Machine Learning and Creativity: Advertising and Marketing Sectors to Undergo a Paradigm Shift

Machine learning has the potential to revolutionize the marketing industry by automating processes and making campaigns more effective. In recent years, such advanced algorithms have also become more adept at creative processes. This has helped integration with and creation of novel advertisement strategies that help brands stand out in the market.

Artificial intelligence known as “machine learning” enables software applications to gain knowledge and execute processes on their own. This allows the software to function without being explicitly programmed for certain tasks.

Machine learning analyses massive volumes of data in digital marketing to gather insights, spot trends, and provide forecasts.

Machine learning (ML) has the power to improve audience targeting, increase personalization, and optimize customer engagement. It is a potent technology that makes use of data analytics to forecast customer behavior and enhance marketing initiatives.

Spotify is a well-known music platform that creates tailored playlists for users. Amazon suggesting items to consumers and Netflix customizing content recommendations are all processes underscored by the extensive use of ML.

Predicting consumer behavior, such as figuring out which clients are most likely to complete a purchase, is one important application field. Upon examining client information, and relevant data like surfing habits, machine learning algorithms can help customize and target brand messaging.

As per Future Market Insights (FMI), the global machine learning as a service market is likely to benefit from supervised learning and surging demand from the retail sector.

Power of Predictive Analytics

Predictive analytics, a subset of machine learning, has the ability to analyze large amounts of data and predict future outcomes with high accuracy. In creative industries, this technology can be used to identify consumer behavior patterns and high-value markets for the best growth opportunities.

Companies can now create more relevant, personalized content that resonates with their target audience, resulting in high engagement rates and increased conversions.

For instance, Netflix is using predictive analytics for its online marketing. Netflix analyses user data, including viewing history and ratings, using machine learning algorithms to forecast which movies and TV episodes a user would like. As a result, they can tailor suggestions for each user, which boosts customer retention.

Nascent Consciousness vs. Algorithms – Can Machines Really Be Creative?

As machine learning algorithms become increasingly sophisticated, there is growing interest in their potential to be creative. Some experts believe that with enough data, machines can not only identify patterns but generate novel ideas and solutions that human minds might overlook.

Others argue that true creativity requires the human touch and that machines can only produce what they have been programmed to do. However, the reality is that machine learning algorithms are already being used to create impactful campaigns in marketing and advertising.

Several companies are already using machine learning algorithms to develop entire marketing campaigns, from concept to execution. These algorithms can create ads, analyze consumer behavior, and optimize campaign performance in real time.

While machines might not yet be able to match the nuances of human creativity, they can certainly supplement it. Its ability to process vast amounts of data rapidly and accurately is the key differentiator from other tools used in marketing.

This has rendered machine learning an indispensable tool for campaign managers looking to make an impact on customers.

End of Interruption Marketing – How Personalization Changing the Game

Personalization is transforming marketing and advertising by allowing brands to tailor messages and experiences to individual customers. This signals an end to the traditional approach of interruption marketing. ‘One size fits all’ strategies have been abandoned for a shift towards relevant and targeted messaging that resonates with customers.

Machine learning is at the heart of this transformation. It allows marketers to gather and analyze vast amounts of customer data to gain insights into their spending and internet habits. This data allows for the delivery of highly personalized content across a range of channels, from email and social media to in-store experiences.

The benefits of personalization are clear. According to research by MarTech, tailored promotional emails increase sales by six times more for each instance than non-personalized emails.

Personalization isn’t a new concept. However, the level of sophistication and scale allowed by machine learning has improved vastly. By using algorithms to analyze customer data in real time, marketers can tailor messages and experiences on the fly. This makes for a highly personalized journey for each individual customer.

Consequently, marketing becomes less about selling and more about creating meaningful connections with customers. Brands that can build these connections are likely to thrive in an age where customers are increasingly sceptical of traditional advertising and sales practices.

Machine Learning Algorithm to Analyze Consumer Behaviour

With the vast amount of data generated from online activity, machine learning algorithms are able to analyze consumer behavior and provide insights that were previously impossible to obtain. By tracking consumer preferences, interests, and behavior patterns, marketing and advertising strategies.

Companies have optimized to reach the right audience at the right time with personalized messaging. Machine learning is allowing marketers to better understand their target audience and make data-driven decisions to drive business growth.

The Future of Machines: Learning & Creating

The future of machine learning in creative industries is exciting and full of potential. With advancements in technology, we can expect to see even more personalized and targeted advertising campaigns that cater to individual needs and preferences.

For instance, Pecan AI stated in February 2023 that its portfolio of automated, low-code predictive analytics tools now includes marketing mix modeling (MMM).

Machine learning algorithms will continue to provide invaluable insights into consumer behavior over the coming years. It enables companies to optimize their marketing strategies to out-sell competition. Machine learning is advancing at a rapid rate and is set to transform the way people think about creativity and innovation.

As machine learning and artificial intelligence continue to evolve, the future of creativity is looking increasingly automated.

Although machines might never fully replace human ingenuity, they will undoubtedly play a significant role in shaping market ploys. Marketing and advertising content is on track to undergo a paradigm shift in how it is delivered to larger audiences. The key will be to find the right balance between human creativity and the power of machine computing.

Author Bio

Mohit Shrivastava has more than 10 years of experience in market research and intelligence in developing and delivering more than 100+ Syndicate and Consulting engagements across ICT, Electronics and Semiconductor industries. His core expertise is in consulting engagements and custom projects, especially in the domains of Cybersecurity, Big Data & Analytics, Artificial Intelligence, and Cloud. He is an avid business data analyst with a keen eye on business modeling and helping in intelligence-driven decision-making for clients.

