5 Must-Haves for Selecting an Enterprise AI Chatbot Platform
So you’ve decided to invest in a smarter AI chatbot solution for your enterprise. Congrats!
You are now among the growing number enterprises looking to use AI-powered chatbots as a method of driving cost-savings, labor efficiencies, and enhanced customer experiences throughout your enterprise.
According to a recent Forrester survey of 7,000 consumers across 12 countries,
“75 percent of service provider AI decision makers say that 85 percent of customer interactions will be with software robots in five years’ time.
65 percent of these decision makers fear they are lagging behind their competitors in the use of AI to improve the customer experience.
49 percent plan to increase their AI budgets by at least six percent in the next 12 months and
87 percent intend to expand their AI workforce.”
As you can see, there is a growing need for enterprises to intelligently manage a dialogue between consumers, employees, and back-office business softwares. In order to do this successfully, choosing the proper chatbot platform for your enterprise is a critical step to ensuring intelligent, scalable, and cost-effective conversations in the future.
Selecting the Right Enterprise AI Chatbot Platform
There are currently hundreds of chatbot platforms to choose from who all claim to provide the “smartest” chatbot solution customized to your enterprise. However, only a handful may actually meet your specific chatbot requirements and deliver the cost-savings, labor efficiencies, and enhanced customer experiences you are looking to achieve for your enterprise.
At Parlo, we understand that choosing the right enterprise AI chatbot platform is a time-consuming, but crucial part of developing a successful AI strategy. In order to ease this decision-making process, we have laid out the following 5 most important criteria for AI decision makers to consider when selecting the right enterprise AI chatbot platform:
- Learning
- Understanding
- Deploying
- Training
- Pricing
1. Learning
An employee’s job effectiveness is dependent on his/her knowledge of the job. Similarly, the effectiveness of a chatbot solution is dependent on its knowledge base and ability to learn on the job.
When evaluating various AI chatbot platforms, it is important to ask “How will my chatbot learn on the job?”
Some platforms require you to manually teach your bot basic skills:
Customer asks question A…chatbot responds with answer A.
While this approach works in theory, it is a very time-consuming process. Unless you have a team of dedicated engineers who can monitor each input and output to become the chatbot’s “English teacher,” this method will lead to more frustration than success.
The better solution is to choose a chatbot platform that can ingest knowledge from your enterprise corpus in the form of chat logs, emails, knowledge bases, CRM data, and documents.
This method allows the chatbot to learn very rapidly and dynamically based on historical conversations between your enterprise customers and employees.
Rather than being an “English teacher” you can simply guide the chatbot to the the right resources and let the chatbot learn independently on the job.
However, even if your chatbot is self-learning, you will still encounter complex business requests that a chatbot will not be adequately prepared handle. This leads us to our second criteria of evaluating a chatbot platform’s natural language understanding (NLU) abilities.
2. Understanding
According to Forrester, “The top need of enterprises using AI bots is the ability to deal with more complex requests.” And when it comes to consumers, they “love the speed and convenience of chatbot but don’t want forced interactions until these chatbots are more human and smarter”.
Chatbots are only as effective as their ability to break down complex language and execute complex tasks.
For this reason, it is important to choose a chatbot platform that builds smarter bots using advanced AI and NLU capabilities.
Let’s take a look at an example:
In this sentence, most chatbots would understand that the user intends to make a purchase. However, a basic NLU service is likely to request more specific product information and completely disregard half of the user’s intent: “Please ship those shoes to my home.”
On the other hand, an advanced enterprise AI chatbot platform would break down complex language by detecting multiple user intents and entities.
The chatbot should recognize the word Minimus as the product type, black as the product color, and 10 as the product size. It should also be smart enough to set your home as your shipping location, leaving the next logical response to be “What is your home address?”
Here is another example involving complex entities:
“I would like to meet you by the Starbucks near the mall.”
A basic chatbot would detect either Starbucks or mall as the location and proceed to ask “Which Starbucks/mall?” as there are several dozen in the area.
A smart chatbot would find the differences between each location entities (Starbucks and mall) and preserve their relationship. It would understand that near the mall is describing a specific Starbucks location.
The ability to
1) break down multiple complex intents and entities and
2) understand unique enterprise terms and language (i.e. Minimus)
is what sets a smart chatbot’s understanding ability apart from more basic chatbots.
3. Deploying
After building a smart chatbot for your enterprise, your next criteria should be how to deploy this chatbot in a quick, safe, secure, and scalable way.
Fully Integrated Solution
The most robust enterprise AI chatbot platforms provide a dialogue manager, NLU service, and behavior engine as an integrated offering in their platform tooling. If any of these pieces are missing from a chatbot platform vendor, then you risk stalling your chatbot project while building out custom integrations to your conversation channels and enterprise software systems.
Here is what a fully integrated chatbot platform should look like:
Time to Market
It is important to understand how long it will take to bring your chatbot solution to market. You should discuss ways to ensure a quick and reliable timeline when launching and refining a chatbot solution.
