Artificial intelligence technologies have many applications. One of the most popular among them is business process management (BPM). AI is capable of quickly and efficiently creating, analyzing, automating and optimizing various work and business processes. With its help, companies can improve their operational efficiency and increase profits. Priority AI tasks in BPM include data analysis, process building and process automation.

Artificial intelligence's ability to quickly process large amounts of information, perform accurate calculations, and generate relevant forecasts makes it an extremely useful tool for companies across various industries. In our article we will tell you how AI can be used in business processes, what advantages and risks there are, and also introduce you to examples of the implementation of these technologies by market leaders.

Current State of AI in Business

Today, the use of AI in business processes is a widespread trend. Many companies around the world are integrating these latest technologies into their enterprise systems that manage core business processes. Many enterprises use artificial intelligence for specific areas or processes. A survey of 254 leading tech industry companies conducted in April 2023 showed that 90% of respondents use generative neural networks like ChatGPT or Bing Chat. 80% plan to increase investments in AI technologies in the next year. According to research and consulting company Grand View Research, the global AI market reached $62 billion in 2020 and is expected to grow by another 40% by 2028.

Forbes has released a large-scale study with an up-to-date assessment of the dynamics of the implementation of artificial intelligence by modern businesses. In preparing the report, owners of 600 American companies were surveyed, and their responses showed the following results:

  • The most common application of AI in business is customer service and support. More than 56% of respondents use it for these purposes.
  • 51% of surveyed entrepreneurs are using AI for cybersecurity and fraud protection.
  • Other areas of greatest use of AI in business processes include customer relationship management (46%), online personal assistants (47%), inventory management (40%) and content production (35%).
  • To a slightly lesser extent, implementation AI is relevant in product recommendation (33%), accounting (30%), supply management (30%), personnel management (26%) and audience segmentation (24%).

Top managers and business owners are looking at artificial intelligence as a tool to improve the efficiency and productivity of their businesses, reduce costs, create competitive advantages and adapt to rapidly changing market dynamics. Rapid advances in AI have made this innovation more accessible and commercially viable. Therefore, in their opinion, artificial intelligence is being increasingly introduced into almost all modern business processes. Entrepreneurs consider natural language processing (NLP) to be the most popular AI technology for business.

Benefits of AI Implementation

AI implementation in business brings tangible benefits to entrepreneurs, helping to optimize key business processes and increase enterprise profitability. Frost & Sullivan conducted a study in 2022. Its results showed that 87% of respondents surveyed are confident in the ability of AI and machine learning to improve customer experience, operational efficiency and profitability.

AI Implementation in Business Processes

The main advantages that the introduction of artificial intelligence technologies provides to businesses:

  • Personalization and improvement of user experience. AI technologies flexibly personalize services and customer interactions by processing large volumes of data. Now, popular chatbots based on neural networks provide prompt support to users 24/7. High speed and efficiency of request processing helps companies strengthen audience loyalty.
  • Business process automation. AI qualitatively optimizes both manufacturing operations and service delivery processes. It is capable of managing robotic lines, processing applications and payments, answering letters and calls, testing programs, generating invoices, and so on. In addition, by integrating AI into ERP systems and other software, the work of staff can be accelerated and simplified.
  • Advanced analytics. Neural networks efficiently process huge amounts of data and interpret them in real time. This helps companies act faster and make better decisions, gaining important competitive advantages.
  • Saving money. AI technologies provide businesses with a range of benefits, which together help them minimize the cost of time/money/other resources and generate more profit. These primarily include monitoring processes 24/7 and eliminating risks associated with the human factor. And also – accelerated processing of large amounts of data, reduced production downtime and reduced operating expenses.
  • Improved data security. Artificial intelligence does a good job of protecting data by instantly detecting unauthorized access attempts and fraudulent activities. This makes it extremely useful for many areas of business where the security of personal data is critical (for example, banks, fintech).
  • Accurate forecasting. AI models perform well in predictive analytics, thanks to their ability to work with big data, identify patterns and generate forecasts. Equally important is their self-learning skills, which allow them to formulate correct conclusions when processing data and create predictive analysis based on them.
  • Reducing the cost of staff training. Neural networks can be used to interact not only with clients, but also with employees of your company. They can train personnel efficiently and quickly with minimal cost of these operations. Adaptation and personalization skills will help them flexibly adapt to any person.

Challenges and Considerations

AI solutions for business have not only advantages and benefits, but also certain challenges and risks. Companies wishing to optimize their processes often face serious obstacles when trying to resort to the help of artificial intelligence. The most common ones are:

  • Integration into existing systems. Upgrading a decades-old supply chain, security module, or workforce training program may require more complexity than installing a few plugins. Often such upgrades require considerable investment, time, and experience. Before they begin, it is worth assessing your infrastructure and paying attention to employee training.
  • Poor or insufficient data. One of the main challenges in implementing AI is the volume and quality of available data. The results produced by neural networks directly depend on them. Data imbalances can lead to significant challenges when implementing AI into your business processes. Companies should invest in robust data management systems and conduct regular audits of information.
  • Ethics and safety issues. Ethical challenges facing businesses typically revolve around data privacy and the responsible use of AI. Before introducing such innovations, entrepreneurs should consider measures to protect the personal data of customers and employees, as well as ethical standards. Particular attention should be paid to the cybersecurity of new technologies.
  • Search for an integration strategy. Today, there is no universal instruction on how to implement AI in business. Each company has its specifics and requirements that their AI implementation strategy must meet. During the pre-implementation phase, businesses should assess infrastructure, data, and organizational readiness. In addition, you need to create a roadmap detailing the actions, timelines, and resources required for integration.

