Copyright: sloanreview.mit.edu – “Learning to Manage Uncertainty, With AI”

The second Artificial Intelligence and Business Strategy report of 2024, from MIT Sloan Management Review and Boston Consulting Group, looks at how organizations that combine organizational learning and AI learning are better prepared to manage uncertainty. It examines how the emergence of generative AI is changing workers’ and organizations’ attitude toward the technology and the opportunities and risks that it poses.

Uncertainty Abounds

Uncertainty is all about the unknown. The less an organization knows, the greater its uncertainty and the less able it is to manage resources effectively. Managing uncertainty, therefore, requires learning. Companies need to learn more, and more quickly, to manage uncertainty.

Addressing uncertainty constitutes a pressing challenge for leadership, especially today, when geopolitical tensions, fast-moving consumer preferences, talent disruptions, shifting regulations, and rapidly evolving technologies complicate the business environment. Companies need better tools and perspectives for learning to manage uncertainty arising from these and other business disruptions. Our research finds that a major source of uncertainty, artificial intelligence, is also critical to meeting this challenge. Specifically:

Companies that boost their learning capabilities with AI are significantly better equipped to handle uncertainty from technological, regulatory, and talent-related disruptions compared with companies that have limited learning capabilities.

The Estée Lauder Companies (ELC) offers a case in point. The cosmetics company has a strategic need to anticipate consumer trends ahead of its competitors. In earlier times, consumer preferences might have shifted seasonally. Now, preferences are less certain; shifts happen more quickly due to social media and digital influencers. Fashion trends can change by the week. If the color peach suddenly captures the public’s interest, the company needs to discern that trend as quickly as possible.[…]

Read more: www.sloanreview.mit.edu

Der Beitrag Learning to Manage Uncertainty, With AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Ashwin Tambe – “Enabling a Smart Consumer with AI based Search Experience”

A smart consumer search experience empowers shoppers to make informed decisions quickly and confidently. It transcends traditional keyword matching to grasp the deeper intent behind a search query. This includes anticipating needs by suggesting relevant products or services before the consumer even realizes they need them, offering personalized recommendations based on past browsing behavior and purchase history, and seamlessly comparing products and prices across different retailers.

Advances in semantic search, AI, and natural language processing are enabling search engines to better understand user intent, deliver personalized recommendations, and facilitate conversational interactions.

Semantic Understanding and Organized Responses

Traditional keyword-based search is giving way to semantic search, where engines try to understand the intent and context behind a query. Search is getting a major upgrade! Imagine a giant encyclopedia that not only stores information but also understands how different concepts are connected. This is what search engines are building: massive knowledge graphs that link people, places, things, and ideas together. By analyzing these connections, search engines can grasp the deeper meaning of your queries, even if you don’t use the perfect words. For example, if you search for “best running shoes for beginners,” the search engine can understand that you’re not just looking for any running shoes, but for shoes that are specifically designed for people who are new to running. This allows the search engine to deliver more insightful results, such as reviews that focus on comfort and support for new runners, or comparisons that highlight features like shock absorption and breathability.

Personalized Recommendations

Search engines are getting to know you better! By remembering your past searches, location, and other bits of information, they can curate results that fit your interests. Imagine searching for “hiking trails” and seeing suggestions for beginner-friendly paths near your city, based on your previous searches for outdoor activities.

Behind the scenes, powerful algorithms are sifting through mountains of data, like detectives looking for clues. They recognize patterns and make predictions to personalize your experience. This might mean suggesting new cookbooks based on past recipe searches, or recommending movies similar to ones you’ve enjoyed before.

Even chatting with search engines is getting a makeover! Instead of clunky text interfaces, AI-powered assistants are emerging that can have natural conversations. These chatbots can answer your questions and offer help in a more conversational way, just like talking to a friend. Imagine asking “What are the best things to do in Paris?” and having a friendly AI chat back with personalized suggestions based on your interests and travel style.

Unified Experience – One destination Endless possibilities

Imagine a world where interacting with technology feels effortless and intuitive, anticipating your needs and desires before you even express them. This is the vision of Artificial Intelligence (AI) and its role in crafting a unified user search experience.

Traditionally, navigating the digital world has often been a disjointed experience. We juggle between various apps, websites, and devices, each with its own logins, interfaces, and functionalities. AI has the potential to bridge these gaps, creating a seamless and unified experience across all interaction points.

One way AI achieves this is through personalization. By intelligently analyzing our behavior, preferences, and past interactions, AI can tailor the user experience to our individual needs. For instance, an AI-powered virtual assistant might proactively suggest restaurants based on our recent searches and past dining habits. Similarly, an e-commerce platform might curate product recommendations that align with our interests and purchase history. This eliminates the need to endlessly search through countless options, saving us time and frustration.

AI also fosters foresight. AI algorithms can anticipate our needs and provide assistance before we even request it. Imagine a smart home system that automatically adjusts the temperature based on your daily routine or a fitness tracker that prompts you for a workout when you’ve been inactive for too long. This level of anticipation creates a sense of flow and removes the need for constant manual interaction.

