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Enterprise Conversational AI Pricing: Calculating the Cost of a Successful Chatbot or Virtual Agent

A conversational AI solution should provide optimized, consistent self-service. It should create value for your organization and customers. But how much does an enterprise solution with this level of sophistication, flexibility, and customization really cost? This article explores budgeting, typical pricing models, and average costs for pilots and full systems.

Enterprise Conversational AI Pricing: Calculating the Cost of a Successful Chatbot or Virtual Agent

Content Summary

Budget for Technology, Development, and Maintenance
Avoid the Dangers of Underfunding
Share the Initial Risk
Consider the Pricing Options for a Full System
Calculate the Return on Investment

Today’s conversational AI market provides buyers with a wide range of technology options from basic do-it-yourself platforms to fully integrated, scalable solutions with pricing to fit just about any budget. However, most of these technologies are not designed to be true enterprise-level solutions. With customer experience (CX) a competitive battleground for companies, creating a joined-up digital experience is essential and many conversational AI solutions on the market can’t deliver that.

An enterprise conversational AI solution should enable you to provide optimized and consistent self-service. It should create value for both your organization and your customers. It should allow you to maximize the benefits of machine learning while keeping a human-in-the-loop. It should deliver the right experience to the right person on the right channel every time.

Conversational AI with this level of sophistication, flexibility, and customization requires a financial commitment from your organization to be successful. However, pricing around enterprise-level solutions can be a mystery.

This article will walk you through budgeting for an enterprise solution, typical pricing models, average costs for pilots and full systems, and calculating your return on investment.

Budget for Technology, Development, and Maintenance

When it comes to budgeting for a conversational AI tool, you need to consider the cost of the technology, the cost of developing and implementing your customized solution, and the cost of ongoing maintenance. Let’s start with a closer look at each of these important pieces and the factors impacting the cost of each.


Earmarking a portion of your budget for the technology itself is a no-brainer. Without a working conversational AI technology, you obviously have no working chatbot! But the amount you allocate for this really depends on how and where you plan to deploy your solution.

Take into consideration your initial project plan as well as how you may want to expand and scale it in the future. Identify your integration points, calculate how many concurrent users you anticipate, estimate how large of a knowledge base your content will require, and select the deployment channels that best serve your users. All these elements will impact which technology is the best fit for you and how much you should budget for that technology. An enterprise-level vendor will provide guidance to help you scope out your technology requirements.


While there are solutions on the market that can be deployed straight off the shelf with very little configuration, an enterprise-level chatbot will never deliver real success without customization. This customization should include integrations with other systems, such as your CRM platform, ticketing systems, or live chat, and conversation flows tailored for your users. You also need to ensure the tool can respond to queries about your products, services, and procedures with specifics unique to your business.

Often enterprises that attempt to delegate the work of building and implementing their tool solely to an internal team discover that their people struggle to deliver. Unless you have a team that has experience creating successful chatbots with the technology you select for your identified use cases and/or channels, you are much better off outsourcing this work to the experts. Working with an expert vendor is more cost-efficient because they already know what they are doing so you aren’t paying them to figure it out. This also means you cut down on the development time and get better, quicker results.


If a conversational AI provider tells you that you can configure and deploy a chatbot with their technology and then leave it alone to do its thing, cross them off your list immediately! Enterprises that invest in these solutions quickly learn that they have wasted money on empty promises. Pure AI solutions that deliver successful customer or employee support do not exist. In fact, the ongoing maintenance of these tools is what enables the long-term success.

Newly implemented chatbots need more attention than well-established ones, so that needs to be reflected in your budget. During that initial period, engaging the expert vendor’s team is recommended. However, a quality enterprise solution should give you options for moving all or some of your chatbot maintenance in-house. If you choose to do that, factor into your budget costs for those internal staff members and any related pieces of training, licenses, etc.

Avoid the Dangers of Underfunding

Without the proper level of financial support and ongoing funding, you will never achieve success with a conversational AI project. An enterprise chatbot that is treated like an unimportant side project not worthy of dedicated resources will perform like one, providing a poor experience and driving users away. If you want to have a conversational AI tool that increases customer satisfaction, contributes to cost savings, generates new revenue, and improves efficiency and productivity, then your enterprise needs to make a commitment to invest in those goals.

Should the price tag of a quality enterprise-level solution create some hesitation within your organization, consider the cost of deploying a chatbot that delivers a negative, frustrating experience for users. Putting time and money into a tool that your customers won’t want to use – even if it is just the bare minimum investment – is a misuse of resources. Not only are you wasting your budget, but you are harming your customer experience and eroding customer loyalty.

