Artificial intelligence (AI) and manufacturing seemed like a rare scene in a movie a few years ago. In fact, no one would imagine a factory with robots doing tasks that were in the past assumed to be done by humans alone, except in a science-fiction movie. This has changed to a real-life scenario, with AI now used to do various tasks in manufacturing companies. With the advances that have been made in AI and the technology landscape in general, manufacturers can benefit in a variety of ways. Here are some ways you can put AI to task in manufacturing.

1. Cobots can work with humans

Collaborative robots, mostly known as cobots, frequently work alongside human employees to function as an extra set of hands. These autonomous robots are programmed to perform specific tasks by learning. They can detect and avoid obstacles, making them agile and capable of working alongside human employees. Cobots are used to perform tasks requiring heavy lifting or assembly lines.

2. Robotic process automation (RPA)

Robotic process automation (RPA) is a powerful software used in the back office. Unlike the cobots, which work on the frontlines, RPA is mainly for the back office. It helps manufacturing companies handle high, repetitive tasks, queries, calculations and records, which can be challenging for humans to deal with. With such software, people do not need to enter data or spend time searching for specific things manually. Rather, they can enter specific queries, which saves time and effort. 

3. Digital twins

Machines used in manufacturing can be complicated. Due to this, companies need a way of understanding the inner workings of such complicated machinery. A digital twin, a virtual model of a physical object that receives information about another physical counterpart using sensors, is the right solution. Digital twin uses AI and other technologies to deliver insight about an object or an object. Manufacturing companies can monitor objects through their lifecycle and receive critical alerts such as the inspection and maintenance of machines.

4. Predictive analytics

Manufacturing plants and other heavy equipment users are increasingly using AI-based predictive maintenance to anticipate when the machines may need to be repaired. If the equipment is not maintained in time, operations risk being ceased and valuable time and money will be lost. Customers will also be affected by the suspension of operations and delays caused by the equipment breakdown. On the other hand, waiting too long can result in the machine’s extensive wear and tear and can also expose employees to risk.

5. Inventory management

Inventory management is a complicated undertaking if done solely by humans. However, companies now use AI systems to manage their inventory needs better because it promises to change things. AI can help keep track of supplies and automatically send alerts when the items in inventory have dropped and require replenishing. They can also use AI to identify bottlenecks in the industry supply chain. In pharmaceutical companies, for example, AI can predict the arrival time of certain ingredients and how delay can affect production.

6. To boost supply chain management

One strong use of AI in manufacturing is in managing the supply chain. Large companies with complicated supply chains may find it hard to manage their orders, purchases, materials and ingredients. A manual approach to supply chain management leads to time wastage and is expensive. However, companies have deployed augmenting their supply chain processes with the help of AI. Vehicle manufacturers, for example, may use it to manage spare parts from different suppliers. For instance,  AI systems can help track the type of vehicles and the spares and track vehicles made with defective bolts or nuts. This makes it easy for manufacturers to recall them.

Decades ago, we grew up thinking that artificial intelligence (AI) was something we would only watch in fiction movies. However, as things pan out, this is no longer the case. AI is now real and is helping us in many areas of our lives. As it turns out, AI is now used in our phones, computers and devices, where it helps more than we could have ever thought. Unlike humans who make mistakes in day-to-day tasks, AI is better in many things, hence the increased adoption in many industries. In the fashion industry, for example, humans make mistakes in sewing and stitching, specifically when cutting fabric. This causes dissatisfaction and frustrations among the employees and customers. However, there is hope with the new capabilities of AI. Artificial intelligence and fashion have converged and now have a new partnership, which has shown potential success in the years to come.

Here are some ways that AI can be used in the fashion industry.

1. Manufacturing

During manufacturing any product, the time taken is often more crucial than the product itself. For instance, rushing to fill an order or hitting a daily quota may affect the quality of the product. When employees are rushed to work on something, they are likely to make mistakes that can mess up a product. AI solves this problem by helping in areas like stitching fabric or by ensuring the colours of the design match the fabric and make everything perfect. AI will do things people initially viewed as difficult and perfect and does it with accuracy and speed that humans can only dream of performing. Furthermore, AI makes manufacturing cost-effective and will do the job better than the entire room of workers.

2. Design

Many brands are implementing artificial intelligence (AI) into their design processes to help collect data about the customers and what best suits them. AI helps customers choose clothes based on the fabric, colours and preferred style. Unlike humans, who are likely to propose clothes to customers based on their liking, AI has no favourite. They only choose what suits the customers better. They help customers design what they have in their minds. This is changing how the fashion industry designs its products as it gives customers exactly what they want.