Furthermore, AI can break down language barriers. Imagine traveling to a foreign country and being able to have a natural conversation with locals through an AI-powered translator that understands context and subtleties. This removes communication hurdles and opens doors to richer cultural experiences.

The possibilities of a unified experience powered by AI extend far beyond personal use cases. In the healthcare industry, AI can analyze patient data to provide more personalized treatment plans and improve overall health outcomes. In the education sector, AI-powered tutors can adapt to individual learning styles, creating a more effective and engaging learning environment.

However, it’s important to acknowledge the ethical considerations surrounding AI and user experience. Data privacy concerns and the potential for prejudice in algorithms need to be addressed to ensure a truly unified and positive experience for all.

Overall, AI has the potential to revolutionize the way we interact with technology. By creating a unified experience that is personalized, proactive, and removes language barriers, AI can empower us to achieve more and unlock a world of endless possibilities.

About the Author:

Ashwin Tambe, (Delivery Management Google , Retail CPG) is a management professional with expertise in enabling customers with AI adoption and bridging business gaps with use of modern technology. Ashwin actively engages in the public discourse on Large Language Models (LLMs) by sharing his insights through articles published on various digital platforms, exploring their consumption, societal impact, and potential role in shaping the future. Beyond his professional experience, Ashwin actively contributes to the academic community.he is serving as a judge and student mentor in University of Arlington Texas.

Der Beitrag Enabling a Smart Consumer with AI based Search Experience erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: cio.com – “10 ways AI can make IT more productive”

Every IT leader wants to build a productive organization. AI is ready to help.

When it comes to maximizing productivity, IT leaders can turn to an array of motivators, including regular breaks, free snacks and beverages, workspace upgrades, mini contests, and so on. Yet there’s now another, cutting-edge tool that can significantly spur both team productivity and innovation: artificial intelligence.

Any task or activity that’s repetitive and can be standardized on a checklist is ripe for automation using AI, says Jeff Orr, director of research for digital technology at ISG’s Ventana Research. “IT team members tend to have better experiences when they’re working on meaningful activities,” he notes. “Better employee engagement leads to employee retention.”

How can AI help your IT team members become more creative and productive? Check out the following 10 ideas.

1. Provide more context to alerts

Receiving an error text message that states nothing more than, “something went wrong,” typically requires IT staff members to review logs and identify the issue. This is highly unproductive, Orr says. Software incorporating observability technology, enabled by generative AI, allows an error message to be visually traced back to its source along with recommended steps to address the cause.

“This approach to better information can benefit IT team KPIs in most areas, ranging from e-commerce store errors to security risks to connectivity outages,” he says.

2. Create self-service options

Automating existing processes with AI gives enterprise departments a powerful new self-service tool. Onboarding a new hire, for example, follows a set of known processes, such as location, role, hours, and so on, Orr says.

“The steps to create employee credentials and access permissions, pre-configure security settings, and prepare the individual for a productive first day on the job really doesn’t require human intervention,” he adds.[…]

Read more: www.cio.com

Der Beitrag 10 Ways AI Can Make IT More Productive erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: diginomica.com – “3 Steps To An Enterprise Gen AI Strategy That Delivers Real-World Business Results”

In a fast-paced global marketplace where the quality of data-driven decision-making regularly separates the ‘haves’ from the ‘have nots’, the ability to keep up with technological advancements is an essential aspect of managing a successful business. With the Artificial Intelligence (AI) boom of the last few years, business leaders are also being faced with a new set of challenges and opportunities. To make the most efficient use of generative AI (gen AI) tooling while mitigating the potential risk of their use, more and more organizations are formulating organization-wide gen AI strategies to better align the technology they use with the business OKRs they ostensibly serve.

At its most basic, a gen AI strategy refers to a strategic approach that incorporates AI technologies and tools to improve operations, enhance customer experience, and drive growth. With a terrain informed by myriad forces ranging from potential budget cuts to new rulings by the Federal Trade Commission (FTC) granting them the use of compulsory measures in investigations related to the use of AI, the imperative that CXOs and other C-Suite executives take ownership of AI-driven business outcomes provides another important justification for the development of coherent AI strategies. AI must be thought of not just as a tool for automating certain manual processes, but as a market-wide disruption that will reshape businesses down to their underlying operational models.

In this blog post, we’ll explore the reasons why having a gen AI strategy is crucial for your business and how it can give you a competitive edge in the market. We will also walk you through how to build your own AI strategy to make the most out of your AI investments. Finally, we will walk you through how Hakkoda has helped one Fortune 500 Consumer Packaged Goods (CPG) company strategically leverage artificial intelligence to streamline its reporting ecosystem and improve operational efficiencies in a modern data stack centered on Snowflake.

Step 1 – understand the risks of gen AI (and how to mitigate them)

The integration of artificial intelligence into your organization’s data practices comes with numerous benefits, but it also presents unique risks that need to be addressed.

