Conversational AI technologies are undisputedly proving to add value to a company. However, there are companies that fail to successfully keep these technologies alive for a long time: usually companies disconnect their conversational bots due to lack of resources or because “they don´t add value” to the business. There are however, other companies in which their conversational bots are a centerpiece of their service provided to both employees and customers.
So what is the difference between these two? There are many factors that influence these outcomes but having a well thought, long-term, 360 degree strategy is one of them.
This article gives an overview of the basic elements each company should consider when defining their Conversational AI strategy
Actually, strategy in Conversational AI is the same as in every other area: a long-term plan to achieve an objective. It is the means for executing a vision and it involves carefully planning and thinking about methodologies, resources, tasks, etc.
Contrary to popular belief, developing the solution is roughly a 20% (tip of the iceberg) of the component of a Conversational AI strategy. There are also other elements that must be considered to guarantee a successful and sustainable adoption of this technology in a company, which is the rest 80% of implementing such technology. Let’s explore the most important ones in the next section.
As it happens with usually every aspect of a business, a successful Conversational AI strategy must be aligned with the main company strategy and values. Understanding what the company needs to achieve will be extremely helpful to align the scope of the project, determine the value the conversational bot delivers and to keep the team and project’s discussions focused.
Business alignment also means knowing who your customer is and what their needs are . This is a fundamental piece that will determine which dimensions (channels, languages, functionalities) the project will have and in which order they will be developed.
Having the right people, at the right moment with the right skills is critical for a successful project implementation. Companies usually start with small teams delivering a conversational bot which centralize most of the needed skills for a project. As the solution grows, the need for specialized resources come in and the complexity increases.
A solid conversational AI strategy should not only consider present and future skills, but also resource availability (some resources may belong to other business units), work methodology and how communication will be handled.
Adopting new technologies in a volatile, uncertain, complex and ambiguous (VUCA) context calls for flexible and scalable architectures. Businesses should choose an AI platform that is able to quickly scale up but also to integrate seamlessly with current (and future) elements of your corporate architecture: what starts with a simple backend integration could end up in also connecting more than one application to your conversational bot to automate tasks or integrating with your call center application. Platforms like Teneo which come with an array of pre-built backend, channel, and data connectors that can be used to fast-track integrations in a project.
“If you can´t measure it, you can´t improve it”. The amount of KPIs in a Conversational AI project can be endless, therefore companies should keep in mind what are the relevant indicators associated to their projects and companies. Usually a combination of performance, user experience and business related KPIs are analyzed together.
Embedding data analysis into your scoping, development and QA processes is fundamental to understand the success of the project, its impact on the company and enhance your conversational bot. It is also an essential element of the continuous improvement process in a project.
Businesses may impose security policies that may end up affecting the projects success, so the sooner these are addressed the better. There might be requirements as to how information should be handled or security mechanisms within the company’s IT architecture that need to be integrated.
Governments impose regulations on businesses, and for good reasons. When interacting with customers and their data, companies will need to think about how customer data is obtained, treated and stored in order to avoid blockers that may impact project development or end up in fines and sanctions.
In the end, each Conversational AI strategy is unique to each business. Two strategies might be similar but never the same and that’s simply because each company has its unique way of reaching their goals.
However, by considering the above explained factors as a baseline for defining a strategy, companies can avoid ugly surprises and delays in their deliveries but most importantly ensure a successful adoption of this technology into their businesses.