Research from Gartner Inc. in 2022 indicated that 80% of entrepreneurs believed any business workflow could be automated. Yet, only 54% of AI startups in that year transitioned from the pilot stage to realization. The most common issue identified for enterprise AI was the inability to measure the return on investment, despite 74% of companies expressing readiness to invest in AI. So the problem was not the expenditures but the value. We will review the types of automation that can be implemented in enterprises and their potential value. Let's start with the terms to further dive into details.

Enterprise AI refers to systems that use artificial intelligence technologies to enhance routine yet complex tasks of software and Internet of Things (IoT) equipment and to improve specific solutions such as customer service or risk prediction. Both AI automation paths have their pitfalls. Companies may encounter problems trying to gather data from many different sources while creating specific solutions might pose the challenge of accurate automation alignment with business needs. How to overcome the challenges for both? Let's start with complex systems.

An enterprise AI system with IoT

Enterprise AI development involves processing data from different enterprise systems and collecting information about suppliers, distributors, stocks, and IoT equipment. The latter refers to the devices that incorporate software and other technologies and are able to exchange information with other systems over the internet. Thus, the velocity of data processing in enterprises is extremely high.

The current most popular platforms assisting with these tasks are AWS and Microsoft Azure. Yet, to fully integrate an AI into the whole enterprise system with information systems, IoT networks, and extraprise data, one needs to combine sets of technologies into a robust and effective architecture.

Microsoft Azure or Azure is a cloud computing platform, which allows for the building of apps and services with global data centers. It offers over 600 services including a variety of customizable tools suited for AI development.

AWS by Amazon is also a cloud computing service that includes analysis, machine learning, database, and other functionalities. It provides virtual access, enables the management of business operations, and offers services that cater to different tasks suitable for startups and enterprises.

Just imagine adding trade or market data and various networks such as weather forecasts in logistics or pricing in retail to inner enterprise systems like ERP, SCADA, and MRP. To combine this diverse range of data, a comprehensive system is required.

In addition to AWS and Azure, there are numerous AI platforms serving different purposes, including Cassandra, TensorFlow, Grafana, and Cloudera. However, none of them, including modern generative AI platforms, can meet all enterprise goals without expert aid. Therefore, a set of tools is often used to integrate AI into the enterprise rather than relying on a single technology. While a single platform may handle a specific task or provide tools for completion, custom AI software development is typically necessary for comprehensive enterprise solutions.

The integration of AI into enterprise systems offers numerous benefits. By combining IoT with AI, data utilization is taken to the next level, enabling real-time tracking, predictions, and insights from a variety of connected devices and networks within the enterprise. This can include predicting machinery maintenance based on usage patterns or analyzing real-time market data for dynamic pricing strategies.

Enterprise AI for specific tasks

Even if a company is not planning to integrate complex solutions for interpreting and handling huge data volumes at all enterprise levels, there are smaller yet effective ways to use artificial intelligence in enterprise software.

Technologies explained

AI is a broad term for performing different tasks that replace human behavior patterns. It has its substitutes and building blocks. These are some concepts that are at the forefront:

Computer vision

Computer vision involves systems where a machine can recognize and process images and videos to substitute visual tasks usually performed by the human eye. What sets it apart from image processing is its ability to "understand" the visuals provided.

Generative AI

Generative AI has unlocked the potential of Large Language Models (LLM), or rather, advancements in LLM development have made generative AI possible. Generative AI models are trained to absorb patterns within data from extensive datasets and can generate new data that resembles the data they were trained on. Machine learning models are utilized to generate new instances.

Machine learning

So, machine learning is a branch of artificial intelligence that creates algorithms by identifying concealed patterns within datasets and using them to make predictions on similar data. It is commonly applied in statistics to gain valuable insights for an organization and can be supervised (with labeled correct answers) or unsupervised.

Deep learning

This is a model that performs data recognition itself. It is trained on a large amount of data, and the more data the deep learning model processes, the more accurate it becomes. The core of deep learning is recognition, with its technology underlying computer vision, speech recognition, and natural language processing. Essentially, deep learning serves as a foundational component of machine learning processes.

Areas where these technologies can be applied

Let's examine the processes that enterprise AI can enhance and add value to.

ERP

Enterprise resource planning (ERP) software aids an enterprise in organizing and automating its business routines, such as financial planning, purchasing, marketing, etc. In the everyday operations of ERP systems, machine learning, and AI algorithms can automate data entry tasks, quickly identify and correct human errors in data inputs, and alert managers about any anomalies in normal procedures. Prediction models can also be built over time to forecast sales, expenses, and revenues.

By implementing artificial intelligence in enterprise software, entrepreneurs access critical performance data promptly. As it is still a real struggle for entrepreneurs to see the full image of the organization's processes, enterprise AI reports can be extremely useful, especially in big organizations. Apart from data interpretation, automating routine tasks can reduce operational costs and provide better decision-making for ERP systems.

SCADA

Supervisory control and data acquisition (SCADA) systems are tools controlling enterprises' processes and equipment. They monitor both software and hardware. The major advantage of embedding AI into SCADA systems is its ability to predict maintenance needs.