One such risk is the potential for AI systems to make errors or provide inaccurate information. Since gen AI and LLM content is the by-product of vast amounts of training material, it is also worth being proactive in addressing discriminatory output, as AI will share any biases commonly found in its source data.[…]

Read more: www.diginomica.com

Der Beitrag 3 Steps To An Enterprise Gen AI Strategy That Delivers Real-World Business Results erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: investopedia.com – “How to Use Artificial Intelligence in Your Portfolio”

Artificial intelligence (AI) is the simulation of human intelligence by machines. This is accomplished by software that attempts to replicate a human process. While artificial intelligence can entail very sophisticated applications, such as OpenAI’s ChatGPT (which can converse with users) or autonomous driving applications, artificial intelligence encompasses a very wide range of applications. One term often associated with financial market artificial intelligence is algorithms, which refers to a set of programmed instructions that provides results from the data analyzed by the program. Artificial intelligence in investing and finance takes many forms, some of which are relatively straightforward.

Hedge funds and other trading operations utilize artificial intelligence at a very high level to, as an example, gain the slightest advantages in fast-moving markets. But artificial intelligence is also widely used in finance and investing because of its ability to process and analyze information from very large data sets. Further, artificial intelligence can be used to help choose stocks, make predictions on market movement, optimize portfolios, manage risk, obtain personalized investment advice, manage trade entry and exit strategies, and automatically build a customized portfolio that meets specific investor criteria like risk tolerances. We will discuss a variety of ways any investor can incorporate artificial intelligence into their investing.

How to Use Artificial Intelligence in Your Portfolio

Here are some ways regular investors can utilize artificial intelligence in their portfolios.

Stock Picking

All of the companies that trade on U.S. stock markets have many data points that investors can use to determine what stocks they want to buy or sell. Artificial intelligence allows investors to efficiently sort through this data to identify stocks that meet their criteria. Stock screeners are sophisticated tools that allow investors to filter stocks on criteria that can include fundamental and technical data points, such as accounting ratios, market capitalization, trading volume, and moving averages, to name just a few of the literally hundreds of data points available. If you’re looking to get started with a stock screener, consider learning how to use these platforms by starting with a one of the many free versions that are available, like ZACKS (NASDAQ).[…]

Read more: www.investopedia.com

Der Beitrag How to Use Artificial Intelligence in Your Portfolio erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Nicolas Maréchal, AI Consultant – “Three AI Trends in the F&B Industry (2023)”

In recent years, we have observed several significant developments that demonstrate the increasing impact of AI in this industry. Artificial intelligence is transforming the food and beverage (F&B) industry. The sector is projected to reach a USD 35.42 billion valuation by 2028 (USD 7 billion in 2023), at a CAGR rate of 38.30% during the forecast period (2023-2028)¹. From that perspective, this article covers three AI trends that are disrupting the global F&B industry’s landscape in 2023.

1. AI-powered chatbots

Chatbots can simulate conversations with human users and are being used by restaurants and food retailers to provide quick and accurate responses to customers. Leveraging Natural Language Processing (NLP) technologies, chatbots can understand a customer’s inquiry in multiple languages and immediately reply with the relevant information, making communication more efficient and accurate.

Amongst others, chatbots can take and process orders, support the booking process, answer specific questions about menu items (such as allergens, sources, etc.) or provide information about special deals and promotions. They can also be combined with recommender systems (intelligent systems based on user preferences) to create personalized offers to retain clients and consolidate loyalty.

With the recent rise of LLMs (Large Language Models), we can expect many more breakthroughs in this field. For example, considering the topic of automatic recipe generation and menu creation, chatbots can suggest ingredient combinations and flavor profiles that are likely to be well-received by customers. This supports chefs to create new relevant and exciting dishes while meeting the dietary requirements of different customers and providing nutritional information.

In conclusion, chatbots save customers time and contribute to improving their overall experience with the brand, in addition to increasing efficiency.

More use cases related to LLMs and chatbots.

2. Cameras as sensors (Computer Vision)

One of the main sub-fields of Artificial Intelligence is known as Computer Vision. It basically refers to the processing, analysis, and understanding of digital images. Based on this technology, major use cases have emerged around the images and videos recorded by (surveillance) cameras.

For example, AI can detect visitors entering and leaving a restaurant to analyze occupancy over time (people counting applications), enabling managers to plan staff based on actual demand. It can also monitor important safety measures (such as hand washing, surface cleaning, gloves detection, mask detection, etc.), to avoid cross-contamination or improper food handling, and assist the workforce in improving overall food safety.

AI-powered cameras can also be used to identify any areas where staff need further training and even to detect and prevent theft. Trained to recognize specific products and packaging, AI models can for example send a real-time alert if any unauthorized items are removed from the kitchen.

However, one important consideration with this kind of model is the risk of privacy violations, depending on where the surveillance cameras are installed and located and what is recorded. These types of projects should be implemented in accordance with national policies and data privacy regulations such as the General Data Protection Regulation (GDPR), The EU Artificial Intelligence Act (EU AI Act), and more.

More use cases on how AI cameras can improve safety in restaurants.

3. AI-driven Robots, Automation, and Smart Kitchen

AI technologies also drive disruption in food production departments, enabling chefs to automatize non-critical and time-consuming tasks. AI Robots or “Robot chefs” (a mix of AI, Robotics, and Computer Vision) are already able to handle some entire cooking procedures end-to-end. This can include monitoring the cooking process or automatizing equipment such as ovens or cleaning vacuums, simply requiring a push of a button or voice activation.