3. Merchandizing

 AI has made virtual reality successful in buying products online and in-store. Anyone who wants to buy specific clothing or shoe can chat with an AI assistant and find what they need. With the advanced chatbots through apps on company websites, customers can get the help they need and receive help when they need it.

4. Inventory

The adoption of AI is changing how stores manage their inventories. This area has always been overlooked, leading to loss of profits and customer dissatisfaction. With Artificial Intelligence, brands can now calculate fashion trends through insights gathered from the market and get the upper hand by suggesting certain products to be brought at the right time. They can also look for the most fashionable pieces for the season and recommend the right course of action.

5. In-store enhancement

Smart mirrors are becoming a thing for fashion brands. They are using it to enhance the user experience by allowing customers to look in the mirror and virtually try different clothes and shoes without going to dressing rooms. The mirror allows customers to compare multiple garments side-by-side and change the colours to see the shade that suits their skin tone, body shape and the outfit that matches them better generally.

Generally, although the fashion industry has lagged in adopting technology for years, it is clear that AI is a technology that will positively affect the industry. While some think that AI will be the end of the fashion industry as we know it today, all signs point to better days for brands and customers.

Artificial intelligence (AI), machine learning (ML), and other digital tools are commonplace in many industries. This is the case even as the world moves to digitization to enhance efficiency, transparency and lower operational costs. Using these tools is becoming part of artists’ life as technology becomes easy and ubiquitous. In music, new technologies, particularly artificial intelligence, are significantly changing the nature of creative processes. With the advancement of AI, computers now play a significant role in areas such as music, architecture, science and even arts. Instead of just being a tool that helps human creators, it has become a creative entity in its own right with the ability to create works of art.

Computational creativity is an area that originates from the abilities of AI to decipher information and work creatively. It is the study of building software that can work with human-like creativity. The software can work autonomously to perform creative tasks like writing poems, composing music, developing mathematical theories and composing music. Furthermore, the study of computational creativity allows us to understand human creativity and produce programs that creative people can use. Historically, it was difficult for society to accept the intelligence of machines and their ability to perform certain tasks. Nowadays, we can all admit that machines can be creative.

Computational creativity in music

AI has been in the music industry since the 1950s. However, it was not until recently that it entered unchartered waters of music composition and improvisation. New AI systems now have these capabilities that allow artists to perform express. This enables significant performances in composing, performance and improvisation of music.

Composing music

Since the 1950s, computers have entered the music industry. One example was the Illiac computer by Hiller and Isaacson in 1958. This computer is known for its Illiac Suite, which composed the famous “generate and test” problem-solving. Fast forward to 2022, various AI composing software are capable of composing music. This has proven the notion that machines cannot do some tasks which were initially meant to be achieved by humans alone. These platforms generate original, copyrighted music that can be used on social media for ads and on YouTube. With the advancement of this technology, you, as a musician, do not have to be a sound designer to produce a soundtrack for your videos. You can simply upload the music that has already been recorded and come up with various variations.

AI in synthesizing music

Despite the earlier breakthroughs that AI had made in arts and music, there was still a challenge in synthesizing music. The computer-generated music has always had a problem with expressiveness, which is a lack of “gesture.”  This is what musicians call the nuances of performance and are often unique and interpretive. Now, AI has sound cloning that allows trained models to speak exactly like musicians with just a short audio clip taken.

Music improvisation with AI

Improvisation is a complex process in music. However, it has also been modelled computationally in what is commonly known as “composition on the fly.” Since the early 1980s, music improvisation has been computerized; however, the current compositions are complex, and so is their improvisation. The current software is capable of being incorporated into music. Google’s AI-powered Piano Genie is an example of such an advanced improvisation platforms. When playing, it acts as if it reads the musician's mind. The accuracy of the model depends on the training data.

From the above examples, it is clear that AI can augment human creativity. It will pair instinctive knowledge of when certain components or notes need to be played and determine what should come next. They also make producing music easy.

Artificial intelligence (AI) is changing almost every industry, one after the other. It is helping in medical research to create vaccines, in education to tutor children and even in the cooking of burgers through machine learning. While these are just a few ways that AI is currently used, there are many other ways and real-world implementations of AI. Machine learning technologies are becoming more accessible than ever. However, it remains a challenge to find a business case for AI. Here are the AI strategies you can implement to improve your desired activity.

