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Conversational AI technologies have evolved rapidly in the last decade, with chatbots, virtual agents, voice assistants and conversational user interfaces now part of our daily lives. This explosive transformation toward AI assistance hasn’t come from an individual technological innovation, but rather multiple innovations developed as an assistive layer between our lives and our digital services, whether we’re asking for directions, purchasing online or banking. In fact, IDC predicts global spend on AI will double from 2020 to 2024, growing to more than $110 billion, with retail banking expected to spend the most.
Surprisingly, for all the benefits conversational AI offers, many projects fail as a result of poor discovery done at the beginning, which is why spending time upfront to examine what’s being built and the value it will deliver to customers is critical. With lessons learned in the field instrumental in improving odds for success, the following seven steps can serve as a guide for enterprises in nearly every industry embarking on or advancing an existing conversational AI platform:
1. Identify the Problems You’re Trying to Solve
Just by numbers, the business value for chatbots is often apparent. But numbers alone will not guarantee success. When deciding where to start or what to do next, it’s vital to balance ROI with customer needs. Many banks, for example, will spend months building a system only to find that customers have no interest in what is delivered. There are a variety of ways to test ideas early with customers. With Botmock or Botsociety, for instance, rapid prototypes can be created and put into customers’ hands in a research setting. Many enterprises use the Wizard of Oz approach, which allows a user in a live environment to interact with a real agent acting as a virtual assistant to test hypotheses and validate risky assumptions they’ve made, from using chatbots to switch their mortgage deal to receiving their account balance from their Alexa. In the long run, this can save months of wasted design and development time.
2. Align the Organization on a Conversational AI Vision
To ensure your conversational AI program isn’t viewed as a digital side-project, create a shared vision and ensure it’s a vital pillar of the broader contact strategy. Whether experimenting with a first conversational interface or developing more sophisticated platforms and experience capabilities, it’s important that stakeholders across the business embrace conversational AI as not just an interface type but as a means for achieving larger organizational goals. To ensure the business is aligned around a common vision and organizationally prepared to make that vision real, proactively address these four core areas:
1. Articulating a realistic yet ambitious future vision
2. Addressing organizational siloes
3. Knowing when to pivot
4. Building future roadmaps with flexibility
3. Think Strategically About Your Conversational Platform Architecture
Beware of the sales pitch from organizations that say implementing this technology is quick and easy. Despite advances in Natural Language Generation (NLG), implementing and training conversational AI is relatively manual and time-consuming without the right approach. A flexible architecture with the right building blocks and a data-driven approach is key to automating as many processes as possible and delivering value fast. Where possible, leverage existing investments and consider the needs of all business units that may want to deploy conversational AI solutions in the future. It is helpful to think about virtual agents as having two distinct architectural phases:
1. A conversational platform that integrates with critical communication channels and can seamlessly hand over to human agents within those channels. It doesn’t have any integrations into back-end enterprise systems, but it can already deliver significant value.
2. A conversational platform with authentication that can hook into back-end enterprise system to unlock end-to-end use cases, such as transactional queries.
It’s important to select the right NLU (IBM Watson, Google Dialogflow or Amazon Alexa, etc.) and dialogue building tooling if the platform providing the NLU does not provide one that meets the required needs. There is no best in market, as the best solution will depend on a business’s requirements, broader ecosystem and technology and cloud providers.
4. Secure the Right Funding and Generate Momentum
While creating a conversational AI program may prove essential, some can be put off by the time and money needed to implement and run a successful one. So, start with a low initial investment, demonstrate the benefits and then scale up to millions of customers if that’s the goal. To help in decision making, answer the following questions:
· Can this technology help leverage data to understand customers better than before?
· How might staff benefit from virtual agents?
· How could conversational AI help boost revenue?
· Are there key complaint drivers that could be solved with automation?
· How can this technology be utilized to leverage other forms of AI and automation to really solve business and customer problems?
· If capacity from human agents is freed up from Conversational AI, how can this be reinvested?
· Can a virtual agent enable a launch or scale more channels that customers want?
· How could a virtual agent help execute proactive marketing or engagement?
· How could conversational AI help reduce fraud or ensure compliance?
5. Staff the Right Talent: Starting with a Conversational Analyst
One of the most important learnings is that the roles and skillsets needed to deliver great conversational experiences are different to web or app teams. Expect to have to build new role profiles and hire externally. The hard part is finding talent with relevant experience in this field when they are in such high demand across the industry. It may be necessary to create new role types that may not currently exist, such as a conversational analyst, who will use machine learning algorithms and natural language processing to study the way your customers speak and use these insights to train your virtual agent, as well as a conversational designer, a copywriter/UX designer who can create the conversational flows, write the dialogue, utilize rich features (such as quick replies, buttons and carousels) and optimize the experience over time.
6. Create a Persona for Your Conversational AI Early
The ultimate goal for implementing conversational AI is to create a virtual agent that is a brand ambassador with an engaging persona. Start by thinking about the demographics and psychographics of the typical customer. Use customer personas if available or create them from scratch if not. Then create a backstory as a guide for this conversational AI program. Think about age, gender, ethnicity, family background, experience, job title, likes, dislikes and personality traits. Run customer testing to gain insight into a multitude of factors that go into a customer’s perception of personality including the size, style and layout of the entry point and interface, the use of rich features, the length of messages being sent and the delivery speed of text bubbles to name just a few. Persona is important from an engagement point of view, but it’s also the only way to encourage customers to talk to your Virtual Agent using natural language and unlock the real power of this technology.
7. Optimize Your Virtual Agent Through Agile Design and Delivery
To deliver a successful conversational AI solution, adopt an agile mindset and embrace design thinking. Many conversational AI teams are still heavily reliant upon process mapping tools, like Visio or Lucid Chart, to create designs. Instead, opt for designing in a no-code, rapid prototyping conversation design tool. This allows designers to create mock-ups quickly and even interact with prototypes using natural language. The most powerful benefit of this is the ability to test the virtual assistant with real customers in hours and shortcut learnings, totally independent from the development team.
Once launched, keep monitoring and make improvements as necessary so that customers’ needs are met — and exceeded. There is no doubt that today’s organizations have the opportunity to be at the forefront of the next wave of transformation, seamlessly integrating conversational AI to solve the complex challenge of serving and assisting time-challenged customers at every step of their journey. Of course, conversational AI is not the solution for everything, but there are almost certainly quick wins to be gained by identifying customer interactions that will deliver maximum value with the lowest effort.
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