Top 4 Conversational AI/Chatbot Challenges For Users in 2024
50% of large companies are considering investing in chatbots. And with the rising interest in generative AI, more companies would likely embrace chatbots and voice assistants across their business processes.
Unfortunately, many users still don’t like chatbots. For instance, 54% of a survey’s respondents said they would interact with a live person rather than a chatbot even if the chatbot saved them 10 minutes.
- Human language understanding difficulty, such as encountering ambiguities, different regional dialects, or variable forms of the same sentence
- Integration with social media applications, ERP, and CRM
- Choosing the right chatbot development tool that meets your applications’ needs, deployment location, and technical expertise
- Purchasing, development, or deployment-related costs
This article will provide you with solutions.
1. Human language understanding
Language understanding allows the chatbot to comprehend and interpret human language inputs for enhanced customer engagement. The main challenges of language understanding in conversational AI systems include:
- Ambiguities: A single phrase can have multiple meanings. For instance, “book” in a sentence could be a noun or a verb depending on how it’s used.
- Handling variability: “Can I book a table?” and “I want to make a reservation” are examples of language input with similar intent but different phrasing.
- Context management: During customer interactions, if a user mentions something early in the conversation, the chatbot should remember it to carry the conversation forward.
- Slangs, typos, and abbreviations: Users might make spelling errors, abbreviations, or say slang terms which the chatbot can’t understand. For example, saying “btw” instead of “by the way.”
- Limited training data: Using limited sets of training data that makes it incapable of handling out-of-scope queries
- Multilingual support: Not supporting multiple languages or dialects of the same language, especially in chatbots deployed in different regions.
- Domain-specific keywords: If the chatbot is deployed in a technical field, and it’s not trained on the domain-specific jargon, it will misunderstand the queries
- Use a diverse training set that includes slangs, technical jargon, different dialects, etc. For that, you can use synthetic data, try different data collection methods, and fine-tune the results.
- Leverage pre-trained NLP models, like GPT and BERT (which also leverage machine learning and deep learning neural networks to create generative AI chatbots), and fine-tune them with domain-specific data as well as models supporting multiple languages.
- Continuously monitor the chatbot’s performance, test different methods (for example, with A/B testing), and analyze the failed interactions.
Chatbot integration is deploying one chatbot into websites, social media platforms, messaging apps, CRMs, ERPs, and other business systems. Integration plays a fundamental role into how conversational AI works because without it, the chatbot’s usability will be limited.
There are 2 main issues with integration:
2.1. Messaging platform integration
This is specific to integrating a chatbot with messaging platforms like WhatsApp, Google Chat, Facebook Messenger, Telegram, Slack, etc. And integration here is a challenge because of platforms’ different API, UI interface, and specific guidelines for bot behavior.
Use no-code chatbot tools that offer one button integration via an easy-to-use developer interface.
2.2. API calls
When connecting to an ERP or CRM, the chatbot makes API calls to GET (retrieve data), POST (send data), PUT (update data), or DELETE (remove data) information upon a user’s specific request. For example, a customer asking a chatbot to update their email address results in a PULL request.
Common API calls’ challenges include latency, breakdowns, and high costs.
- Setting limit rates: Conversation AI chatbots like ChatGPT and Bing only handle a certain number of hourly requests to prevent API overload. You, too, should create mechanisms to cache results, queue requests, or increase request intervals during rush periods to prevent breakdowns.
- Optimize API calls: Train the API to only fetch the necessary data through pagination, filtering, or specific fields selection. Unoptimized API results in calls that take too long, fetch too much unnecessary data (thus also creating security risks), and break down.
- Caching: Via caching, you can store frequently accessed data/results temporarily so requests for similar data will be handled from the cache instead of a new API call. The indirect implication will be lowered costs because APIs might charge based on the number of calls made or the amount of data fetched.
- API documentation and testing: Use APIs with thorough documentations and utilize tools and platforms that allow for API testing, mock calls, and environment simulations.