Mohit holds an MBA in Marketing and Finance. He is also a Graduate in Engineering in Electronics & Communication.

Employee in a logistics company working on his computer and using AI for inventory forecasting.

Global Logistics, Global Trade Daily, Technology

Pros and Cons of Using AI for Inventory Forecasting

May 30th, 2023 Sam Clement Leave a comment

Are you tired of overstocking or running out of inventory? Then you need to consider using AI for inventory forecasting. Artificial Intelligence (AI) can help you optimize inventory levels, increase efficiency, and improve customer satisfaction. But as with any technology, there will always be pros and cons. In this article, we will explore the advantages and disadvantages of this innovative approach to inventory management.

Using AI for Inventory Forecasting: A Game Changer

AI is revolutionizing inventory forecasting by providing more accurate and efficient predictions. With AI, you can analyze large data sets and identify patterns that would be challenging for humans to detect. This technology uses algorithms and machine learning to predict demand and optimize inventory levels, reducing the chances of stockouts or overstocking. The current inventory management software is already showing amazing results, and we can expect this trend to continue.

Pros of Using AI for Inventory Forecasting

One of the main advantages of using AI for inventory forecasting is its increased accuracy and efficiency. Traditional inventory forecasting methods rely on historical data and simple algorithms, which can result in inaccurate predictions. Conversely, AI can analyze large amounts of data in real-time, ensuring that inventory levels are always optimized.

With AI, you can respond quickly to changes in demand and supply. AI models can analyze data in real time and adjust inventory levels accordingly. That means you can reduce the risk of stockouts or overstocking and ensure you always have the right products. Overstocking can lead to wasted resources and increased costs, while stockouts can lead to lost sales and decreased customer satisfaction. AI can help you optimize your inventory levels, reducing the risk of overstocking and stockouts.

Optimizing inventory levels and supply chain performance is critical to the success of any business. With AI, you can analyze data from different sources, including customer behavior and market trends, to optimize inventory levels and improve supply chain performance. Many businesses want to implement AI forecasting with structural supply chain flexibility to achieve the best results.

AI can help you improve customer satisfaction and retention by ensuring you always have the products your customers want in stock. Optimizing your inventory levels ensures that your customers are always happy with your products and services. AI can also help identify any potential choke points in your supply chain, further helping you improve customer satisfaction.

Cons of Using AI for Inventory Forecasting

The accuracy and efficiency of AI-based inventory forecasting depend on the data quality and algorithms used. If your data is incomplete or inaccurate, your predictions may be unreliable. Similarly, if your algorithms are not well-designed, they may not provide accurate predictions.

Implementing AI-based inventory forecasting can be expensive. You need to invest in the technology, hire experts to manage it and ensure it is always up to date. That can be a significant expense for many businesses, especially small ones.

AI models may have difficulty incorporating external factors, such as supply chain disruptions caused by market trends and economic changes. While AI can analyze large amounts of data, it may not be able to detect external factors that may impact demand or supply.

AI can automate many inventory forecasting tasks, which may lead to job loss and workforce disruption. That can be a significant concern for businesses, especially those that rely on manual labor.

While AI can provide more accurate and efficient predictions, it still requires human intervention and oversight. Human experts are needed to ensure that the AI models are performing correctly and to identify any issues that may arise. AI models may provide inaccurate predictions without human oversight, leading to stockouts or overstocking.

Implementing AI for Inventory Forecasting

Implementing AI for inventory forecasting requires careful planning and execution. Before implementing AI forecasting software, you must identify your business needs and objectives. What are your current inventory management challenges, and how can AI help you overcome them? What are your long-term goals for your business, and how can AI support these goals?

Find the AI tools that best fit your business needs and objectives. Some popular tools and techniques include time-series forecasting, regression analysis, and neural networks. However, you should remember that AI relies on quality data for accurate predictions. You need to gather, clean, and process your data to ensure it is accurate and complete. You also need to ensure that your data is stored in a format compatible with your AI-based forecasting tools and techniques.

Conclusion and Future of AI-Based Inventory Forecasting

Using AI for inventory forecasting can be a game-changer for your business. AI can provide more accurate and efficient predictions, reducing the risk of overstocking or stockouts. Furthermore, as AI continues to evolve, we can expect new and innovative inventory forecasting approaches. AI has the potential to transform inventory management, improving efficiency and customer satisfaction. By carefully considering the pros and cons of using AI for inventory forecasting and implementing it to align with your business needs and objectives, you can take advantage of this exciting technology and stay ahead of the competition.

Author bio

Sam Clement is a relocation coordinator for Harris Movers. Sam also has previous experience in supply chain management and transportation planning, giving him a deeper appreciation of what is required to ensure timely delivery. Sam likes staying physically active and in shape when he isn’t trying to find an optimal route.

Global Trade Daily, Technology

Small Businesses – Here’s How You Can Improve your Digital Marketing in the Blink of an AI

May 23rd, 2023 Tom Schwarzweller Leave a comment

Technology is advancing at an extremely fast pace – just look at how quickly ChatGPT has become a household name. It has dominated news cycles and in only 4-5 months, it’s also heightened our awareness and understanding of where, and how, artificial intelligence (AI) is impacting our lives. From digital assistants like Siri, to predicting customer behavior and preferences, AI is everywhere now.

Whether you know it or not, AI has also been a core technology in marketing for years. Most product recommendation engines run on some form of AI, and we have used AI for years at Constant Contact to power things like subject line recommendations in our email marketing platform. The difference between where AI has been used in the past and its applications today is that it is much more capable – particularly when it comes to creating content.

