Deploying conversational artificial intelligence (AI) is not an IT upgrade in the conventional sense. Conversational AI is ushering in sophisticated robotics into the front office. You’re basically giving your customers a blank text box or open mic and asking for their voice across channels and in real-time. All virtual assistants, as well as the teams that manage them, are put through their paces responding to all of the idiosyncratic ways users communicate. This is why it’s crucial to make sure you have a system that can exceed your customers’ expectations by rising to every challenge.
Understanding the difference between conventional chatbots and conversational AI is important. To anticipated statements and requests from humans, Chatbots serve up canned responses. The systems are not integrated with front-office platforms or data isolated applications meaning they can’t reply without that additional information as they don't understand context, making them too one-dimensional to offer the kind of support demanded now. On the other hand, conversational AI platforms communicate across most corporate channels, including voice interfaces, text messaging, social media and corporate websites as they are deeply integrated into information systems. Whether you choose to license a conversational AI platform with in-built expertise in your sector or build your conversational AI systems internally, it’s important that the employees and executives across functions are aligned about what it takes to maintain and deploy a virtual assistant.
Deploying virtual assistants demands a myriad of functions at your company to align, customer care, compliance, product lines, tech teams, and channels to name a few. Together, they navigate a number of stages, from integrating and piloting, designing to deploying, improving and expanding the virtual assistant to new channels, products, and markets. Every company should carefully consider these factors after assessing their organizational readiness.
To create contextual and personalized conversations with your virtual assistant or bot, access to real-time data is crucial. To identify which data is necessary to feed the platform to allow for successful interaction, first determine your most high-value, frequent use cases and then work backward. Then assess that data quality then decide if it should be augmented to support the conversational experience you would like to provide. Where does the data reside? How is that data structured? Also, try and give your AI systems unhindered access to every database that they need to perform tasks successfully after you’ve made it as secure as possible. And different use cases require different data sets. Regardless of your use cases, you will likely need to structure and augment data to meet your objectives. Fortunately, tools you already use for cleansing and enhancing data to prepare your data for conversational AI, can be leveraged.
You have to develop a strategy for the content that’s going to flow through to the virtual assistant, once the pipes are in place. Determine the ideal sources to integrate into the platform after assessing your bank’s existing content. For conversational interactions, you'll have to refine and shape the content. Meaning bite-size, short replies, paragraph-long answers are out of place in a conversation with a virtual assistant. This virtual assistant must also mirror the personality of your brand since the virtual assistant is an extension of your company. How the bot engages can contribute to the delightful customer experience. Bots are an important new face for a company, like switchboards and websites before them. Many big firms have capitalized on their popular, quirky advertising mascots, that chirpily banters and aims to sell their products. By communicating efficiently and accurately with many, unpredictable human beings, conversational AI’s “persona” must reflect the firm’s competence and embody its values. A synthetic voice built with a personality that is too far too different from your brand can be just as bad as a tone-deaf advertising campaign.
Without users ever coming to know there was a problem, certain technical issues of a company can be fixed. Deploying conversational AI won't feature in that list as its issues will be your brand’s issues. An AI system has to be foolproof. Saturation integration is part of this. If conversational AI platforms are not integrated extensively with and through CRM and ERP systems, little happens. Real-time access to all relevant corporate databases, amounting to gigabytes if not petabytes of data is an important part of successful implementations. Their interfaces ideally or synthesized voices are realistic enough to keep consumers focused not on the software, but on the task.
Your virtual assistant won't ever see the light of day without buy-in from compliance, security, and legal. As early as possible, involve these key stakeholders. And the sooner you inform and educate these teams, the smoother the process will go as conversational AI is a new breed of consumer service. Take into account extra time into this process with your compliance and legal teams in particular, for content approval and such. They will have to adjust to the intricacies of conversational AI even though they are accustomed to approving every word of content. As most interactions will be machine-generated depending on contextual situations, you cannot just provide exactly everything the virtual assistant will “say.” To avoid a possible delay-causing stalemate down the road, educate these teams early about the inherent differences of a conversational AI experience.
There are other aspects to consider too, even though ease and speed of development is crucial for companies looking to gain a foothold in their AI strategy. You’ll need language support if you’re a global company. The same goes with porting to different devices or services. Enterprises have to ensure that intelligent agents will work across them as the number of devices that users interact with grows everyday.
This will be a harder point to digest, but executives have to be ready to destroy then remake what their companies have just built. Don't hesitate to reevaluate when a chatbot you’ve implemented is working incorrectly or in tone. If necessary, begin again as it will be one of the most visible parts of your brand.
All of us need to move beyond lip service about data’s value. The really savvy ones don’t act on partial data, and this applies for successful customer-facing AI systems too. Ensure that the system has access to the necessary data sets when deploying conversational AI otherwise, its effectiveness will be limited. Getting conversational AI off the ground is worth the struggle, although its no small feat. You too can start reaping the rewards of conversational AI with the right technical and operational readiness levels.