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A Guide to Making the Buy v. Build Natural Language Processing Chatbot Decision


Build or Buy? Bring your company up to speed with Artificial Intelligence-powered Machine Learning Conversational Platforms.

Machine Learning and Natural Language Processing have become more accessible than ever before, but the fact remains that implementing AI is complicated. Businesses live in a world of limited time, limited data, and limited engineering and resources.

Focusing in-house resources on AI-powered projects that will make the biggest impact to your company, and to do so without losing your core competencies, is vital. But where to start?

Chatbot Customer Success Solutions

AI-powered chatbots are being use extensively to improve customer success and personalize customer engagement. Affecting change in customer success is an effort that must transcend company sectors, crossing the divide between sales, marketing, on boarding, customer education and customer service. It is vital that company sectors no longer be siloed in individual efforts. All divisions affected by implementing AI to improve customer experience must buy into the process.

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Chatbot Software Value Analysis

AI and ML can streamline and automate workflows company wide.

The first step is to determine where and how exactly AI-powered chatbot software can demonstrate the highest value within your business processes. Teams across the company must review its key technology to understand how AI and ML can help streamline and automate workflows in order to improve communication and personalization in their customer success. An in-person workshop that brings together all impacted company groups is often a good first step. As well, it is critical that team members company-wide see strong C-Suite buy in.

Assessing Value of AI-powered Chatbots

Assessing potential financial value of AI is one of the most challenging tasks.

Assessing the potential financial value as well as the time needed to implement AI platforms is one of the most challenging tasks. This will include understanding cost savings and improved profitability forecasts, as well as time requirements and cost allocation to get the platform up and running.

Rule-Based vs. AI-Powered Chatbots

Many companies start simply with rule-based chatbots while others move directly into AI-powered platforms. Companies need to assess internal resources to make the best first step. For example, creating customer personas and conversational flows for AI-powered conversational platforms can require a long lead time and subsequent refinements. It is important to understand the resources needed based on the specific AI platform.

Start Small to Succeed

Defined, limited scope are the best first AI pilot projects.

Once you’ve achieved buy-in across company sectors, the next step is to determine whether you are going to build or purchase a conversational solution. Whichever the case, we recommend that your first AI pilot project starts with a narrowly defined, limited scope and a specific timeframe — often less than 4 months.

The Partnership between Human and Machine Intelligence

ML based platforms necessitate a partnership between Human and Machine Intelligence.

A ML-based AI conversational platform is software that learns. It must be trained, it cannot simply be programmed and truly requires a partnership between humans and machines. To teach these platforms to become smarter, humans must feed these systems examples, many examples. Robin Bordoli, CEO of CrowdFlower Inc., a company providing human labor to train and maintain AI algorithms states,

“An algorithm can only be as good as the quantity and quality of the training data to get [it] going,”

Even when a chatbot or Intelligent Agent (IA) has been trained, its judgment is never perfect. Human oversight and interaction is still needed, especially with contextual material and situations requiring empathy.

Human Interaction is needed to understand context.

To illustrate, within the swivl AI-powered platform, if the algorithm has less than a 70% confidence level understanding a question, it is called an “edge case”. A human agent is then notified via Slack, SMS, WhatsApp, or the medium of choice and can take over the conversation. They can then answer questions in real time. A human agent is easily able to step in any number of situations:

  • the IA is uncertain because an unknown inquiry has been entered,
  • the conversation has reached a designated touchpoint,
  • or the interaction simply requires a human touch.

Data Training Requires Human-in-the-Loop

Bringing a Human-in-the-Loop also provides the opportunity to further train the data. Data can be sourced from the CRM, updated, and then be fed back into the algorithm to improve it. This makes the AI-powered chatbot software smarter and smarter over time.

Data is trained and fed back into algorithms to make the AI platform smarter over time

AI-powered platforms must be assessed based on their ease and ability to allow humans to:

  • step in while understanding the context of the conversation,
  • access and update customer data,
  • deliver a more personalized interaction when empathy and understanding is required,
  • and train the system by adding new intents and entities to a database, making it smarter over time.

Companies implementing AI-powered conversational solutions are increasingly recognizing that they are most effective when the solutions complement humans, not replace them.

Data is the Lifeblood of AI

Data is the lifeblood of AI-platforms. An essential key to successfully implementing AI-powered platforms also lies in understanding how to harness and use customer data to make it actionable. Mary Meeker’s June presentation of Internet Trends at Code2019 made more than a few interesting conclusions surrounding data.

