Blog / Generative AI Use Cases that are Transforming Enterprise Data Management

Generative AI Use Cases that are Transforming Enterprise Data Management

The universe of artificial intelligence (AI) is an ever-changing landscape, continuously shaped by the ebbs and flows of technological innovation. Today, the rising tide of generative AI, or Gen AI, is heralding the onset of a transformative epoch in technology and its applications.

This new wave of AI has been met with widespread enthusiasm, as evidenced by the flurry of requests we receive daily at Pluto7. Customers are curious, excited, and eager to learn about how they can harness the capabilities of Gen AI for their enterprises. Fueling this excitement is the momentum generated by major players like OpenAI and Google, with the former unveiling ChatGPT and the latter introducing its Gen AI model, Bard.

Everywhere you look, Gen AI is infiltrating our technological ecosystem. From Snapchat to Salesforce, companies are discovering unique applications of Gen AI, each finding its niche. Microsoft has endowed Bing with Gen AI capabilities, and there are rumblings about Google integrating Gen AI into everyday tools like Docs and Sheets.

As the potential of Gen AI expands, the question facing businesses is no longer about whether it can be applied to enterprise data but where (use cases) and how soon (time to value).

At Pluto7, our guiding principle is to always prioritize real, tangible value for the end user. We strive to understand how new technologies can seamlessly integrate and co-exist with existing systems. After all, innovation is not merely about adopting the newest technology, but also about harmoniously blending it with the existing landscape to generate the maximum impact and value.

Leveraging the Power of Google Cloud

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Leading the charge on the Gen AI front is Google Cloud, which has demonstrated its commitment to AI innovation through a suite of exciting new features. The spotlight during the Google I/O conference was on PaLM 2, Google’s state-of-the-art language model. Offering improved multilingual, reasoning, and coding capabilities, PaLM 2 not only represents a breakthrough in AI technology but also offers deep security and direct access to the underlying training models.

This level of control and transparency makes it particularly suitable for enterprise applications, setting it apart from other models like ChatGPT. But while the tech is in place, leveraging it for business intelligence brings its own set of challenges.

The Enterprise Data Conundrum

The application of AI to enterprise data, especially ERP data, is a Herculean task. The hurdle here is not just the sheer volume of data but the lack of inherent interconnectedness between the applications that hold the data.

Unifying this data, cleansing it, blending it with external data, and applying AI and ML models to it are all complex tasks. It’s no surprise that many companies have steered clear of these challenges due to various constraints, such as the lack of skilled resources, data security concerns, and governance issues.

Yet, the potential for AI and ML to revolutionize business operations makes it an enticing prospect that’s too good to pass up.

Read Case Study: Digital transformation journey of a large business conglomerate

Navigating the AI Evolution with Pluto7

At Pluto7, we are at the helm of this AI revolution. For years, we’ve been creating AI and machine learning algorithms designed to assist businesses in improving their supply chain and marketing operations. By connecting various data sources together and tapping into external datasets, we’ve been able to improve forecasting accuracy and optimize inventory positioning, among others.

However, we are fully aware that the successful deployment of AI doesn’t lie in simply adopting the latest tools. 

A strong data foundation is a prerequisite for any successful AI project. Rushing into AI implementation without ensuring the quality and structure of the underlying data is a surefire way for projects to fail.

We work primarily with business operations data which includes ERP data sets and non-ERP data sets like SAP, Salesforce, Google Ad Tech etc. For each project we undertake, our first step is always to clean the data and establish the correct data model. This foundation work, critical to the success of the subsequent steps, is made more efficient with the help of the Google Cloud Cortex Framework. With our data platform, we can speed up data modeling, enabling us to bring the benefits of Google Cloud’s public datasets to the line of business users worldwide.

Generative AI and Supply Chain

Standing on the brink of yet another technological revolution, we’re ready to take a leap forward, embracing Gen AI to supercharge our solutions. We’re currently exploring the application of Gen AI along with our customers to enhance supply chain and marketing intelligence, one of the most promising areas for AI deployment.

We’re working on a unique question-answering model that merges supply chain and marketing data with real-time information. The model’s objective is to anticipate and analyze the potential impact of events that could disrupt the delivery of purchase orders, such as port strikes or hurricanes and 100s of such potential use cases and questions that customers have.

The model operates on a matrix of data, linking the dots between real-time events and pertinent supply chain and marketing data with details, such as purchase orders, port of origin, and shipment dates, ad spend and its impact on demand leading to inventory positioning. It’s then tasked with drawing insights on potential impacts, highlighting which purchase orders are likely to be affected, and assessing the broader implications on overall revenue or margin.

This fusion of data and AI has the potential to revolutionize supply chain, sales and marketing management, adding a layer of predictive intelligence that can save businesses from unforeseen disruptions.

Read: Transform data into decision intelligence

Generative AI Use Cases for Marketing

Beyond supply chain management and marketing, our Gen AI solutions have also found applications in sales, finance and manufacturing functions.

In marketing, the pressing challenge is understanding customer behavior and preferences, gauging the effectiveness of campaigns, spotting emerging market trends, and driving conversions. Our data platform dives deep into customer behavior patterns and tracks campaign performance to help you get better visibility and tailor your marketing strategy.

At Pluto7, we have long recognized the power of AI in marketing, using it to blend marketing data with sales data and external sources, resulting in deeper insights and more effective strategies for our customers. As we stand on the cusp of a new horizon, we look forward to amplifying these achievements and pushing the boundaries even further with Gen AI. The business drivers remain the same as it has always been to improve revenue and margins while reducing costs.

Read: Advanced Marketing Analytics on Google Cloud

While there are many use cases that a customer can start with, our focus starts with demand sensing and inventory positioning as those seem to have the most bottom-line impacts in enterprises, especially if they deal with physical goods.

Our core focus remains on three key marketing use cases that drive revenue growth and margin improvements:

1. Understanding Customer Behavior and Preferences

With Gen AI, we aim to tap into the rich linguistic and contextual understanding these models possess. The goal is to decipher nuanced customer interactions, offering a more comprehensive understanding of their behavior. 

Gen AI’s potential to learn and adapt with each interaction holds the promise of hyper personalized marketing campaigns that resonate with customers at an individual level. All of this will continue to rely on enterprise foundational data while building on external data signals insights.

Read Case Study: A travel agency improved their customer engagement with personalized messages.

2. Measuring the Effectiveness of Marketing Campaigns

We have leveraged AI to measure and optimize the effectiveness of marketing campaigns accurately. Gen AI is set to enhance this capability by offering real-time, adaptive feedback on campaign performance. 

By analyzing campaign data dynamically, Gen AI models could pinpoint successful strategies and suggest necessary modifications to underperforming campaigns. This continues to be about assisting marketing teams in finding answers and insights more quickly from their marketing datasets, which include Google Ad Tech data and other vendor data they manage.

3. Identifying and Capitalizing on Emerging Market Trends

AI has been instrumental in helping us identify emerging market trends. With Gen AI, we aim to boost this ability, tapping into its capability to analyze vast and diverse datasets. 

The anticipation is that Gen AI models will offer early identification of subtle shifts in market dynamics and customer preferences. Such insights can arm us with the knowledge to proactively adapt our marketing strategies, ensuring we maintain a competitive edge. The key here is to blend internal data (ERP, Marketing datasets) with external data at the right level.