AI is sophisticated enough now, and has a big enough dataset, that it can decipher text, react to engagement patterns, anticipate responses and recognize images. As you can imagine, this lends itself perfectly to digital marketing, where the objective is to deliver the perfect marketing message to every customer every time.

With such an in-depth understanding of how a consumer behaves, AI can help businesses make purchase recommendations, analyze product fit, and create better content with more precise targeting. If you’re a small business marketer, there’s no reason to not use this technology to your advantage.

Here are some easy ways small businesses can start using AI in their marketing strategy.

Personalize messages and offers for your customers

Leverage AI’s deep learning capabilities to amp up the experience your customers have when they interact with your business. One of the best ways to do that is by tailoring your marketing to share content that meets their needs.

AI can recognize if a person abandoned their online cart, and what types of products they have purchased in the past. It can also determine whether they prefer to be texted, or if email is their go-to channel for reading messages from their favorite brands. That type of analysis is invaluable as a marketer because instead of guessing what your customers will engage with, you can make decisions based on real-world data. Leverage that information to send more applicable messages and deals to your customers, and in doing so, they will be more likely to convert to a sale.

Banish writer’s block once and for all

Writing is difficult, and as a small business marketer, it can feel daunting to think about having to sit down and write an entire email or text campaign to your customers. And, it has to be good! Luckily, AI is here to help.

Instead of spending hours fighting writer’s block for your next campaign, let AI do the heavy lifting for you. There are several AI-powered writing tools available that can help businesses get a jump start on content creation and save valuable time in the process. Our AI Content Generator at Constant Contact is purpose-built for small businesses. It allows our customers to automate the writing process for their marketing campaigns, which saves time and resources while still creating high-quality content.

Write social media posts

Sometimes, you just don’t have the energy to create an engaging and informative social media post – let alone multiple posts for all the different platforms you are managing. That’s ok, you’re human! So, why not ask AI to do it for you?

Using AI to write social media posts can help small businesses maintain a consistent presence on social media, engage with their audience and even attract new followers. The best AI content generators will even give you multiple options to choose from. However, it’s still important to ensure the content feels authentic to your voice and doesn’t sound like it was written by a chatbot. Double-check before posting, and then move on to other areas of running your business.

One last note on AI content generation

AI-generated content can be informational, informative, and even fun. However, it’s important to note that AI is often just a baseline to get you started. The real value still comes from sharing your story and connecting with your audience.

It’s up to you to ensure that any content you generate using artificial intelligence is not only original and helpful, but that it’s also accurate, honest, and aligns with your brand values. Most importantly, you want to make sure that the content sounds like you and speaks to your customers in a way that makes them feel valued.

Dave Charest is the Director of Small Business Success at Constant Contact which delivers everything small businesses and nonprofits need to build, grow, and succeed.

Global Logistics, Global Trade Daily, Technology

Generative AI Up and Down (and Within) Supply Chains

May 18th, 2023 Peter Frerichs Leave a comment

Improving decision-making along supply chains is strategic work. Inventory management, procurement, and forecasting are some of the main areas large software providers such as SAP, Manhattan Associates, and Blue Yonder Group among others are pouring resources into. Unsurprisingly, artificial intelligence (AI) is front and center.

Supply chains are chock-full of data. Making sense of said data and arriving at speedy conclusions drives competition. Traditional AI was characterized by its ability to compress information (numbers primarily) into even fewer numbers. At that point, however, a human needed to step in and process the results. Generative AI is the process of algorithms (within traditional AI) creating and generating an output. This could be a photo, video, 3d renderings as well as “advice.” The word advice is still up for interpretation as programs like ChatGPT (an all-purpose chatbot) are not 100% accurate. Yet, its proponents argue, neither are human beings.

The future of chatbots is both exciting and downright frightening. Nestlé is rumored to be considering how chatbots and generative AI can help in evaluating the company’s security. Meanwhile, Manhattan Associates out of Atlanta is pondering ChatGPT to help streamline its warehousing and transportation operations. Simple questions such as, “where is customer A’s February shipment” could theoretically be answered faster and perhaps more accurately via a generative AI bot and unfettered access to the company’s data. \

Back in late 2019 the medical systems and consumer electronics products manufacturer Koninklijke Philips needed to ramp up the production of medical equipment, specifically ventilators. To do this they needed to scale down production in other areas. The company turned to AI to model the effects as forecasting models became more and more complex during the early-pandemic era. The accuracy is comparable if not better than traditional models and this is the potential power of this next generation of AI – generative AI.

The biggest challenge ChatGPT faces is its power. While supply chain management can certainly be bolstered, the very people using ChatGPT and similar chatbots have management worried. Some fear the data that is being shared (and potentially leaked or compromised). A January 2023 survey by Fishbowl of roughly 12,000 workers revealed over 40% had used ChatGPT and other AI tools while nearly 70% indicated they do so without informing their boss.

This new iteration of AI is here to stay, but solving supply chain bottlenecks will rely on a release of data that was previously unimaginable.

Global Trade Daily, Technology, Warehousing

Futuristic Technologies That Are Skyrocketing Warehouse Efficiency In 2023

May 9th, 2023 Joe Firth Leave a comment

Introduction

Warehouse efficiency is increasingly important in the modern economy, as companies look to optimize operations and deliver goods faster than ever before. In the coming years, technological advancements are expected to revolutionize warehouse operations, leading to unprecedented efficiency gains. From artificial intelligence (AI) and robotics to advanced data analytics and automated systems, here’s a look at some of the futuristic technologies that are skyrocketing warehouse efficiency in 2023.