1. The efficiency strategy

Most people think automation is the first thing that comes to their minds when they think about AI. However, AI has many other applications apart from automation. Despite this, the efficiency strategy is more about automation. It can be used for activities that have clearly defined rules and routines. Companies that adopt the efficiency strategy to optimize their processes do so with the intention of reducing costs. For example, with the taxi industry at risk of becoming fully automated soon, self-driving cars are increasingly becoming better than humans. Clearly defined rules for driving a vehicle are in place. Furthermore, fraud detection has been automated. As most routines are being discovered, automation algorithms are also becoming sophisticated.

2. Effectiveness strategy

This strategy revolves around using AI to enhance the communication and coordination of workers. Here, AI takes the role of an assistant. This strategy is to make humans effective by eliminating or simplifying the act of scheduling, monitoring and communicating. Due to the rise of chatbots, it has become normal for companies to implement basic AI solutions with this strategy. You can also deploy this strategy for more complex activities. For instance, AI can be used to schedule meetings between workers. In large consultancy firms, AI can be deployed to suggest which person can be assigned a project based on employees’ skills, experience and desires. Apps built for services like Google Assistant, Alexa and Siri are some of the services in this category. With the rising number of people owning smart devices such as speakers, products built on effectiveness strategy have become important for customer-focused companies.

3. Expert strategy

Unlike the two strategies above, this strategy is closely associated with augmentation. Expert strategy can help elevate human decision-making for activities containing complex work processes. While systems help in making decisions, humans have the final say. They can help in almost everything, from human lives to money matters. With the sensitivity of these areas, humans must always be responsible for the consequences of their decisions. The systems using this strategy empower professionals in different industries, such as lawyers, doctors, financial advisors and teachers, among other individuals, through advice. For example, AI solutions can help teachers create tests for individual students and evaluate them accordingly.

4. Innovation strategy

This is the most advanced of all AI strategies. It is the opposite of the efficiency strategy and revolves around augmenting humans to enhance creativity. For example, a music composer can use software to create a new song. This can be done through a machine learning algorithm which learns the composer’s style of music by observing how he adds piano, guitar, bass or other musical instruments. The composer can then listen to the AI and decide whether it can be added to the song or not. With the decision of the composer, the AI can also learn and improve itself for the future. Like the expert strategy, the innovation strategy gives humans the final say in decision-making.

Currently, tools enhanced with AI aren’t all that helpful to frontline workers, reports STAT News.

Artificial intelligence is a transformative tool in the workplace — except when it isn’t. For top managers, state-of-the art AI tools are a no-brainer: in theory, they increase revenues, decrease costs, and improve the quality of products and services.

Read the article STAT News

Old analog chips perfect for AI, reports HPC Wire.

The need for speed is a hot topic among participants at this week’s AI Hardware Summit – larger AI language models, faster chips and more bandwidth for AI machines to make accurate prediction

Read the article HPC Wire

According to Nature, artificial intelligence is helping to create new proteins.

In June, South Korean regulators authorized the first-ever medicine, a COVID-19 vaccine, to be made from a novel protein designed by humans

Read the article Nature

The technology behind the Dall-E image generator is complex, reports MIT. 

The internet had a collective feel-good moment with the introduction of DALL-E, an artificial intelligence-based image generator inspired by artist Salvador Dali and the lovable robot WALL-E that uses natural language to produce whatever mysterious and beautiful image your heart desires. 

Read the article MIT

Artificial intelligence (AI) is changing many aspects of the business. It allows business owners to offer a personalized experience to their customers. With the potential of this technology, patterns of information can be identified, including customers’ buying history, their preferences, credit scores and other common trends. With the pandemic ravaging the world today, businesses are adopting AI faster as the urge to automate processes continues gaining momentum. Here are some trends and innovations that reflect how AI is changing businesses.

Economic growth will drive enterprise IT spending

About two years after the pandemic began, the global economy has faced many challenges. In countries including the US, Germany, China, South Korea and others, it is expected that the economy will recover in 2022, despite the emergence of the new variants of the pandemic. Subsequently, IT spending is expected to go up to $4.072 trillion, an 8.4% increase compared to 2020. Out of this, businesses are expected to invest heavily in AI as they seek to automate services. The global spending on AI could soon reach $90 billion.  

