How AI Can Transform Your Business: Benefits, Use Cases & Challenges

If you contemplate using artificial intelligence (AI) in business processes, this article is for you! 

 

Here, you can learn about trending artificial intelligence business applications, businesses that use AI successfully, and primary considerations before implementing AI for your business goals.

 

According to IBM’s 2023 Global AI Adoption Index, about 42% of enterprise-scale companies have deployed AI already. Early adopters who have apparently benefitted from integrating AI in business processes are making further investments. Another 40% are actively exploring the technology. 

As a machine learning (ML) development company with broad experience, Onix can advise you on implementing artificial intelligence and build a solution to meet your specific needs.

If you are not convinced yet, let’s review the primary use cases for AI in business operations.

How Artificial Intelligence Is Used in Business Processes

AI business considerations driving the adoption cut across many key operations.

Source

 

Rob Thomas, Senior VP of IBM Software, expects the quickest profound artificial intelligence impact on business from IT automation, digital labor, and customer care applications.

Automation of IT processes, documentation, and other business processes 

According to IBM’s 2023 Global AI Adoption Index, among enterprises interested in automation software or tools,
 

• 31% are considering or using AI to enhance business operations and task efficiencies
• 30% expect or enjoy higher efficiencies in IT
• 30% aim at accelerated decision-making to improve customer experiences
• 29% report or expect cost savings
• 29% are saving employees’ time for higher-value work

 

81% of IT professionals reported a positive impact of AI on businesses’ productivity. 

 

Among businesses using AI to address labor or skills shortages, 55% use AI automation tools to reduce manual or repetitive tasks and 47% – to automate customer support.

 

For instance, AI-powered bots and voice assistants can act as personal assistants to help employees manage emails, maintain calendars, and perform other daily tasks.

A social network auto-reply assistant built by Onix can answer messages and keep chatting on a user’s social networks when they are offline. The software uses natural language processing (NLP) and deep learning (DL).

“The assistant adapts its communication style over time by continually learning from interactions with individual contacts. So it can deliver increasingly nuanced and personalized responses that align with individual preferences and conversational contexts,”said Oleksii Sheremet, the Tech Lead at Onix’s ML Department. Oleksii is a Doctor of Technical Science, Professor, and Head of Department at the Donbas State Engineering Academy.

Such virtual assistants may also anticipate extensive use of artificial intelligence in business communications.

 

Read also: The Benefits and Challenges of AI Agent Implementation

 

Source

 

According to the same IBM Report, 38% of IT professionals reported that their company was actively implementing generative artificial intelligence (GenAI), and 42% were exploring it. Most use in-house technology (43%) or open-source technology (32%).

The industrial, telecommunications, financial, automotive, and travel and transportation Industries are the leaders in generative AI adoption.

Read also: Generative AI in Travel: Unlocking Personalized Experiences

 

Fraud detection and security

With the growing scale and complexity of cyber threats, AI has become indispensable. Intelligence software can recognize hacking activities, ransomware attacks, other cyber threats, and holes in computer network defenses by monitoring patterns and identifying anomalies in data input.

Once an AI system detects a threat, it can backtrack through the data to find the source and alert the personnel in charge. AI-infused solutions also offer self-healing capabilities for infrastructure and future threat prevention. 

 

Financial services use AI and ML for fraud detection and digital and data security. Analyzing historical and real-time data, they make near-instantaneous decisions regarding individual transactions. Banks deploy ML models that can detect suspicious transactions nearly in real time, stop them immediately, and alert the authorities.

 

Read also: AI-Powered Banking: Transformative Trends, Insights and Use Cases

Business intelligence

More and more companies are tapping into the power of customer data and realizing the benefits of artificial intelligence in business planning and decision-making.

 

For example, banks, financial services, and fintech companies employ AI for predictive analytics and more niche applications, such as wealth management, trading decisions, and loan approvals.

 

Businesses have also long tried to foresee market changes. AI can process billions of data points in real time or use historical data to project future outcomes with a high level of accuracy, enabling better-informed decisions.

 

These insights help companies increase their marketing effectiveness, create more personalized customer experiences, and manage their enterprises more effectively. 

 

Even security and other cameras can provide useful information, such as customer behavior patterns and possible threats. AI can quickly extract behavior patterns or other valuable info, generate visually appealing reports, or issue important real-time alerts.

