Enterprises are constantly investing in solutions that can help scale up their operations and automate their internal as well as customer level interactions. Deploying chatbots across different enterprise usecases - accessing data from repository, handling customer queries, collecting feedback, booking tickets etc. - has emerged as one of they key ways to optimize operations. It is estimated that 80% of enterprises will be using chatbots by 2020, to solve a diverse range of business challenges.
While that’s a great number, there are things you need to consider before deploying bots for your enterprise. Here’s a look:
Why Do You Need a Chatbot
Before you start with what kind of chatbot to deploy, and which platform to use, it is important to answer the first basic question: why do you need a chatbot? What is the business problem that you are trying to solve? Is it to conduct research, answer queries, give reminders, or something else? Starting with a clear definition of your business problem will give you clarity on how chatbots can solve that problem for you.
Clearly defining the 'why' will involve specifying:
- The exact use cases of your bot. This will help define the first set of features and capabilities your bot should have
- The users of the bot. This will help define additional features that might be valuable for the intended users. It will also help create the right conversation flow for the bot.
Once the 'why' answered, the next question is how? Based on your tech stack capabilities, and the above factors, you can decide whether you want to build a DIY (do it yourself) drag-and-drop chatbot using any of the available bot platforms, or a customised bot from scratch.
We take a look at the two ways to build a chatbot, and which one you should choose.
Proprietary Vs Open Source Platforms
Chatbots make use of machine learning and natural language processing engines to perform enterprise tasks, and solve related business problems. While typically this would involve a skilled team of developers, there are a number of DIY chatbot platforms that are gaining popularity.
Understanding Proprietary DIY Bot Platforms
Beginners and non-technical users can simply use platforms like Chatfuel, Motion.ai, Aivo, Botsify etc to build and deploy bots without any coding. The key aspects of machine learning and natural language programming are incorporated into the platform, and all that you have to do is create the conversation flow and the tasks that you want the bot to perform. Designing these bots is as simple as dragging and dropping from a set of pre-defined functionalities, with some scope to modify and customize them for your specific business objectives.
For example, on Chatfuel, all you need to do is write use cases and user stories, follow tutorials, and run some testing. These kind of chatbots can be built using a drag-and-drop interface, and also integrate easily with third party integrations like Salesforce, Zendesk, WhatsApp etc.
Using these platforms, you can create a basic bot in minutes and then tailor it for your usecase. But even with these capabilities and ease of deployment, it may not always be the right choice for your business. Why you ask?
DIY bot platforms come with certain challenges:
Limited Functionality: Building chatbots using these platforms means limiting your bot's capapilities to what the platform can do. There are high chances of your bot missing out on elements like self-learning, responding based on user intent, or carrying out contextual conversations.
And this can severely affect your customer experience, especially if compared to competing organizations that deploy self-learning and intelligent bots.
Limited Extensibility: Most enterprise solutions need to take into account concerns around integration, scalability and extensibility. While your current chatbot usecase might be a simple one, and adequately served by a DIY platform, is it scalable in the long run? Given that most DIY platform offer only a specific set of functionalities, it becomes challenging to scale a DIY bot to perform tasks with greater complexity.
Compounding this is the fact that DIY platform bots also have limited integration options. In a scenario where an enterprise has used different DIY platforms to build bots for different tasks, the complete bot ecosystem becomes a jumble of different systems straining to work cohesively. Frequent integration challenges with each other as well as with the existing enterprise architecture will likely become a major drain on enterprise resources and productivity.
Building Intelligent Bots from Scratch
Companies like Google and Amazon are investing heavily in to develop extraordinary capabilities in their voice assistants. Alongside, they have created products that bring in powerful machine learning and NLP capabilities for developers. AWS solutions like Amazon Lex and Sagemaker, along with Alexa skills gives enterprise development teams a complete toolbox to conceptualize and design bots from scratch, with a wide range of features.
What's more important is that these solutions are focused on delivering capabilities like self-learning, understanding user intent, advanced analytics and also customized for people with speech disabilities. So the level of fine-tuned customer experience you can generate with these tools if your build your bot from scratch cannot be matched by DIY bot platforms.
Yes, building a chatbot from scratch can seem like a complex and time consuming task upfront, but the gains for your business intelligence processes, operations, and user interactions are also higher. With code-based frameworks like AWS, Wit.ai, API.ai, or Microsoft Bot, a skilled team of developers can help you create a bot that's tailored to your organization’s needs. It can work across multiple platforms, solve complex use cases, generate analytics, and extend in close collaboration with your enterprise IT infrastructure.
Summing up, here's a look at the proprietary DIY bot platforms vs. building bots from scratch
What Should You Choose?
Choosing either of these two depends largely on your enterprise requirements, team skills, and project limitations. So if you need a chatbot for a simple task, like feedback collection or setting reminders, it might make sense to use a DIY platform. But its benefits are only for short term. In the long run, you cannot scale up your bots, nor have innumerable use cases, or integration with other platforms, and cannot solve complex enterprise problems with it.
There are also chances that in an effort to keep all bots interoperable, you create all of them on the same platform. But then again, you get locked within a walled garden in terms of functionality and hinder the scalability of your bot ecosystem.
So if you want to ensure that your bots are future ready, and create a foundation that can scale with your enterprise requirements, it makes sense to build your bots from scratch, using an advanced set of machine learning and NLP solutions. And if you do not have a team of developers who can do that for you, you can always get in touch with qualified third party development teams.
Srijan's expert team of certified AWS engineers are working with machine learning and NLP to create interesting enterprise chatbots for diverse industry use cases. We recently built chatbots to access asset performance data for a large cleaning and hygiene solutions enterprise. AWS solutions like Amazon Lex, Amazon Cognito, AWS Lambda, AWS Translate and Amazon S3 were leveraged for the same, eventually leading the client to upsell to a business worth 90 million USD.
Looking to develop an effective enterprise bot ecosystem? Just drop us a line and our team will get in touch.