The global labor market will see a significant change

Since the last quarter of 2019, job openings in the US have increased by 33%. On the other hand, unemployed individuals hit 8 million in August of 2019. This could be explained by the change that the COVID-19 pandemic has brought to many sectors of the economy, including different industries such as hospitality, healthcare and education. In the meantime, the demand for  IT workers has gone up, reaching over 25,000 towards the end of 2021. These factors are influencing the trends in AI this year and beyond.

Cybercrimes will be on the rise

The past two years have seen a rapid shift of everything to online. This led to the rise of global e-commerce that grew by 27.6% in 2020. With this shift, the number of people working from home and using personal devices for work has increased to 67% from only 45% before the pandemic. Thus, security has become important in organizations than ever.  With this shift, cybercrimes have been on the rise. According to reports by the FBI, there were 69% more cybercrime complaints in 2020 compared to 2019. These cases include those caused by distributed denial of service attacks, malware attacks and phishing. This is expected to continue rising. The good thing is that AI is expected to help businesses fight cybercrime by automating some tasks, including DDoS attacks. 

Responsible AI will take center-stage

As AI adoption continues gaining ground, there have been concerns about responsible and ethical uses of AI. According to Gartner, responsible AI helps achieve fairness and avoid biases in data, building trust, transparency and explainability. Although AI has shown a lot of potential, governments and responsible agencies will need to work on the moral implications of this technology. In 2022 and beyond, governments will adopt strategies for ensuring automation outcomes have positive moral implications, and that AI does not discriminate against specific groups of people based on their ethnicity, gender, age or religion. According to Gartner, as we head to 2022, every company could have up to 35 AI projects in development.

National AI strategies are emerging

As more and more countries realize the potential of AI in boosting growth, resilience and productivity across both the public and private sectors, national AI strategies are beginning to gain traction. More than 50 countries, including the US, China and UK, have already developed AI strategies, with others already implemented. For example, in the US, the strategy is focused on fostering AI talent, addressing the semiconductor shortage, fighting cybercrime, and ensuring the country leads in the AI race. Last year, the Biden administration poured billions of dollars into AI projects and initiatives. This is expected to influence AI trends for decades to come.

Artificial intelligence (AI) is undoubtedly one of the revolutionary concepts of the twenty-first century. For this reason, we hear talks about AI and how it can revolutionize almost every industry in the world. While you might have heard how AI will revolutionize manufacturing, learning, and other industries, not much has been said about the positive impact of AI on the hiring process. There has been an increase in the use of AI in employment circles in recruiting and hiring. Here are a few areas that AI is changing in the hiring process.

1. Candidate scanning

A single job post may receive more than 200 resumes, yet only four or even fewer candidates may be selected for the interview. This makes it hard for the recruiters to find the right candidates for the job in a short time. However, recruitment AI can help recruiters screen candidates using an algorithm that finds certain keywords in the resume of a particular candidate. This not only saves time but also enhances efficiency in the recruitment process. Some AI software can also analyze candidates' data sources and online presence to understand their history and abilities. This information is critical in discovering a suitable candidate for the job.

2. Candidate evaluation

AI-powered applications can speed up the interview process by increasing the speed with which potential candidates are evaluated. The pre-screening process can remove the interviewer's biases that may lead to the elimination of the right candidates from the selection. The pre-screening digital interviews can also be shared among hiring teams, allowing the members of the teams to conduct a transparent interview process and allow candidates to participate in remote interviewing.

3. Predictive intelligence

Recommender systems will provide a shortlist of the top-ranked candidates, cutting through the noise. This will minimize the work of sorting and reviewing, which usually takes a lot of time, even in instances where hundreds or even thousands of applications have been made. Predictive intelligence can share additional insights about candidates, including their values, traits, personality and communication skills. Simply, it guides and helps simplify the process of hiring.

4. Minimize the cost of every hire

Automation of candidate screening and interview processes during hiring is a big step in cutting costs. It allows companies to reap big due to a more streamlined and stress-free hiring process. Automation also offers an immediate pay-off in time and enhances efficiency. In frontline customer service and retail roles where there is a recurrent need for staff to fill the positions, automation is the best alternative to quickly conclude the hiring process and help get the best staff to fill specific company needs. Interview automation can enhance a company’s high-volume recruitment process and help make it a more efficient and enjoyable experience for those involved in the process.

5. Targeting and outreach

Using AI in the recruitment process helps businesses and talent scouts avoid mistakes while targeting potential talent for the organization. Scouts can use refined searches to seek candidates with certain attributes through AI. Candidate targeting can help companies increase the diversity of their workforce through the elimination of biases.  Furthermore, AI in the recruitment process can help discover, reach and engage relevant talent and potential employees. Instead of eliminating human touch in the hiring process, AI eliminates the time-consuming process and allows human professionals to focus on finding the right candidates to fill specific posts within an organization.