The fundamental concept around Smart Kitchens concerns the transformation of select kitchen appliances into “sensors” and their integration with Internet of Things (IoT) technologies. This process facilitates the smooth interconnection of these devices, enabling the collection and analysis of real-time data. Smart Kitchen can ingest and process data from these interconnected appliances and subsequently provide prescriptive or predictive alerts and insights to users. This dynamic integration of technology empowers restaurants and individuals to optimize their cooking experiences, enhance efficiency, and make informed decisions. Tailored for individuals, we observe a rising demand for this type of device, such as Smart Fridge, making the cooking experience smarter and simpler.

More info on AI-driven robot chefs.

Conclusion

As Artificial Intelligence (AI) brings obvious benefits to the F&B sector, it is also important to underline the potential drawbacks that it can have on humans. In fact, AI could lead to reducing the demand for human labor, with a significant impact on the workforce needs in the industry. However, with the appropriate planning and management, restaurants and food retailers can adopt AI without necessarily negatively affecting jobs but rather helping to improve the industry overall.

The F&B industry is projected to skyrocket to a staggering USD 35.42 billion valuation by 2028, and embracing AI as well as aligning with expert partners becomes not just a choice but an imperative. By seizing this transformative technology, players can unlock unparalleled growth and secure their position in this increasingly fiercely competitive landscape.

If you are interested in AI trends and use cases applied to the Security sector, check out this article from SwissCognitive.

Sources

¹https://www.mordorintelligence.com/industry-reports/artificial-intelligence-in-food-and-beverages-market

https://www.mordorintelligence.com/industry-reports/artificial-intelligence-in-food-and-beverages-market

https://www.technavio.com/report/artificial-intelligence-market-in-food-and-beverage-industry-analysis

https://www.forbes.com/sites/jennysplitter/2020/04/23/this-ai-camera-can-help-restaurants-show-that-their-food-is-safe-from-covid-19/?sh=52973f0925b9

https://www.columbusglobal.com/en-us/blog/blog/6-ai-use-cases-in-the-food-and-beverage-manufacturing-industry

https://www.qsrmagazine.com/outside-insights/how-artificial-intelligence-reshaping-restaurant-world

https://timesofindia.indiatimes.com/blogs/voices/how-artificial-intelligence-is-revolutionizing-the-food-and-beverage-industry/

https://www.sourcesecurity.com/insights/staying-speed-utilising-ai-video-analytics-co-1538138049-ga.1629100806.html

https://goliathconsulting.blog/2020/12/12/five-ways-ai-cameras-will-help-your-restaurant-in-2021/

https://thespoon.tech/expect-more-restaurants-to-use-ai-cameras-like-dragontails-to-monitor-kitchen-cleanliness/

https://www.aiplusinfo.com/blog/ai-enabled-smart-kitchens/

https://aicontentfy.com/en/blog/chatgpt-in-food-industry-recipe-generation-and-personalization

https://fortune.com/2022/10/18/tech-forward-everyday-ai-robots-pizza/

About the Author:

Nicolas Maréchal is an AI Consultant at Artificialy SA, a Swiss company specializing in Artificial Intelligence. From consultancy, all the way to custom turn-key AI solutions, Artificialy SA delivers value to clients’ processes, products, data, and decisions. Nicolas is also Visiting Lecturer at EHL Hospitality Business School and teaches Python Programming.

Der Beitrag 3 AI Trends in the F&B Industry (2023) erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Arek Skuza  – “From Startup to Industry Leader: Drizly’s Success Story Through Data Analytics and Innovation – Beyond Efficiency: AI’s Creative Potential”

Drizly is an online drink delivery service that has seen great success capitalizing on technology and making shopping for drink products easier than ever before. Founded in 2012, Drizly has since become a leader in online drink delivery, offering over 4,000 beers, wines, and spirits from local retailers in more than 70 cities across the United States and Canada. 

The company’s innovative approach to online drink delivery has made them a leader in the food delivery industry, and its focus on customer convenience has paid off. In 2021, the company was acquired by Uber, the largest ridesharing, food delivery, and transportation network company in the United States, for 1.1 billion USD. During the COVID-19 pandemic, the company’s sales grew 800% due to the enhanced demand to buy drinks with limited exposure to the virus. Of course, the idea behind Drizly was innovative and popular. However, their business model and use of data analytics made the company stand out in a competitive market with many other product delivery and e-commerce startups.

The graphic below highlights Drizly’s success timeline. Companies often take years and years until they reach the so-called “breakthrough,” if they’re lucky. As you can see, Drizly was able to achieve this in just nine short years, which further highlights the effectiveness of data analytics.

Business Model and Strategy

Drizly’s business model revolves around providing customers with a convenient, fast, and easy way to purchase drinks online. Unlike traditional retail stores or licensed premises, Drizly has no physical locations. Instead, they partner with local retailers who carry the necessary licenses to be able to provide delivery services. Customers can order from Drizly and deliver their orders to their homes, offices, or designated pickup locations. 

