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Think about how much time of your day is spent completing productive tasks versus interacting with co-workers, including meetings, calls, or brainstorming sessions. And how much time is spent on information-gathering legwork to get your projects off the ground?
According to a recent report, employees dedicate an average of 16% of their work hours to internal communication and collaboration, and another 19% to information gathering and research. To optimize time management, companies are looking for ways to make collaboration more efficient. Along those lines, Shopify introduced an internal “Meeting Cost Calculator” to raise awareness on the cost of huddling versus emailing. By getting rid of even three meetings a week, the company estimated that overall costs decreased by 15%.
But what if finding answers within your company’s data could be as simple and fast as asking a question and receiving answers right away?
Conversational AI platforms are transforming the way companies access and analyze Business Intelligence (BI). Traditionally, data analysis was a time-consuming process requiring specialized skills. Analysts had to sift through dense reports and dashboards to uncover actionable insights, leaving non-technical users dependent on them for interpretation.
With conversational AI platforms, users are no longer bound by complex, counterintuitive formats and jargon, and they can now get the information they need by simply asking questions – no technical expertise needed. In fact, Bloomfire reports that conversational AI significantly cuts down time spent on tasks involving internal communication, research, and information gathering.
In this article, we’ll explore how conversational AI empowers business users to simplify interactions with data and metrics by providing quick, intuitive answers in natural language.
What is conversational AI?
Conversational AI is a technology that enables computers to simulate real-time human conversations. Through advanced Artificial Intelligence, conversational AI platforms simplify the creation, training, and deployment of conversational self-service tools, such as chatbots or virtual agents. As a result, these tools can understand, process, and respond in plain human language.
For companies looking to leverage BI, conversational AI is a game-changer because it allows users of all skill levels to interact with data and analytics in a way that feels intuitive and immediate. Instead of relying on complex BI dashboards, anyone can either type or speak their question and receive answers in real time. This shift represents a major leap forward in how businesses access their data.
How does a conversational AI platform work?
Conversational AI platforms combine advanced natural language processing and machine learning techniques to understand and generate answers in natural language. Not only can they interpret the words of users but they can also grasp the context and the intent of these words. This is made possible with the use of semantic layers learned from deep neural networks, such as transformer models, which are trained on vast amounts of textual data. These models accurately analyze the user’s intent, improving the quality of the generated answers, with updates based on the latest data available, even when continuous learning takes place offline. Additionally, when an AI platform is integrated with RAG (Retrieval-Augmented Generation), it acquires an advanced capacity to provide answers that are even more accurate. The RAG model fetches specific information – through retrieval systems – to then generate text, ensuring precise, contextualized, and relevant answers.
Examples of conversational AI use cases
The most widespread use of conversational AI is in applications such as chatbots or virtual assistants. But this technology’s impact extends far beyond. Here are some examples of common conversational AI use cases:
1. Chatbots
Widely used in customer support, chatbots leverage conversational AI to answer questions, troubleshoot issues, and provide information in an automated way.
2. Virtual assistants
AI-powered virtual assistants help users manage tasks, generate content, answer questions, and control devices through natural language interactions. These assistants leverage contextual understanding to provide responses in line with users’ history and preferences.
3. Text-to-speech and voice recognition
By converting spoken language into text – and vice versa – these applications let users dictate messages, search queries or notes with voice commands.
4. Language translation
Multilanguage support has been key for the development of platforms leveraging conversational AI’s ability to understand spoken or written text across languages in real time.
5. Business Intelligence and Analytics
Conversational AI simplifies BI and analytics by breaking down complex datasets and answering questions in plain, straightforward language, broadening data access across a business organization.
Benefits of conversational AI for numbers and analytics
Adopting conversational AI for BI or Decision Intelligence offers numerous advantages, beyond time-saving benefits:
1. Operational efficiency
Conversational AI significantly reduces the time spent searching for data and analyzing reports. A recent Gartner survey found that businesses using AI to gather insights spend 40% less time on data analysis, freeing employees for more complex or strategic work.
2. Bridging the digital divide
Conversational AI democratizes data access, enabling users of all technical backgrounds to get data-driven insights by simply asking questions and getting answers in natural language, regardless of their technical or data analytics expertise.
3. Insight suggestions and exploration in real time
Conversational AI can do more than fetch metrics from data sources. It can answer users’ questions that dive deeper, exploring data and releasing recommendations and trend forecasts. Moreover, conversational AI can “guide” the exchange in its own way, by suggesting questions that might help users get the information they were searching for.
4. Continuity
Interaction with a conversational AI tool can really feel like a back-and-forth dialogue, with continuity capabilities allowing for longer exchanges with natural flow and context retention from the platform. Also, continuity allows conversational AI solutions to adapt responses to meet users’ needs.
5. Improved decision-making
Ultimately, conversational AI platforms empower all company departments to make informed decisions on the fly, by getting straightforward answers – just like asking another team member – resulting in a swift, more responsive organization overall.
Which conversational AI platform is right for your business?
For regulated industries, AI-powered tools with advanced conversational capabilities can transform workflows by boosting efficiency and supporting data-driven decision-making. For example, with the Decision Intelligence tool Crystal you can literally “talk to your business data”. You can ask questions just like you would with a team member. The features allowing you to explore insights include:
1. Plain language
Access your information in natural language, which Crystal can understand, process, and answer with human language too.
2. Language of your choice
Not only can you talk to your data, but you can do it in a language of your choice too. Crystal’s multilingual capabilities include options for English, Italian, French, Spanish or German.
3. Voice or text
Interact with your data naturally, whether through voice or text, making it possible to get answers to questions in all circumstances, for maximum flexibility.
4. Real-time response
Crystal’s real-time answers only consider current, updated data. As such, they only reflect the latest information available.
5. Trusted reliability
With Crystal, you can depend on accurate insights, in a safe space that’ll ensure data protection, based on your data sources and nothing else.
What will a workday look like when the increasingly fluent and sophisticated conversational capabilities of AI are fully incorporated into our workflows? Chances are, we’ll spend less time on the clock researching and gathering information, while interactions with co-workers may very well shift exclusively to casual coffee machine catch-ups. In return, we’ll have faster answers, streamlined groundwork, and accurate insights delivered with AI-powered tools.
Keep the conversations going – make them more efficient, meaningful, and use your time and resources wisely.
Frequently Asked Questions
Rule-based chatbots – also known as decision-tree, menu-based, or basic chatbots – follow predefined scripts and pre-set rules to respond to specific keywords or phrases. For example, the rules or script could be “if the user types in X, answer with Y”, which would limit interactions and exclude complex requests. However, AI-powered chatbots understand context, interpret intent, and can respond to a much wider range of questions, making them more flexible and conversational in tone, closely imitating the flow of a real dialogue.
While conversational AI platforms can understand context and intent, it cannot understand subtleties of tone, such as sarcasm and frustration. It cannot deliver empathetic answers nor can it connect emotionally. Also, while conversational AI has advanced capabilities to understand context, it still requires thorough and detailed input from a user to obtain the most accurate answers possible.
When exchanging sensitive data, it is important to select tools that will ensure data safety and protection, especially for businesses operating in highly regulated industries. Fortunately, there are a number of conversational AI solutions that will champion security and confidentiality, such as Crystal.
