Lower the Cost of Chatbot Development & Management
Conversational AI along the Road to Digital Transformation
Conversational AI (or chatbots) leverage natural language processing (NLP) and machine learning (ML) technology to emulate human conversation and automate business processes and workflows in new and intelligent ways. As a result, the demand for chatbot software has grown as they have proven the potential to reduce support costs, automate customer engagement, and deliver attractive business results.
More importantly, this artificial intelligence technology is providing companies with a means to achieve quick wins in their digital transformation journeys, which can be protracted. The cost of building chatbots and their time to market is low relative to other software and IT projects so an enterprise can still realize some of the fruits of digital transformation in smaller and more manageable chunks.
A New Era of Creating Better Chatbots, Faster
As the tools and approaches to chatbot development have matured, the time and cost of chatbot development have been reduced. It is also now easier to add advanced chatbot features and skills that improve the overall customer experience. With low-code bot building and graphical workflow tools, the reliance on coding skills has diminished, allowing tech-savvy business people to build their own prototypes.
Welcome to the new era where we can create better chatbots, faster. No longer is there a need for lengthy development cycles. Nor is it essential to hire a team of expensive and scarce AI and enterprise developer skills.
Advancements in NLP and machine learning have helped achieve this current state but it is the emergence of conversational AI platforms that have largely been responsible for lowering the cost of chatbot development and making it faster and easier to deploy conversational AI solutions.
When conceived and designed properly, a chatbot project can achieve almost immediate results on launch. And with the proper development tools and platforms, they can be built in a matter of days and weeks. There’s no need for 1 -2 year implementation cycles and hefty professional services and developer costs.
While early enterprise adopters may have found themselves in this position, times have changed and the market has evolved. Today, speed to market, agility, and flexibility are critical factors that separate successful chatbot implementations from failures.
The Many Facets of Chatbot Development
There are many aspects and tasks involved in building chatbots – from bot design to bot building, integration with systems and channels, adding business workflows and logic, infrastructure, analytics, testing, and deployment.
You can explore more about this in our new EBook: The 8 Essential Components of Enterprise Conversational AI and their Impact on Total Cost of Ownership
Natural language processing (NLP) toolkits like Google DialogFlow, Amazon Lex, Microsoft Luis, or IBM Watson enable users to build conversational interfaces that recognize and understand human language and can respond and learn. In the context of enterprise conversational AI, developers use these tools to handle the natural language understanding but there is more that goes into creating advanced solutions than just the NLP. Hence, companies that create their own conversational AI solutions rely on additional enterprise developer- and AI skills to build the AI models, integrations to business systems, security, and management features, and analytics dashboards.
For example, there’s a list that extends to at least 22 items to be checked off by a business before even reaching the stage of a bot being deployed to production.
- Design the conversation
- Design data flow & fulfillment
- Design the user interface
- Design the solution architecture
- Build intents & utterances
- Build the integration to data/systems
- Integrate the NLP to channels (SMS, web, facebook messenger, slack, Microsoft teams etc.)
- Integrate the Identity Provider (IdP)
- Integrate with contact center systems
- Build business logic & rules
- Build operations
- Build a reporting dashboard
- Build logging & test scripts
- Build DevOps
- Manage infrastructure
- Build application monitoring
- Bot testing
- Test the technology
- Train the bot
- End-to-end Testing
- Deploy to production
For those that want to build a custom solution from scratch, it’s easy to see how this quickly becomes a large project requiring multiple skills and significant costs.
Chatbot Development: Buy versus Build Approaches
The effort involved in creating custom chatbots is highly dependent on the business use case and the unique characteristics of the business. This determines everything from conversational design to business logic, workflows, data/system integration, security, storage, channels, and user interface.
More significantly, it is the development approach that a business takes that can make a difference to the effort, time to market, and costs.
Here are some different approaches that organizations take to chatbot technology:
- Build custom chatbots with your in-house team of developers and AI specialists.
- Contract with a consulting or systems integrator to build your solution.
- Buy a bot platform to build your chatbots using low-code tools and reusable components.
- Buy a bot platform subscription and use the vendor’s professional services to build your chatbot.
The term “Build versus Buy” is used often to describe the approach that a company takes to software projects. In this case, options 1 and 2 are “build” decisions where the organization decides to invest in developing their own bots from scratch, either using their own team or contractors. Generally, they will choose an NLP toolkit from the likes of Google DialogFlow, Amazon Lex, Microsoft Luis, IBM Watson, Rasa, or WIT.ai but they may even create their own NLU technology. This approach is favored by organizations that want a high degree of control and customization around their solutions and that have the financial resources and potentially the in-house skills to manage this.
Options 3 and 4 are “buy” decisions where a company may not have in-house AI and developer skills or doesn’t have the resources to hire these skills. Instead, they purchase a conversational AI platform, leveraging the building blocks and low-code tooling to create their solutions, either using their own resources or those of the vendor’s professional services team. This approach requires a subscription payment that covers operational and hosting costs on top of all bot building, management, and analytics functionality.
Let’s explore the two approaches in terms of their impact on the overall cost of development and time to market.
Calculating Chatbot Cost and ROI
Organizations that buy a conversational AI platform can avail of the low-code bot building tools, out-of-the-box chatbot features, API connectors, and other centralized services around security, management, and analytics. This enables them to create their bots faster and more efficiently, getting solutions to market quickly so that they can generate business results in a faster timeframe.
Because much of the heavy lifting for AI models, backend integrations, workflow automation, deployment, and management is taken care of by the platform, an organization often just needs business analysts, bot builders, and tester skills.
On the contrary, in the case of a business that builds its own DIY or custom bot solutions, more advanced and costly resources such as enterprise developers, enterprise architects, conversation designers, and AI specialists are needed.
The skills requirement and associated costs and the length of time it takes to get bots into production are the key differentiators in the two approaches. And because a shorter time to market means that a business can realize earlier returns, the two approaches have different ROI impacts.
In summary, building bespoke solutions means higher development costs and longer time to returns (for example, through revenue gains, higher conversion rates, or lower costs) while buying a platform means lower cost of development and quicker realization of returns.
In an era where organizations can’t afford to wait 12 months or more to get self-service solutions to market there is a compelling reason to choose a path that can shorten the time from concept to production to a matter of weeks.