The adoption of artificial intelligence (AI) is moving fast from an experiential technology to an important part of the business. In every industry, more use cases are under development for AI to drive business efficiencies, data optimization, enhancement of customer experience and support business goals and initiatives. Heading to 2023, AI continues to be an important point of conversation, with various trends showing that AI is the present and future of technology. Watch these trends in AI that shows the direction this technology is taking.

1. Improving human work

AI has come at the right time when human work needs a boost. With remarkable assistive tools, artificial intelligence has increased the productivity of humans while equally impacting various professions. For artists, for example, AI image generation tools like OpenAI’s DALL-E, among others, while programmers have tools like GitHub Copilot, which helps software developers to refine their code, write and test them in most computer languages. As AI continues advancing, human work will be boosted significantly.

2. Automation

Machine learning which is part of AI, is a valuable tool which is benefiting businesses in different industries. This area of AI has become widely popular recently. In the new era of digitization, companies are looking for ways to improve operational efficiency, which is made possible by automation. There is a misconception that machine learning and automation will displace humans from most of their jobs. Although AI will perform many tasks currently done by humans, it frees employees to focus on other tasks. Therefore, as companies seek ways to tackle routine tasks, automation made possible by AI will continue to be adopted.

3. Automated code generation

The progress that has been made in modern machine learning is fast. However, GitHub’s release of AI pair programmer has shocked many, showing how AI’s deep learning models can be used to autocomplete coding. This will benefit highly skilled software developers. The Copilot suggests how a developer can finish a specific line of code and generates multiple lines from plaintext descriptions of what the code does. The Copilot proves that AI has more capabilities yet to be explored. As such, more AI applications in this area will emerge in the coming days.

4. More growth of AI in 5G

5G is one of the unique technologies changing how we communicate. As the world transitions from 4G to 5G, AI is proving to be an important component that will increase application performance and security. Furthermore, automation tolling and telemetry, all set to benefit from 5G, will take advantage of AI. Some emerging trends in AI include edge AI, which will become one of the IT jobs in the future.

5. Digital twins in clinical trials

Artificial intelligence is already transforming the use of pharmaceuticals in treating human ailments. When we think about the link between AI and pharma, the main area that comes to mind is AI-driven drug discovery, which has shown immense potential. However, there is a new area where machine learning and AI can benefit humans by enabling faster therapeutics and efficiency in treatment. This is the use of digital twins to enhance clinical trials.

Clinical trials today are known to be expensive and inefficient, and the average new drug requires about a decade and over $2 billion to bring to the market. Digital twin offers a solution to this challenge through a simple machine learning model, which simulates placebo outcomes for patients in a medical trial. This means that medical trials require only a few participants because the testing process can be simulated. This trend is gaining momentum and is expected to dominate the medical field in the coming years as pharmaceutical companies seek cheaper alternatives to testing drugs.

Artificial intelligence (AI) has indeed come from far. From being a technology thought by many as only applicable in fiction movies to the one that is now changing almost every aspect of our lives, AI is touted as one of the best technologies of the century. Through AI, we now see various applications such as self-driving cars, smart virtual assistants, smart robots and chatbots, to name a few. Since AI became a go-to technology in various industries and our daily lives, a debate pitting AI and human intelligence has emerged and continues to increase with time. While we all agree that human intelligence and AI are different, we need to go deeper by discussing what these differences are. But first, let us start by understanding what artificial intelligence and human intelligence are.

Artificial intelligence (AI)

Artificial intelligence (AI) is a data science branch focused on building smart machines capable of handling tasks traditionally reserved for humans. They can learn from experience through historical data, analyze their environments and perform actions based on this knowledge.

Human intelligence

Human intelligence refers to humans’ intellectual capabilities that allow them to think, learn, understand complex concepts, apply logic, and solve problems. This intelligence also allows humans to recognize patterns, make inferences and decisions, store information and communicate with fellow humans. The uniqueness of human intelligence is backed by emotions such as passion, motivation and self-awareness that allow them to accomplish specific tasks.

Artificial intelligence vs Human Intelligence

Nature

While human intelligence can adapt to different environments using different cognitive processes, AI, on the other hand, can do the same by building machines that can mimic humans to adapt to new environments and make decisions. While human brains are analogous, machines are digital. Therefore, the difference between AI and human intelligence is the process of functionality and time taken to solve a problem by both these two.

Functioning

Human beings use their brain for computing power, memory and thinking abilities. On the other hand, AI-powered machines rely on data and specific instructions that are fed into them via data. Another key difference between the two is that while it takes humans a long time to process and understand data or information, artificial intelligence is often faster and can process inputs and data quickly and provide accurate results.

Learning power

Human intelligence entails learning from various past incidences and experiences. Humans learn from mistakes made through a trial-and-error approach in an individual life. Intelligent behaviour is the core of human ability and is mostly inborn. On the other hand, artificial intelligence (AI) falls behind since machines cannot think independently. Rather, they are programmed to think through models and algorithms developed by humans. This is the core difference between the two. Therefore, as humans learn from experience, machines can learn from data and through continuous training. However, machines cannot achieve a thought process similar to that of humans. Although AI-powered systems can perform certain tasks excellently, they take many years to learn different functions for a particular application area.