Drizly’s strategy is focused on customer convenience and providing a seamless online shopping experience. They offer a wide selection of beverages from local retailers, ensuring customers get the freshest products. The company also has a comprehensive delivery network, ensuring that orders are delivered quickly and efficiently. Additionally, Drizly offers a variety of promotional codes that customers can use to get discounts on orders. It’s no wonder that Uber was eager to make the acquisition. With the success Uber Eats, Uber’s food delivery service, has had, adding Drizly to its arsenal was a strategic business decision to maintain its position as the leading food and now beverage delivery company in the United States.

Drizly’s revenue model can be seen in the graphic below. As you’ll see in the next section, advanced analytics fuels this model.

Data Analytics

Data analytics has been a key part of Drizly’s success. Data has allowed them to create an effective and efficient supply chain, optimize prices based on customer demand, and ensure that they provide customers with the best possible products. 

Drizly consulted Hashpath, a data analytics consulting company, to speed up their time-to-market. Hashpath helped ease and speed processes like authentication and onboarding. The advantage of utilizing Hashpath’s services was that it ensured the longevity and long-term success of these processes for users and administrators. In the case of Drizly, we can see that it’s often advantageous to outsource the incorporation of data analytics to another firm. Startups should not feel intimidated by the need to adopt analytics-based tools. In today’s business landscape, data analytics is at the forefront of success. As seen in the graph below, the advanced analytics market is growing at a compound annual growth rate (CAGR) of 15%, which is very fast. Companies like Amazon and Meta are industry giants due to their use of advanced analytics. Furthermore, the jobs with the highest demand are all data-oriented positions. Therefore, outsourcing data analytics-based approaches to outside firms is an effective way startups can integrate these profitable techniques. 

While Hashpath’s services were extremely valuable to Drizly, the firm was not working alone. They consulted Google’s Looker, a data exploration and discovery company. Looker partnered with Hashpath to help Drizly create new revenue streams, including direct monetization. Drizly is able to take advantage of direct monetization to sell its customer reports to vendors. The company receives loads of Big Data about customers, which is a highly valuable asset to various companies. 

Another advantage of Big Data is to evaluate customer behavior and preferences, analyze sales data, and track delivery times. This analysis helps the company make informed decisions about how to expand its service offering, determine what products customers are looking for, and ensure that orders are delivered on time. 

In addition to using Big Data for tactical decision-making, Drizly also uses data insights to inform its marketing strategies. The company can quickly determine what types of customers respond best to certain promotions and tailor messages and offers to meet the needs of each customer segment. This level of personalization has been a key factor in the tremendous success that Drizly has seen over the past few years. 

Drizly uses data from customer orders to make informed decisions about product selection and pricing. This information allows them to stock the products customers are looking for and ensure they have competitive pricing. The company also uses customer feedback to make product and service improvements, which further boosts customer loyalty. 

Conclusion

Since its founding in 2012, Drizly has become the leader in online drink delivery due to its innovative approach to convenience and data analytics and customer-centric focus. The company has created an efficient delivery system and optimized pricing strategies by leveraging the services of Hashpath and Google’s Looker. It now stands as a major player in the food industry thanks to Uber’s acquisition of the firm in 2021. With its unique data-driven approach, Drizly will continue to grow and expand its operations in the future. 

Without question, Drizly has proven that adopting an analytics-driven approach is essential for startups to succeed in today’s crowded business landscape. Startups can benefit from the use of data analytics and should not be afraid to outsource these services to experienced firms. By utilizing advanced data tools, companies like Drizly are sure to remain at the top of their industry and maintain a competitive edge.  

References

https://cloud.google.com/customers/drizly

https://cloud.google.com/looker/

https://drizly.com/

https://www.contentstack.com/blog/all-about-headless/use-predictive-analytics-augmented-analytics-make-most-of-data/

https://www.apptunix.com/blog/drizly-business-model/

Arek will speak at the SwissCognitive World-Leading AI Network AI Conference focused on Beyond Efficiency: AI’s Creative Potential on 5th September.

Der Beitrag From Startup to Industry Leader: Drizly’s Success Story Through Data Analytics and Innovation – Beyond Efficiency: AI’s Creative Potential erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Thomas Helfrich, Expert in intelligent automation and Artificial Intelligence Innovation systems – “Entrepreneurship in the Age of Generative AI”

Bridging Boundaries Across Business Domains

Entrepreneurs are celebrated for their versatile strategies, and generative AI is a tool that brings transformative potential across a multitude of business areas. Around 75% of the projected value of this technology resides in four key domains: customer operations, marketing and sales, software engineering, and research and development (R&D).

Imagine an innovative startup leveraging AI to personalize customer service experiences, generate dynamic marketing content, or even accelerate software development through AI-driven automation. The applications are broad and transformative, offering unprecedented growth opportunities.

For instance, an entrepreneur running an e-commerce platform can implement generative AI to enhance customer interactions. The AI could analyze customers’ preferences, offer personalized product recommendations, and create more engaging shopping experiences. In marketing, generative AI could create compelling ad copy or social media posts, boosting customer engagement and brand loyalty.

