The Enterprise Challenge: Ready for Generative AI, But Where’s the Infrastructure?

Blog / The Enterprise Challenge: Ready for Generative AI, But Where’s the Infrastructure?

In the rush to embrace the latest technological marvels, many enterprises are leaping without looking, overlooking crucial prerequisites. First, there’s the business case – or rather, the lack of one. Companies are often so dazzled by the promise of cutting-edge tech that they fail to align it with their actual business needs. It’s like acquiring a sports car when you really need a pickup truck. Then, there’s the current data infrastructure, often neglected in the excitement. It’s similar to trying to run the latest software on an outdated computer. Without addressing these foundational aspects, these enterprises are setting themselves up for a tech journey that’s all flash and no substance, potentially leading to costly missteps and underutilized innovations.

What enterprises are eager to achieve today: 

…But this is where their current data infrastructure is: 

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Enterprises everywhere are buzzing about the promise of Generative AI (Gen AI). However, despite this enthusiasm, many are confronted with the sobering realization that their data infrastructures are far from ready. Here’s why:

• Disconnected Data Silos: Multiple systems, ranging from CRM tools like Salesforce to ERPs like Oracle and SAP, are functioning in isolation. These fragmented systems make it a challenge to aggregate data, resulting in inconsistent or incomplete views.
• Absence of a Unified Data Model: The sheer volume and variety of data warrant a comprehensive data model. However, many enterprises lack this, making data interpretation and use inconsistent across departments.
• Outdated Systems: Legacy systems, not built for real-time processing, cannot handle the demands of Gen AI. They slow down operations, hinder real-time insights, and often lead to decision-making based on outdated information.

Google Cloud’s Answer: The Data and AI Cloud for Supply Chain

Bridging the gap between where enterprises stand today and where they aspire to be, Google Cloud offers the Data and AI Cloud for Supply Chain. Let’s delve deeper.

• Data Integration: Pulling in data becomes seamless, consolidating information from varied sources into one unified view using the Google Cloud Cortex Framework. 
• Data Enrichment: Enhancing raw data, adding value, and ensuring every byte is primed for analysis.
• Insight Extraction: Leveraging the prowess of advanced analytics tools like BigQuery, this environment digs deep, unveiling patterns and offering solutions.
• Insight Sharing: With a few clicks, insights can be shared, ensuring that every stakeholder is informed and empowered.

Pinpointing the Pain Points

Google Cloud has created a robust environment that streamlines the entire data process. It starts with efficiently pulling in data using the Google Cloud Cortex Framework, transitions to enriching it for higher quality insights, moves to extracting those insights, and finally, ensures those insights reach the right stakeholders. 

By addressing challenges like excess inventory and blind spots in the consumer journey, Google taps into the prowess of tools like BigQuery combined with the finesse of Machine Learning, AI, and Gen AI via Vertex AI.

Empowering the Frontline Decision Makers

For those who sit at the heart of the supply chain – demand planners, procurement officers, merchandise planners, logistics coordinators, and distribution managers – Google Cloud’s offering is a game-changer. No longer will they have to wade through outdated spreadsheets or make decisions based on stale data. With real-time insights at their fingertips, decisions are timely, accurate, and impactful.

The ultimate aim is to simplify insight access for business users while also giving data teams the robust tools they need to support them.

Two Must-Haves for Supply Chain Optimization

Before diving deep into Google Cloud’s “Data and AI Cloud for Supply Chain,” there are two critical components that must be in place:

1. Comprehensive Data Integration: In modern supply chains, data is the keystone. It’s essential to have a system that not only collects data from varied sources but can also merge them to yield actionable insights. What does this mean? It’s about syncing inventory data with sales, integrating vendor performance metrics with procurement strategies, and making sure every data point aligns with the larger organizational objectives.
2. A Flexible Data Platform: Flexibility is key. Data platforms should be nimble, accommodating sudden market changes, influxes of new data, or rapid shifts in consumer behavior. They should integrate new sources without hiccups and pivot strategies on the fly. In scenarios like unexpected supply chain disruptions or sudden shifts in consumer demand, the platform must adjust and adapt seamlessly. 

Now, let’s talk about a tool that’s designed with these exact prerequisites in mind: Planning in a Box.

Planning in a Box: Bridging Data Potential with Business Outcomes

Planning in a Box is a decision intelligence platform tailored for supply chain complexities. As a data platform on Google Cloud, it integrates seamlessly with Google Cloud’s Data and AI tools. It uses the Google Cloud Cortex Framework to pull in data from business systems such as Salesforce, Oracle EBS, and SAP. With the added Gen AI layer, it delivers faster insights to address business challenges like excess inventory, optimizing marketing spend, and improving demand forecasting. Here are some of its features: 

Real-time Visibility: A dashboard that tracks demand, procurement, manufacturing, and inventory. It provides real-time alerts and recommendations.

Comprehensive Analysis: Aggregate analysis capability across inventory, customer orders, marketing, finance, procurement, and vendor performance management.

Gen AI Platform: Enables users to ask questions in natural language and receive instant answers.

Intuitive Planning Tool: Spreadsheet-like tool for planners to slice, dice, and analyze data.

Insights and Recommendations: Offers actionable insights and business recommendations.

Unified Data Integration: Uses the Google Cloud Cortex Framework to integrate various data sets, such as sales metrics, customer behavior, and operational stats, providing a singular, strategic view.

Agile Data Ecosystem: Designed for real-time adjustments and reactions to changing market dynamics, providing real-time inventory visibility.

Holistic Data Integration: Champions a comprehensive data perspective, absorbing diverse datasets and synthesizing them into actionable insights.

Decision Intelligence Platform: Built on Google Cloud, combining domain knowledge of supply chain and tech expertise of Google Cloud tools like BigQuery and Vertex AI.

Solves Key Business Problems: Addresses demand forecasting, inventory visibility, customer experience, and marketing and sales challenges.

User-friendly Design: Intuitive interface designed for a range of user expertise, ensuring wide accessibility.

Strategic Recommendations: Provides advice aligned with overarching business objectives.

Supply Chain Optimization with Gen AI on Google Cloud: Where to Begin?

Begin with our hands-on workshop. At Pluto7, our expertise runs deep into Google Cloud, and our track record showcases real-world solutions for global companies like Levi’s, AbInBev, Ulta Beauty, and more. Most of these companies transitioned from identifying use cases to actual implementation in just 4 weeks or less. By joining our workshop, you’re not just saving time and money but also ensuring efficiency throughout your operations. Harness the full power of Gen AI, integrating it into every layer of your decision-making process. Reserve your place today.