While AI has advanced significantly, there are things it still cannot do without human intervention. For instance, AI cannot make rational decisions like humans, although they were trained to mimic human behaviour. The main difference between human and machine intelligence is human intelligence is the main contributor to machine intelligence. In this regard, to completely mimic humans in every sense will take time and effort. Without knowledge of its models and algorithms, machines will still depend on humans to make critical decisions.

Much of our popular media today has a bleak view of the future of artificial intelligence (AI). Many depictions predict the technology will become sentient and will decide to kill or enslave all of humanity. In other words, to many, it’s only a matter of time before our robot overlords doom us all, and there’s not much we can do. 

These bleak arguments usually follow three lines of thinking:

1. AI systems have already surpassed human intelligence, which is inherently bad. For example, AI systems can beat the world’s best chess and Go players.
2. AI and machine learning has crossed a point of no return and will eventually allow us to automate everything, leaving nothing for people to do.
3. AI models have become so complex that even the scientists who create them no longer understand their operations.

These arguments may seem intuitive, but they aren’t rooted in fact. Let’s briefly discuss them.

Argument 1

This argument posits that AI has finally surpassed human intelligence – and that that’s an unquestionable source of trouble. The reality is that AI has been around for decades, doing things better than people in tasks where people tend to make errors, exercise poor judgement, or simply can’t meet work demands. For example, the financial industry has been using AI to automate and streamline credit scoring and loan offering decisions since the 1970’s. This has made the credit and loan application process more secure and more accessible to millions of people. That AI systems can make better, faster, and more secure decisions than people in this industry have been a powerful force for good, and the same holds true in other industries and applications, too.

Argument 2

The pace of technological development – and the fear associated with it – is a tale as old as time. The fact is that the pace of AI development should be a cause for celebration and motivation to keep pushing forward. After all, we’ve all benefitted from AI-driven systems that deliver better medical outcomes and recommend treatments and consultations for a host of chronic diseases like high cholesterol or diabetes. In fact, AI systems produce most of today’s lab work, which alerts healthcare professionals to abnormal or noteworthy results without them needing to comb through it manually. This is another example of AI helping unburden already-overworked people, which helps them perform their jobs better, more accurately, and in less time. In general, the medical field is an ideal example of AI and human intuition combining to deliver optimal outcomes.

Argument 3

The last, and most popular argument AI doomsayers often raise is that AI models have grown so complex that data scientists and software developers can no longer understand them. This is hyperbole – and in any case, any data scientist or developer worth their salt wouldn’t let this happen. Today’s AI experts work with explainable AI technology, which gives users exact insight into how AI models gather data and make decisions. Explainable AI is built into many of today’s common software tools and is becoming the industry norm. In fact, many of today’s vendors and tools can explain every decision right down to an individual data point. So far from becoming more convoluted, today’s AI systems are becoming both more powerful, and more explainable.

Conclusion

Even though AI technology isn’t new, it has grown by leaps and bounds because computing power has done the same. When technologies make great strides, there will always be a natural fear of the unknown. But when we implement technology in disciplined, responsible ways – and pair that with laws and best practices that ensure a standardized, ethical landscape – we can make the world a better place. Rather than an opaque dark cloud hanging over the future, we should view AI as a gateway leading to opportunity and progress.

By Mamdouh Refaat, chief data scientist, Altair

Keeping track of the latest advances in artificial intelligence (AI) may be difficult for ordinary people. However, for a business leader, it is a good thing to do despite the challenges you can encounter because learning how new developments can transform the business landscape in your industry can open your eyes to potential opportunities you can invest in. It is no secret that AI is changing the business landscape in many ways. Thus, there is a need to carry out activities such as measuring the return on investment (ROI) for investing in a given AI application on business. As the end of the year comes closer, here is what we see in AI for the rest of this year.

1. AI will converge with data and cloud- and demand a new approach to management

Big data alone is not enough to create a difference in today's competitive environment. Rather, it must be organized before being used. AI can do these things. Investing in AI to help in this area can pay off only if an application is developed to help work on data 24 hours a day, every day of the year. With this capability of AI, cloud computing can easily adjust to changing storage needs. This is the reason why top companies are investing in the cloud for the management of their data. With AI and cloud proving to be an amazing pair for businesses, leading companies are significantly increasing their investment in these areas. This will continue towards the end of this year and beyond.

2. AI will help in finding, using and monetizing data

Data is a raw material for artificial intelligence. However, in the past, you had to collect, curate, confirm, standardize and label the data so that AI could use it. However, this is changing. The procedure is what is viewed as the thing that holds back AI. AI has evolved to the extent that it is turning data that was once regarded as "messy" and unstructured into something that can be used to make decisions. AI can be used to collect data from various sources, turn unstructured data into useful information, and confirm and standardize it for easy use and to govern.

