Surface What Matters: How Business Can Navigate the Data Depths
- Shawn Duffy
- Mar 31
- 4 min read
Updated: Apr 2
Using LLMs, ML, and Automation to generate insights in an AI saturated world
In today's business environment, the reality of "fast-paced" decision-making is undermined when leaders wait days or weeks for critical insights. Yet paradoxically, companies are sitting on vast goldmines of data while struggling to convert it into timely, actionable intelligence. This post explores how looking backwards to utilize business fundamentals can help us leverage AI to revolutionize businesses of all sizes. As a former leader of mine would say, "Go slow now, to go fast later."
Businesses Are Drowning in Data while Starving for Insight
Volume & Complexity
Data is growing at an unprecedented rate. From endless transactional systems with years of historical data fed from marketing analytics and sales pipelines to financial metrics and operational KPIs, businesses now collect more information than ever before. This exponential growth creates both opportunity and challenge, as valuable signals are often buried in noise.
Manual Limitations
Traditional business intelligence approaches can be useful in describing what has already happened but are not keeping pace with forward-looking innovations. Dashboards and reports require manual preparation and involve inherently reactive, time-consuming development. By the time a quarterly analysis reaches decision-makers, the business landscape has often already shifted. The most valuable insights frequently arrive too late to drive meaningful action. Is your report data refreshed nightly or every few hours? Are you waiting for the month to close identify success or failure? What if you did not have to wait for answers?
The Insight Gap
Today's leaders need real-time intelligence to be competitive, but most analytics teams, if you even have a team, are stretched thin trying to keep up with the dynamic technical landscape. We have shifted from reactive request-response to user-driven analytics to now future-predicting analysis and automation. This evolution has created a new insight gap that hampers business agility in 2025.
AI's Innovation Dilemma
Clayton Christensen's Book
In looking forward, we start by looking in reverse. Clayton Christensen released his groundbreaking book "The Innovator's Dilemma" back in 1997, which highlighted how disruptive technology could erode established companies' market share. His core insight was revolutionary: the very management practices that made businesses successful could contribute to their downfall when facing disruptive innovations.
How AI Applies
Today, this principle is alive and evolved with the acceleration of AI technologies. When Christensen authored his book, Business Intelligence had only started gaining momentum as we "recovered" from a decade of OLAP Cubes and Kimball vs. Inmon architecture debates. Machine Learning, Good Old-Fashioned AI (GOFAI), and automation tools now dramatically compress the timeline of disruption that he described. While his core principles remain valid, the speed at which market dynamics can shift has increased exponentially, leaving businesses with even less time to adapt.
How to Leverage
The key to navigating this accelerated landscape lies in applying Christensen's framework to AI adoption. This approach means focusing on the "right customers" with the "right solutions" to achieve "real value" for the business. Success requires using technology as an acceleration tool toward disruption, not just applying it indiscriminately.
Utilizing his "fail fast" mentality is crucial to success as it preserves precious resources by encouraging us to start small, focused, and emphasize "fast" evaluation of success or failure. By identifying targeted use cases and success criteria before implementation begins, businesses can create immediate value. Equally important, organizations can learn and adapt without overcommitting to unproven approaches.
Navigating the Journey
Start Focused
Begin by identifying where your team currently spends the most time on manual processes or analyses. These pain points represent prime opportunities for automation and augmentation with AI.
Successful Use Cases will include:
Repeatable, predictable, and high-volume activities (i.e., customer service email responses, or data forecasting or predictions).
Processes that require significant manual effort (i.e., customer retention analysis, or email marketing campaign analysis).
Activities that deliver value but are often less actionable as delays increase (i.e., product pricing analysis and optimizations, fraud detection, or customer service escalation predictions).
Build Smart
The next step involves connecting your core data systems to enable a unified view, exploring predictive and prompt-driven analytics tools that leverage commonplace technologies (i.e., LLMs, automation, scalable resources, etc.) to surface patterns the team might miss or not have time to find. Focus on solutions that integrate with your existing workflows rather than requiring wholesale transformation.
Choose a Pilot
At Baleen Data, we help businesses move from "data-rich, insight-poor" to "data-smart and decision-ready." Our approach focuses on practical implementations, data governance, classification, and business fundamentals that focus on business value using an automated intelligence strategy to accelerate efficiency realizing competitive advantage.
We are the right partner to pilot you with practical implementation, ensuring you avoid common pitfalls and reduce time to value.
Are you ready to accelerate your business? Do you want to implement AI but unsure where or how? Are you losing margin to rising costs? Contact Baleen Data today to schedule a consultation and discover how we have helped others unlock the full potential of their data.