The generative AI revolution in content creation

December 12, 2023

In the era of digital transformation, artificial intelligence (AI) plays a crucial role as a driver of business innovation and efficiency. One of the latest market advancements is generative AI, a groundbreaking technology that can boost fields ranging from digital art creation to process automation. Its impact extends to the supply chain, where it has the power to enhance efficiency and decision-making.

Generative AI enhances decision-making for logistics process organization

“Rapid technological advancements in AI, as well as other advancing technologies such as robotics, cloud computing, and the Internet of Things, are transforming disciplines, economies, and industries, and challenging ideas about what it means to be human,” states UNESCO.

AI systems can identify patterns, make probabilistic predictions, and operate unsupervised in certain scenarios. AI is being used in many fields, such as computer vision and automatic speech recognition.

Differences between generative AI and traditional AI

  • Objectives and approach. Generative AI focuses on creating new and original content. On the other hand, traditional AI centers around solving specific tasks through algorithms designed to perform concrete actions based on predefined rules.
  • Training and data. Generative AI is trained using extensive datasets, enabling it to learn the information’s structure and characteristics. Meanwhile, traditional AI often requires structured datasets to train supervised algorithms.
  • Flexibility and adaptability. Generative models stand out for their remarkable flexibility and ability to address a wide range of tasks related to content generation, from creating realistic images to producing coherent text. Traditional AI tends to specialize in specific tasks. Furthermore, it requires the manual definition of rules and characteristics for each action, limiting its adaptability to new tasks without major reprogramming.
  • Creativity and originality. Generative AI can create inventive and original content, such as artwork generated by generative adversarial networks or text produced by language models. Traditional AI focuses on automating rule-based tasks and is not conceived to autonomously generate creative content.

Potential of generative AI for businesses

Joint research conducted by Microsoft, GitHub, and the MIT Sloan School of Management confirms the enormous potential of generative AI for industry: “Artificial intelligence applications hold promise to increase human productivity. A variety of AI models have demonstrated human-level capabilities in fields ranging from natural language understanding to image recognition.”

The Microsoft study provides evidence that generative AI tools have positive effects on productivity. For example, the authors write that programmers who used Copilot “completed the task 55.8% faster.” Developed by GitHub and OpenAI, Copilot is an AI-based assistant for writing code used by software programmers.

Companies driving generative AI seek to tailor language models to their specific needs and use cases. Their goal is to achieve natural interactions in human language using their own data and documents. To do so, there are three options:

  • Train a customized model from scratch. This is the most challenging option, with equipment and computing costs that may be beyond the reach of many enterprises.
  • Fine-tune an existing model. This option involves updating an existing model with proprietary data. It represents an area with considerable potential for development in the business world.
  • Use a pre-trained model and add contextual information. Instead of having a proprietary language model, the organization leverages a previously developed model to analyze relevant information at the right moment.

This modern technology could infuse billions of dollars into the world economy, as per a McKinsey report. The consulting firm found that this technology “will have a significant impact across all industry sectors. Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues.”

Generative AI is a branch of artificial intelligence focused on creating original content from existing data

Thanks to generative AI, companies will be able to develop products more quickly, improve the customer experience, and increase employee throughput. A Gartner survey of over 2,500 executives highlighted the main reasons why organizations should invest in generative AI. These include customer experience and retention (38%), revenue growth (26%), cost optimization (17%), and business continuity (7%). The consulting firm says: “By 2025, 30% of enterprises will have implemented an AI-augmented development and testing strategy, up from 5% in 2021.”

Gartner underlines the opportunities that can arise within companies through generative AI:

  • Greater revenue. Businesses with higher degrees of AI maturity will profit more. How so? They’ll be able to create new products at a faster pace and scale up the services they offer. “By 2025, more than 30% of new drugs and materials will be systematically discovered using generative AI techniques, up from zero today. Generative AI looks promising for the pharmaceutical industry, given the opportunity to reduce costs and time in drug discovery,” notes the analysis.
  • Cost reductions and productivity growth. Generative AI is capable of enhancing workers’ skills when writing and editing texts and producing images. Additionally, it can “summarize, simplify, and classify content; generate, translate, and verify software code; and improve chatbot performance.”
  • Risk mitigation. One of generative AI’s capabilities is to “analyze and provide broader and deeper visibility of data” (from customer transactions to software code). Using this information, the system swiftly identifies patterns and possible risks to an organization.

Generative AI in the supply chain

What repercussions will generative AI have on the supply chain? Advancements and research in generative AI suggest the potential for substantial progress in developing creative ideas. This paves the way for the integration of this technology into various scenarios, including logistics.