AI and Robotics

Robots have been used to automate processes in warehouses for years, but their capabilities are becoming more sophisticated each year. In 2023, robotics and AI will be used to streamline operations by automating mundane tasks such as sorting and organizing inventory, picking orders, labeling packages and more!

Artificial intelligence is also being used to help warehouses become more efficient. AI-powered systems can analyze data in real time and make decisions based on past performance, allowing warehouses to identify and address issues quickly.

The future of market leaders like Amazon will be driven by robots, AI and the integration of both. Amazon’s director of robotics fulfillment Scott Anderson said fully automated warehouses will be commonplace within the next 10 years, as the use of robots and advanced AI enables orders to be packed in a fraction of the time it takes with human hands.

Radio Frequency Identification Systems (RFID)

Although RFIDs are relatively new it seems like they are being used everywhere from small-scale E-commerce operations to the huge warehouses of industry leaders like Amazon.

RFIDs can be used to quickly identify and track items as they move through the warehouse, allowing employees to locate products faster than ever before!

Radio-frequency identification tags are undoubtedly one of the most important advancements for the warehousing industry, and when you integrate them with some of the other warehouse automation systems available in 2023, it’s easy to see why they are so important.

Machine Learning and Predictive Analytics

Machine learning and predictive analytics are being used to enhance warehouse efficiency in 2023. By leveraging data from sensors, RFID tags, and other sources, warehouses can gain valuable insights into their operations.

This data can be used to identify areas of inefficiency, predict future demand for products, optimize storage layouts and more. Additionally, predictive analytics can help warehouses anticipate customer needs and stock the right inventory at the right time.

Advanced logistics software is an invaluable tool that helps warehouses stay ahead of the competition, organized and efficient In 2023.

Automated Labour Planning Systems

Labour is one of the biggest costs in any warehouse and automating labor planning can help reduce these costs. Automated systems are being used to optimize staffing levels and shift patterns, helping warehouses reduce their total labor costs while still delivering a high level of customer service.

In addition, automated systems can be used to ensure that employees are working efficiently by tracking their performance and providing them with feedback when needed. This helps streamline operations and lower costs.

Data Analytics

Data analytics has become an essential tool for managing warehouse operations. In 2023, companies will integrate advanced data analytics into their operations to gain insights into inventory, customer demand and other aspects of the business.

This data can then be used to optimize processes, reduce costs and increase efficiency, in every aspect from customer service to product storage.

Electric Vehicles And Renewable Energy

The warehousing industry is experiencing a surge in investments towards electric vehicles and renewable energy. The trend began in 2021 and 2022, and there’s no sign of it slowing down in 2023.

One of the major reasons for this shift is cost savings. Electric vehicles offer significant long-term savings in fuel and maintenance costs compared to traditional vehicles.

Renewable energy sources, such as solar and wind power, are also gaining popularity in the warehousing industry. These sources provide a sustainable and cost-effective alternative to traditional energy sources, further reducing operating costs.

The role of batteries in this transformational shift cannot be understated. There are multiple types of batteries available for use in electric vehicles, such as lead-acid, nickel-cadmium, and lithium-ion batteries. Each type has its advantages and disadvantages, making it vitally important to choose the most appropriate for specific applications in the warehouse.

Autonomous Systems

Autonomous systems are increasingly being used in warehouses for tasks such as product scanning, order fulfillment and more. In 2023, these systems will become even more sophisticated, allowing robots to take on more complex tasks such as loading shelves with products or navigating tight warehouse aisles.

Autonomous systems are expected to greatly improve speed while reducing errors and boosting safety.

The Positive Impact Of New Warehouse Technology

As warehouse technology continues to evolve, it will become increasingly easier for companies to stay ahead of the competition and remain efficient. These new technologies are making warehouses smarter, more organized and more cost-effective, leading to greater efficiency and improved safety.

By investing in state-of-the-art technologies for their warehouses, companies can ensure that they are always operating at peak efficiency and remain competitive in 2023. The integration of innovative tools such as robotics, AI, predictive analytics and automated systems will help warehouses increase speed while reducing errors, costs and labor expenses.

In addition, data analytics can provide invaluable insights into customer demand, inventory levels and other aspects of the business. With these advancements leading the way to greater warehouse efficiency in 2023, companies around the world are sure to benefit greatly from these new

The Negative Impacts Of New Warehouse Technology

Although these new technologies have the potential to revolutionize warehouse operations, there are some drawbacks. For example, automated systems can be expensive to integrate and maintain, which can lead to increased costs for companies.

Additionally, incorporating new technology requires a significant amount of training for employees for them to use it effectively.

Lastly, warehouses may become overly dependent on automation, leading to problems if the technology fails or malfunctions.

Despite these potential issues, the benefits that come with implementing modern technologies outweigh the risks.

By investing in innovative solutions such as autonomous systems and predictive analytics, companies can ensure that their warehouses remain competitive and efficient in 2023.

Final Thoughts

The advances in technology over the next few years are expected to revolutionize warehouse operations. With AI robotics, advanced data analytics and autonomous systems leading the way, warehouses will become increasingly efficient, leading to faster delivery times and lower costs. As these technologies continue to evolve, warehouse efficiency is sure to reach new heights in 2023 and beyond!

supply lending edge coriolis intelligence AI lenders

Archives, Global Trade Daily, Technology

Edge AI Market Expected to hit US$70 Bn by 2032

February 24th, 2023 Global Market Insights Leave a comment

As per the report by Global Market Insights, Inc. “Worldwide Edge AI market was valued USD 5 billion in 2022 and will surpass a revenue collection of USD 100 billion by 2032 with an annual growth rate of 35% over 2023 to 2032.”