 

“Our development experience has shown that robust AI solutions can transform customer data streams into actionable insights that drive strategic decisions. By harnessing predictive analytics and real-time processing, we’ve helped companies not only anticipate market trends but also elevate personalized customer experiences, optimize marketing efforts, and enhance operational security,” said Oleksii Sheremet, Onix’s ML Tech Lead.

 

For instance, a shopping mall owner wanted to understand their customers’ behavior better. Onix developed a video-analytical service that uses computer vision to recognize crowd behavior patterns, estimate customers’ ages, and determine their gender.

Entities can use the crowd video analysis system built by Onix for marketing or public space and event security.

 

Targeted marketing and personalized sales

The efficiency of ChatGPT gives reason to believe that someday, AI may become the ultimate search engine, understanding everything on the web, knowing what exactly users want, and giving it to them instantly.

 

Meanwhile, companies can deploy artificial intelligence in brand applications, personalizing offers and streamlining sales. So did Onix’s client, for whom our mobile developers built an AI-powered solution for the cosmetics industry.

 

With thousands of beauty and skincare products, consumers are often confused and make costly mistakes. Our client came up with a big idea: a one-of-a-kind skincare-focused AI platform and mobile app that helps users:

 

1. create skincare routines for their unique skin type and needs based on scientific data and up-to-date regulatory resources
2. easily find safe beauty products suitable for their age, skin type, needs, and lifestyle. 

The skincare & cosmetics analysis app built by Onix selects the most suitable products for each consumer.

 

With the help of ML technologies, Onix’s experts enhanced one of the largest global ingredient databases that helps understand products’ benefits, toxicity, and other characteristics.

 

They also developed a classification system for all cosmetic products on the market. Multiple data scrapers assisted in obtaining information from the Internet, scientific literature, and PDF articles.

 

After a user fills in a questionnaire, the science-backed AI-powered recommendation engine analyzes 800,000+ ingredients to find the most suitable product. Users can quickly search for desired cosmetics across thousands of brands and compare prices with verified vendors.

 

The results to this date include:

 

• 100,000+ ingredients cataloged
• 200,000+ product formulas benchmarked
• 45,000+ products analyzed
• 100,000+ happy customers

 

This collaboration helped our client achieve a leading position as an ingredient transparency solution for the cosmetics industry.

 

Read also: How Machines Can Boost the Online Shopping Experience in Ecommerce

 

Online search providers, online stores, and others use AI to understand consumers and their buying patterns. AI can use data from their online activities to predict their needs and interests, select optimal ads for specific products, and market particular products to particular consumers, increasing marketing efficiency and sales while reducing costs.

 

For retailers, AI technology drives targeted marketing campaigns and personalized product recommendations based on consumers’ shopping and browsing history, social media interactions, and predictive analytics.

 

Read also: AI-Driven Customer Engagement: Strategies and Examples

 

After an online shopper spends some time browsing products, clicking links, comparing options, and adding/removing items from the cart, the AI can generate valuable insights and re-target ads accordingly.

 

For Adoric, one of our long-standing clients, Onix built an AI-driven personalized product recommendations feature to deliver highly targeted and relevant product suggestions.

 

Adoric’s AI algorithms can now analyze user behavior, purchase history, and preferences to discover patterns and make predictions about products that may appeal to users. These personalized recommendations can be displayed to consumers on websites, mobile apps, or emails, increasing customer satisfaction and boosting sales.

 

Adoric's AI-powered recommendations feature, programmed by Onix, empowered its customers to tailor shopping experiences to each shopper’s unique needs and preferences.

 

Intelligent machines that place advertisements online also design them to optimize click-through rates.

 

Social media and content streaming services also use AI to determine what content to offer to individual users first. For instance, Netflix is known to make AI-powered viewing recommendations and dynamically populate content that appeals to each user. Spotify monitors the users’ listening behavior and song preferences to suggest songs that each user will enjoy.

Learn more: How Netflix Uses AI: Lessons Businesses Can Learn

Customer relationships and customer service

The prevalence of online research, contactless options, hyper-personalization, comparison shopping, and other consumer trends drive more companies to adopt AI to streamline the purchasing journey and customer service.