Most advanced enterprise chatbot platforms take between roughly 1–2 months to build and deploy a fully integrated AI-powered enterprise chabot solution. You must also budget another 1–2 months to refine each iteration of your chatbot solution to meet customer and employee expectations.
Be aware of how other chatbot platform vendors approach the deploying, testing, and refining phases of the chatbot development cycle. Ultimately, you must determine whether your chatbot solution can be deployed in accordance with the timeline and objectives set for your enterprise.
Data Security
The most robust enterprise chatbot platforms allow customers to build their chatbot on-premise. This allows the enterprise to have full control over the chatbot experience and securely manage all chatbot conversations within their enterprise environment. This is especially important if your enterprise deals with financial data, healthcare records, or other personal customer account information.
More basic platforms will only allow chatbots to be built on a cloud server, allowing minimal access and visibility into the chatbot data being collected. This could leave your enterprise vulnerable and exposed to critical security threats.
Deployment Channels
Finally, an important aspect of deployment is the various channels where a chatbot can live. Most basic chatbot platforms today allow for chatbots to live on Facebook Messenger. This channel works well if you are primarily interested in building simple marketing and support bots. However, if you are a large enterprise planning to drive conversations relevant to customer experience, IT service, finance, payroll, or other internal employee interactions, Facebook Messenger alone will not cut it.
Enterprise chatbots must be able to live on messaging applications, websites, mobile apps, and voice assistants in order to create an end-to-end conversational experience for your customers and employees.
4. Training (AKA continuous learning)
The fourth and arguably most important criteria for evaluating an enterprise chatbot platform, is the ability to train the chatbot for future conversations.
Similar to the learning criteria, many chatbots require hand-holding in order to be consistently accurate when responding to customer and employee requests.
For example, let’s say your chatbot generates 10,000 messages per day. There may be up to 6 natural language understanding (NLU) predictions that appear for each response. This would mean that you would have to manually train up to 60,000 NLU inputs daily in order to adequately train your chatbot.
Ask yourself, “Is this really scalable?”
Enterprises need a chatbot solution that reduces its dependence on a human-in-the-loop to support a chatbot’s continuous learning. The best enterprise AI chatbots automatically become smarter with each conversation (using machine learning and semantic modeling) which lessens the burden of constantly having a human-in-the-loop for training purposes. The only instance where manual training would be required is to teach the chatbot unknown or new vocabulary.
It is the combination of learning, understanding, and training which indicates the intelligence of an enterprise chatbot.
If you are searching for an enterprise chatbot platform that can create human-like chatbots, you will want to choose a platform that does all of these well.
5. Pricing
Finally, we must mention what is on every decision maker’s mind when evaluating a new enterprise AI chatbot platform: pricing.
Today’s enterprise AI chatbot platforms use a combination of the following three pricing models:
Monthly license fee:
-Build limited/unlimited bots for a fixed monthly cost
A platform license fee will provide you with regular chatbot support and server maintenance so you can build and manage chatbot conversations at scale.
Pay-per-call:
-Cost is based on usage of the chatbot and number of software API calls
A pay-per call pricing will give you flexibility to pay-as-you-go when launching your chatbot.
Pay-per-performance:
-Cost is paid out of the savings generated by the chatbot
A pay-per-performance pricing monitors and optimizes your chatbot conversations to attain certain performance metrics (KPMs). You are guaranteed savings using this model, and will be charged accordingly to the goals you do or do not achieve.
As you look for an enterprise AI chatbot platform, you should find a model flexible to your needs.
Ultimately, while there are merits to each of these pricing models, we believe that an investment in any enterprise AI chatbot platform is also an investment in a strategic partnership. Regardless of the pricing model you agree upon, it is important to be working with an experienced team who understands your enterprise’s needs, priorities, budget, and strategic goals.
In Conclusion
Evaluating an enterprise AI chatbot platform is a challenging task for even the most knowledgeable AI experts. Therefore, we believe you should focus on the learning, understanding, training, deploying, and pricing value that each enterprise AI chatbot platform brings to your enterprise’s strategic AI needs.
There are many large enterprise AI chatbot platforms to choose from including Microsoft LUIS, IBM Watson, RASA, Amazon Lex, Google Dialogflow, and Facebook Wit.ai to name a few.
All of these platforms approach our 5 criteria with varied success. However, there is no one-size-fits-all platform to satisfy the AI needs of all enterprises.
The Parlo AI bot platform proudly differentiates itself by creating custom AI chatbot solutions for each enterprise we work with. Talk to us to learn why Parlo is the preferred AI chatbot platform for our largest enterprise clients and technical service partners.
If you found this article useful, please let us know your thoughts below. We regularly publish content like this on our blog in our mission to bring smarter AI to smarter enterprises.
Parlo builds smarter bots for smarter enterprises. We build AI chatbots that employ cutting-edge machine learning to seamlessly integrate with your business. They’ll support your human workforce, delight your customers, and save you time and money.