Global Edge AI Market size is predicted to expand considerably throughout the forecast period 2023 to 2032 due to the rising consumer inclination towards smart homes.

Integration of supports monitoring of temperature, gas density, humidity, and home security. Increased customer expectations and a rise in the availability of such advanced gadgets are predicted to boost the market share for edge AI. Furthermore, the growing demand for consistent data access and low latency across industries is fueling the need for powerful infrastructure. Using technologies, businesses can achieve significant cost and performance savings.

In April 2021, multinational technology company, Intel, introduced third-generation Xeon scalable CPU which allows for the implementation of a flexible and scalable infrastructure. It can enhance workloads related to 5G, AI, and other high-performance computing, network security, and clouds.

Moreover, the increased acceptance of remote working since the COVID-19 outbreak is another factor influencing the industry’s growth. Edge AI enables businesses to securely grant access of internal resources to their distant workers. Additionally, it lowers the price and maintenance expenses related to physical network infrastructure.

Based on component, the services segment of the market is anticipated to grow at a rapid pace during the forecast period resulting from the global development of edge-based technology. Further, the subdivisions comprising training and consulting, system integration and testing, and support and maintenance make up the service market segment.

Due to the rising demand for dependable and low-latency data access throughout all companies, the system integration and testing segment will continue to expand. Furthermore, the expansion of the support and maintenance segment is attributed to its capacity to help organizations give speedy remote assistance for reducing network outages which will boost the overall market share.

In terms of end user, the government segment is anticipated to exceed USD 3 billion by 2032 owing to the increasing adoption of helping government offices to consolidate and centralize their IT resources to enable users to access the system anytime from anywhere. The growing digitization and need for robust wireless connectivity across government agencies are fueling market growth. For instance, MEA governments are digitalizing all of their departments and services, improving their infrastructure, and providing new services like e-applications that speed up administrative processes.

Depending on application the market the segmented as video surveillance, remote monitoring, predictive maintenance, and other segments. The adoption of telemetry in the healthcare industry is covered in the other category. This technology aids in the automatic detection of abnormalities in the operation of telemedicine devices. Telemetry in medical equipment also helps with patient management, diagnosis, and treatment from a distance. Such a wide range of edge AI applications will promote industry growth.

Regionally, the Middle East edge was valued at over USD 500 million in 2022 as several telecom operators are increasingly emphasizing on trials and deployment of 5G across the region accelerating revenue growth. Soaring demand for edge computing and data centers owing to the development of smart cities in Saudi Arabia and the UAE has led to massive investments in the IT sector, especially in GCC countries like Saudi Arabia, the UAE, and Qatar will push the market growth further.

The Edge AI market consists of Anagog Ltd., Amazon Web Services (AWS), Dell, Google (Alphabet Inc), Gorilla Technology Group, Huawei Technologies Co., Ltd., IBM Corporation, Imagimob AB, Intel Corporation, Microsoft Corporation, MediaTek Inc,

Archives, Education, Global Trade Daily, Technology

How to Successfully Incorporate AI in Procurement

December 20th, 2022 Emily Newton Leave a comment

People looking to optimize their procurement processes sometimes turn to artificial intelligence (AI). This option is not yet widespread, but it’s becoming more common, especially as decision-makers explore new technology investments. Here are some things for individuals to keep in mind before and during their use of AI in procurement.

Know That AI Does Not Replace Human Input and Expertise

Artificial intelligence is a subject with much hype surrounding it. The excitement and buzz are not wholly unwarranted. When used well, the technology can change business processes and results. However, people must remember that even the most advanced AI cannot and should not replace human judgment and knowledge.

The best approaches involve AI supporting human input and helping them make better, faster decisions. Decision-makers mistakenly believing AI can work unattended without ongoing supervision from humans will almost certainly end up disappointed due to unrealistic expectations.

A good starting point is to consider any areas of procurement that need improvement due to a high probability of errors and inefficiencies. Taking a closer look at those shortcomings can help people understand what causes the issues and how AI might help resolve them.

Moreover, it’s helpful for people to designate individuals or teams who will oversee the AI deployment process and solve issues that arise. Carefully planned tech implementations are more likely to proceed with fewer problems, but people should still expect a few surprises. Having a point person or team to solve those mishaps will prevent the matter from getting out of hand.

Work With Experienced Service Providers

People at many organizations are still relatively new to using AI in procurement or for any other reason. That’s why it’s smart for them to find and hire companies or individuals with the experience to guide them through the project from start to finish. It’s ideal if these selected service providers have direct expertise in applying AI to similar use cases or at least have worked with other clients with procurement needs.

In one case, the U.S. Department of Health and Human Services (HHS) worked with an external consultant as part of a larger procurement improvement process. The service provider combined microservices and AI to meet the department’s goals. An AI algorithm enabled the department to compare and contrast previous purchases. It analyzed the content of more than 2 million current contracts and assessed 5-7 billion words.

Since the AI used language directly from existing contracts, it allowed procurement professionals to more effectively enter into new agreements with suppliers or to extend current partnerships.

The tool’s design and functionality kept user-friendliness at the center. People interacting with it don’t need to know the exact terms to search for. Instead, they go through a guided process that helps them find the most relevant information quickly and easily.

This example shows why it often makes sense to seek providers with the expertise to create solutions that fit specific needs. Finding a company with the right experience often speeds the development process and improves the chances of the AI product working as expected.