Companies in the automotive, chemicals, oil and gas, utility, and environmental sectors use AI to create more personalized experiences, improve customer service agent productivity, and streamline information search and FAQs for customers and employees.

Even simple rule-based chatbots that provide pre-determined answers to specific questions faster than humans save time and resources. Intelligent bots use machine learning algorithms and NLP to obtain information from customers, understand words and phrases, respond appropriately, and improve over time as they handle more conversations.

AI-powered chatbots combine the previous two types. They can conduct “sentiment analysis” of customer conversations and remember the context and users’ preferences.

Read also: Transform Your Business with Onix’s Chatbot Development

For instance, Onix has developed a conversational AI platform that facilitates personalized, automated customer interactions. Based on the robust RASA framework, this enterprise-grade bot provides the essential infrastructure and advanced tools for outstanding customer communication assistants.

Onix’s ML Tech Lead Oleksii Sheremet explains: “RASA-based solutions offer a level of customization and control that is critical when precision and data security are paramount. With RASA, we can tailor every interaction to fit specific industry needs – from advanced intent recognition to seamless integration with existing systems – ensuring that our bots deliver consistently accurate, personalized, and efficient customer experiences.”

Moreover, by integrating SpaCy, the platform significantly enhances its ability to recognize and process user inputs. This results in more precise responses and an overall improved customer experience.

Enterprise chatbots like this, built by Onix, may exemplify the future role of artificial intelligence in businesses’ communications with customers.

 

AI-powered chatbots that effectively engage with leads, provide immediate customer support 24/7, and resolve issues in real time can raise conversion rates, customer satisfaction, and retention. Many companies use them on their websites or social media.

Read also: The Future of Chatbots: 10 Trends, Latest Stats & Market Size

 

Amazon’s Alexa, Apple’s Siri, Google Assistant, and other voice user interfaces also show how artificial intelligence will transform business operations further.

 

AI-powered digital assistants that recognize customers by face and voice across channels and partners may soon be resolving complaints in real time, placing orders, monitoring processes, and doing anything that previously required interactions with human customer-care representatives.

 

The financial sector is one of the best areas for this application of artificial intelligence in business. 

 

AI also helps companies conduct sentiment analysis of social media comments to understand people’s perceptions of their brand, products, and services.

Read also: Customize AI for Your Brand Fueling Business Growth
 

AI systems can also assist human employees in serving customers, drawing on analytics and using “recommendation engines” of sorts to suggest next-best actions, how to take a conversation with the customer further, or how to present a specific offer. AI can also route customer calls not to randomly available representatives but to those best suited to handle a particular customer’s needs.

 

Customer relationship management (CRM) systems like Salesforce require human intervention to remain up-to-date and accurate. AI can make these platforms self-updating, auto-correcting systems.

Optimization across enterprises

Optimization is another artificial intelligence use case that spans industries, businesses, and processes. Companies are also using AI to reduce costs and consumption, streamline operations, manage reporting, and solve sustainability challenges.

Business AI applications can analyze complex business processes and turn various data into actionable insights, enabling organizations to streamline workflows, optimize their functions ranging from worker schedules to IT infrastructure, and reduce operational costs.

 

For example, AI-driven digital advertising platforms can optimize ad placements to reach the most relevant audience, increasing businesses' return on ad spend.

 

Improved decision-making is a high priority for supply chain managers worldwide. ML algorithms help forecast what will be needed, when, and the optimal time to move supplies. This helps minimize or eliminate overstocking and prevent a deficit of in-demand products.

In logistics, AI solutions analyzing real-time data on traffic conditions, weather forecasts, and delivery schedules can optimize route planning, minimizing transportation costs and reducing delivery times.

Retailers employ AI capabilities for intelligent store design, optimized product selection, monitoring in-store activities and inventory on shelves, and controlling the freshness of perishable goods.

Businesses are also looking to leverage AI to improve recruiting and human resources and to enhance their skills, training, and safety. For example, AI-driven recruitment tools can automate candidate screening, shortlisting the best matches, and save recruiters significant time.

 

 

Manufacturing, construction, mining, utility companies, farms, and other enterprises garner data from cameras, motion detectors, thermometers, weather sensors, and other endpoint devices.

