September 28, 2018
What you need to know about building a conversational AI team
Editor’s note: This page is a summary of our playbook – Building Conversational AI Teams. You can grab your copy of the full playbook below.
Conversational AI has the potential to change the way you do business and build relationships with your customers. But these systems will not build themselves (yet). Startups that focus on conversational AI need teams that can articulate an inspiring and differentiated product vision. They’ll need to design experiences that are effective, memorable and delightful, and train high-performing machine learning and Natural Language Processing models.
Building Conversational AI Teams
In this playbook you’ll find:
- Role descriptions, reporting structure, and sample job descriptions
- Advice on where to source the best talent
- Interview questions
- Tips on how to onboard your conversational team
- Steps to create a culture to attract top conversational AI talent
People with these conversational skills are in high demand and short supply. Where do you begin? Is conversational AI even a profession yet?
With more business models and product avenues available, nuanced strategies are emerging for different conversational objectives. A company focused on growth will aim to acquire users quickly, whereas a company focused on engagement and LTV will need to deliver subtle, meaningful interactions with users before scaling. Each of these strategies will require a different set of skills and a different type of conversational product team.
Get Conversational Internally
Before we dive in, let’s say a few words about using conversational AI in internal tools. Unless you’re a really large organization with very specialized functions, you’ll be best served by using existing solutions instead of building your own. For instance, instead of relying on email you might consider X.ai to automate meeting scheduling, or Skipflag to build an up-to-date knowledge base from your interactions in messaging apps.
While you don’t need to do custom development, we recommend you give internal conversational AI tools a try. Not only can they make your employees more productive, but they’ll also give your team a feel for what’s possible in a great conversational experience.
Building a Conversational Roadmap
To win the war for talent in this space, it’s important to develop a clear plan for how you are going to build your team.
If done right, you’ll be able to leverage the shortage of conversational talent to your advantage and deliver unique experiences that increase engagement and further your competitive advantage.
Creating a conversational vision, strategy, and product roadmap will allow you to solidify your approach. You should consider whether you plan to buy, build, or take a hybrid approach.
Before you hire, identify where you’ll have the greatest impact. Map out how your customers interact with your business and products. Include all channels across the organization. Then identify where there are opportunities to improve through the use of conversational AI.
Next, conduct an exercise to assess these opportunities and prioritize them based on what is strategically important to your business. Use this to map out a plan for conversational AI. This will help you decide how to grow and evolve your team and set a path to maturity. You can learn more about this process by reading our 9 Principles of Conversational AI.
Planning for the Right Skills Mix
With your plan in hand, there’s good reason to focus on your team next. Without the right skills and team as part of your organization, it will be difficult to achieve the promise of conversational AI, opening the opportunity for faster moving competitors.
If you’re building a conversational AI team, you’ll know that there’s a lot to consider and not much information available to help you.
Your Team Planning To-dos:
- Acquiring the right skills for your team while balancing budgets
- Writing a strong job description that will bring top-tier talent in the door
- Sourcing talent, including the best nontraditional options
- Asking interview questions to help you identify the highest-potential employees
- Developing a culture and compensation strategy that will retain your team and keep them happy and productive
Software Development Teams with a Conversational Twist
You can get started with the skills you have, but as conversational AI starts to become core to your business, consider supercharging your team with more specialized skill sets. Before we discuss how you should prioritize these hires, let’s take a look at what these roles can offer and how to know when to bring them on board.
There are many different flavors of data scientists, but they’ll all be using your data to answer questions or solve problems. In general, once you have a significant amount of data flowing through your conversational AI system, you might want a data scientist (or several) to analyze and improve its performance. For instance, you might task a data scientist with:
- Segmenting your users into groups based on their behavior.
- Analyzing the other data you have about a user to inform future conversations.
- Understanding how people are using your conversational AI application.
Data engineers do the heavy lifting to manage your data storage and get it aggregated, cleaned up and ready to use in your conversational AI application. You might need a data engineer if you have data spread across a lot of different backends or if your data scientists find they’re spending more time cleaning data than actually analyzing it or using it in models.
Computational linguists / Natural Language Processing scientists
Although terminology varies, I’m talking about someone who has a deep knowledge of linguistics combined with technical chops. Consider adding someone like this to your team if you’re building a broader conversational experience vs. one with only a few options.
You’d also bring people in this area on board if improving your performance on key language technology components would yield meaningful business results. For instance, let’s say you’re building a customer service experience that routes users differently if they’re unhappy vs. happy. In this case, you want to invest in someone who deeply understands sentiment analysis and can build a really accurate classifier.
When making this hire, be sure to focus on candidates with a background in the languages, channels and modes (text or speech) that are most important to your conversational AI strategy.
Conversational UX designer / researchers
If your conversational AI experience isn’t meeting your goals for user engagement, you might invest in a designer with previous background in designing conversational experiences for text or speech. Designers with a background in call center or IVR applications or video game dialogue often have highly relevant skill sets.
For some conversational AI applications, you’ll have access to ample training data. For instance, you can develop content for a customer support chatbot from your existing customer support conversations. However, if you’re starting a totally new type of conversation, an experienced writer might be required to handle what you’re going to communicate in a way that is consistent with the persona of your bot.
If you’re ready to hire for one of these roles or a Conversational Product Manager, Integration Engineer, Audio Designer / Engineer, Taxonomist or Conversational Success Manager, you’ll find job descriptions and sample interview questions in our guide which you can access here.
