What are the challenges of implementing chatbots for enterprise?
Conversational AI chatbots handle larger volumes and more complex queries. This is due to the nature of the organizations they serve. Whilst chatbots normally meet customer needs by tackling simple FAQ type queries that are deemed as straightforward, more complex queries can lead to more complex issues.
Determining viable use cases
When businesses fail to construct and validate viable use cases, chatbot failure is inevitable. Chatbot implementation must add value to customer interactions and enhance customer experiences; organizations should never compromise on these tenets in the simple pursuit to reduce support costs.
When chatbot implementation is successful, cost reduction is a likely by-product, but it is not the driving force.
Unfortunately, many enterprises have had to learn about the limitations of chatbot technology from 2016’s failed ‘chatbot revolution’. Enterprises faced the harsh reality that pursuing chatbot implementation to replace customer support agents is not practical or sustainable.
Chatbots are not sentient beings that can be trained to handle the entirety of customer support needs. This fact has been proved time and again, evidenced by the failures of Enterprise chatbots like Facebook’s personal assistant, “M”.
“M” was created to handle user queries at scale, but ultimately, 70% of the queries were handled by human staff, and the virtual assistant was inevitably relieved of its duties. There is no litmus test for use case viability
There is no litmus test for use case viability
If there were, many costly mistakes could have – and can be – avoided. The only true test for determining a chatbot’s success is to deploy the bot and observe its performance. However, enterprises can reduce the risks of chatbot implementation by refining their criteria for evaluating potential use cases.
Since chatbots are meant to augment customer support operations, we can begin our viability assessment by analyzing a given interaction’s complexity. Ask yourself:
- Is the progression of this interaction linear?
- Can the exchanges in this interaction be standardized?
A straightforward example of a linear, standardized process is a user checking their bank account balance. This simple process is a viable use case for chatbot implementation, as both the question and its resolution are standardized.
Of course, enterprise chatbots cater to significantly more interactions at scale. For example, a finance chatbot may facilitate a variety of digital banking functions. In this case, each function must be similarly vetted.
Lastly, when analyzing customer interactions and the customer’s journey through support channels, we must consider touchpoints at which human intervention is necessary. These touchpoints must be factored into chatbot implementation, as human agents will need to intervene via live chat handover.
This blueprint is by no means complete, but it lays the skeleton for chatbot viability assessment. Other considerations include analyzing the time saving potential for the user, etc.
It is a costly exercise
A use case can be viable, but not worth pursuing, when implementation is possible but the scope for achieving meaningful business outcomes is limited.
Enterprise digital transformation initiatives require substantial investment, including the costs of the technology itself, change management, and internal training. Thus, businesses must evaluate how much value an enterprise chatbot can add – both for the organization and, more importantly, its customers.
When determining the worth of a chatbot’s implementation, one of the simplest (and most important) questions you can ask is:
“Will adding a chatbot save time here?”
If the answer is ‘no’, then the chatbot’s purpose is already negated. It cannot serve your customers better nor enhance their experience.
However, successful chatbot implementation has the potential for substantial business benefits. Chatbots Magazine estimates that U.S. businesses can potentially save $23 billion as chatbots have the capability to automate 30% of the tasks carried out by contact center agents.
A chatbot’s success is most accurately determined after its deployment – businesses must track and analyze specific Key Performance Indicators (KPIs) to assess the bot’s performance.
We need to ask, “Is the chatbot adding positively to the customer experience?”, which can be determined by user engagement, leads, sales, customer feedback, increased usage, resolutions, employee feedback, or the duration of interaction.
We can best determine the worth of chatbot implementation by setting realistic expectations of what business goals the technology is being used to achieve.
Conversational AI and interpreting user intent
Simple rule-based chatbots and mythical sentient virtual assistants have a realistic middle-ground: the more elegant, practical ‘hybrid’ chatbot. These chatbots are powered by a combination of rule-based interaction structures and conversational artificial intelligence (AI).
However, we must stress yet again that chatbots, even those powered by conversational AI, have limited abilities. Time and again, the potential of artificial intelligence and similar technologies is drastically overestimated. This is true for conversational AI too.
Earlier, we stressed how chatbots are best suited for straightforward interactions. Conversational chatbots are no different – they cannot manage complex interactions and are no substitute for human intervention. They are also not empathic or sentient.
But, used appropriately, conversational chatbots can significantly enhance the customer experience. They can be trained for specific use cases and can thrive in straightforward interactions. Moreover, conversational chatbots offer a key advantage over simple rule-based ones; intent recognition.
Conversational chatbots can interpret the intent behind user queries and provide relevant responses, provided the bots are trained appropriately. As you might expect, the scope for training conversational chatbots is limited by context, so we yet again confine ourselves to simple use cases.
It is feasible to train a conversational chatbot to recognize queries in, say, specific digital banking functions. But it is not feasible to create an all-knowing enterprise chatbot that eliminates the need for human agents.