A combination of AI, data from on-site IoT (Internet-of-things) systems, and computer vision enables monitoring workers’ behaviors and conditions to ensure compliance with safety protocols and quickly detect hazards.

 

Intelligent systems can process the data to identify dangerous situations, problematic behaviors, or business opportunities, make recommendations, or even take preventative or corrective actions.

AI-powered robots can perform assembly line and construction tasks precisely and quickly, increasing production rates and reducing errors.

 

Read also: Innovation in Construction: 5 Trends to Embrace in 2025

AI-driven predictive maintenance software enables companies to repair or replace machinery before failures occur, optimizing service schedules, reducing downtime, cutting costs, and improving efficiency in industries like manufacturing and aviation.

Manufacturing machinery and devices connected to networks continuously stream operational data, which ML systems analyze to detect patterns and anomalies in nearly real time. When a machine underperforms, AI alerts decision-makers to schedule preventive maintenance.

AI can determine when equipment needs service by processing sensor data, maintenance records, and weather conditions.

Smart energy management systems collect data from sensors affixed to various assets. ML algorithms then contextualize the datasets, helping decision-makers understand energy usage and maintenance demands.

Companies also apply AI to address environmental, social, and corporate governance (ESG) initiatives and reduce their environmental impact.

 

Read also: AI-Powered Mobile App Development [Expert Tips]

 

These are just a few applications of artificial intelligence in business operations. As technology advances and becomes more accessible, more innovative applications will emerge. It’s vital to keep abreast of the latest AI solutions for business transformation and growth. 

 

It’s equally important to know how to implement AI in your business ecosystem. Successful AI deployment involves a comprehensive understanding of the technology’s benefits and limitations, thoughtful planning, strategic foresight, and other AI business considerations.

 

Main Considerations Before Implementing AI Solutions

IBM’s 2023 Global AI Adoption Index lists six main factors hindering the adoption of AI for business operations:

 

1. limited AI skills and expertise (reported by 33% of respondents)
2. excessive data complexity (25%)
3. ethical concerns (23%)
4. difficulty tackling, integrating, or scaling complex projects (22%)
5. high price (21%)
6. lack of tools for AI model development (21%)

 

Let’s take a look at these and related difficulties and how companies can alleviate them.
 

Limited AI skills, expertise, or knowledge

The lack of skills remains the most significant barrier to AI adoption within organizations. 20% reportedly do not have employees with the right skills in place to use new AI or automation tools, and 16% cannot find new talent to address that gap.

 

Learn more: How to Build Teams for AI Projects 

 

Data scientists and professionals with particular technical abilities may be hard to find, and it’s even harder to hire people with an understanding of business strategy and digital technology who can generate insights from corporate data.

39% of companies reportedly respond to this challenge by preparing and reskilling personnel to work with AI and automation software and tools.

Others, especially smaller companies and startups with limited budgets, can outsource the work. Besides quick access to seasoned professionals knowing all the ins and outs of data science, integrating AI with business applications, and other technicalities, this approach can help save up to 50% of a project budget.

Onix’s ML developers can help you navigate the challenges of implementing artificial intelligence in business applications while you grow your business. We have completed hundreds of projects for international clients and will properly address all critical considerations when developing an AI solution for you.

Data availability and management difficulties

Data should be any entity’s first thought before implementing artificial intelligence: ML powering most AI applications requires vast amounts of data to train the models. 

Main article: What Are the Risks and Limitations of Machine Learning?

72% of large organizations are using 20 or more data sources to inform their AI, business intelligence, and analytics systems. This limits the use of AI in business areas that are new or  or don’t have much available data.

 

Without sufficient clean data sets, ML algorithms’ ability to learn and analyze will be limited. However, data still largely comes unstructured and unlabeled.

 

AI requires more sophisticated cloud architecture and more complex data architecture.

 

For example, businesses that use AI are more likely to utilize a data fabric, i.e. architecture that facilitates the end-to-end integration of various data pipelines and cloud environments by using intelligent and automated systems. Companies that have deployed AI are more likely to use a mix of databases, data lakes, data warehouses, and data lakehouses than those that have not.

 

AI initiatives involving the collection of sensitive information raise additional concerns. IBM’s 2023 Global AI Adoption Index lists the same main challenges of organizational data management: ensuring data security, governance, compliance, and privacy, integrating data across a cloud, and managing disparate data sources and formats.