As an employee looking to upskill in your career or who wants to become more competitive in the job market, AI and machine learning are some of the areas that you might want to look into. Jobs that require AI or machine learning skills are on the rise, and they are expected to increase by 71% in the next five years. With this, people with AI skills will be in high demand in the future.

We live in a world where customers have become increasingly aware of what they want. As such, pressure is mounting on businesses to deliver quality and develop products and services customized to meet the needs of the enlightened customer. Sales and marketing have become complex, and conversion rates are often determined by how you understand your customer. To address these challenges, companies have started adopting artificial intelligence to give them the push they need to thrive. AI in sales allows businesses to use modern technologies to help machines perform cognitive duties like humans or even more efficiently. As a salesperson, various smart technologies in AI can help you improve your performance considerably. These technologies use AI to predict leads and customers that can buy from you.

Role of automation in sales

Generally speaking, sales and marketing have been slow in adopting artificial intelligence and AI. Although there is immense potential in adopting technology to solve problems in sales, it was not until recently that companies and individuals saw the need to adopt it. Large multinationals have already put themselves in a strategic position by capitalizing on this technology to make an impact. JPMorgan Chase, for example, has managed to use AI to increase headline clicks by more than 400%. According to Salesforce, the recent craze in chatbots will see more organizations adopting this technology to enhance customer service and sales. According to Gartner, 70% of customer experiences in the next three years will involve machine learning components in the next three years.

What are the benefits of using AI in your sales efforts?

As a business that wants to grow, AI is fast becoming one of the secrets that allow you to navigate the ever-growing entrepreneurial world. Here is what AI can benefit your business

1. It gives room for the development of business strategy

Artificial intelligence allows you to focus on creating strategies to generate more leads. It offloads monotonous, repetitive tasks that take sales reps' time through machine learning programs that work autonomously. Therefore, the sales reps will have enough time to develop and implement strategies. With the help of AI, you can effortlessly perform tasks such as data consolidation, honing customer profiles, choosing the right content for leads, and sending content leads. With the AI successfully doing this, you get more time to develop strategies that improve the customer experience. This increases the profits for businesses.

2. It increases the leads

Connecting each piece of information is time-consuming. This is also the case when following up the qualifying leads, building them, and sustaining business relationships. However, adopting AI for sales increases the speed of the delivery of results and reduces the time consumed in such duties. Furthermore, AI increases leads because it allows businesses to target and reach specific prospects.

3. Significantly drops costs

Adopting AI automates most activities that salespeople engage in. This will reduce the cost of operations and will cut down many processes, which consumes time and money. With the automation of such activities, you will save money to invest in more significant activities and remain with profit to expand your business.

4. Eases-upselling and cross-selling

With AI, sales reps are better placed to identify the prospects likely to buy their products or services. Salespersons can also use AI to determine when they are likely to make sales and when clients are likely to buy from them. The advanced AI algorithms pick prospects that can sign up for specific products or services from your organizations.

Generally, your sales efforts are likely to benefit significantly from the advancement in AI. It is likely to increase returns and bring other promising deals in sales. Therefore, identify the areas that this technology can help in your sales efforts and invest in them.

Artificial intelligence (AI) has become one of the leading technologies that have positively affected many industries. Among the sectors that have dramatically benefited from technology over the years is the retail sector. The rise of online shopping, whose adoption rose with the pandemic, has provided both the sellers and the buyers with the right atmosphere to embrace technology like never before. Lately, it has become clear that there is no technology to boost online shopping like artificial intelligence. Here is how AI helps make sales more intuitive.

1. Lead generation and scoring

Under normal circumstances, sales representatives take a lot of time trying to sell to leads, most of whom are not interested in their product. These leads will not think of buying the product, no matter how talented or skillful the sales rep is in convincing them. This is a lot of time wasted, and resources lost chasing the wrong people. Without enough data on the leads, sales reps will not differentiate the right potential customers from others, and their efforts will not pay off. They follow the leads and hope for the best. AI software scores leads based on the data obtained. It, therefore, makes the scoring more data-driven and helps sales teams focus on potential buyers. AI also compares the obtained insights with the accounts reports, therefore, making targeting more straightforward.