3. AI will be assessed and forecasted based on its full value, not just cost savings

There are many advantages to investing in AI. However, it is often challenging to ascertain the technology's return on investment because it is still evolving. Fortunately, there are new assessment methods that can capture the "hard" returns and costs, like increased productivity and hardware costs, and "soft" returns, like improved employee experiences or demands on specialists' time. This has been problematic in the past because it was difficult to assess the value of AI.

4. Human augmentation will play a critical role

There is a misconception that AI has taken over many areas, such as online advertising, innovation, and other applications. While AI has entered many areas of our lives, there is still a huge gap that needs the human touch to experience efficiency. Over 90 percent of human touch is still needed in many areas where AI has been adopted. Although AI can do many things autonomously, there must be someone behind the scene to check the output and make the final call. Humans will play a crucial role in AI adoption now and in the future, even as this technology advances as we seek to deploy it in many other areas.

5. AI will be an important area for AI specialists to govern

Governing AI comes with special challenges, considering it is a fast-advancing technology that inputs new, sensitive data while being adopted to make critical business decisions. With the sensitivity of this technology, governance teams may not have the requisite skills and business and technical skills to keep up with the speed of changes. As such, AI governance will continue to be challenging and require a new and advanced approach.

According to Euronet, a Hong Kong company is experimenting with an artificial intelligence executive.

It’s Monday morning and you’re in Hong Kong having coffee with your new boss, a virtual robot powered by artificial intelligence

Read the article Euronet News

In the first year of the pandemic, science happened at light speed. More than 100,000 papers were published on COVID in those first 12 months -- an unprecedented human effort that produced an unprecedented deluge of new information

Read the article CNet

According to Analytics Insight, artificial intelligence is playing a key role in gene sequencing. 

Next-generation sequencing has greatly improved genomics studies by sequencing a gene in a short time. As a result, the machine learning approach is being used to discover gene coding areas in a genome. 

Read the article Analytics Insight

AI is used to identify sex pests on trains, reports Daily Mail.

It will mean staff do not need to sit through hours of footage but will instead be alerted to any potential incidents.

Read the article Daily Mail

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

Technology is indeed changing many industries. With technological advances, all industries are doing their best to find ways to deliver better quality, attract more customers and make more profits. Artificial intelligence (AI) is leading the way in changing fortunes for many industries. One such area that is receiving massive backing from this technology is the fashion industry. Although this industry has lagged in the adoption of technology before, it is benefitting immensely from AI. Here are some ways the clothing industry is taking advantage of AI to offer competitiveness.

AI helps to promote and sell fashion products

The fashion industry is more about creating demand and brand awareness, just as it is the case in fashion industry. Clothing and fashion industry constantly looks for new ways to get their products to the buyers, create awareness and build demand in the market. The brands in this industry are increasingly using AI and machine learning to improve users’ shopping experience, enhance efficiency of sales systems through automation and use predictive analytics to guide sales processes and predict demand.

Customer support

Fashion brands are leveraging conversational assistants through chatbots. Voice assistants such as Amazon’s Alexa, Microsoft’s Cortana and Apple Siri are also key in customer support. Using these conversational interfaces allows fashion brands to understand what the customer wants, their trends, purchase patterns and suggest items they may want to buy. For example, instead of a customer interacting with a website when purchasing new products, they can have a conversation with the chatbot or intelligent conversational agent about the same. Through this dialog, the customer can find the fashion product or accessory that suits their needs. This interaction provides customer satisfaction and valuable information for a particular fashion brand.

Smart recognition

AI is making way into mobile applications. With these apps, customers are now able to take pictures of the cloths they want or styles they to imitate and match the photos with the available items that are sold. This eases the way customers find cloths or other products of their choice within a shop or store. AI-enabled shopping applications allows customers to take screenshots they see online and identify the same outfit in a shop before they can purchase them. 

Personalized shopping

Online shopping is challenging and has infinite choices that often overwhelms customers. This can sometimes leave them less satisfied and thus hesitant to buy items online or face countless challenges making buying decisions. The solution that AI provides for this challenge is personalization of shopping experience by offering more engaging and positive shopping experience to the customer. AI uses machine learning to analyze the shopping history and remember the customers’ likes, page clicks, purchases and the length of stay in a particular page. These parameters are used to gauge interest in certain products. With this knowledge, AI then provides recommendations to the customer on products that they may like.

Trend forecasting

Fashion trend forecasting entails predicting possible trends for fashion. Conventionally, fashion trend forecasters combined their knowledge in fashion, intuition and historical data to predict the possible trends in the industry. This approach was not effective and accurate and ended up providing wrong predictions. In the era of AI, this has changed significantly. Through machine learning, AI can capitalize of different types of data to predict trends. For instance, some modern AI-enabled apps can analyze millions of social media pages within a short time and provide patterns of demand and predict the possible direction in the future, this is a critical technology for the fashion brands, which can stock their stores of manufacture products based on the potential demands. This is a notable development that only AI can offer, minimizing losses that was the case when using traditional approaches.

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.