 

Onix’s developers are familiar with these challenges. For example, a robust database and integration with existing services were our critical considerations when developing an AI-powered news aggregator for a Kazakh news platform.

 

The Onix team built a robust system for collecting articles from diverse sources, processing them through ML models, and storing the information in a database. Various APIs and services were integrated to ensure an extensive news source pool.

 

We also successfully integrated the news aggregator with the client’s existing infrastructure, ensuring a cohesive user journey and enabling user identification within the media app.

“The successful integration of LSTM models elevated the platform's accuracy, resulting in precise news categorization and enhanced user engagement,” said Oleksii Sheremet, Onix’s ML Tech Lead. 

Onix’s work on the LSTM news categorizer showcases our expertise in implementing artificial intelligence for online media.

 

Ethical concerns

The promise of AI can be delivered only when its use is trusted, supported, and safe. According to IBM’s 2023 Global AI Adoption Index, the biggest inhibitions of organizations not exploring or implementing GenAI are data privacy (57% of respondents) and trust and transparency concerns (43%).

 

85% of the surveyed IT professionals believe consumers are more likely to choose providers with transparent and ethical AI practices. 83% of companies exploring or deploying AI say that being able to explain how their AI reached a decision is important to their business.

 

Read also: AI Development Life Cycle: Key Stages for Achieving Business Success

 

Among companies working towards trustworthy AI, 44% are developing ethical AI policies, 41% are making sure they can explain the decisions of their AI models, 37% track data provenance, and 27% focus on reducing bias. A staff member responsible for ethical AI implementation and accountability may become mandatory for businesses that use AI.

 

Implementing artificial intelligence also comes with regulatory responsibilities. Your business’s use of AI must meet ethical standards and comply with the General Data Protection Regulation (GDPR) or other relevant data privacy and security regulations.

 

Legal experts can help you navigate the complex landscape of AI regulations and ensure a compliant and ethical use of AI in your business processes. For example, lawmakers worldwide struggle with regulating government and commercial use of facial recognition technology and continue investigating its possible risks.

 

Read also: Deepfake Threats: How to Protect Your Business?

Voice recognition technologies also raise the sensitive issue of privacy. AI, in general, can bring the analysis of personal information to new levels that can intrude on privacy interests.

AI systems that make automated decisions can also raise legal and ethical concerns. For example, an AI system denying someone a loan based on biased data could violate anti-discrimination laws. 

Other AI-related concerns include the possible rise of fake media and disinformation, security holes and other vulnerabilities in AI systems, and the ability to manipulate or fool them.

Budget issues

Investments remain a key consideration of AI adopters. According to IBM’s 2023 Global AI Adoption Index, the primary investments at organizations exploring or deploying AI are:

 

• research and development (44%)
• reskilling/and workforce development (39%) 
• building proprietary AI solutions (38%)

Cost is the biggest barrier to NLP adoption for enterprises worldwide.
 
If a company ventures to build and integrate its own AI solutions, it should objectively assess the level of research and development it can sustain. AI innovation is only feasible if a company is big enough and has unique business needs, appropriate capabilities, and a strapping budget.

However, the AI technology market already has a lot to offer. Small and middle-sized companies can find state-of-the-art AI solutions that are open-source and free or pay for solutions developed by Amazon and other AI champions based on usage.

As AI matures, the technology and necessary expertise will become increasingly accessible. More vendors will replicate successful AI applications and offer products at increasingly affordable prices.

The development and maintenance of custom AI systems is computationally intensive, requiring significant processing power and storage capabilities, a modern networking infrastructure, data engineers, and skillful project management. AI-compatible infrastructure must be flexible and scalable and have sufficient data processing power. Enterprise-level computing resources are expensive.

 

It’s crucial to ensure that your infrastructure can handle the computational demands of AI and ML algorithms and accommodate any potential scalability needs.

 

The good news is that cloud-based services eliminate the costs of acquiring new servers and powerful processors. AI can also be made more affordable through partnerships with universities and tech firms that possess the knowledge and infrastructure for building robust AI models.

If a whole in-house team seems too significant an investment, an experienced remote dedicated team can be a perfect solution.

However, the primary way to reduce (or eliminate) the cost and risks of AI-powered digital transformations is to select the right task for AI in your business.