3. Choosing the right development tool
A development framework – the tools and libraries that assist developers in building a chatbot, Wit.ai, Dialogflow, Argos Labs, and Rasa – offer different components, like:
- NLP (natural language processing), NLG (natural language generation), and NLU (natural language understanding)
- Knowledge and databases for data storage and retrieval
- Dialog manager for maintaining conversation flow
- On-premise or cloud-based hosts like AWS and Google Cloud
And because of each:
- Chatbot’s different requirements based on its use case, target audience, etc.
- Technology’s different learning curve, flexibility, and customizability
It’s difficult to pick the right development framework and implementation tool.
- Clearly define your chatbot’s use case, functionalities, and objectives.
- For instance, a Q&A bot has a different architecture than a customer service bots and this should be taken into account
- If you need multilingual bots, choose an NLP platform with multilingual support
- If your team is proficient in Python, pick a dialogue manager that can run on Python like DeepPavlov
- Study each framework’s user review
- Check each framework’s documentation to ensure compatibility with your tools, like CRM, databases, and third-party services
- Explore open-source tools if you want more customization
- Build a PoC version of the chatbot before making a large investment
- Understand the total cost of ownership, including initial costs, licensing fees, potential scaling costs, and other associated expenses
- Pick a tServes your audience’s needs across their customer journey (i.e., if they need multilingual bots, you should choose an NLP platform with multilingual support)
- Fits your team’s skills and expertise (i.e., if your developers are proficient in Python, pick a dialogue manager ran on Python like DeepPavlov’s)
Conversational artificial intelligence supporters cited deployment cost and acquisition/purchase cost as their major implementation hurdles. Creating a conversational AI platform can be done through:
- In-house development
- Outsourced development
- Small business chatbot platform
- Enterprise-level chatbot platform
We can’t provide exact estimates of how much in-house or outsourced development costs, and most chatbot providers only give pricing details on sales calls. But we have identified some vendors that cost around $2,000 annually.
Therefore, the chatbot costs vary based on complexity, deployment method, maintenance needs, and additional features such as training data costs, customer support, analytics and more.
- Explore the chatbot ecosystem. Pick the solution best tailored to your needs to avoid overpaying.
Reach out to us to help you find a vendor:
- Get a detailed overview of chatbot’s cost breakdown
- Use open-source tools to reduce licensing and bot building costs
- Use template solutions for common use cases to reduce the development costs
- Choose the right server usage when choosing cloud providers
- Integrate only essential services and APIs
- Expand the deployment and undertake reiterative development only after running a pilot
- Take advantage of online communities and forums for insights, solutions, and best practices to handle maintenance in-house
- Deploy the chatbots on communication channels that bring you the most traffic
- Regularly monitor chatbot’s performance to address issues early and avoid cost buildup
- Top Differences Between Conversational AI vs Generative AI
- 9 Epic Chatbot/Conversational Bot Failures (2023 Update) (aimultiple.com)
- Top 7 Conversational AI Platforms
And explore our data-driven lists of:
What is conversational AI?
Conversational AI uses artificial intelligence technologies to understand, interpret, and respond to human language in a contextual and meaningful way.
What are the different types of conversational AI?
Conversational AI can generally be categorized into chatbots, virtual assistants, and voice bots.
What happens when the conversational AI fails to understand us?
Depending on the intelligence of the conversational AI system, it can:
– Give a default response
– Ask the user to repeat themselves
– Escalate to a human agent
– Ask alternative questions
– Make a guess
– Offer a different interaction mode
– Give a combination of these factors.
How can bias affect a conversational AI system?
It would lead to responses that are partial, stereotypical, or discriminatory, reflecting the bias in the training data. This would limit its usability and damage the tool and the developer’s reputation. It is crucial to carefully audit and curate the training data to minimize biases and to constantly monitor the system to ensure it is treating all users fairly.
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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