Greatest Challenges to Chatbot Development in 2022 and beyond
Building a chatbot for quick deployment on any business website is relatively easy. Relatively. The requirement for good chatbot development, not just any chatbot, is usually glossed over when interested parties advertise the advantages to other businesses that are “catching up to the norm.”
So, despite the level of advancement and prevalence in almost any industry, organization, or business today, there are still big hurdles to chatbot development. Even as we go further towards the latter half of 2021, these challenges keep developers and builders on their toes.
Let’s take a look at some of the biggest challenges involved in chatbot development.
Context Data Analytics versus User Unknowns
In-depth analytical features are a huge plus, but analytics are pretty much required to accurately gauge any chatbot performance over a set period of time. Context analytics can be quite tricky on its own, but it is possible, and you can still learn a lot about what makes your chatbot tick by looking at the interactions it deals with regularly.
The problem lies within doing something meaningful with these critical observations. Analyzing unrecognized user interactions on free input chatbots, for example, can be challenging to do because:
- Categorizing interactions needs a vastly different sets of instructions.
- Numerical values do not show specific points of improvement.
- Solving the highest occurring incidences can’t guarantee big results.
In this case, you usually are better off just letting the advanced AI do its thing (and hope it learns enough), go on a case-to-case basis, or simply block users from inputting them in the first place.
Another possible wildcard metric that is difficult to plan and strategize for in chatbot development is session complexity. While data from such metrics can be reinforced by other analytic elements such as chat duration and chat volume, deciding on a specific development activity from an observed result (like if the user unnecessarily articulates, for example) can be vague.
How Users Expect Chatbots to Function
What is the expectation of the average tech user when faced with some cold, faceless corporate chatbot? If they expect a rudimentary text-based interface, they might skip the interaction game and simply opt for “button” keywords. If they want something like Alexa or Google Assistant, the natural language processing (NLP) level of the chatbot might not be sufficient to form convincingly human responses. In both cases, how are you going to balance form and function to maintain artificial eloquence and perfect utility?
Structurally simple yet complex implementation-wise, the consideration for the type (sentences, phrases, keywords) and not just level (duration, chat length) of usage is also a challenge that still puts considerable time and investment strain on chatbots. At our current rate of industry integration, where chatbot provider services can boast near-instantaneous deployment, it is often overlooked.
After all, lead generation is considered a means, not an end. Anything that could achieve that at the fastest rate possible with the lowest amount of investment is fair game. Even if satisfaction is sacrificed somewhat.
The issue isn’t as complex as AI learning context or hitting the wrong set of customers. But, in the long run, businesses attempting to implement a chatbot with query-solving eloquence should take a step back and reconsider the possible ways that users will optimize the “game”… that is its own chatbot engine.
Consistent Real-World Chatbot Testing
The difficulty of testing chatbots prior to setting them out in a real-world setting is pretty easy in terms of options. You could try automated chatbot test engines, which would provide analytics and reports of whatever it is in your chatbot design that might require improvement. Or, you can set up a group of testers just as with any software development cycle.
Both have their advantages and disadvantages, and you can even combine them to produce optimal maintenance results. But the real challenge is the consistency in staying up-to-date with the interaction game. Anticipate any potential bugs and errors, re-implement, optimize, and go back to step one.
As with any online software platform, the time investment may not be so easy to give away. But it is a technical necessity as the years go by, even if the team decides on a simple visual overhaul.
Then there is the manpower issue for the tester group. Verification and validation tests once again require dynamic consistency and a good amount of time to conduct. This is whether they’re doing response tests, error handling, or testing new stuff, as indirectly suggested by the most recent analytics results.
And lastly, a basic testing checklist. Chatbot testing metrics vary per application, of course. But a linear conversational flow-based testing procedure may help save time.
… or at least make the most out of each test conducted.
Simple but Efficient Security Measures
Decking out your guns and using layers upon layers of authentication systems to protect your chatbot platform is indeed a surefire solution to protect your customer service automation asset in the short term. But simpler measures that don’t complicate overall chatbot development are definitely the more efficient way to go.
A few ideas are:
- Allow the chatbot to handle self-destructive messages with near-perfect accuracy. Chat responses that contain sensitive information, such as account numbers or authentication codes, may sometimes need to be erased after a set amount of time. Chat time logs and other metadata are still there for analysis. But the exact information written by the user would no longer be retrievable after a set time.
- Omitting any confidential information encoded within the chatbot workflow.
- Practicing end-to-end encryption. What the name suggests exactly is, information is encrypted on both ends of the communication lines.
- Double-check where and how information on other platforms is being processed. You might also want to check if the platform supports end-to-end encryptions as well.
- Using brute-force methods to try and break into your own security measures during testing.
- Using secure protocols, such as HTTPS, is often already provided by default.
Even as a non-tech savvy user, you should also familiarize yourself with the potential threats and risks that you may encounter:
- Denial of Service – blockage of authorized/target users from accessing the chatbot
- Elevation of Privilege – using loopholes to gain a higher level of access
- Information Disclosure – exposure of data to potentially malicious parties, data theft
- Repudiation – using loopholes to attempt something illegal or completely unintended
- Spoofing – impersonation, gaining access by illegally using another user’s credentials
- Tampering – defacing online assets, modifying system data without permission
Nurturing the Chatbot Through Time
As your chatbot handles more users, it gains more experience and records more analytics. From this generalized perspective, chatbot development doesn’t seem like it requires the rigorous design and testing that traditional software development does. This is particularly true since coding knowledge is no longer really a requirement, and there are lots of templates to go around that you can deploy much more easily.
But that is not a reason to completely ignore the challenges that chatbots still face today. As we have time and again prefaced in our chatbot articles, the massive benefits of automation and human resource reduction are only effectively achieved if a good chatbot is implemented.
Looking at these hurdles and integrating ideas surrounding them would then be an excellent start in treading that road.