 

AI is not a one-size-fits-all solution. Business leaders must think independently about where it may benefit their company most and how it can fit into their business model. They should also recognize the complexity of AI deployment and manage expectations.

Before investing in any AI applications, it’s essential to identify the most pressing issues and your goal and determine whether AI is the right solution. Learn what is viable and what is possible. Try to predict the AI solution’s possible impact on customers and employees. 

 

Research businesses that use AI to solve your kind of problem to figure out how they’re putting AI technology to work. Try to learn what it takes to implement such AI applications and what to expect.

 

When communicating with vendors, ask them what companies like yours deliver ROI from using their AI solutions for business operations.

 

Entrepreneurs who don’t have the time, competence, or confidence for all this research and decisions should delegate the job to professionals. For example, Onix offers project discovery services that help address most AI implementation challenges early on, accelerate the deployment, and save money. 

Read also: The Ultimate Guide to AI Proof of Concept

 

The process of deploying artificial intelligence for business use can be lengthy. After the initial planning, research, and development, tailoring and configuring the AI solution will take time. 

The integration of a custom AI system into your business processes and IT architecture will require additional time. You will also have to redesign the processes around the AI solution.

A lot of initial training and work with data is required before an AI solution can work with the existing system and deliver tangible results. Afterward, subject matter experts must monitor it to ensure the machine correctly interprets the change in the business context.

 

Entirely autonomous AI systems are rare. That will typically mean new roles for the employees who work alongside them and retraining workers on the new process and system. 

Even a fully autonomous AI system is likely to require some augmentation. During this period, interactions between the system, the users, and observers should occur. The collection of new data sets and baking them into ML algorithms may take months on end.

 

Other AI business considerations may include:

 

• uncertain value of AI implementation in business processes
• extended AI deployment timelines 
• cultural challenges and lack of trust in AI technology
• resistance from employees
• customer preference for human interaction
• rapidly changing business environment

The challenges faced by organizations and businesses using AI are many and various, yet more and more entities seem to overcome them. Let’s look into several artificial intelligence-related business transformations.

 

Businesses that Have Transformed Their Operations with AI

 

Alibaba

Alibaba earned over 243 billion yuan (USD$33.8 billion) in Q2 2024. 

Alibaba’s active research and implementation of AI and machine learning solutions are evident in

 

• a $15 billion investment in its global R&D DAMO Academy in 2017, including in research areas that focus on data intelligence, IoT, human-machine interaction, and quantum computing;
• a $600 million investment in SenseTime, a DL and computer vision company, in 2018;
• a $1.41 billion investment in its AI and IoT system centered around smart speakers in 2020;
• a 2021 investment in the autonomous driving startup DeepRoute.ai.
• leading a $1 billion funding round for Moonshot AI, a Chinese startup developing GenAI, in 2024. 

The giant ecommerce platform is preoccupied with offering the most relevant products and enhancing the online shopping experience on its Tmall website and mobile app. 

Managing traffic from over a billion users while optimizing the shopping experience requires intensive use of ML and cloud AI functions. Alibaba’s Recommendation System Framework was designed to optimize traffic flow and promptly offer consumers the desired products, increasing customer satisfaction, click-through rate, and revenue.

Before 2019, Alibaba used a “relevance of recommendation” function that calculated the degree of similarity between the products previously clicked on or purchased with Tmall’s inventory.

 

The company changed it to include the diversity of recommendations and discovery optimization using the Artificial Intelligence Recommendation (AIRec) engine.

AIRec algorithms outperform “self-managed” algorithms by 20-100%. The engine can analyze and capture user behavior in seconds and promptly provide personalized recommendations for more diverse products with a high click-through rate.

 

In 2024, Alibaba became a leading investor in China’s GenAI by acquiring stakes in Moonshot, Zhipu, MiniMax, and 01.AI, all focused on producing alternatives to popular American AI applications.

Instead of traditional cash-for-equity funding, Alibaba used its vast cloud computing infrastructure: the provided credits give these startups access to the network resources essential for training AI models.

This strategic move by the ecommerce giant aims to replicate the success of Microsoft’s investment in OpenAI with its ChatGPT.