2. Enhances communication with customers and prospects

While recognizing promising leads is a good thing, it is never enough. Following them and reaching out to both the customers is equally important. AI can help sales teams achieve this by easing communication with customers. For instance, chatbots that use natural language processing enable the collection and processing of information from consumers. While not all chatbots are sophisticated enough, they help collect critical information from customers around the clock, which can be used as feedback to improve service delivery. Chatbots can also be very useful and fast in answering the questions that the customers have at any time, which is something that sales reps cannot manage.

3. Personalized recommendations

New technologies are improving customer experiences. Through data collected from different sources, a good customer experience can be achieved by processing data and using the information to target leads. This makes sales much more straightforward and eases the process of gathering data needed to make decisions. AI-based algorithms generate personalized recommendations based on the data available, with an example being those thrown at us every time we visit e-commerce websites.

An algorithm that takes a snapshot of user browsing history, demographics, interests, and preferences makes it easy for retailers to identify what the customer might be interested in and recommend in advance. This helps customers buy exactly what they want and spend the shortest time possible shopping. AI is sometimes so efficient that as much as 35% of consumers in sites like Amazon get precisely what they want from recommendations and will be willing to pay for it.

4. AI improves the productivity of salespersons

Apart from improving the interaction between sales teams and the customers and the interaction with the rest of the world, AI also enhances the work of sales reps. AI can automate workflow in the entire organization. It can automate task delegation by prioritizing tasks based on different parameters such as objectives and deadlines. AI can also recognize promising leads and assign them to the best sales reps automatically. It can also delegate tasks or even set up meetings based on the availability of sales reps. This saves a company a lot of time and money while also streamlining operations. It reduces noise and chaos in communication and leads to precision in operations.

Source

https://learn.g2.com/ai-for-sales

The leading organizations today are using artificial intelligence and machine learning technologies to enhance their operations in different ways. These two technologies promise to help transform workplaces by automating work and guiding business executives in their decision-making responsibilities. Furthermore, it will improve the efficiency of staff and enhance their productivity. Data visualization ability using AI and 3D applications has been one of the key areas when it comes to machine learning. It gives clarity to workplace planning and offers profitability for companies, especially in production and marketing. The competitive nature of modern workplaces demands that they adapt and work smarter. Machine learning and AI will augment jobs and make work more interesting and attractive. However, it will also displace some people and create new jobs. Some of the uses of machine learning and AI in workplaces include the following.

1. Finding and hiring new staff

Machine learning and AI improve the hiring process. With this technology, human resource managers can quickly identify the right talent from a large pool by analyzing millions of profiles and resumes and detecting potential candidates for the job. AI can then interact with the candidates engagingly to identify the candidates that are best suited for the job. Arya is one of the companies that offer intelligence to organizations on the right talent. Once you have a list of potential candidates or a group of candidates that fit the job description, AI can assist you to pick the right ones for your organization.

2. Machine learning and workplace training

Employee training is one of the areas that are central to the performance of the business. Although this area is critical to the performance of an organization, conventional methods have been used for decades to train staff. Some of these approaches are not effective. However, with the advances in AI and machine learning technologies, the training of staff has received a boost. Although the traditional training approaches made a huge impact, about 74 percent of staff do not feel that they are reaching their full potential at work.  With the global pandemic that has affected face-to-face training, machine learning and other online-based approaches are critical.

3. Intelligent reporting

Aside from being used in hiring remote workers, AI can be useful in improving remote work itself. It can help remote workers save time by automating admin tasks that would normally have to be completed manually. Furthermore, it enables telerobotics (machines remotely operated by humans). These robots can completely change the workspace, especially when they are integrated with virtual reality (VR). This technology can enable employees to work from home.

4. Employee productivity

Every organization seeks to earn the maximum profit using minimal resources, including employees and finances. Sadly, this is easier said than done. However, with the help of software and automation software, managing multiple tasks in the office can be achieved with ease. Companies are adopting centralized cloud-based AI systems that will help in establishing an employee-friendly environment. Machine learning and AI-enabled robots are helping employees finish their tasks quickly and with minimal costs compared to traditional methods.

5. Marketing automation

According to the Digital Marketing Institute, most business leaders (97%) believe that the future of marketing is made up of marketers and machines that work together to improve business outcomes. Companies are nowadays using machine learning to identify and target new audiences, reach customers and find new ones. These are the tasks that were once done manually, ending up being slow and ineffective. However, thanks to machine learning, these tasks can be done efficiently and time can be saved. Some tools can streamline how you find and message prospects after analyzing the existing data.

