What augmented analytics really means for modern business
Augmented analytics integrates AI into analytics and business intelligence (ABI) platforms, transforming how organizations handle data. Traditional data analysis methods rely heavily on manual processes, but augmented analytics shifts this dynamic by using AI to assist human analysis.
This integration brings a more user-friendly experience to non-technical users, democratizing data access across the organization.
Through embedding AI into ABI platforms, augmented analytics speeds up data processing and reduces the time to insights. Automating routine tasks and applying advanced algorithms helps analysts surface insights faster and with greater accuracy.
According to a report by Gartner, companies using augmented analytics see up to a 30% increase in the speed of data analysis—directly translating into better decision-making and a stronger competitive position.
How analytics tools were shaping up before generative AI
Before generative AI emerged, ABI platforms were already evolving by using natural language processing (NLP) and machine learning (ML)—helping users interpret complex queries and provide explanatory results without needing advanced technical skills.
For example, NLP lets users ask questions in plain language and receive understandable outputs, making data analysis more accessible to business users and non-data scientists.
Machine learning models helped improve the accuracy and relevance of insights by identifying patterns in data that might be missed by human analysts. This focus on accessibility and speed helped organizations begin moving toward real-time decision-making capabilities, letting more people use data effectively.
Research shows that companies using NLP in their BI platforms have seen a 25% reduction in time spent on data preparation and query formulation, enabling faster insights for decision-makers.
Generative AI is supercharging analytics platforms in powerful new ways
Generative AI is unleashing new powers for analytics
Generative AI brings new dimensions to ABI platforms that extend beyond previous capabilities, offering tools for deeper, more intuitive engagement with data.
- Data storytelling and summarization: Generative AI can automatically create data stories, metadata, code, executive summaries, and visual storyboards. This helps executives and stakeholders quickly grasp complex data narratives without needing to dive into raw data. For instance, AI-generated summaries save up to 70% of the time that data analysts typically spend on report creation.
- Conversational analytics: With generative AI, conversational analytics become a reality. Users can interact with data using natural language, receiving automated insights in real-time. This interaction happens without a traditional dashboard, enabling ongoing dialogue with data that retains context. Research shows that conversational analytics can reduce the time needed to find actionable insights by up to 40%.
- Integration with third-party and proprietary LLMs: Platforms now leverage large language models (LLMs) from leading providers such as OpenAI, Microsoft, and Google. These LLMs improve the understanding and generation of natural language, improving the platform’s ability to provide nuanced insights. ABI platforms incorporating these models report higher user satisfaction and engagement due to the more intuitive experience.
- Improved offerings by platform providers: Many platform providers are acquiring point solutions, including GenAI companies, to enhance their capabilities. These acquisitions then create a more comprehensive ABI offering, providing users with a broad range of tools that go beyond standard analytics and dashboards.
Why generative AI is a game-changer for your organization’s data
Generative AI enhances data utilization by uncovering insights that might otherwise remain hidden. AI models can identify patterns, correlations, and anomalies in vast datasets faster than human analysts. This accelerated insight discovery allows organizations to make more informed decisions, often in real-time.
For instance, companies using generative AI in their data processes report a 20-40% reduction in decision-making time—stemming from AI’s ability to analyze data continuously and deliver actionable insights promptly. Deploying AI-driven insights also helps to reduce human biases in analysis, supporting more objective and strategic business decisions.
How credibly is winning big with generative AI
Inside Credibly’s strategy for using gen AI
Credibly, an SMB lending platform, uses generative AI integrated with supervised models to refine its business operations. The company focuses on combining AI with existing data models to improve its risk assessment and customer profiling processes.
As a result, Credibly achieved faster approval times, moving from several days to just a few hours.
Accuracy has also improved significantly in creating business profiles, thanks to AI’s capacity to process and analyze large datasets more efficiently—ultimately leading to a deeper understanding of customer behavior, enabling more precise risk assessments.
In a high-stakes industry like lending, these capabilities reduce default rates and boost profitability.
Real-world wins from Credibly’s AI moves
Credibly has made several innovative applications of generative AI, driving tangible business outcomes:
- Use of vector databases and retrieval augmented generation: Through developing more comprehensive business profiles, Credibly has improved its ability to analyze customer data and identify high-risk clients effectively.
- Development of a proprietary genAI-driven search engine: This engine ingests and summarizes metadata from both internal and external sources, allowing more accurate, risk-adjusted underwriting determinations. As a result, Credibly reduced the time required to process applications from several minutes to under 30 seconds.1
- Quantifiable improvements: The complexity of decision-making processes decreased, with the number of selections required dropping from thousands to fewer than a hundred. These optimizations then led to a notable increase in productivity and revenue per employee, while also bringing previously outsourced tasks back to onshore employees, reducing costs and improving operational efficiency.
Overcoming the toughest challenges in gen AI adoption
While the benefits are clear, Credibly faced several challenges in adopting generative AI:
- GenAI challenges: Issues like hallucination—where the AI generates inaccurate or nonsensical outputs—and nondeterminism, where results can vary unpredictably, present major hurdles. User adoption also required careful management, involving extensive training and education to uphold high engagement and trust in the AI’s outputs.
- Mitigation strategies: Credibly mitigated these challenges by leveraging existing databases and supervised models to rank and validate AI-generated answers—helping maintain the reliability and accuracy of insights, supporting user trust and confidence in the AI’s capabilities.
What’s next for gen AI in the world of data analytics
How generative AI is spreading fast across industries
Generative AI is rapidly becoming a staple in analytics and BI platforms, extending benefits to a broader range of users, including data scientists, analysts, and “citizen” data scientists. Improved natural language capabilities let users interact more intuitively with data, driving higher engagement levels and faster adoption.
Different ways companies are jumping on the gen AI bandwagon
Some organizations are deploying AI broadly across multiple functions, while others are taking a more targeted approach.
Despite these variations, the trend shows swift advancement as companies recognize the potential of AI to drive efficiency and growth.
Analysts predict that within the next few years, generative AI will move from being a temporary competitive advantage to a standard feature in ABI platforms.
When will generative AI become just another tool in the analytics kit?
As with previous innovations in analytics, such as predictive modeling, data visualizations, and dashboards, generative AI is expected to become a commodity. Over time, it will be viewed as a necessary tool in the analytics toolkit, accessible to organizations of all sizes and industries, further democratizing access to advanced data insights.
Final thoughts
As generative AI continues to transform data analytics as we know it, the question isn’t whether your brand should adapt, but how quickly it can.
Will you be one of the organizations that harness this technology to tap into deeper insights, streamline operations, and outpace competitors? Or will you watch from the sidelines as others leverage AI to redefine their markets?