Read also: ChatGPT for Startups: How to Harness the Potential of AI for Your Business

Anheuser-Busch InBev

AB InBev is the largest beer brewer, with brands such as Beck’s, Budweiser, Corona, Hoegaarden, and Stella Artois. The company operates in nearly 50 countries, and made USD$15.33 billion in revenue in Q3 2024.

 

Started in 1366, AB InBev might seem an unlikely pioneer of digital transformations, but it is. Tassilo Festetics, then AB InBev’s vice president for global solutions, advised: “Really start looking at your data early, because data is the fundamental part. [...] Then start looking at the areas where you have the best business cases, where can you drive the most value for your company.” 

 

They began in 2013 by collaborating with farmers through the Smart Barley platform. Its AI algorithm analyzes data from past yields to help improve future yields, reduce water and fertilizer usage, and create a more sustainable environment.

 

Then, AB InBev brought AI, analytics, and predictive modeling to their back-office operations and the supply chain with the help of Microsoft Azure and Google Cloud Platform’s services. By optimizing its data pipeline and deploying AI solutions from Azure, AB InBev reduced shipping times and fuel costs.

 

Google Cloud Platform helped AB InBev enhance its beer filtration process. They used AI and ML on data collected from sensors in the filtration equipment to find the sweet spot for filtering the finished product. This resulted in lower filtering costs and a competitive advantage. AB InBev then deployed an AI solution to maintain brewing machinery.

 

AB InBev established a Growth Analytics Center in Bangalore, India. Its data scientists use AI and Azure ML capabilities to analyze current and historical data from multiple sources. They develop forecasting models and identify patterns that can help optimize prices and promotions and achieve the highest possible customer retention rates.

 

In 2023, to celebrate Beck’s 150-year milestone, AI helped create the limited-edition Beck’s Autonomous. The technology generated millions of flavor combinations and selected a possible favorite. AI also created the name and designed the logo, container, and advertising campaign.

 

 

Beck’s Autonomous is an excellent example of artificial intelligence-related business promotion. The novel product that “sold out in 10 minutes” generated publicity, and tapping into the global AI trend helped the brand stand out.

 

Read also: Generative AI in Pharma: How It Boosts Productivity and Drug Development

 

X (formerly Twitter)

Founded in 2006, Twitter quickly developed into an alternative to Facebook. However, as the platform grew, it saw an increase in hate speech, fake news, and terrorism-related and illegal content.

 

To mitigate possible legal and social implications, Twitter started leveraging AI and ML tools to transform its content management. For example, the company acquired

 

• the image search startup Madbits in 2014
• machine learning startup Whetlab in 2015
• Magic Pony Technology that used neural networks to improve images (2016)
• Fabula AI using ML to detect fake news online (2019)

 

In the first six months of using AI algorithms, the platform eliminated 300K accounts related to terrorists or promoting such activities.

 

AI is also used to crop images in a specific way, offering users a nearly accurate crop that helps drive engagement. If the system detects adult content in an image, it displays a message but hides the image. Video recognition software facilitated an accurate categorization of videos, improving their searchability.

 

Read also: Image Classification: 6 Industries & 26 Use Cases You Can Try

 

Showing relevant post suggestions was another challenge. They made a complex DL algorithm powered by deep neural networks to drive the X timeline. It runs millions of posts and reactions to them through these systems to determine which users want to see what type of content.

 

AI algorithms scan and score every post to determine whether a user would like to see it. The ranking model considers the post’s content, the number of likes and reposts, and any connection or previous interactions between its author and the user. A post with a high relevancy score will likely appear in the user’s feed.

 

Onix also has experience in Twitter/X sentiment analysis and news categorization.

The latter was necessary for the news mobile app mentioned above. The concept is an ML model that categorizes news from several sources by topics, such as health, society, politics, business, sports, etc. This feature was envisioned to promote efficient news consumption based on readers’ preferences.

Read also: Text Classification: 6 Use Cases You Can Try for Your Industry

The categorizer model is based on LSTM (long short-term memory), a cutting-edge neural network architecture. The language of the news is determined based on a perceptron trained on data in Russian and Kazakh. The language detector ensured that users can effortlessly access news in their preferred language.

The model has proven effective at news categorization (validation accuracy over 0.93), provided it is multi-label and multi-category.  

After Onix implemented the LSTM news categorizer, the client saw a substantial boost in user engagement and remarkable user growth.