Leading enterprise conversational AI providers understand both the risks of underfunding these projects and the challenges you may face when seeking budget approval for the proper level of investment. They know achieving long-term success requires a significant commitment from everyone involved. Select a vendor who will help you build a realistic business case for your project and share the financial risk of getting started.

Share the Initial Risk

To get your conversational AI project started, the vendor should provide a complimentary consultation to discuss your goals, scope out the project, and help build your business case. If you have transcripts from an existing live chat tool or your contact center, the vendor will analyze that data for insights into what types of questions are being asked and how many of those can realistically be automated with a self-service tool. This analysis will also help with identifying key performance indicators (KPIs).

Before you go all-in with your conversational AI project and budget, look to do a pilot or proof-of-concept (POC) with the vendor. This gives your enterprise the opportunity to test out the solution on a limited basis to make sure it is a good fit for you and your digital strategy. The financial risk associated with this pilot should be shared by the vendor.

The average cost of an enterprise-level pilot is typically between $30,000 – 50,000 (USD).

This fee will cover all hosting, software licenses, content development, technical consultancy, and transactional fees for the agreed period. Typical pilots run for 30-60 days from their launch which provides sufficient time for you to see results, evaluate initial performance, and make decisions about taking the next step in your conversational AI plan. When you convert from the pilot stage to a full system, you should expect the financial investment you already made to be credited against the cost of the full deployment.

Consider the Pricing Options for a Full System

A conversational AI vendor offering enterprise-level technology and consultation will have several different pricing models for a full system. You should consider which option fits best with your customized solution and internal budgeting needs. There is no one pricing model that is optimal for every enterprise, so the vendor should work with you to determine the best choice for your full system.

There are two main pricing options for an enterprise conversational AI system: transactional and software as a service (SaaS).

Transactional pricing is a pay-for-performance option. This is typically a tiered model based on volume with a per session cost. In this option, a session is identified as a single interaction with a user, with unlimited questions and integration calls during that session. The per session cost may vary depending on session volumes, giving you a reduced session rate when your traffic reaches a certain threshold.

SaaS is a more traditional pricing model and a better choice for enterprises preferring an option without a variable rate. This pricing gives you the costs for the software and ongoing consultancy upfront, typically calculated as an annual cost. The size of the system and an anticipated number of user sessions are key factors in determining the pricing.

Since success with enterprise-level conversational AI requires customization, the cost of building and deploying your solution is going to depend on how complex and customized your chatbot or virtual agent will be. A standard system should be integrated with a live chat platform and your CRM to ensure customers get personalized answers, can complete transactions, and are able to seamlessly escalate to a human when needed. Additional integration points, the number of solutions contained in the knowledgebase, where your tool is hosted, and the number of concurrent users you want to support are just some of the other elements that impact pricing.

Keeping this in mind, the average annual cost for a full enterprise-level system with transactional and SaaS pricing options can be broken down as follows based on knowledgebase size:

≈ 150 Solutions: $150,000 – 250,000 (USD)

≈ 300 Solutions: $250,000 – 350,000 (USD)

≈ 450+ Solutions: $350,000 – 500,000 (USD)

A true enterprise provider will also have perpetual licensing and white label/ OEM licensing options available. These models are less popular, and this pricing is highly customized. If you are interested in one of these licensing options, the vendor will collaborate with you on a personalized package and cost breakdown.

Calculate the Return on Investment

Regardless of the pricing model, you select, an important measure of your project’s value comes with calculating the return on investment (ROI). A successful conversational AI deployment will deliver a positive ROI in less than 12 months.

The KPIs you identified for the project as part of the initial consultation workshop is essential for this discussion. They will have informed what metrics you selected to track and how you are tracking them. With the vendor’s expert guidance, you will have the right business intelligence tools in place to collect and analyze the rich conversational data to garner a variety of important insights.

Keep in mind that statistics such as call deflection and reductions in live chat sessions may be important in your ROI evaluations but should never be the only focus. The user experience and customer satisfaction are just as critical in determining success. There is no value to your organization if you are reducing live chat volumes but creating a negative experience that drives customers away. Always take a close look at satisfaction scores when evaluating your conversational AI tool.

Customer experience is a crucial competitive differentiator for enterprises. Customers expect a joined-up digital experience that includes conversational AI tools. Enterprise-level solutions require an ongoing commitment of financial support and resources to be successful. That commitment enables you to deliver the right experience to the right person on the right channel every time with enterprise conversational AI, creating a wealth of value for your organization and your customers.

Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

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