Meeker acknowledges that companies that use data with the intention of improving customer experiences have seen the best results. Pre-1995, successful businesses used solicited human data and insights to improve customer experience. The 2000’s saw a move into using digital data and insights. As we look to the 2020’s, Meeker states that companies are creating “data plumbing tools” that provide a framework for customer data collection and utilization efforts. AI-platforms can be of tremendous use to bring clarity and purpose to the job of making customer data actionable.

However, we are seeing a slow adoption rate for AI conversational platforms that successfully lever customer data. A recent Forbes survey found that only 13% of companies are successfully leveraging customer data. This low number magnifies the gap between competency and expertise in effectively utilizing AI-powered platforms to access customer data. As well, many organizations are only at the beginning of their efforts to implement AI to convert customer data into actionable insights. This pace will accelerate. AI-powered platforms are already proving to yield greater returns on investment. They will continue to drive market disruption. Per Tom Davis, Chief Marketing Officer at Forbes Media,

“Customer data has become the key ingredient in providing a better customer experience. Those who fail to adapt to this will fall behind.”

Build vs. Buy Natural Language Processing Chatbots

Build vs. Buy Natural Language Processing Chatbots?

It’s an age old conundrum in the IT world: when you’re planning out a project, you must decide whether to build or buy. Few, if any companies build in-house payment processors, customer survey tools, or CRM systems anymore. Why?

  • Building a payment processor is hard,
  • Existing survey tools are easy to use, and
  • CRM systems are increasingly complex in the back end.

AI and Machine Learning are just the next piece of an ever complex business building toolset.

Outsourced ML models can be piloted more quickly.

In-house or outsourced machine learning models can both be used successfully. Outsourcing makes starting pilot projects quicker and provides access to external talent. In deciding whether or not to build AI in-house, it’s important to understand the limitations of time, data, and engineering and financial resources.

Natural Language Processing Cloud Services

Building personalization technology, particularly with all the complexities involved, is likely not a core part of your business.

Most platforms sit atop Natural Language Processing cloud services such as, Dialogflow, Microsoft LUIS, RASA, etc. As well, platforms should integrate into a company’s CRM.

It’s unlikely that smaller companies have personnel with the skillset to build this type of platform internally. Typical AI specialists are expensive. Both PhD’s fresh out of school and those with less education and just a few years of experience, can be paid from $300,000 to $500,000 a year or more in salary and company stock by larger tech companies. Many smaller companies may employ Data Scientists to lead their Machine Learning efforts. This, too, can be expensive, with average salaries upwards of $140,000.

AI-Powered SaaS Options

What are the options for SMB’s who want to harness AI-powered solutions such as chatbots to deliver highly relevant, contextualized customer experiences without these high costs?

Many companies have developed no-code solutions to simplify AI training and turn customer data into Machine Learning-Ready models. swivl is one option that empowers product and support teams to seamlessly “clean” data, labeling it to better understand their customers.

With swivlStudio, companies can build the right type of customer experiences using a simple annotation interface and proactive feedback loops. These tools allow businesses to continuously improve their automation while keeping Humans-in-the-Loop.

Three Step Approach to Implementing AI Chatbots

The swivl team typically follows a simple 3-step approach when consulting with a company looking to use AI and Machine Learning to achieve its business objectives:

  1. Use specific short pilot project timelines (1–3 months) with limited customer personas and defined goals.
  2. Achieve buy-in from all team members and the C-Suite. Work hand in hand with internal teams to onboard and provide training on AI and Machine Learning concepts.
  3. Test assumptions, experiment and reiterate. Repeat. Map out future implementation steps.

Experiment and Optimize

Businesses today invested in AI and Machine Learning Models can reach what we call a virtuous cycle of AI. Virtuous cycles of AI enable company data streams to be a constant source of value without causing a drain on resources. In order to achieve this level, businesses must continually iterate and optimize their AI-powered platforms.

Virtuous Cycle ofAI enable data streams to be a constant source of value.

One of the biggest barriers companies have in implementing AI is simply getting started. We recommend trying outsourced AI-powered chatbots as a first step to optimizing customer experience. This step will let you dip your toe in the water and dive into the big black box called AI easily and more inexpensively.

Meet swivl’s Intelligent Agent, Hoover.

We invite you to visit our new website and learn more about swivl, an AI-powered conversational platform to make your customer data actionable and optimize customer experience.

Don’t forget to give us your 👏 !

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