Industries Poised to Benefit

The adoption and application of generative AI are not confined to specific industries. Businesses across sectors – from banking and high tech to life sciences – can substantially benefit from this technology. McKinsey’s study projects that in the banking industry alone, generative AI could provide an additional annual value of $200 to $340 billion. The retail and consumer packaged goods sectors also stand to gain, with potential benefits ranging from $400 to $660 billion per year.

Workplace Evolution Through AI

Generative AI is set to redefine our work paradigms, offering significant automation capabilities. This technology could take over tasks that currently consume 60% to 70% of a worker’s day. For entrepreneurs, this means that more of their time, and that of their team, can be directed towards strategic thinking and creative endeavors, the vital ingredients for innovation and growth.

The Accelerating Pace of Automation

The adoption of generative AI is expected to hasten the onset of the era of automation. Predictions suggest that as early as 2045, half of the tasks performed today could be automated, signaling a major shift occurring a decade earlier than previously anticipated. This accelerated transformation brings opportunities for increased efficiency and cost savings, offering entrepreneurs significant strategic advantages.

Boosting Productivity & Fostering Economic Growth

Generative AI holds the potential to supercharge labor productivity. Still, realizing this potential requires strategic investment in training and transitioning workers to adapt to AI-augmented workplaces. With a well-planned approach, generative AI adoption could catalyze labor productivity growth of 0.1% to 0.6% annually through 2040.

The key lies in managing the transition effectively. By helping workers acquire new skills and adapt to changing occupational roles, entrepreneurs can steer their companies toward significant economic growth. The result? A more inclusive, sustainable world – an ideal environment for entrepreneurial success.

Navigating the AI Era: An Entrepreneur’s Guide

The excitement around generative AI is palpable, with early results showing great promise. However, the full realization of its benefits will take time. Entrepreneurs must skillfully navigate the risks associated with AI, identify new skills required in the AI-augmented workforce, and rethink core business processes.

For those ready to embrace this shift, generative AI offers immense potential. Entrepreneurs can leverage this technology to reinvent their businesses, spark innovation, and drive productivity. As we step into the era of generative AI, adaptable entrepreneurs could lead the charge toward a transformative wave of innovation and prosperity.

Final Thoughts

The advent of generative AI offers a golden opportunity for entrepreneurs. By streamlining operations, boosting efficiency, and augmenting profitability, this technology is poised to redefine entrepreneurship as we know it. The integration of generative AI marks a paradigm shift in how business operations are conducted. Entrepreneurs ready to harness the power of AI can position themselves at the forefront of their industries, steering their businesses into a prosperous era powered by intelligent automation.

This exciting phase of digital transformation presents a unique opportunity for entrepreneurs to reimagine their businesses, utilizing AI to drive innovation and competitiveness. As we stand on the brink of a generative AI revolution, the entrepreneurial landscape is teeming with opportunities that promise to reshape the global business landscape in unimaginably profound ways.

Der Beitrag Entrepreneurship in the Age of Generative AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Purushottam Raj Gaurav – “The Future of Dining: AI-Driven Solutions for Personalized Food and Beverage Experiences”

Introduction

Artificial intelligence (AI) has the potential to transform the food and beverage industry, providing personalized experiences to customers in ways never before possible. From menu recommendations based on customer preferences to kitchen automation and even entertainment, the future of dining is looking increasingly AI-driven.

Brief overview of the current state of the food and beverage industry

The food and beverage industry is a global sector that covers a wide range of enterprises involved in the production, distribution, and sale of food and drinks. The industry is constantly evolving, with trends such as healthier and more sustainable food options gaining popularity.

The COVID-19 pandemic had a significant impact on the industry, with many restaurants and other food service businesses forced to close or limit their operations. As a result of the pandemic, many restaurants have adapted to offer takeout and delivery services as well as technology-enhanced customer experiences.

The industry also continues to encounter difficulties like rising food prices, disruptions in the supply chain, and shifting consumer tastes. Businesses need to be flexible and adapt to these changes in order to stay competitive. They also need to concentrate on delivering excellent customer service and high-quality goods.

Explanation of how AI can revolutionize personalized food and beverage experiences

1. Assessing consumer data: To better understand each client’s tastes and preferences, AI can evaluate customer data such as order histories, culinary preferences, dietary restrictions, and even social media activity.
2. Creating personalised menus: With this data, AI can generate personalised menus and recommendations that cater to each customer’s unique preferences. These can include recommendations for specific dishes, drinks, and even flavour pairings.
3. Enhancing the dining experience: AI can also enhance the dining experience through personalised ambiance and entertainment. To create a distinctive and memorable dining experience, AI-generated music playlists and lighting designs, for instance, can be customised to the interests of specific clients.

Overall, AI has the potential to create truly personalized food and beverage experiences, improving customer satisfaction and loyalty, and increasing revenue for businesses in the industry.

AI-Driven Personalization in Food and Beverage

Using AI to analyze customer data and preferences

Analyzing client information and preferences is one of the most important ways AI may transform tailored food and beverage experiences. To obtain insights into each customer’s preferences, AI systems can process enormous volumes of data from numerous sources, including social media, purchase histories, and loyalty programmes.