Machine learning (ML) could not have come at a better time than now. As businesses move towards a new era of digitization, ML is needed to help in various operations of organizations, starting with data processing to customer support. Machine learning algorithms learn from data and find different hidden patterns and insights, which is crucial for businesses to make critical decisions. ML and artificial intelligence tools and algorithms have gained fame over the past few years in business circles and the analytics community due to their potential to help sift through massive amounts of data from different sources. With growing data volumes, higher and faster computational power and affordable data storage, machine learning has become more feasible now than ever.

Here are some ways that your business can benefit from machine learning.

1. Prediction of Customer lifetime value

Prediction of customer value is one of the challenges that marketers face today. This is the same case for customer segmentation. Companies get massive data and access to huge volumes from different sources. This data can be used to derive meaning and gain insights through the identification of patterns.ML and data mining are critical in aiding businesses in predicting the customer's behaviour and purchasing patterns. They also help send offers to specific customers based on their browsing and purchase history.

2. Predicting maintenance

Manufacturing, transport, and logistics companies always focus on preventive and corrective maintenance practices. With no automation, these practices are expensive and mostly inefficient. However, with the emergence of machine learning, companies can now discover patterns and the possibility of machines breaking up. This is known as predictive maintenance. It helps reduce the risks associated with unexpected machine failures and eliminates unnecessary machine expenses. 

3. Eliminates manual entry of data

With machine learning, you can sift through massive amounts of data in a short time and remove duplicates and inaccurate data. Data duplication and inaccuracy are some of the biggest problems that most businesses today due to massive amounts of data coming from different sources. However, predictive modelling algorithms and machine learning minimizes errors and eliminates errors caused by manual data entry. Machine learning improves processes by using the discovered data and allowing employees to use their time to do other tasks.

4. Spam detection

In the age of technology, cybercrime has gone up more than ever. However, machine learning has shown some potential in helping avert challenges emerging from spam attacks. Previously, email service providers used rule-based techniques to discover spam and filter them out. However, pre-existing rule-based techniques are not efficient in detecting spam and phishing messages. With the rise of machine learning algorithms that can adjust based on the data presented, fighting spam has been simplified. 

5. Product recommendations

If you have visited any e-commerce website, you must have seen that products are recommended based on your previous search. This is the work of machine learning. Most e-commerce websites today use machine learning to recommend products to their customers. Unsupervised learning helps develop product-based recommendation systems. These algorithms use purchase history and match it with large product inventory to identify hidden patterns and group products together. These products are then suggested to customers to motivate customer product purchases.

6. Financial analysis

With large volumes of data coming from various sources, machine learning can be used in financial analysis. ML is used by banking and finance institutions for portfolio management, algorithmic trading, detection of fraud and underwriting. Chatbots and other security interfaces are also making their way into these institutions.

As machine learning continues to grow in terms of its abilities, it is clear that more applications will emerge along the way. As a business, take advantage of this technology to boost your productivity and effectiveness.

When it comes to procurement, machine learning is one of the most promising technologies to improve operations. A data analysis machine learning is the application of self-learning automated statistics to solve defined challenges or improve efficiency. Unlike robotic process automation, machine learning can improve its abilities over time. This technology can mirror human decision-making abilities and enable businesses to leverage it in automating tasks and decisions. In procurement, organizations can use machine learning to save time that could have been wasted on tedious tasks and provide insights through the analysis of data sets. Here are some roles that machine learning can perform in procurement.

1. Spend classifications

Spend classification is the process of grouping related spending data into categories using a given approach. This process is important in the preparation of data to spend analyses. The process of classifying data starts with gathering ERP data from contracts, purchase orders, and internal and external data and putting related items together. Spend classification is a tedious and repetitive process which may strain an organization in terms of human resources if done manually. However, with the help of machine learning, it is possible to gather data from different sources and classify it faster and accurately. The procurement staff can be left to do other useful things.  

2. Analyzing stock in transit

Companies that send and receive products must follow the status of materials and products in transit in near real-time. This will help the address emerging problems such as delays and other issues before they can occur. Predictive analytics give them exactly that. It allows them to ensure orders are delivered in time and at the right state.

3. Automated sourcing

Through an increment of supply assignment sourcing, procurement departments can reduce the need for interacting manually while securing materials at the best price, faster and in high quality. With machine learning, the system automatically creates a bidding event when an internal source of supply is exhausted. It will act like a human purchaser, sending the bidding invitation to a specific list of suppliers.

