled independently.
Finally, don’t forget about caching frequently used data and queue systems to manage user requests. This way, your chatbot can fetch common answers quickly without slowing down to process every single request from scratch.
Challenge 7: ‘Training’ Takes Time (Technology)
Training a chatbot isn’t a one-and-done job—it takes time, effort, and a whole lot of data. There are also many different approaches you can take to specialise your model for a specific task, such as customer/ patient support, appointment scheduling, financial services, data collection, and surveys.
Solution: Pick Your Training Strategy
Retrieval-Augmented Generation (RAG) RAG allows the model to retrieve relevant information from external sources before generating a response. Think of it as a well-informed chatbot that checks its notes before answering. This approach is perfect when your chatbot needs access to a large amount of information without storing everything in its own memory.
If you already have a pre-trained model, fine-tuning lets you customise it for your specific needs. You don’t need to build everything from the ground up – just tweak the existing model to suit your business’s voice, tone, and requirements.
For those willing to put in the work, training a model from scratch is still an option. While it’s the most time-consuming approach, it gives you complete control over the chatbot’s knowledge and behaviour. If your business has highly specialised needs that off-the-shelf solutions can’t meet, this might be the way to go. But be warned – it’s the marathon of chatbot development!
Prompt engineering is like giving your chatbot a cheat sheet. Rather than re-training or fine-tuning the entire model, you just create prompts that guide the chatbot in providing better responses. It’s a quick, cost-effective way to make an existing model perform specific tasks without needing to reinvent the wheel.
Challenge 8: User Engagement (Technology & Business)
User engagement is about making chatbot responses feel less like a machine and more like a human that understands. Now this is important because if it’s not engaging then your target audience may as well look at an FAQ page.
To have a good user experience, your chatbot needs to feel human. A big tasks for a chatbot because you also need to be transparent and inform users, they are speaking with a chatbot. Customer experience is often the determining factor for chatbot fails.
Solution: Personalisation and Conversational Context
Start by mapping out common user journeys. Imagine your chatbot as a tour guide – it needs to anticipate where the user wants to go next. Plan for everything. Both yes/no and open-ended queries, and always provide clear, concise answers. Don’t give them an essay! Using quick reply buttons can also streamline the conversation guiding users through their options without getting bogged down in lengthy responses.
Personalisation is key to making your chatbot feel more like a helpful assistant and less like a customer service robot. Your bot can use natural language processing (NLP) to understand user intent and provide relevant responses. Having contextual understanding allows your bot to remember details from earlier in the conversation – so it doesn’t ask you to repeat details you’ve already provided.
Plan for confusion. Bots aren’t perfect, and misunderstandings can happen.
However, how a chatbot handles these moments is critical. Implement fallback responses for when it doesn’t understand a query and offer a handover to human agents when needed. This shows users that the bot is smart enough to know its limits and can gracefully step aside when it’s out of its depth. You can even program a chatbot to trigger an automatic handover if it detects frustration or confusion, making for a much smoother user experience.
Sentiment analysis helps your bot respond in a way that feels more empathetic, whether a user is happy, frustrated, or confused. If the user seems upset, the chatbot can soften its tone or offer to escalate the issue to a human agent.
When your bot is unsure of something, it’s better to ask for clarification than to give the wrong answer. Simple clarification prompts (“Did you mean…?”) help keep the conversation on track and prevent misunderstandings.
Don’t forget that even the most polished chatbots need ongoing improvement. By using continuous learning, your chatbot can learn from real conversations and adapt over time. This improves engagement and ensures your bot stays relevant as your business evolves.
Challenge 9: Testing and Refining (Technology)
After all the hard work of building and training your chatbot, you can’t just set it loose; never look at it again and hope for the best. Without thorough testing, your bot could end up making errors – and that’s no good for user experience or your reputation. Testing ensures your chatbot is running smoothly, from handling complex queries to managing a flood of users.
Solution: Test, Test, and Test Again
Simulate real conversations by testing how your chatbot responds to various scenarios. How does it handle simple and complex queries? What happens when it encounters something unexpected? Simulating real conversations will help you identify weak points in your chatbot’s logic.
Chatbots need to be prepared for anything, including unexpected or nonsensical inputs (typos and random emojis!). Testing your chatbot on edge cases will ensure it doesn’t crash or freeze when users throw it a curveball. Make sure it responds gracefully with helpful fallback responses rather than giving users the dreaded “I’m sorry. I don’t understand” over and over.
Your chatbot may work perfectly during calm periods, but how does it handle the lunchtime rush? Load testing is crucial for simulating high-traffic scenarios, ensuring your chatbot doesn’t slow down or break when faced with multiple users. Use techniques like load balancing and cloud scaling (as discussed in Challenge 6) to keep things running smoothly during peak usage.
Just because a chatbot works technically doesn’t mean it’s user-friendly. User testing involves getting real users to interact with the chatbot and give feedback on how easy and intuitive it is to use. Are the instructions clear? Are responses timely and relevant?
Chatbots often handle sensitive information, so security testing is essential. Ensure your bot doesn’t accidentally expose personal data and follows all the best security practices you’ve implemented (like encryption and multi-factor authentication from Challenge 4). Run through potential attack scenarios to ensure your chatbot’s security is tighter than a vault.
Even after launching, the testing doesn’t stop. Continuous monitoring ensures your chatbot remains reliable and performs as expected over time. You’re giving your bot regular check-ups to make sure it’s in tip-top shape.
In Summary
Using a chatbot may seem daunting at first. However, with proper preparation, you can make it successful! Knowing your goals, planning for scale, and prioritising security will ensure your intelligent chatbot doesn’t miss a beat.
