Beye® Skills
Use Cases
Blog
Whitepapers
Pricing
Sign In
Book a Demo with Us
Thought Leadership
February 21, 2025
5 min read
Our Thoughts on AI Agents in the Context of Generative Business Intelligence
Demystifying AI Agents and Generative BI with practical guidance for
business and technical leaders in CPG, retail, and distribution
Layer 1
Application Focus
Crawl-Walk-Run
Implementation Strategy
Outcome-Driven
Approach
There is a lot of hype surrounding AI Agents and Generative BI right now, and with that hype comes skepticism. Many business and technical leaders in CPG, retail, and distribution are under pressure to embed Generative BI into their organizations, but they're struggling to figure out what is actionable and how to implement it without disrupting everything that is already working.
My aim is to demystify AI Agents and Generative BI, set the context for how I see this space evolving, and provide practical guidance on how organizations can approach implementation. I want to outline the challenges you should be ready to face and suggest steps to help address them. This is meant to be valuable to business teams responsible for driving operational or financial outcomes and to give a realistic perspective on what works and what doesn't.
Setting the Stage: Layer 0 and Layer 1
Layer 0 is the foundation of AI models. It is a mixture of open-source and closed-source solutions, and the pace of improvements in these models is outpacing Moore's Law. We are seeing meaningful releases to reasoning models every six months. With competition driving down costs, these foundation models will increasingly become commoditized.
The real value, then, lies in Layer 1, the application layer. This is where we are building at Beye.ai. We are leveraging the strengths of Generative AI by using it as an API into intelligence and developing software applications around it. But the focus isn't just on the AI model — it's on creating purposeful, proactive, and reliable experiences that drive business outcomes.
Generative BI is about bringing proactivity and purposefulness to workflows. It is not just about automation; it is about leveraging AI as an extension of your team to find opportunities you didn't even know existed. It's about factoring in business context, organizational data points, the outcomes you aim to achieve, and the integration of first- and third-party data.
The goal is to design workflows that fit how your core users already work, rather than asking them to change how they operate. This approach makes Generative BI an enabler of strategic goals, not just another tool in the tech stack.
Understanding Workflows and Contextualization
Understanding workflows is critical. How do business units currently analyze data? What are the key operational KPIs and organizational goals, and how are they tracked? How are you driving improvements? To build effective AI applications, it is essential to understand existing business workflows and identify the immediate bottlenecks in those processes. This isn't about replacing workflows but enhancing them with automation and insights that empower teams.
I've seen firsthand how empowering teams with self-serve BI enabled them to react faster to supply chain disruptions, turning a potential loss into a strategic advantage. This is the power of proactive, purpose-driven Generative BI. It's about finding opportunities faster and driving action in real-time.
We also focus on contextualizing the experience. Not all KPIs are equally meaningful, and the importance of specific metrics varies across teams and roles. The organizational goals that are relevant to one group may not matter to another, so building this nuance into the application is crucial.
This requires the experience to be outcome-oriented. It should be able to surface insights that are relevant to business objectives, contextualize them with both first-party data and third-party data sources (like weather, macroeconomic indicators, inflation, interest rates, or tariff data), and help users see the bigger picture.
The third component is structuring organizational data — both structured and unstructured — in an attribute-rich, well-tagged format. Data reliability is critical, and this requires great data attribution richness and the ability to bring together information across multiple modalities. The data architecture must be robust enough to handle complex business questions while ensuring transparency in how insights are generated.
So What? Implementing Generative BI Where It Matters Most
This isn't just about theory. To truly embed Generative BI, you have to be strategic about where and how you implement it. The focus should be on driving outcomes, not just deploying new technology.
I recommend a Crawl-Walk-Run approach to strategically implement Generative BI within your organization. Start with financial and operational goals — not just quarterly mandates, but the strategic outcomes that drive your business unit. These could be anything from revenue growth and cost optimization to improved inventory management or enhanced customer engagement.
Once these goals are clear, work backwards. How are these goals tracked and analyzed today? Is the process formal or ad-hoc? Which cross-functional teams are involved, and what systems of record are critical for sourcing this information? This may include ERP systems, CRM platforms, accounting systems, or data warehouses. Understanding this landscape is crucial because it reveals the underlying workflows and dependencies.
After mapping out the goals and data sources, evaluate the current BI process. If you needed to analyze ways to achieve a strategic goal, what would you do today? What systems of data are critical to get that work done? Once you have the data, how is it analyzed? Is it through Excel, Power BI, Tableau, or a mixture of tools? Often, requests are routed through IT, data engineering, or BI teams. This creates bottlenecks, slowing down decision-making.
The question to ask is: Can this process be optimized? Can we shorten the distance from business questions to actionable answers? Can we bring some self-service enablement to the business side so they can react more quickly to dynamic changes, such as macroeconomic shifts or tariff changes? If there are bottlenecks or inefficiencies, that's where Generative BI can deliver the most impact.
Final Thoughts: From Strategy to Action
Generative BI isn't just about deploying AI; it's about strategically embedding intelligence into your workflows to drive action and deliver real business value. Start by understanding the strategic goals that drive your business. Map out your existing workflows, identify the bottlenecks, and pinpoint the systems of record that are critical for decision-making. This strategic approach ensures that you're implementing Generative BI where it matters most, driving outcomes that are meaningful to your organization.
Let's figure out how to make Generative BI work for you, not the other way around.
Article Info
Published:
Author:
Farhad Hussain, CEO
Category:
Thought Leadership
Reading Time:
5 minutes
Topics:
AI Agents, Generative BI, Implementation Strategy
Key Topics
AI Agents
Generative BI
Layer 1 Applications
Workflow Optimization
Implementation Strategy
Business Outcomes
Share This Post
Ready to Discuss Implementation?
Connect with our team to explore how Generative BI can drive outcomes for your organization.
Book a Demo
Continue the Conversation
Explore how Beye's Generative BI platform can transform your data workflows and drive strategic outcomes.
Book a Demo with Us
View Case Studies
Transform complex data into actionable insights that drive effective decisions and superior business outcomes.
Platform
Connected Dataverses
Collaborative Channels
Cognitive Analytics
Resources
Blog
Case Studies
Trust Center
Documentation
Support
Company
About
Contact
Privacy Policy
Platform Terms of Service
© 2025 Beye Analytics Inc. DBA Beye Inc. All rights reserved.
Made with ❤️ by the Beye.ai team
Published:
February 21, 2025