Moreover, intent recognition is further limited by the nuances of human language. Customers have different communication styles – dialects may differ, or they may use slang, etc.
Thus, conversational chatbots are programmed to respond when they are reasonably certain of a query’s intent – e.g., when they are 70% certain. And when intent fractures occur, human intervention is yet again necessary.
As your conversational chatbot learns over time, it can better identify these nuances. But the possibility of misinterpretation remains.
Data Security
Enterprises have to build confidence for a customer to feel happy and able to ‘trust’ a chatbot. Data collection laws are in place in different parts of the world, but data breaches are still possible.
Data collection is essential to assist with customer satisfaction and improving chatbot usability. However, the same data can be manipulated and used with malicious intent. This would require enterprises to be very clear about the data they will accumulate and which data is never to be disclosed.
Data security can be enhanced by:
- End to end encryption- this prevents a third party seeing messages.
- Authentication-such as one time password (OTP) to your email, or registered mobile number.
- Disclaimers- about what should not be disclosed during the chatbot interaction, for example ‘Our agents will never ask you for your PIN, please report any such conversations’.
- Staff training that ensures the system is used securely.
Transparency with customers through regular communications such as email and messages about data security is also a way to build trust and show your commitment to this.
How can you successfully implement chatbots for enterprise?
Enterprises are often multifaceted, dynamic organizations that run successfully thanks to the synergy among different departments and operation streams. To achieve their profit-making goals, enterprises must also continue to fulfill customer expectations and demands.
Therefore, to implement chatbots for enterprises, so that they are helpful to customers, they must be integrated into the internal and external ecosystems of the organization and be able to address customer demands.
Here’s our roadmap for how to successfully implement chatbots for enterprise:
1. Build a thorough understanding of customer requirements
The fundamental purpose of a chatbot is to help customers by simulating conversations to understand and resolve their concerns. To do so, it is crucial that the chatbot being implemented has been developed on a robust understanding of your customers.
The chatbot should, for instance, understand the kinds of questions customers ask for specific intents, the goods and services the organization offers, and what products or services can be used to address various concerns
2. Train staff and create an AI culture
To successfully implement a chatbot, the organization’s staff must be on board. The company should foster an AI-friendly culture, with staff aware of the benefits that artificial intelligence can offer them and their company.
It is important to communicate that AI and chatbots are not meant to replace staff, but to facilitate them and introduce efficiency in company operations. Therefore, staff should be trained to evolve from performing repetitive tasks to managing bots to derive useful information about customer intents that can further be used to improve chatbot utility.
3. Customize user interfaces according to the platform
Chatbots will often be deployed across multiple platforms such as the website, phone app, e-mail, text messaging, and social media pages. When implementing a chatbot, it is important to ensure that it is accessible on each platform.
Within different platforms customers may use a chat window, SMS text messages or social messaging services.
Considering the different dynamics of each platform, the chatbot interface should be customized so that customers can interact with the bot with ease through the different platforms.
4. Develop a measurement framework to gauge the bot’s success
A critical part of successfully implementing a chatbot is ensuring that it is doing the work it was meant to do, i.e. improve the customer service experience and resolution of complaints.
KPIs are used to measure the success of a goal, and can be employed to gauge whether the chatbot is meeting business objectives.
To get a more comprehensive picture of the chatbot’s success, companies can look at the completion rate, which is the rate of successful outcomes achieved through the chatbot.
In addition, organizations can also consider the fallout or bounce rate, which refers to how often a customer terminates an interaction with a chatbot in favor of another channel or requires human intervention.
5. Invest time in bot learning
At the beginning when the chatbot is implemented, it will have a lower rate of accuracy, and thus, a lower completion rate. The more interactions and data that the chatbot is exposed to, the more it will learn. To ensure that customers do not bear the brunt of the chatbot’s limited utility, organizations should conduct additional testing of the bot.
To do so, company staff and selected customers can use the bot to improve its artificial intelligence and learning. Using natural language processing, the bot will learn to handle new queries and phrases and match them to customer intent.
Thus, crowd-source bot learning would help the chatbot build its artificial intelligence before being launched to customers.
6. Work on continuous improvement
The work does not end once the chatbot is implemented. Chatbots improve with time. To improve the chatbot’s utility, staff must continue to gather data and identify new search intents.
By checking chat history and adding new utterances (ways of expressing intent), chatbot improvement is possible.
The organization should also ensure that the processes underlying query resolution remain consistent across channels so that customers crossing channels face little confusion and have a smooth customer service experience.
Feedback is crucial. This is possible through conversations within the chat, asking how the session was, and rating it. And also external feedback, such as how a chatbot was able to successfully sell a product or service, customer satisfaction surveys via social media or email channels.
Final Thoughts
Chatbots will serve your organization well if there is a clear digital strategy. Challenges that are foreseen and tackled quickly will lead to better success rates. Customer focus is key to ensuring the chatbot is a success as its primary value is to ensure better customer experience.