Agree on the Primary Objectives

People should not apply AI in procurement solely because they know competitors already have. Instead, they must develop several concrete reasons why they want to use artificial intelligence and what they’ll achieve in the best-case scenarios.

One way to do that is to figure out how AI fits in with a company’s main business goals, whether those milestones occur in the short or long term. All organizations have specific priorities, but sustainability is an issue coming to the forefront more often lately. That’s true in terms of procurement and throughout the wider supply chain. For example, AI might identify suppliers with the lowest environmental impacts or those with less waste than competitors.

It’s not always easy to zoom in on those finer details without technological assistance. The beauty of AI is it can process huge quantities of data much faster than humans working independently. A well-trained algorithm might confirm whether a company has a long history of operating sustainably or could uncover some previously unnoticed red flags.

Everyone involved must agree about the AI’s primary functions, regardless of whether a company’s goals relate to sustainability or other matters. Ironing that out early will make it easier to get executive buy-in and ensure top-down support for the technology from the beginning.

Seeing how similar companies and industries have used AI in procurement could provide some necessary inspiration. For example, a hospital will have different procurement processes than a government agency.

Assess How Using AI Could Boost Resilience

COVID-19 and numerous other factors have made supply chains far less stable than they once were. There’s no easy fix for that challenging situation, but AI could reduce some uncertainties.

For example, people have successfully used supply chain AI tools to improve demand forecasting. Companies are then less likely to run out of the supplies they need or have too many of certain items on hand.

However, AI could increase resilience in other ways, too. Perhaps a company has previously had issues with counterfeit parts or supplies. Artificial intelligence is excellent at spotting deviations from the norm, picking up on things people can’t.

One product developed to screen for fake luxury handbags assesses anywhere from 500 to 1,500 features per item. It then gives the results of a check in as quickly as 60 seconds, although it sometimes takes an hour, depending on the brand. The tool also gets smarter with use.

Even the most sharp-eyed and conscientious people can’t check such minute details so fast. There’s also a high likelihood of individuals getting fatigued and missing out on flaws or fakery. AI is not foolproof but does not get tired or distracted like humans.

These are a couple of the many ways companies can and have depended on AI in procurement to become stronger and less vulnerable to problems. People should set key performance indicators (KPIs) related to the changes they want to make in the organization with the help of AI. That will make it easier to see if the AI is working as expected or needs further tweaking.

Using AI in Procurement Requires a Careful Strategy

Artificial intelligence-related procurement applications certainly have potential. However, people cannot expect to have AI deployed within their organizations in a matter of weeks. It could take months to figure out the best ways to use the technology and get people accustomed to working with it. That’s K. It’s far better to take care when using AI than to rush the rollout and risk experiencing frequent setbacks.

Author’s Bio

Emily Newton is an industrial journalist. As Editor-in-Chief of Revolutionized, she regularly covers how technology is changing the industry.

Archives, Global Trade Daily, News

CLAOC To Introduce AI Workforce Training Programs

February 9th, 2022 CLAOC Leave a comment

CEO Leadership Alliance Orange County (CLAOC) announced plans to launch Artificial Intelligence (AI) skills development programs early next year in partnership with Intel, along with local educational, community and workforce partners. According to CLAOC SVP Amy Kaufman, the new programs will focus on providing necessary AI skills to empower the future workforce in the growing digital economy.

Kaufman said regional workforce education is key to the OC region’s global competitiveness as companies accelerate their use of AI. “Demand for AI skills is expected to grow exponentially over the next three years and drive a need for workers to learn new technical skills across industries,” she said.

A recent Edscoop survey of higher education leaders and IT decision-makers found that 69% of all respondents sensed increasing demand from employers for graduates with AI technical skills. In January, CLAOC and its partners began a series of awareness and training sessions on how to equip the future workforce with the necessary technical, social, and career growth skills to succeed. As a partner in the effort, Intel will supply its expertise and intellectual property for the development of curriculum to be introduced by CLAOCs education partners at the high schools and community colleges with a goal to train and certify AI for Workforce skills (including non-coding), and enable access to work-based learning opportunities for at least 3,000 students by October 2026.

Carlos Contreras, Senior Director of AI and Digital Readiness at Intel said, “the next-generation workforce will need this kind of specialized training to develop solutions to the world’s greatest challenges, and community colleges have a huge role to play in unleashing innovative thinking.”

Intel’s corporate responsibility commitment to positive global impact is embedded in its purpose to create world-changing technology that improves the life of every person on the planet. This partnership builds on Intel’s commitment to expanding digital readiness to reach 30 million people in 30,000 institutions in 30 countries as part of the company’s 2030 Goals that underscore Intel’s aim to make technology fully inclusive and to expand digital readiness.

CLAOC is a not-for-profit membership organization of CEOs from some of the region’s most prominent public and private companies who are committed to leading change. CLAOCs mission is to collaborate to cultivate Orange County into a premier, inclusive, innovative talent hub. The group’s organizational priorities include creating an AI Talent Development Center of Excellence to bring together civic and business leaders, AI-focused entrepreneurs, and education organizations to cultivate and recruit the diverse talent needed now and in the future to support a robust AI-driven economy in OC. The AI Talent Development Center of Excellence will be the premier source of talent cultivation, providing OC residents the opportunity to build and flourish in AI-infused careers. Through this strategy, the group hopes to create a well-defined talent pipeline and promote a thriving local economy and environment for the region.

CLAOCs members include Edwards Lifesciences, Johnson & Johnson, EY, City of Hope Orange County, Pacific Life, PIMCO, Skyworks, Golden State Foods, Ingram Micro, and a host of others working strategically to help others succeed.
More information about CLAOC is available at www.claoc.org.

