Over the past year, there has been a sudden surge in interest in Artificial Intelligence (AI), particularly in generative AI. We are witnessing how generative AI models like ChatGPT are revolutionizing how we produce content in the form of text, images, and audio.
Despite being in the nascent stage, generative AI does have loads of promise and potential in the technology space. Kuldeep Deshpande, the founder of Ellicium, talks about how the “team has piloted 10 applications of generative AI in business functions such as legal, contracting, finance, purchase, and HR.”
Can generative AI also transform the way we approach data science? For instance, ChatGPT can be used in data analytics to create data summaries and natural language explanations. Besides that, ChatGPT can also generate synthetic data for training machine learning models.
To that end, let’s discuss how generative AI can transform data science.
How Generative AI Will Transform Data Science
Generative AI has the potential to transform how enterprises process their business data. For instance, companies can replace existing data (fed into generative AI models) with their business-generated data.
David McGraw, in an interview with Datavarsity, outlines that “Any company using generative AI technology with similar models and the same dataset as ChatGPT will see its products converge to match ChatGPT outputs.” He further explains that companies that train generative AI models on their data can hyperfocus the model to answer questions related to their business objectives.
Effectively, generative AI can overcome bottlenecks in enterprise-level data analytics, which otherwise limit their problem-solving abilities. For instance, most data scientists can explore useful data patterns – but can only evaluate certain hypotheses. This leads to plenty of unexplored areas.
On its part, generative AI can overcome these limitations by bypassing “human” biases and providing alternative ways to leverage the available data. This means generative AI models can develop and test hypotheses from all the available data sources. What’s more, generative AI models can also create new data sources by gathering unstructured data into new structured data sources, which are critical for any enterprise.
Here are some of the capabilities of generative AI useful in data analytics:
Generate and update business insights over time – and as and when the data changes.
- Produce and analyze a number of hypotheses each minute – and retain the strongest ones.
- Identify and augment relevant data sources (including external sources) to generate a composite hypothesis.
- Analyze data from complex ecosystems and sources, including spreadsheets, databases, and cloud-powered data storage.
- Ingest different data dimensions including text, images, videos, and time-series data – and identify useful patterns and connections. This begs the question – how can generative AI improve data analytics in areas like supply chains, logistics, and demand forecasting? Let’s discuss that next.
The Use of Generative AI and Data Analytics in Supply Chains
For a long time, AI has been used to improve the efficiency and productivity of modern supply chains. In fact, AI solutions in supply chain management have improved inventory management, logistics, and smart manufacturing. A McKinsey study found that AI-enabled supply chain management has benefited companies with:
- 15% reduction in their logistics costs
- 35% improvement in inventory levels
- 65% increase in customer service levels
Jan Burian of IDC believes that “expectations related to generative AI are high in the supply chain arena.” Supply chain and logistics companies expect generative AI to provide more visibility into their supply chains. This can help them overcome challenges like cost escalation and volatile demand.
Here are some supply chain areas where generative AI and data analytics can play a productive role:
1. Data Analysis
Generative AI technology can help supply chain companies analyze data from diverse sources like purchase orders, shipment data, and invoices. This is useful for identifying accurate data patterns and areas of improvement.
2. Eliminate Supply Chain Bottlenecks
For each product or material, generative AI can provide a detailed summary, which is useful for warehouse operations and logistics planning. This can potentially eliminate any bottlenecks in the overall workflow.
3. Supply Chain Training and Process Guidelines
4. Mutual Understanding Among Supply Chain Personnel
Next, let’s discuss how Ellicium can help in your next-gen AI implementation.
How Ellicium can help with Generative AI
- Predicting supply chain blockages
- Identifying alternative sources of supply
- Identifying any vendor-specific risks
- Optimizing supply chain strategies
Learn how a Chicago-based legal research company leveraged our AI and Big Data expertise to improve their LPO efficiency and cost efficiency.
Going forward, generative AI solutions like ChatGPT can maximize the benefits of data science and analytics across every industry. In this blog, we have discussed how generative AI can impact the world of supply chains and logistics.
With expertise in next-gen AI solutions, Ellicium Solutions is now creating pilot projects for generative AI across legal consulting, financial operations, and HR domains. With these advanced AI solutions, we are setting the benchmark in business efficiency and accuracy.
Looking for the right partner for your next AI project? We can help. Contact us today