The benefit claims of a product is a critical driver of consumers' purchase behavior. Creating product claims is an intense task that requires substantial time and funding. We have developed the $\textbf{Claim Advisor}$ web application to accelerate claim creations using in-context learning and fine-tuning of large language models (LLM). $\textbf{Claim Advisor}$ was designed to disrupt the speed and economics of claim search, generation, optimization, and simulation. It has three functions: (1) semantically searching and identifying existing claims and/or visuals that resonate with the voice of consumers; (2) generating and/or optimizing claims based on a product description and a consumer profile; and (3) ranking generated and/or manually created claims using simulations via synthetic consumers. Applications in a consumer packaged goods (CPG) company have shown very promising results. We believe that this capability is broadly useful and applicable across product categories and industries. We share our learning to encourage the research and application of generative AI in different industries.

The evolving banking landscape has pushed traditional banks to digitalize in order to cater to consumer demand for personalized and value-added services. This is especially prominent given the effects of the Covid-19 pandemic, which has driven a majority of the customer base towards digital services. Whilst many emerging technologies exist, there have been variations in adoption rates by traditional banks around the world, with trends showing that they have been generally slower to adopt these technologies. Typically, traditional banks have been rolling out their digitalization efforts conservatively, usually with a multi-channel approach. These include improving existing digital channels or ramping up efforts to launch independent digital banking businesses.

"Technology is a great slave, but a poor master" The above line is a common phrase in any discussion involving AI. It doesn't seem that the discussion is one to be resolved anytime soon. With the concept of self learning technologies taking precedence in a digital driven world, AI finds special usage in Marketing Automation. Marketing refers to the process of promotion of a particular enterprise's products through research, analysis and interpretation of consumer data. In short, it is the art of convincing a consumer to buy something they do not need.

With AI taking the world by storm, it can be said that sci-fi novels and movies weren't wrong about its adoption and the subsequent rise. What if there were other books and films that showcased a vision of artificial intelligence under the guise of a different genre? But first, "The content expressed here are my opinions and not hard facts." Now that's out of the way; I can share my controversial opinion in a risk-free and carefree manner. Could I please get a drum roll first?

A Swiss fragrance company is counting on artificial intelligence to help it perfect the plant-based burger. Firmenich, one of the world's leading flavor manufacturers, says recreating the sensation of beef relies not only on flavor, texture and color, but also on how it responds to cooking and the way it feels in the mouth. 'Finding a protein that resembles meat from a vegetable protein is highly complex,' Emmanuel Butstraen, head of Firmenich's flavors unit, told AFP. One of the toughest challenges is avoiding an unpleasant aftertaste--pea proteins tend to release bitterness, which the taste buds are quick to pick up, Butstraen noted. Vegetable proteins can give off hints of green apples or pears, an aftertaste of beans, bitterness or sourness, or even a feeling of dryness, said Jerome Barra, the company's innovation director.

The industry as a whole is facing huge challenges, from rising costs of supplies, a shortage of labor, and changes in consumer preferences for transparency and sustainability. There is increasing recognition from agriculture corporations that solutions are needed for these challenges. In the last 10 years, agriculture technology has seen a huge growth in investment, with $6.7 billion invested in the last 5 years and $1.9 billion in the last year alone. Major technology innovations in the space have focused around areas such as indoor vertical farming, automation and robotics, livestock technology, modern greenhouse practices, precision agriculture and artificial intelligence, and blockchain. Farm automation, often associated with "smart farming", is technology that makes farms more efficient and automates the crop or livestock production cycle.

They have to strike a balance between maintaining top-line revenue growth and managing sustainable profit margins. The reason why the task becomes difficult is because they have to do so while managing a dynamic set of operations. Moreover, a shift in consumer preferences, advances in data & analytics, channel shifts, and pandemic-led disruptions have created new challenges for retail and CPG companies. However, in the current market scenario, they also have the opportunity to upgrade RGM, thereby creating equilibrium between growth and efficiency. They can increase the use of complex and action-oriented analytics across the product catalogue, optimize value capture approaches, use automated technology, and partner with retailers for shared value creation. Competitiveness has considerably intensified within the CPG industry.

Neuromarketing aims to understand consumer behavior using neuroscience. Brain imaging tools such as EEG have been used to better understand consumer behavior that goes beyond self-report measures which can be a more accurate measure to understand how and why consumers prefer choosing one product over another. Previous studies have shown that consumer preferences can be effectively predicted by understanding changes in evoked responses as captured by EEG. However, understanding ordered preference of choices was not studied earlier. In this study, we try to decipher the evoked responses using EEG while participants were presented with naturalistic stimuli i.e. movie trailers. Using Machine Learning tech niques to mine the patterns in EEG signals, we predicted the movie rating with more than above-chance, 72% accuracy. Our research shows that neural correlates can be an effective predictor of consumer choices and can significantly enhance our understanding of consumer behavior.

If you are a major manufacturer in 2020 and you have employed the likes of Deloitte, McKinsey or PWC, it is safe to assume that they have advised you to invest big in artificial intelligence and machine learning. According to reports by Deloitte and McKinsey, machine learning improves product quality and has the potential to double cash flow. Let's take a look at three global manufacturers who are already on board. SEE ALSO: Introduction to machine learning in Node.js Siemens is the largest industrial manufacturer in Europe, and whether they are putting together planes, trains or automobiles, their goal is to solve production challenges efficiently and sustainably.

As the rise of e-commerce continues, companies around the globe have become increasingly sensitive to evolving consumer preferences. In a world where instant gratification has come to represent a generation, autonomous technologies are set to make a significant impact. When it comes to consumer shipping, McKinsey reports that 25 percent of all consumers would pay a premium for same-day or instant delivery made possible by autonomous tech. However, this figure is likely to grow, given that 30 percent of younger consumers are willing to pay more for the same shipping options. As industry use cases continue to expand, many have come to define the ecosystem as the autonomous "last-mile."