Now you know who you’re looking for, let’s look at which order to hire them in.
Matching Hires to Priorities
Chances are, you’re already shipping software with a team structure and process that works for you, involving product management, engineering, design and QA. The good news is that you’ll be able to use a similar structure and process for conversational AI purposes.
You don’t need a full team to get started. You’ll need product management, design and engineering for any project, but it’s possible to make headway using more generally trained resources that are willing to come up to speed on basic conversational AI best practices. That said, adding experienced conversational AI specialists when you’re able to will help maximize your chances of success. Prioritize these depending on your maturity and product goals. Think about the path your product will take to maturity.
As your product evolves the mix of skills you require will change with it. You’ll need to make decisions about how to balance resources and budgets to meet your objectives, using full-time, contract and consulting staff.
Stage 1: Early Stage Companies, or New to Conversational AI
At this stage, you’re developing your product vision and roadmap, gathering requirements and developing the foundation for your initial conversational experience. Your focus is on getting a basic experience up and running on a single platform. This will allow you to start gathering data and user feedback and validate your product-market fit at a high level.
Hire a conversation-friendly product manager. Using high-quality conversational AI platforms, you can make a lot of progress with a general purpose engineering team. But because conversational interfaces have unique challenges and opportunities, we recommend using a product manager with previous expertise. Look for someone who has built their own chatbot, is a heavy user of messaging and conversational AI products, and understands some basic tenets of natural language processing.
Design for conversation. Similarly, conversational design has several aspects that differ from other types of interface design. These include persona design, dialogue flow design, and conversational repair design. For speech, defining the right voice, pausing and earcons for the conversation are also relevant. If you can get a designer who already has some experience with conversation, that will help. Otherwise, you might need to build in time for your design team to ramp up on best practices in this area.
Personify your business (with input from the marketing team). Since users treat conversational agents like people, the personality of your chatbot or voice interface really matters. Even if the goals of the product aren’t specifically marketing-oriented, you’ll want marketing at the table to make sure your persona design aligns well with your brand.
Customize your QA process. Quality testing for conversational interfaces can be more complicated than for traditional web or mobile interfaces, for a couple of reasons. First, the range of responses a user can give (when natural language input is supported) is much less constrained since they can say whatever they want instead of choosing from a limited menu of inputs. Second, your conversational AI application is likely to rely on several machine learning models. You’ll need to make sure your testers can handle assessing the performance of your system quantitatively, or add a data scientist with complementary skills to help out.
Your next hires will depend on the path you take to maturity – we’ve outlined the two most common approaches below.
Stage 2A: Scaling a Narrow-purpose Conversational Experience
On this path, your aim is to reach additional user groups. For instance, if you are building a customer service chatbot, you might want to automate a handful of the most common interactions and roll that out to a large user base, then focus on adding additional functionality afterwards.
Double down on the quality of your experience — address intents that produce errors, getting the use cases that are part of the product right, and creating a successful, memorable, differentiated user experience. Design resources will help to optimize each part of the experience. Consider adding customer success, so that you can incorporate user feedback into the product.
A data scientist can help you prioritize improvements, and a data engineer will help you capture data to drive improvements.
Stage 2B: Building Intelligence or Personalization
To build an intelligent and personalized experience that inspires user trust, you’ll focus on more sophisticated interactions that take advantage of rich contextual data. If you’re building a trusted virtual assistant that requires a high degree of intelligence to add significant value for users, you’ll want to build out a robust set of functionality with a smaller user group before attempting to scale.
To build proprietary NLP/NLU components and domain-specific or user-level models – going beyond standard features available in common APIs such as Dialogflow or IBM Watson – you’ll want to add some NLP scientists or similar resources to your team. They will define an applied research roadmap, conduct experiments and hand off new features to engineering.
Bring on a dedicated taxonomist to maintain, organize, analyze and extend the taxonomy. This will allow you to expand the use cases your application can handle and determine how your taxonomy should grow. Your taxonomist will work closely with NLP scientists to improve the underlying NLP and technical features of the platform to support the identified taxonomy changes and evolve the system’s intelligence.
Stage 3: Your Mature Conversational AI Team
Your mature team should be equipped to support an expanding set of users and intents, expansion of use cases to adjacent steps in the business process, a recognizable and compelling persona, and increasingly personalized experiences.
Competition for talent in these areas is fierce, so you may need to get creative. While you might find hires from academia, consider looking to post-graduate certificate programs or other alternative paths as well. Post a challenge to Kaggle, or attend conferences as a way to engage with the community. Make sure your recruiters are up on the latest language in the field. Eventually, you might even want to think about working with a dedicated AI-focused recruiter.
Ready to get started?
Hopefully, you now have a good sense of how to start integrating conversational AI into your business. You can dive into conversational AI for employee efficiency with a minimal team and off-the-shelf products. As you move into customer-facing applications, you don’t need to wait to get started until you have a specialized team in place. Start with people you already have, and add as it becomes necessary.
For a deeper dive on everything I’ve covered in this post, there’s tons more information in our playbook – Building Conversational AI Teams. You’ll find:
- Role descriptions, reporting structure and sample job descriptions
- Advice on where to source the best talent
- Interview questions
- Tips for onboarding conversational AI teams
- Steps to create a culture to attract top conversational AI talent
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