AI, Bots, Chatbots, Large Language Model

The Fundamentals of a Successful Chatbot Strategy

By 5 Minute Read

chatbot strategy


The Hype and The Reality of Chatbot Implementations

Although the first chatbot, ELIZA, was conceived in 1966, the true power of chatbots has only recently become highly significant to business operations thanks to the ongoing advancements in technologies relating to natural language processing (NLP), natural language understanding (NLU), generative AI models, Large Language Models (LLM), processing power, speech recognition and synthesis, and messaging devices and applications.

AI Chatbot deployments have been wildly successful but also been responsible for some classic failures. Some companies jumped in too quickly without considering some of the longer-term implications of having a poor bot experience. Some under-estimated how easy it actually is to implement bots that can respond successfully to a customer and execute the necessary business tasks, without having to immediately hand over to a human agent. And for others, it’s been a combination of factors that have led to bots not meeting the desired expectations.

Some Considerations for Your Chatbot Strategy

Here are some perspectives on how you can avoid some of the common pitfalls in your chatbot implementation strategy and strive for success. Think of your chatbot strategy as a living plan that is not set in stone. It doesn’t have to be a lengthy or tedious exercise but these tips will help pave the way.

1. Don’t Boil the Ocean 

Like all new technologies, starting small is often the best approach. It can be tempting to yield to pressure from business leadership and try to bring multiple AI projects to market as quickly as possible. However, by picking a manageable chatbot use case and rolling it out to a small customer- or user- base before doing a broader rollout, kinks can be ironed out and poor experiences can be averted. The learnings gained in starting small usually open up new ideas for subsequent use cases, for broader reach, a better chatbot architecture, or for additional features.

2. Prioritize the Use Cases 

Since all chatbots are not created equal, it can be helpful to think of them in terms of different skills. Each industry can have unique needs for deploying digital AI assistants and then each business has its own unique priorities and objectives. An industry-specific example of use cases is that of AI insurance bots that can be deployed at different stages of the customer lifecycle, assisting customers with online quotations, onboarding new policyholders, helping them file a claim, or renewing their coverage. There are also use cases that are more generic and applicable across multiple industries, for example, AI customer service bots that can be as simple as an FAQ bot or involve a more complex customer journey, like customer onboarding. Identifying the use cases across departments and prioritizing these in terms of the business value and complexity of development, helps tie bot investments to business strategy and key performance indicators (KPIs). Your KPIs or bot metrics will help you track chatbot success and set you up for continuous improvements.

3. Think Transformation

AI Chatbots have the potential to change the way a business engages with customers so when considering the use cases for bots, don’t just think of a bot as something that replaces a human agent and works 24/7. In fact some of the best applications of chatbots often involve some form of “human in the loop” or the ability to handover to a human agent when necessary. Conversational AI and Large Language Models (LLMs) are enabling a whole new model for engaging with your customers through fluid and frictionless conversations, and for automating key business processes or workflows. So, rather than having chatbots emulate your current workflows, consider how the power of conversation can eliminate or automate some process tasks, remove friction, and transform how you meet your customers’ needs.

3. Prototype and Iterate

Proof of Concept (PoC) is often a preferred route for organizations to prototype a conversational Ai solution before investing in a full-blown project. This allows the business to experiment and agree on features, design, and technology, gathering feedback from early users and stakeholders to improve the bot accuracy and performance. Remember that your bots are also your brand ambassadors so careful consideration to how they properly reflect your brand is an important part of early design and prototyping phases.

4. Communicate Early and Often

As with any new and disruptive technology that has the power to replace humans and automate processes, the fear factor around AI is palpable. In particular, Generative AI solutions can be a particularly sensitive topic for senior business managers who often see ChatGPT and similar models as high risk. Gaining buy-in and understanding for AI chatbot projects from your employees, especially those that will be directly affected, is critical to company culture. Communicating the bot strategy and even including frontline employees in decisions around bot deployments, helps avert unrest and assure staff of their continued role. The best business leaders understand that the workforce will continue to be their greatest asset and like any game-changing technology, the manner in which organizations implement AI will set them apart.

5. Be Proactive about Data Security and Privacy

One of the many downfalls of early chatbot deployments points to them being too simple to be effective. Unless a chatbot can execute the necessary business tasks, it will quickly fail or need to hand over to a human agent. And executing a business process requires rules and data, necessitating integration, security, and governance. As customers chat via messaging or voice, they may not provide, or even have, all the information needed for a chatbot to fulfill their need, requiring the bot to access data via an appropriate API. For example, if a customer asks via SMS chat to change their flight, the bot can retrieve the data needed to do this, filling in the information gaps such as PNR, payment details, itinerary, flight schedule, etc. Needless to say, data security becomes paramount as customer data moves from the business system to the customer so authentication, data isolation, governance, and control become important. 

Data security is a critical consideration when using generative AI models due to the potential risks associated with the generation of synthetic data. Generative AI models, such as large language models (LLMs), have the capability to create highly realistic and convincing text, images, and other forms of data based on patterns learned from training data. While this ability can be beneficial for various applications, it also raises concerns regarding data privacy, security, and misuse.

One of the primary concerns with generative AI is the generation of synthetic data that may inadvertently reveal sensitive information or personally identifiable details. For example, if a generative language model is trained on a dataset containing personal emails, financial records, or other confidential information, there is a risk that the model could generate synthetic text that inadvertently leaks sensitive details.

To address these concerns, organizations must prioritize data security when developing and deploying generative AI solutions. This includes implementing encryption techniques to protect sensitive data, enforcing access controls to restrict model access to authorized users, and implementing robust authentication mechanisms to verify user identities. Furthermore, organizations should conduct thorough risk assessments and ethical reviews to identify potential risks associated with generative AI and implement appropriate safeguards to mitigate these risks effectively. By adopting a proactive approach to data security, organizations can leverage the benefits of generative AI while safeguarding against potential security threats and data privacy risks.

6. Choose the Right Technology

When planning chatbot implementations, consider how they may need to scale and extend their reach beyond the initial use case or audience. Underlying infrastructure and architecture decisions, while not the skillset or priority for lines of business, can often make or break the success of any software project, chatbots included. When deciding on building conversational Ai or chatbot solutions think about the underlying technologies and models. Will you want to employ Generative AI or Large Language Models (LLMs) in your solutions? How about using a Conversational AI Platform that makes bot building easier and faster? If so consider the pros and cons and how you can best create solutions that offer the maximum in user experience and business impact while minimizing security risks and the cost of AI chatbot solutions.

AI Chatbots are Here to Stay

There’s no doubt that conversational AI and generative AI technologies are a hot topic for today’s enterprise. Crafting and implementing an enterprise chatbot strategy doesn’t have to be a long or tedious exercise and ServisBOT is here to help support you. Please reach out and we’ll be happy to help.

If you’d like to learn more about how to progress your conversational AI journey and maturity please check out our new Guide for a Successful Conversational AI Journey.

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