Archives, Commentary, Global Trade Daily

Taking The Mystery Out Of AI: 4 Ways It Makes Life Easier

February 7th, 2022 Rajeev Ronanki Leave a comment

Artificial intelligence is significantly impacting the world, yet there’s still a great amount of mystery – and misconceptions – about it.

The public’s imagination has been heavily shaped by science fiction, with the term AI evoking images of robots like WALL-E, C3PO from Star Wars, and David from Stephen Spielberg’s movie A.I. Scientists and technologists refer to this kind of humanlike AI as “general artificial intelligence.” General AI attempts to mimic the kind of abstract thought and typical problem-solving skills seen in humans.

I say attempts because there is currently no existing general AI system even approaching the sophistication of the human brain. The technology is nowhere close to creating a system capable of abstract thought or general intelligence. But “applied AI” is rapidly becoming a mainstream technology, improving the efficiency and profitability of businesses in many industries.

Why applied AI is faster but not yet smarter than us

Current AI systems, for all their computational ability, do not have the ability to understand and analyze context the way human brains do. For example, we can see a barking dog and instantly determine the threat level. Sufficiently advanced AI can recognize the dog but may be unable to determine the breed, its physical agility, and whether it has been encountered before. Unlike humans, AI cannot connect dots and judge context to solve problems creatively.

But AI systems can make decisions far more rapidly and accurately than humans. The strengths and limitations of the current generation of artificial intelligence make it most applicable for solving fit-for-purpose business problems. (Fit-for-purpose is the concept in which a product or service is adequate for the purpose for which the consumer selected it.) AI systems are designed to handle a specific task while operating within imposed contextual constraints. These fit-for-purpose systems and tools are known as applied AI.

There are four primary business applications of applied AI in industry. Here is a look at each and how they create efficiencies and value:

Automation

Automation saves countless man hours and resources, as computers can process

data in a fraction of the time it takes humans. Some of these processes require decision-making. This is where AI comes into play. Most business processes involve a structured set of inputs, and decisions are made based on defined policies and guidelines. The variables in the equations are well known and operate within a narrow context. Algorithms can make these decisions rapidly and accurately.

These algorithms allow much of the workload that companies do to be offloaded onto automated systems. That advancement provides conveniences for consumers in the age of online shopping. Capital One Shopping, for example, offers customers a browser add-on claiming to save money by automatically comparing prices, applying promo codes and providing deal alerts.

Banks use AI to automate the loan process. Relevant financial records are collected automatically, validated, and analyzed. The system can give a recommendation on the loan before a lender ever sees the application. This may sound frightening and impersonal, but these systems actually assess loans more accurately and fairly than humans. They look at a borrower’s credit history, credibility, liabilities, and other factors and make an impartial decision. Quite often, these individual metrics are also calculated by AI. Credit scores, for example, are calculated automatically.

Insights

The data produced by automated business processes holds valuable insights. These insights can be about almost anything; they may reveal something about a business process, unlock untapped opportunities, or even allow companies to make predictions about the future.

The data analysis that leads to insights is mostly automated. Given the volume of data that companies now work with, we need these automated AI systems to find the signal in the noise. AI looks for patterns and makes decisions based on training and defined guidelines. The exponential power of these systems can be seen in machine learning. IBM Watson, for example, can predict when an elevator is going to fail. AI systems are able to return insights about insights into their own operation, and this allows them to improve their data sets and algorithms. As a result, AI systems draw insights that the designers may not have ever considered or been looking for.

Personalization

Insights have many applications, but one that is rapidly transforming industries is personalization of user engagement. The insights drawn from consumers, users, and other stakeholders can be used to improve their experiences by engaging with them in a personalized manner.

There are many ways companies can use data to personalize the experience for the stakeholders they serve. Google maps knows through habits a driver’s route and daily schedule and will give them an outlook for the day based on the current data. Retail companies have their websites feature inventory designed to appeal to the specific viewer. Online ads are micro-targeted at individuals based on insights into their specific consumer behavior. Social media platforms also customize news feeds to increase user engagement. Search engines provide results that are tailored to the user’s location, demographics, search habits, online shopping history, and other data.

Sensing

Sensing produces a kind of insight that deserves special mention due to its revolutionary potential. The exponential explosion in data and computing power now allows us to better recognize patterns as they are forming and predict how they will develop, which in turn allows us to actually sense trends as they form and develop.

This ability has huge business applications. Consider the sudden rise and success of TikTok. ByteDance, the Beijing-based parent company that developed the app, hit it big by filling an emerging niche for teens who could use a platform for recording and sharing short videos. TikTok noticed the trend among teens, built a dedicated app, and marketed it to the emerging user base as the trend developed. TikTok is now worth billions of dollars and is changing the social media landscape.

Many companies using applied AI have built fortunes and improved the world for billions of people. They did so by leveraging exponential technology and riding the development curve. We can likely achieve higher levels of AI-powered efficiency and improvement in all industries.

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Rajeev Ronanki (https://rajeevronanki.com/) is the president of digital platforms at Anthem Inc., and the ForbesBooks author of You and AI: A Citizen’s Guide to AI, Blockchain, and Puzzling Together the Future of Healthcare. Before joining Anthem, Ronanki was a partner at Deloitte Consulting, where he spearheaded a myriad of technological healthcare innovations. Ronanki is a frequent contributor to Forbes and other publications. He earned a bachelor’s degree in mechanical engineering from Osmania University and a master’s in computer science from the University of Pennsylvania.