Steps for Effective Implementation

The implementation of artificial intelligence in business is a complex process that requires a consistent approach. Understanding the key factors and conditions for its implementation will help achieve the desired result within the planned time frame.

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Main stages of implementation of business intelligence and AI:

  1. Identification. First, identify the areas of your business that particularly need AI integration. These may include repetitive or labor-intensive processes, including data analysis, customer service, lead processing, etc. Often these tools are in demand in industries where speed and accuracy of results are significant.
  2. Technologies. In the second step, move on to finding and comparing suitable AI technologies and their suppliers. When choosing a provider, you should consider its experience and the availability of solutions relevant to your industry. Research how they can be customized to suit your needs, safety measures and compliance, as well as the cost and expected ROI of these tools.
  3. Planning. A project plan should then be developed, defining realistic timelines/milestones/deliverables and assigning responsibilities to staff. In this case, it is necessary to consider potential risks/problems and consider ways to eliminate them.
  4. Data. Before launching integration, be sure to conduct a thorough audit of your data – make sure that its quantity and quality allow you to successfully achieve your goals. If you are not confident in your data, do not rush into the project. Instead, consider collecting more information or improving it.
  5. Team. The next important stage in the implementation of AI in business process management is the formation of an effective team to manage and support integration processes. You will need experienced data scientists, engineers, and project managers. In addition to performing technical tasks, the integrator team must train other company employees on the correct use of AI tools.
  6. Testing. Before starting full-scale integration, it is recommended to test the main components of the AI system to evaluate its performance and identify potential problems. The pilot project allows us to collect feedback from staff and clients, using this data to refine the technology.

Case Studies

Artificial intelligence technologies have successfully proven themselves in many business sectors. They help entrepreneurs optimize processes, reduce costs and increase profits. We present to your attention several worthy examples of AI integration in business:

Customer Service and Support

KLM Royal Dutch Airlines has implemented an AI-powered chatbot into its Facebook Messenger account. BlueBot processes various customer requests: about booking confirmations, refunds, flight schedules, baggage allowances, and so on. The bot understands text messages and generates a response using NLP technology. After implementation, KLM delegates up to 60% of requests to AI. So its support staff can focus on complex or unusual requests.

Advertising and Marketing

Next on the list of the best examples of artificial intelligence in business is the case of the Coca-Cola company. It implemented the Albert AI platform to optimize digital marketing campaigns. The neural network analyzes customer data and generates insights to improve marketing campaigns. In addition, it is able to independently change their parameters in real time, focusing on the behavior, preferences and purchases of customers. The implementation of AI allowed Coca-Cola to increase return on investment (ROI) by reducing advertising costs.


UPS has integrated the AI-powered ORION logistics platform to optimize cargo delivery routes and improve overall business process efficiency. Using machine learning algorithms, it collects and analyzes information about customers, traffic conditions, weather conditions and other types of data. This allows it to build and adjust optimal routes for UPS drivers in real time. Thanks to the implementation of ORION, the company is reducing its routes by millions of miles per year, saving considerable money.


JPMorgan Chase has implemented an AI assistant called COiN to automate back office work and improve process efficiency. The tool is capable of processing different types of data, including receipts, invoices and other financial documents. The platform increases employee productivity by automating a number of operations: data entry and reconciliation, compliance checking, and so on. COiN helped the bank speed up and improve the quality of analysis of large volumes of financial data, reducing the number of errors in them.


Another interesting example of AI business implementation was shown by Siemens Corporation. The Siemens Digital Enterprise Suite artificial intelligence platform implemented by it effectively copes with the optimization of production operations. It reads and analyzes information from sensors, machines and other equipment, obtaining information about production processes in real time and identifying opportunities for their improvement. The launch of the AI system allowed the company to optimize processes, minimize downtime and increase equipment efficiency.


The spread of artificial intelligence in business has a very positive effect on business processes, as evidenced by both surveys of entrepreneurs and cases published by them. The implementation of AI brings a number of significant benefits to companies, including improved user experience, automation of complex or routine operations, more accurate and productive analytics, cost reduction, and so on.

However, such implementation also has some pitfalls, including difficulties in integrating with existing systems, lack of or low quality data, as well as security risks and ethical use of neural networks. To obtain maximum benefits and mitigate the challenges of implementing AI, businesses should do a lot of work to prepare for such a merger and involve experienced industry professionals in it.


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