4. Making purchase recommendations

Machine learning can be helpful in making quick purchasing decisions. It helps create systems that act like search engines where customers can search products. With these search engine-lie abilities, the system can be refined to show a list of suppliers that meet specific criteria. A recommendation system can be built to support quick and accurate purchase decisions. A recommendation system recommends the right supplier for a company to source products based on the prevailing market conditions.

5. Matching Capabilities

Machine learning can help in capabilities matching. Since all buyers want their suppliers to fully meet their needs while delivering exceptional service, having the right systems in place to match capabilities of each supplier will be crucial for operations. However, it can be tough to determine the potential of suppliers based on the manual systems. Machine learning can scan the industry for new competencies and align them with business requirements. With this information, the abilities of the existing and new partnerships can be tested and understood. 

6. Tracking and monitoring efficiencies

Efficiencies of operations is what every company seeks to achieve. However, this cannot be possible with manual systems. Machine learning allows organizations to track and monitor the efficiency of every company within the supply chain and rate them based on their abilities and performance. With this information, procurements are made efficient and fast. Furthermore, having information of every organization ensures operations run at their peak in terms of standards.

Generally, as technology continues evolving, we should expect more value and increasing efficiency in machine learning. As it is evident today, procurement companies can no longer ignore machine learning. It is upon them to work harder to benefit themselves with it.

Machine learning is indeed breaking barriers in almost every industry today. In the manufacturing sector, machine learning applications, platforms, and algorithms help find new models, fine-tune the quality of products and optimize manufacturing operations. It enables manufacturers to grow the quality of the product while taking short lead-time production runs. With the advancements in machine learning technology, manufacturers are deploying it to improve the end-to-end performance of their operations and end find solutions to various problems they face.

Here are some ways that machine learning is changing the manufacturing sector.

1. Makes forecasting accurate

One of the biggest and most important areas in the manufacturing sector is forecasting. It is crucial for companies to identify the number of products that would be needed in a specific period. Manufacturing too many products or purchasing many raw materials can lead to extra costs. On the other hand, if a company purchases too little inventory, the chances of failing to meet the customer's demands reduces. This means lost revenue and damaged company reputation. With forecasting made possible by ML systems, companies can start manufacturing the needed products. This will reduce wastage and shortages.

2. Enables optimization of workflows

The majority of people assume that manufacturing has become efficient with time. This is not true because manufacturing capacity utilization has gone down over the years despite various improvements in other aspects of manufacturing. Industrial ML can help optimize workflows and eventually improve the areas that are inefficient. ML solutions allow manufacturers to create digital twins and model various scenarios in these areas. The results of modelling allow manufacturers to determine the best course of action.

3. Reduction of supply chain errors

According to McKinsey, machine learning will reduce errors in supply chain forecasting by over 50% and minimize the lost sales as a result of bad forecasting by 65%. It will enhance product availability and minimize the costs associated with transport, warehousing and administration of supply chains by 5 to 10% and 25 to 40%, respectively. ML will lead to overall inventory reductions of 20- 50%.

4. Predicting maintenance needs

One of the most promising areas that ML can be used in manufacturing is predictive analytics. ML algorithms can predict the time that an equipment may fail by analyzing performance data gathered by sensors in machines. The predictions can allow manufacturers to adopt an optimal approach to repair machines and avoid machine downtimes caused by unscheduled maintenance. Corrective maintenance is cheaper than fixing breakdowns and downtimes. Correct prediction through ML allows preventive maintenance to prevent machines from breaking down.

5. Enhancing quality control

Quality control is an important part of any manufacturing operation. Therefore, spotting problems before the products are shipped to the customer minimizes costs and enhances customer satisfaction through quality. Unlike manual quality control which is flawed, ML-based quality control systems are accurate and can compare products to ideal models based on data-based approaches. According to studies, ML-based quality inspections increase defect detection rates by over 90% compared to humans.

6. Enables more product personalization

Over the recent years, customers have begun to demand more personalized products, and manufacturers have no option but to comply. Consumers' reliance on digital services has led them to expect personalized products and services. This can be a big challenge to manufacturers. However, machine learning can help them meet these demands and increase satisfaction and loyalty. With ML, manufacturers can develop hyper-personalized products for their customers and increase sales. Machine learning models can infer features that customers did not mention but would love if added. With the advancement of machine learning models, there will be an increase in customization, and companies will benefit from it.