These details could be anything from dietary constraints to culinary preferences to favoured cuisines to allergies. AI can build a thorough profile of each consumer by analysing this data, enabling highly customised recommendations and experiences.

Creating personalized menus and recommendations

AI is able to analyse customer data and produce individualised menus and recommendations for every single client. Based on the client’s tastes and previous orders, this may entail making recommendations for particular foods, beverages, or even full meals.

Moreover, AI-driven digital ordering systems can offer tailored recommendations and facilitate the ordering of food and beverages that adhere to a customer’s tastes and dietary requirements. The customer’s order history can be used to update these recommendations in real-time, ensuring that they are always accurate and current.

Enhancing the dining experience through AI-generated ambiance and entertainment

AI can improve the dining experience by personalising the environment and entertainment for each patron. To create a distinctive and memorable dining experience, AI-generated music playlists and lighting designs, for instance, can be customised to the interests of specific clients.

By making recommendations for games, films, or other digital entertainment options that fit the user’s interests and preferences, AI can further personalise the entertainment experience.

In general, AI-driven customization in the food and beverage sector has the potential to revolutionise the sector by producing highly customised experiences that are catered to the distinct tastes and preferences of each individual client. This can increase client happiness and loyalty while also boosting sales for organisations in the sector.

Advancements in Kitchen Automation

AI-enabled appliances and equipment for efficient cooking and preparation

Commercial kitchens may be able to expedite cooking and preparation with the help of AI-enabled equipment, increasing productivity and lowering labour expenses. In order to reduce the need for manual modifications by chefs, smart ovens and grills, for instance, can automatically modify cooking times and temperatures based on the type and quantity of food being cooked.

Additionally, AI-driven inventory management systems can track items automatically and notify chefs when they run low on supplies, minimising waste and guaranteeing that the kitchen always has the essential ingredients on hand.

Robotics and automation in food service

The use of robotic automation is spreading rapidly in the food service sector. Robots may carry out a variety of duties, including meal preparation, dishwashing, and even delivery, which will save labour costs and boost productivity.

Robotic arms, for instance, can slice vegetables and put together sandwiches, freeing up chefs to work on more difficult duties. In addition to moving food and beverages from the kitchen to the consumer, autonomous delivery robots can help save wait times and enhance the overall customer experience.

Reducing waste and optimizing food inventory through AI

AI can also assist commercial kitchens optimise their food inventories and lessen food waste. Using historical data and current patterns, AI-powered inventory management systems can forecast demand for particular components, ensuring that the kitchen has the proper quantity of each ingredient on hand.

AI can also assist chefs in finding creative methods to utilise up leftover ingredients and minimise waste. The amount of food that goes to waste can be decreased, for instance, by using AI to recommend recipes that use components that are about to expire.

In the long run, improvements in kitchen automation have the potential to boost productivity, cut waste, and improve the clientele experience in the food service sector.

AI in Food Safety and Quality Control

Real-time monitoring of food safety and freshness

Artificial intelligence (AI) has the potential to monitor food quality and freshness in real-time, ensuring that food products are wholesome and up to snuff. Sensors can be used, for instance, to keep an eye on the temperature, humidity, and other conditions that affect the quality and safety of food. Then, in order to spot any irregularities or potential hazards, this data may be instantly examined using AI algorithms.

AI may also be used to monitor the conditions of food storage and transportation, ensuring that goods are kept fresh by storing and transporting them at the proper temperatures and humidity levels.

Predictive maintenance to prevent equipment malfunctions

AI may also be used to predict when equipment will need repair, assisting in preventing issues that could jeopardise the quality and safety of food. AI systems can identify early warning indications of probable equipment breakdowns and notify maintenance crews before a breakdown happens by evaluating data from sensors and other sources.

This can ensure that food production doesn’t stop and assist minimise downtime and expensive equipment maintenance.

Automating quality control and compliance monitoring

In food production facilities, artificial intelligence (AI) may automate quality control and compliance monitoring to make sure that the goods adhere to both legal requirements and quality standards. AI can be used, for instance, to automatically check products for flaws like broken packing or infection.

AI can also be used to follow product movement across the supply chain and check compliance with food safety rules, ensuring that products are handled and labelled properly.

Therefore, AI has enormous promise for enhancing food safety and quality assurance in the food manufacturing sector. Businesses may lower the risk of product recalls and make sure that their goods meet the highest standards of quality and safety by utilising real-time monitoring, predictive maintenance, and automation.

AI in Restaurant Management

AI-powered scheduling and staffing optimization

AI can be used to streamline restaurant staffing and scheduling, ensuring that the appropriate amount of employees is on hand at the appropriate times to meet consumer demand. For instance, managers might plan extra personnel during peak hours by using AI algorithms to examine previous sales data and forecast when consumer traffic is expected to be at its maximum.

AI can also be used to optimise staff schedules based on variables like employee availability, skill level, and preferred shifts, which will lower labour costs and boost employee happiness.

