Making the Build v. Buy Decision for a Natural Language Processing Tool
Build or Buy? Bring your company up to speed with Machine Learning-powered 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 on your company, and to do so without losing your core competencies, is vital. But where to start?
Chatbot Customer Experience Solutions
AI-powered Chatbots are being used extensively to improve the Customer Experience and personalize customer engagement. Affecting change in Customer Success is an effort that must transcend company sectors, crossing the divide between sales, marketing, onboarding, 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 the customer experience must buy into the process.
Chatbot Software Value Analysis
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 the potential financial value as well as the time needed to implement AI platforms is one of the most challenging tasks. This will understand cost savings and improved profitability forecasts, and 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 depending on the AI platform.
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
An ML-based AI conversational platform is software that learns. This type of platform requires a human to clean and label data, it cannot simply be programmed; a true partnership between humans and machines. To teach these platforms to become smarter, humans must feed these systems examples, many many examples. Robin Bordoli, CEO of CrowdFlower, a company which provides 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 are still needed, especially with the material in which context matters and when empathy is required.
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 and can take over the conversation via Slack, SMS, WhatsApp, or the medium of choice. They can then answer questions in real-time. A human agent is easily able to step in when 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. This data can then be fed back into the algorithm to improve it, making the platform smarter and 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 the best results. Pre-1995, successful businesses used solicited human data and insights to improve Customer Experience. The 2000s saw a move into using digital data and insights. As we look to the 2020s, 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 customer data.
However, there is 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 their 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
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 pieces of an ever-complex business building toolset.
In-house or outsourced machine learning models can both be used successfully. Outsourcing makes starting pilot projects more quickly 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, 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 wit.ai, Dialogflow, Microsoft LUIS, RASA, etc. As well, platforms should integrate into your CRM to make that data actionable.
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:
- Use specific short pilot project timelines (1-3 months) with limited personas and defined goals.
- 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.
- Test assumptions and experiment and reiterate. Repeat. Map out future implementation steps.
Experiment and Optimize ML Models
Businesses today invested in AI and Machine Learning Models can reach what we call a virtuous cycle of AI. Virtuous cycles 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.
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 the 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.
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