Conversational AI is the technology that makes human-computer conversations emotionally intelligent, and less scripted. It allows AI-led chatbots to interact with people in a human-like manner, thereby keeping this human-computer interaction as natural as possible, yet high on its emotional quotient.
Conversational AI is trained to comprehend and engage in contextual dialogue using Natural Language Processing (NLP) and additional AI algorithms.
Conversational AI is one among a few other new-age technologies that continue to emerge on the scene including augmented intelligence, edge AI, data labeling, and explainable AI.
How Conversational AI helps with better CX?
Conversational AI uses a combination of natural language processing (NLP), machine learning (ML), speech recognition, natural language understanding (NLU), among other language technologies to process and contextualize voice or text messages and accordingly respond with the most suitable answer.
Through NLP, the computer can ascertain the intents and entities of the customer on the other end, while identifying statistically significant patterns that it has been trained on. This enables it to learn your business needs and get smarter with time, thus helping a more evolved customer experience.
Why is it important to invest in Conversational AI?
Gartner had predicted that, by the year 2020, customers would manage 85% of their interactions with businesses without interacting with a human. It’s predicted that in the upcoming decade, basic automation and apps will be replaced with advanced AI technologies with an objective to improve the overall customer experience metric by proactively gauging the customer’s needs and intent and engaging on an emotional level.
An increased amount of AR and VR innovations across industries are set to be the norm by 2025 as part of an expected customer experience offering.
“The greatest advantage of having a conversational AI solution is the instant response rate. Answering inquiries within an hour means 7X greater probability of converting over a lead. Clients are bound to discuss a negative encounter than a positive one,” reports AnalyticsInsight.net.
Image Courtesy: www.kore.ai
Conversational AI in Insurance
Conversational AI is an ideal addition for service, healthcare, insurance organizations to name a few, as it helps with helping support agents streamline their work, take after-call notes in the CRM system, or complete the missing details that will help by building a seamless customer service system. The technology also helps predict or react to changing call patterns and/or any real-time guidance the agent might require.
Additionally, customers can first engage in self-service aided by conversational intelligence to save time, improve efficiency and get the desired results sooner. While rebranding Care Health Insurance’s (erstwhile Religare) website and app, Mantra Labs deployed Hitee, their AR-based virtual support that helped with the first-level solution for the customer and in turn, led to higher New Business Conversions by a factor of 10X and an overall drop in customer queries over voice support by 20%.
2021 Customer Support Trends Report says that inferior customer experience costs companies at least $62 billion annually. 56% of support leaders shared that their current chatbot implementations don’t carry intelligent tools in this report, and customers are increasingly demanding convenience from businesses they interact with.
Intelligent chatbots make apps simple, and more human to use. Their USP is to create device-agnostic experiences across channels, thus becoming a key factor in driving intelligent customer experiences.
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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground
In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?
This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.
Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.
Why was Data Warehouse the Best Move on the Board?
Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.
The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.
The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.
The True Depth of Data Lakes
While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.
Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.
However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.
Is the Data Lakehouse the Best of Both Worlds?
This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.
Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.
As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:
Feature
Data Warehouse
Data Lake
Data Lakehouse
Data Type
Structured
Structured, Semi-Structured, Unstructured
Both
Schema Approach
Schema-on-Write
Schema-on-Read
Both
Query Performance
Optimized for BI
Slower; requires specialized tools
High performance for both BI and AI
Accessibility
Easy for analysts with SQL tools
Requires technical expertise
Accessible to both analysts and data scientists
Cost Efficiency
High
Low
Moderate
Scalability
Limited
High
High
Governance
Strong
Weak
Strong
Use Cases
BI, Compliance
AI/ML, Data Exploration
Real-Time Analytics, Unified Workloads
Best Fit For
Finance, Healthcare
Media, IoT, Research
Retail, E-commerce, Multi-Industry
Conclusion
The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.
Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.
At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.
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