Forecasting and predicting customer demand

Restaurant operators can utilise AI to estimate and predict client demand and then change their inventory and personnel numbers as necessary. For instance, AI systems can forecast when consumer traffic is expected to be at its peak and change inventory and personnel levels accordingly by looking at past sales data, weather trends, and other factors.

By doing this, restaurants may cut down on waste and streamline their processes, ensuring that they have enough food and employees on hand to fulfil consumer demand.

Optimizing pricing and profitability through AI analysis

In order to maximise pricing and profitability in restaurants, AI can be used to analyse data from sales, inventory, and other sources. For instance, AI algorithms can detect the most popular items on the menu and modify pricing and advertising methods in accordance with client behaviour and sales data analysis.

AI can also be used to find ways to cut costs, for as by minimising waste or maximising inventory levels. In a congested market, this can help eateries become more profitable and competitive.

Overall, AI has the potential to significantly enhance restaurant management through personnel optimisation, demand forecasting, and pricing and profitability optimisation. Restaurants can enhance their operations and offer a better customer experience by utilising AI, which will also boost their profitability and competitiveness.

The Challenges and Future of AI in Food and Beverage

Ethical considerations and data privacy concerns

The use of AI in the food and beverage industry raises ethical questions and data privacy issues, as with any technology. Concerns exist, for instance, regarding the usage of customer data and the possibility of bias in AI algorithms. Businesses need to address these issues and make sure AI is being used ethically and sensibly.

Adapting to changing technology and industry trends

Businesses must be able to adjust to shifting technology and market trends because the food and beverage sector is always changing. Businesses must be ready to make investments in new technology and make sure that their staff is properly taught and equipped to use them.

Opportunities for further innovation and research

Notwithstanding the difficulties, the subject of AI in food and beverage offers numerous chances for more innovation and study. For instance, the creation of new AI-powered products and technology, like robotic chefs or smart kitchens, is a possibility. In addition, more study is required to understand how AI will affect the sector, including any potential advantages as well as hazards or difficulties.

Overall, businesses have many chances to use AI to enhance their operations and offer a better customer experience, even though there are obstacles to its application in the food and beverage sector. Businesses may stay ahead of the curve and assure their future success by tackling ethical issues, adjusting to shifting technology and industry trends, and investing in more research and innovation.

Conclusion

Recap of the potential benefits of AI-driven personalized food and beverage experiences

The potential impact of AI on the food and beverage business has been examined in this article. Personalized experiences, increased efficiency, enhanced safety and quality control, and improved restaurant management are just a few ways that AI can change the sector. Businesses can gain a competitive edge and improve customer experiences by utilising the power of AI.

Implications for the future of the industry

The impact of AI on the food and beverage sector is substantial. Customers’ interactions with food and beverage companies may change as a result of AI-driven tailored experiences, which can also give businesses new levels of data-driven insights and optimisation. AI is probably going to play a bigger and bigger role in the sector as the technology advances.

Final thoughts on the importance of embracing AI in the food and beverage industry

It is clear that AI has the potential to transform the food and beverage industry, providing businesses with new ways to provide personalized experiences, optimize their operations, and improve safety and quality control. As a result, it is crucial for companies in the sector to adopt this technology and consider how it can affect their operations. By doing this, they can improve productivity and profitability, acquire a competitive edge, and offer their customers a better experience.

About the Author:

Purushottam Raj Gaurav is part of a talented team of content writers working at Emergen Research, fastest growing market research firms in the industry. He has experience in developing quality content and is currently involved in writing articles, press releases, and blogs for the company. He is highly motivated and enjoys putting ideas and thoughts into words to enable the reader to experience a seamless perusal.

Der Beitrag The Future of Dining: AI-Driven Solutions for Personalized Food and Beverage Experiences erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

“Preparing for the Future of Generative AI in Business: Industry Experts’ Insights – SwissCognitive AI Radar”

Since the burgeoning Generative AI hype, the terms “ChatGPT” and “Generative AI” are the most commonly used phrases in the news and in social media.

Many believe that this is a passing trend that is quickly fading or that the attention it is receiving is overrated.

However, the growing number of publications backed up by experts does not show that it is just another one-off buzzphrase. Every day, more and more giant companies are announcing that they are incorporating Generative AI into their daily lives.

Google Bard was just released yesterday, Microsoft announced Copilot last week, and Adobe has just released their own AI image generator, “Firefly”, as well.

While this news is all just from the last few days, several corporations like Coca-Cola, BCG, Snapchat, AWS and Hugging Face and much more have previously announced similar initiatives.

In this AI Radar edition, we’ve assembled a selection of articles from our artificial intelligence experts. These thought leaders share their valuable insights, experiences and forecasts while also highlighting the doubts surrounding generative AI.

Discover the exciting promises of this technology, along with the ethical and creative considerations that come with it, and learn how to develop the necessary skills to keep pace with its rapid evolution. Deep dive into the next phase of the Smart Technology Era and explore the power of generative AI in transforming the world around us.

Der Beitrag Preparing for the Future of Generative AI in Business: Industry Experts’ Insights – SwissCognitive AI Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.