Generative AI is at a critical juncture when it comes to the supply chain. A survey from IBM reveals that 85% of executives consider the implementation of generative AI capabilities to be a major driver of investments in automation. Meanwhile, 20% affirmed that generative AI is pivotal for their future in automation. Why? Generative AI has the potential to augment workers’ skills by automating numerous tasks that previously required human intervention. This technology can gather information and assist in decision-making related to the organization of production processes, management of available resources, and inventory administration.

IBM identifies three fundamental areas in which generative AI can affect the supply chain:

  1. Support. Generative AI is being applied to increase productivity in tasks such as market research development, trend analysis, customer service, and basic coding. "We have seen 90% improvement in speed of coding. With AI, we could take something that can take three months down to a few hours and get real-time analytics,” say the authors of the study.
  2. Flows. Generative AI analyzes the best course of action for a company based on large internal and external datasets. It can also optimize complex decision-making and facilitate natural (and multilingual) language communication in global supply chains.
  3. Collaboration. The most noteworthy value of generative AI will likely come from the global exchange of AI-generated intelligence among the various supply chain stakeholders. “Generative AI technology could play a very interesting role in sustainability if it becomes the platform for collaboration rather than competition,” write the authors.

Generative AI applications in logistics

A study by TBS Business School in Toulouse, France, indicates that one of the most promising applications of generative AI in the supply chain is data analysis. The study indicates that generative AI benefits the supply chain in several ways, including enhancements in process efficiency, forecasting, order fulfillment, and employee assistance and training, “as well as quick analysis of a large amount of data to support quick and better decisions.”

Consulting firm Ernst & Young also stresses how AI-based data analysis contributes to improvement in enterprises. The study How supply chains benefit from using generative AI finds that “many organizations are using AI to analyze large historical sales data sets, market trends, and other variables to create real-time demand models. With generative AI, optimal inventory levels, production schedules, and distribution plans can be created to meet the customer demand efficiently.”

Generative AI produces unique, realistic content: images, videos, music, or text

Companies also employ data analysis with generative AI to optimize predictive maintenance tasks. "By learning from data collected from machines on the factory floor, generative AI models can create new maintenance plans to correlate with the time that equipment is likely to fail. This allows manufacturers to adjust their maintenance schedules to only when it is necessary, reducing downtime and costs while also extending the life of their equipment,” writes Ernst & Young.

By analyzing data — and thanks to the chat function — generative AI reaches conclusions that help enterprises make strategic decisions for their logistics processes. An optimal scenario would be one in which chatbots provide logistics managers with recommendations, such as the inventory they need to serve their customers.

Ernst & Young emphasizes the importance of generative AI’s chat function when predicting demand. That is, businesses can ask questions that help make forecasts more accurate. “For example, the company can run what-if scenarios on getting specific chemicals for its products and what might happen if certain global shocks or other events occur that change or disrupt daily operations. Today’s generative AI tools can even suggest several courses of action if things go awry,” explain the authors. The chat function could also upgrade customer service by generating personalized responses automatically. This would reduce both the time and resources needed to attend to customers.

Moreover, generative AI offers the chance to strengthen supplier relationships and management by automating the creation of emails and messages. Ernst & Young says: “These tools are useful to quickly extract information from large contracts and help you better prepare for renewal discussions, for example.”

Ultimately, in supply chain management, generative AI “will help enterprises become more resilient, sustainable, and transform cost structures,” finds the Ernst & Young study.

How does the Mecalux Group conduct research on generative AI?

Mecalux, as a leading intralogistics solutions company, is examining the potential of generative AI to expand technological capabilities in its clients’ warehouses.

Mecalux Software Solutions’ technical team has begun exploring three generative AI use cases:

  • Document management: create an internal tool to assist Mecalux’s Operations and Remote Support teams. Generative AI will analyze all technical documentation on each logistics facility. Through a user interface, it will provide experts with all the information they need to make well-informed decisions more rapidly.
  • Software programming and development: assist Easy WMS programmers in performing their tasks. The aim is for generative AI to streamline the process of programming new features, generating source code based on natural language descriptions. Generative models can speed up the software development process by providing code suggestions.
  • End-customer care: integrate generative AI into Easy WMS to answer questions conversationally, simulating human interaction. For instance, logistics managers would be able to request the creation of specific dashboards to evaluate the average time spent on order fulfillment.

Generative AI in warehouse management software

The most modern warehouse management systems (WMSs) on the market already incorporate AI functionalities. Thus, enterprises can make use of the WMS software’s big data analytics tools to facilitate the interpretation of the information generated on the different activities in their facilities. With a thorough analysis of operations, organizations can plan resources, measure business performance, and make strategic decisions.

The real power of generative AI lies in its ability to understand and respond to questions like a human. As a result, it can revolutionize the way users interact with warehouse management software. Logistics managers could pose broader, more complex questions to obtain personalized answers through text, graphics, or charts. According to a study from the company Master of Code, “by 2025, 90% of the material in quarterly reports will be synthetically generated.”




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