Archives, Commentary, Global Trade Daily, Software

Understanding How Immersive Mixed Reality Will Power the Metaverse

January 30th, 2022 Dijam Panigrahi Leave a comment

The way in which businesses, enterprises, industry leaders and consumers utilize technology for everyday tasks is set to undergo one of the most drastic evolutions ever. Just a few short years ago it was nearly impossible to think any sort of technology could have a greater impact than networked computers, the Internet or even mobile computing, but now immersive mixed reality powering the Metaverse is challenging just that.

How Companies Will Leverage “Digital Twins”

Today’s IT leaders are building the Metaverse – knowledge workers and things being represented by “digital twins” – a virtual world where people, consumers, workers all gather to communicate, collaborate, and share through a virtual presence on any device. This means companies will build immersive virtual spaces, aka metaverses, and it will allow employees to virtually collaborate using their digital twin through chats, emails, video calls and even face-to-face meetings.

Well-known companies like Microsoft, Accenture, and Facebook, which itself is now called Meta, are all paving the way toward this new reality of business, but there are companies working behind the scenes building immersive reality, modeling and simulation technologies that will ultimately power this new Metaverse.

What Companies Can Do with the Metaverse

Microsoft in particular believes individuals will engage with one another in an immersive experience once they can co-exist in a virtual setting where they exist as avatars, perhaps even one day as holograms. The company expects people to access virtual settings from its Mesh for Teams application through mixed-reality headsets like HoloLens, as well as everyday smartphones and laptops.

In one of the earlier enterprise-level buildouts, Accenture has been developing a “virtual campus” where its employees meet for coffee, parties, presentations and other virtual events. The company also leverages this virtual meeting space when onboarding new employees so they can build their virtual twins.

Modeling is at the center of powering the Metaverse

In this virtual Metaverse, digital twins based on modeling and simulation play a leading role. Simulation allows companies to take copies of the digital twin, run simulations on it and then identify optimizations that are too complex to find by monitoring the physical environment alone.

The power of simulation will be an exact game-changer for enterprises and businesses throughout the Metaverse in a variety of industries, such as optimizing production planning in the automotive sector, accelerating design in the aerospace industry, improving overall production efficiency for manufacturers, and increasing accuracy for consumer packaged goods companies, many companies are poised to leverage virtual simulation to make better business decisions and generate the greatest return on investment.

Optimum immersive reality systems are needed to support ultra-realistic, high-fidelity digital twin visuals during the modeling and simulation process; precise fusion of the virtual on real world in a multi-platform environment and the ability to demonstrate a variety of realistic environments.

Metaverse is a new kind of application which is enabled by tight integration between real and virtual worlds. Metaverse is enabled by multitude of new technologies broadly in five groups as shown below:

1. Communications and computing infrastructure: Metaverse will need to perform large scale compute-heavy tasks, and access large databases to merge the real and virtual world.

2. Management Technology: Metaverse will need a lot of resources like energy, compute etc. This layer manages and allocates most optimum resources to run Metaverse.

3. Fundamental common technology: AI, Spatio-Temporal consistency are fundamental common technology for Metaverse.

4. Virtual Reality object connection: Metaverse will create 1:1 connection between real and virtual world objects and technologies like blockchain, and identity modeling will enable that.

5. Virtual Reality Space Convergence: Metaverse will fundamentally need a new medium to interact. AR/VR/MR, BCI, Gaming technologies will enable this.

Immersive reality solution providers offer the following foundational technologies to run industrial enterprise Metaverse:

1: Virtual Reality Space convergence:

a: AR/VR:

Ultra-low Latency High Fidelity Rendering: Low latency is extremely critical to provide an immersive experience in Metaverse. AR/VR partners provide unparalleled realism of environments by leveraging ultra-low latency remote rendering on cloud/on premise in full fidelity and wirelessly streaming the solution to affordable commercial-off-the-shelf (COTS) devices – HMD, Tablet and Desktop.

High Precision 3D Artificial Intelligence (AI) based Spatial Mapping: Uses high-fidelity remote spatial mapping with high fidelity 3D scene reconstruction, scene segmentation and 3D object recognition using 3D vision and deep learning-based AI with precise fusion of the real and virtual worlds to merge real world and virtual worlds.

Game engine: Consumer game engines have limitations that they can only handle a Metaverse that can fit in a single server. The metaverse will be ever growing as more digital twins are created to simulate real objects in the virtual world. The right AR/VR partners have created a data-centric simulation engine which scales for any complexity of metaverse.

2: Communications and computing infrastructure:

a: Cloud computing/edge computing: Industrial enterprises will always subscribe to multi-cloud, edge cloud. Depending on different factors like data sensitivity, latency, cost, different parts of the Metaverse need to be run at different clouds/edge in a distributed manner. AR/VR partners automate running the Metaverse for industrial enterprise.

b: Messaging framework: In the distributed Metaverse there is a need to update the Metaverse at global scale so users can collaborate seamlessly. AR/VR partners have messaging framework updates distributed to the Metaverse at global scale.

Fundamental common technology:

Security and privacy: Security and privacy is one of the biggest issues facing today’s world. Since Metaverse has the digital twin as an integral part, the Metaverse will have much richer data. The security and privacy in Metaverse cannot be solved by traditional security tools. AR/VR partners have built tools that handle security and privacy related to digital twins.

The Metaverse is going to be important for all businesses, enterprises and consumers. Today, people and employees can only experience the internet when they log online on their computer or mobile device, but with new connectivity, devices and technologies powered by immersive mixed, we’ll be able to experience the internet all around every single day.