 

Conclusion: What Business Can Expect from AI

Amazon, Alibaba, Facebook, Google, Netflix, Spotify, and X/Twitter likely would not have become global leaders without machine learning. Banking, automotive, food, pharmaceutical, and other companies also increasingly use AI to achieve their business goals. 

Data analytics, personalization, and automation are the main current AI applications in business. Existing AI solutions already have the potential to revolutionize marketing, sales, customer services, IT, human resources, decision-making, administration, finance, manufacturing, quality control, workplace safety, and cybersecurity. 

 

We’re just scratching the surface of how artificial intelligence will transform businesses. From using AI to enhance business operations, leaders are moving to uncovering new opportunities and creating a competitive advantage.

Although powerful and impressive, today’s AI needs much work to be done, starting with reducing bias. However, “commonsense” tasks are becoming easier for computers to process. Graphic processing units (GPUs) will become faster, improving the applications of AI software across businesses. 

 

AI is expected to move increasingly from two-dimensional screens to the three-dimensional world. The combination of AI with IoT, augmented and virtual reality, and other technologies will replace traditional displays as the primary interface. New user interfaces, digital assistants, and robots will proliferate across industries and businesses.

The artificial intelligence impact on business will grow slowly for innovators and too fast for laggards. The shift toward AI-based systems will fragment long-standing workflows; new jobs will be required to facilitate that transition, integrate those workflows, and support innovation.

Massive innovation will fuel existing industries and may even create new sectors for growth. We will see new artificial intelligence business applications, consumer uses, and startups. AI may create a knowledge-based economy and, through automation, a better way of life.

The future is coming quickly, and the playing field will only become more competitive. By the time a late adopter is poised to leverage the benefits of AI in business operations, early adopters will be operating at lower costs with better performance. Laggards may never be able to catch up.

It’s best to start exploring AI’s abilities right now. If you don’t know how to implement AI in your business, ask for expert advice. Please don’t hesitate to contact Onix – we can provide a consultation, help hire software engineers, or build an AI solution for your business from A to Z.

 

Frequently Asked Questions

 

What artificial intelligence business applications are developing now?

 

• business intelligence (forecasts and predictions)
• personalized customer experiences, service, and support
• smart searches, product recommendations, and purchase predictions
• ad targeting and customer segmentation
• content recommendations, curation, and feed personalization
• supply chain optimization
• pattern, voice, and image recognition
• chatbots and digital assistants 
• cybersecurity and fraud detection
• business processes automation
• workplace safety
• predictive maintenance
• route optimization and delivery by robots

 

What are the primary benefits of AI for businesses?

The implementation of AI in business processes can bring about the following results:

 

• cost savings
• increased efficiencies and productivity
• improvements in IT or network performance
• business functions optimization
• better experiences for customers
• employees freed to focus on higher-value tasks
• mitigating labor and skills shortages
• streamlined workflows
• reduced operational time
• faster delivery and scaling of new services
• competitive advantage
• reduction in outages
• reduction in data center emissions
• reduced human error
• better decision-making
• increased revenue

What are the primary barriers to AI adoption?

 

• a costly but limited AI talent pool
• insufficient training data and data management issues
• limitations in current computing capability
• difficulty tackling, integrating, or scaling complex projects
• ethical concerns, including biases and possible loss of jobs
• threats to data security and concerns about privacy
• mistrust of machine intelligence
• ROI uncertainty

 

Are there examples of successful applications of artificial intelligence in business to date?

Some prominent businesses that use AI to the greatest effect are Alibaba, Amazon, Google, IBM, Meta Platforms, Microsoft, Netflix, Salesforce, and Starbucks.

 

What should I consider before implementing AI?

A business’s primary considerations before implementing AI should be the following:

 

• Business goals. Align AI with your organization's specific business objectives.
• Data readiness. Evaluate the availability and quality of data needed for your AI training.
• Technical infrastructure. Ensure your systems can support AI integration.
• Ethical and regulatory compliance. Address ethical and legal considerations in AI usage.
• Workforce. Plan for workforce readiness and potential job role changes.

 

Does Onix prepare businesses for AI implementation?

Yes. Onix provides expertise, guidance, qualified personnel, and software development services to ensure successful and responsible AI integration in business operations.