Conversational AI is an ongoing journey not a one-time project
From whiteboarding to launching a minimum viable product (MVP), let’s look at the path this journey typically takes
Conversational AI is the next frontier of engagement, productivity, and innovation. To get the most out of this emerging tech, however, requires you to think of it as a journey, one in which your employees and reps work hand in hand with AI-powered bots to automate interactions, streamline workflows, and achieve long-range business goals.
Considering conversational AI to be a quick fix – or a short term initiative – will hinder your ability to enjoy both immediate successes and long-term tangible benefits, such as minimizing churn, improving containment and deflection rates, reducing costs, and much more.
Undertaking a conversational AI journey enables enterprises to: plan and execute AI-powered bots on an enterprise-wide basis to assist teams, streamline office operations, and deliver and receive information; deploy chatbots to the front-line to enhance the customer experience; integrate siloed systems and external apps; build internal language capabilities to adapt and scale; and, most importantly, to create business value and become market leaders through innovation.
Effective preparation is key to achieving success with conversational AI. Enterprises need to craft a deliberate plan of action, find the right mix of technology and tools for the job, and empower a team of people to achieve that plan. Here are the most important things you need to do to get your journey off to a good start:
Every bot journey is unique, reflecting differences in industry, organizational capabilities, strategic priorities, budget, and more. Timelines are also unique, varying in accordance with your use case, integration and workflow complexities, available resources, and compliance and change management requirements. However, we’ve identified these 5 stages as part of every conversational AI journey.
Now that you’ve effectively prepared for your conversational AI journey by creating your strategy, establishing a team, identifying and gathering your training data, and getting needed buy-in, it’s time to get down to the nitty gritty and plan out your first text or voice bots. Major components of this step include:
The second step is about more than just exploring the potential impact of bots on your organization. It is about exploring potential use cases and key requirements, deciding the role you should play in the creation and implementation process (in-house vs. outsourced), determining what conversational AI partners you need to achieve your goals, building out your first bots, and more. At a glance, key activities include:
At this point, your first bot has been deployed to limited internal audiences or focus groups. Now, it’s time to expand and roll out additional tasks and dialog flows to a somewhat wider employee or customer audience.
Pilot performance should be closely monitored to ensure alignment with your overall success metrics. In addition, all stakeholders should be given sufficient time for testing, and then subsequently surveyed for feedback. It can be helpful to instruct them to try to “break” the bot, by being creative with their responses.
This input provides you with valuable qualitative and quantitative data. This data, when coupled with a thorough review of your methodology, intents, entities, ML models, and more, allows you to document lessons learned, shows room for further expansion, and provides insight into what needs more work before advancing to the ramp up stage.
This step involves finally pushing out your conversational agent to your intended audience. The primary focus includes:
This final step is all about establishing conversational AI as a baseline competitive advantage within the organization. This includes setting up a function dedicated to continual monitoring and training of conversational agents; disaster recovery and response plans in case of outages or other issues; and continuous improvement based on key metrics, including intent recognition, handover, conversion, and abandonment rates, sentiment analysis, and more.
Beyond including these best practices into your operations and culture, this step should include a continual evangelizing of the benefits of conversational AI, while promoting additional bots as a key performance objective across all relevant internal and customer-facing business lines.
The world of AI is moving fast, and it’s easy to overlook the small things and make missteps. To further complicate this, the challenges you’ll face deploying conversational agents is, in many ways, tied to the complexity of your use case – meaning no two implementations are the same. Nevertheless, we’ve helped a lot of companies over the years, and have learned a thing or two. We’ve identified several common implementation mistakes to avoid.