~9 minute read
Today’s data-driven businesses need faster, streamlined access to the information that helps them uncover insights, make informed decisions, and take strategic action towards their goals. In the world of customer service and marketing, conversational AI has already ushered in a new era of information accessibility: purpose-built chatbots can instantly answer customer questions and guide visitors to make purchases.
Though most of us are familiar with conversational experiences like these, new applications of conversational AI technology reach far beyond the customer service and marketing domains.
Conversational AI refers to an AI-backed system that (usually) includes machine learning (ML) and natural language understanding (NLU) technologies that enable computers to understand human language (also called natural language, or NL) and respond in natural ways that mimic human-to-human conversation.
Users experience immediate value when they communicate in natural language and the system returns exactly what they’re looking for. Some systems return artificially generated conversation, others simply return the information itself. This could be something like a list of all flights to New York City this week or a summary of the most popular menu items at a restaurant.
As conversational AI technology advances, we see it reshaping the way people use computers and interface with the digital world.
The power of conversational AI can be unleashed on the growing problem of data inaccessibility in the enterprise.
It’s difficult to get answers and extract information from ever-expanding databases. Tools that have been developed to solve this problem are often rigid and complex, requiring specific expertise to leverage them and, ultimately, failing to meet the demands of today’s data-driven businesses.
We’ve already seen chatbots transform information accessibility in the customer service domain. In much the same way, we can expect a paradigm shift in the realm of data accessibility and business intelligence through the adoption of AI systems built to facilitate easy, efficient access to data through conversational interactions.
Here are seven reasons why data-driven businesses need conversational AI if they want to improve data engagement and accessibility:
#1 Democratize data at all levels of the enterprise
In an ever-evolving market, agile teams need access to information that will help them make decisions day-to-day and inform long-term strategy. Executives are no longer the only decision-makers in a company: every individual in a data-driven business needs to be empowered to make the right choices for the organization, daily. Access to great data is fundamental in this process.
With the power of conversational AI, users don’t need to learn how to use complex BI tools or call on technical teams to handle ad hoc data requests and run custom reports, they can simply ask for the data they need to get the job done, and get it done faster.
#2 Adopt a data-driven culture, fast
Gartner predicts that the competitive edge data gives businesses will catalyze digital transformation over the next three years. The faster a company can adopt a data-driven culture where critical business decisions are based on insights and trends from a single source of truth––the data––the more likely it will outstrip the competition.
McKinsey Global Institute reports that data-driven organizations are 19 times more likely to be profitable than companies slow to adopt a data-centric culture. Conversational AI built for database access makes it easy to scale data initiatives throughout a company and by leveraging this technology, discovering insights and diving into the metrics that matter is as easy as starting a conversation.
#3 Cut costs and recover your team’s time
IBM reports that companies spend $1.3 trillion to answer 265 billion customer service calls every year, but there’s evidence that leveraging AI to address routine issues and requests can cut this cost by 30%.
A conversational AI solution built for interacting with databases has the same implications: it allows data-driven businesses to drastically reduce the number of hours expensive technical teams (or anyone else, for that matter) spend acting like “customer service agents” for the database, fielding incessant data requests from users and struggling to keep up with internal reporting needs.
In addition, users can easily get the detailed reports or quick one-off answers they need in seconds, rather than waiting hours, or days (sometimes far longer) for other team members to compile and provide that same data.
#4 Level-up the BI tools and systems you already use
Most data-driven businesses are already using powerful tools for data access and analysis. If your company has seen success with your own BI systems or third-party tools, or if you’re leveraging the expertise of in-house BI specialists, conversational AI can augment the functionality these systems offer so you get even more out of them.
With the right integration, conversational interfaces backed by AI can be embedded within the same reporting or dashboard interface your system already supports, allowing users to query their data conversationally to gain deeper insights from their visualizations and summaries.
BI tools can provide initial information that users can then delve further into, using the conversational AI system to seamlessly conduct deeper exploratory analysis. Additionally, such systems can help everyone from business analysts to “citizen data scientists” (individuals not officially trained to use data) find answers, fast, so they can spend more time thinking strategically and getting more done.
#5 Explore your data, don’t just report on it
Building on our previous point, conversational AI can transform the way you and your teams interact with data entirely. Rather than relying on inflexible dashboards or rigid reports, conversational AI allows users to follow their individual train of thought through the data analysis process to get exactly the information they’re really after.
Users can begin with questions they already have about the data and input them directly into the conversational AI system. The answer to those questions are likely to spark more questions and spur further insights.
Instead of being stuck with only the information made available in a default template or an outdated dashboard, users are free to slice and dice data and discover more in-depth answers from their data that might not otherwise be accessible through ready-made reports.
#6 Enable data-driven decision-making everywhere
Most embedded BI tools have interfaces that take up a lot of screen real estate in order to show complexity and detail. With conversational AI capabilities, users can ask questions, receive meaningful answers, and easily explore details on a much smaller screen.
Instead of trying to fit every piece of data into a comprehensive dashboard, conversational AI makes it possible to pinpoint the data you need in the moment and analyze it further. Employing an intuitive conversational interface means users benefit from access to information, anywhere at anytime. It’s simple to offer great mobile data experiences that empower users out in the field, working remotely, or on the go, to get important answers right when they need them.
Because conversational AI can provide access to information and insights in an instant, pulling up data in the middle of off-site meetings or quickly checking for updates en route to an event is as seamless as checking the weather. That means data-driven businesses can thrive both inside and outside of the office as the nature of work continues to evolve.
#7 Embrace change to stay ahead of the curve
Thanks to digital messaging systems from SMS to Facebook Messenger, conversational interfaces have quickly become the norm rather than a novelty. Furthermore, we know that many users are more than happy to interact with conversational interfaces backed by AI.
Like 91% of businesses, your team might already be using solutions for remote work like Slack or Microsoft Teams to interface with each other. Conversational AI technology makes it possible to integrate conversational access to data within messenger-style workflows that everyone already finds value in.
Gartner predicts that by 2021, 70% of organizations will assist their employees’ productivity by integrating AI in the workplace. By rolling out innovative technology now, you can set up your team for future success as industries evolve.
Successful businesses of the future will rely heavily on data for strategic decision-making. Conversational AI will play a starring role in the data access landscape as adoption of this innovation permeates every industry. As the nature of work changes rapidly and AI technologies continue to improve, there are more and more opportunities to make data work as hard as the people who use it.