Gaining ROI on Generative AI: A Quick Guide for Business Leaders

The promise of generative AI is enticing. With the help of complex algorithms and machine learning techniques, generative AI can create new content, images, sounds, and even entire virtual worlds. With the cost involved and the technical challenges of implementing it, however, is it worth the investment for your business? This guide will provide a detailed road map for businesses embarking on this innovative but complex endeavor, highlighting the crucial factors that determine the success of generative AI implementations.

The Cost of Generative AI

While generative AI’s advantages seem to been compassing, it still has some limitations. For one, there’s the cost associated with fine-tuning and adapting the models to suit specific needs. This includes the cost of data transformation for prompts, as well as the cost of fine-tuning based on the company’s data. Training the models, too, is time-consuming. In terms of API limitations, the official ChatGPT API forfine-tuning is not yet available as of this writing, and there may be limitations to accessing Azure OpenAI services. There are growing pains, too. Releasing new versions of models may require changes to the API, and in some cases, model explainability, repeatability, and interpretability can be challenging. Before investing in generative AI, businesses should consider the expenses that could arise, such as significant computing power and cloud resources needed to train models, as illustrated below.

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Refocusing Data Strategy to Value Realization as Generative AI Ramps Up

In a Gartner survey centered on the adoption of generative AI by the data and analytics functions of enterprises, 40% of the respondents claimed that they’ve either experimented with generative AI, integrated it into some of their processes, or have completely integrated it across their operations. Lingaro Senior Director of Technology Consulting Practice Carlos Navarro shares his insights on the latest developments in AI and machine learning and how they spur the need to modernize their tech stack and create more business value out of it.

What are the most pressing challenges that businesses face today when it comes to data management and strategy?

We see our customers needing to modernize their data platforms. But, at the same time, they have to prove the value of previous and new investments. Generative AI has accelerated their need and increased the visibility of gaps in their data foundations. By gaps, we mean to say challenges like overreliance on technical resources when consuming data and difficulties in data governance, data platform modernization, and data value realization.

Data value realization, in particular, is a critical concern, as many companies are realizing that the huge investments done to date do not let them fulfill the new data consumption needs of tools like generative AI. This puts leaders in a pinch as they need to justify their new investments or work on their modernization with limited budget.

As the data and AI partner of our clients, we aim to increase the value of their data, modernize their data platforms to utilize new generation technology and architecture in a cost-effective manner, and enable their organization to scale up data consumption.

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Revolutionizing Supply Chains With Digital Twins

In the fast-paced and increasingly complex consumer-packaged goods (CPG) industry, the supply chain acts as the backbone of operations. That is, it dictates the efficiency, responsiveness, and ultimately, the success of enterprises. As the practice of supply chain management and strategy improves over time, transformative advancements occur every now and then to change the game. One of the more recent and most disruptive of these advancements is the integration of digital twin technology into this practice.

A digital twin, in essence, is a virtual replica of a physical system. For supply chain and manufacturing analytics, a digital twin can become a dynamic, digital mirror of the entire supply chain network by continuously collecting real-time data from internet-of-things (IoT) sensors and other sources across the supply chain — from production lines and distribution to storage network. This technology goes beyond mere simulation. It creates a living model of operational systems that managers can interact with, learn from, and optimize via predictive AI.

Digital twins vastly differ from traditional modeling. While conventional models offer relatively static, often one-off frames, digital twins provide a live, constantly updated representation of an interconnected ecosystem. For supply chains, digital twins provide comprehensive visibility, which fosters cross-functional collaboration and seamless alignment of various supply chain components. Moreover, advanced analytics can be used on digital twins to produce dynamic and predictive insights that enable continuous optimization and real-time, proactive decision-making. This makes digital twins a more integrated and responsive tool in comparison to traditional modeling and simulation methods.?

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