With AI algorithms, any potential malfunction or breakdown can be detected in advance by identifying hidden patterns in large datasets, saving the company valuable time and resources and preventing production losses. Implementation of artificial intelligence in enterprise software will help operators concentrate on critical issues and detect deviations in the equipment before they turn into major problems.

MRP

Material requirements planning (MRP) is a software-based system that involves complex decision-making for forecasting the amount of materials needed and when it's required. Here, AI can learn from past material ordering, usage, and waste patterns to make accurate recommendations for future actions.

It can help to eliminate human errors, reduce waste, and increase the overall effectiveness of the production process. Even in such early research as of 2006, the need for automation was recognized and AI was proposed as a solution. According to the authors, AI techniques can reduce uncertainties in areas such as lot size determination, customer demand, material procurement, timing, capacity planning, and identifying the quantity and location of inventory. By mitigating these uncertainties, businesses can operate more efficiently, productively, and realistically under existing conditions.

Human resources

Human Resources, a field that deals with a significant amount of data, can utilize AI to automate numerous tasks like payroll processing, employee attendance tracking, recruitment, and reducing bias in hiring processes.

Using machine learning algorithms for predictive analysis in areas such as employee turnover, performance management, and talent acquisition can dramatically enhance the overall efficiency of HR functions. This improvement significantly influences employee satisfaction with work processes. In 2023, 35% of US employees reported experiencing moderate burnout, with 22% of respondents reporting high levels. Therefore, refining HR processes to perform seamlessly will undoubtedly yield valuable results, including reduced turnover.

Customer relationship management

In CRM, AI can accurately predict customer behavior by analyzing their past interactions, thus assisting in providing personalized experiences. This can lead to greater customer satisfaction and loyalty. Moreover, social media sentiments can be analyzed using AI to understand the current market trends and customer preferences. Generative AI can be applied in the customer relations field to produce answers and provide immediate support.

In 2023, nearly 70% of customer service leaders stated that the most significant positive effect of AI on customer service metrics was a reduction in resolution time. Cost reduction and improved employee satisfaction were jointly ranked second, with 65% of respondents indicating that these customer service metrics had been positively impacted by AI.

Supply chain management

For supply chain management, AI can aid in optimizing logistical operations, reducing transport costs, and predicting future demand patterns. It can also assist in managing and tracking inventory in real time.

Statistics of AI and ML impact on supply chains are also impressive, showing a high percentage of expected AI technology influence by 2025. Therefore, it means that artificial intelligence in enterprise software will not only help with logistics process automation but also compete with rivals in the next few years.

Fraudulent detection

Fraud detection is another crucial area where artificial intelligence can be implemented. The main field utilizing AI for fraud detection is definitely financial institutions. Mastercard shares the survey which shows that 93% of institutions are planning to invest in fraud detection. The main AI application in fraud prevention today is the use of machine learning models to identify suspicious transactions, behaviors, activities, or contents that deviate from the norm. Typically, historical data is input into these machine learning models to enable them to detect potential fraud based on past occurrences.

Apart from earlier fraud detection, in their research, PwC named such benefits of implementing AI as more sophisticated detection due to trained models, freeing up expert capacity, and AI fakes (voice clones and fake images) detection.

Cybersecurit

AI is a powerful tool for the cybersecurity field. It can quickly identify and prioritize potential security threats, analyze attack patterns, and enact automated responses to quell any breaches. It can guard the system by continuously learning and improving its defense mechanisms, keeping the balance between user needs and security, and detecting shadow data.

Among operations that slow down the effectiveness of the security operation center workflow, the manual detection of threats was named to be the first. The surveyed security professionals saw the best and almost the only way to enhance the workflow in many ways which is implementing more AI and automation throughout the toolset. In their opinion, it would help to improve the response time, collaboration between teams, integration between tools, and alert prioritization.

What is the benefit of artificial intelligence in enterprise software here? Well, the earlier the AI can identify a potential threat, the faster the response time and the lesser the potential damage.

Diagnostics

Medical diagnostics can benefit immensely from deep learning algorithms, mainly in image recognition. AI has the potential to identify medical conditions from radiology images or pathological slides far more accurately and rapidly than humans. Artificial intelligence has already proved a better accuracy in image-based disease detection in 2023.

Studies have used ML techniques like SVM and ANN for early detection of different heart diseases in 170 patients, with varying levels of accuracy ranging from 71.2% to 89.1%. Another study has used these techniques on the South African Heart Disease dataset of 462 samples, with an accuracy of 83.9% for heart diseases and 95.7% for diabetes.

The advantage of implementing AI in healthcare cannot be overrated, as it helps to enhance diagnosis accuracy, increase treatment efficacy, and improve patient outcomes. AI is enabling faster detection and diagnosis of diseases, hence reducing the waiting times for patients. It can also process and analyze large datasets swiftly, thereby providing invaluable insights that can guide medical treatment. These diagnostic tools are not just efficient, but also consistent, minimizing human errors that can have massive implications.

What's more?

Generative AI also has great perspectives in fields like content creation, designing, and other creative processes, where new content can be generated based on the patterns and inputs from existing data.

In essence, integrating enterprise AI technologies can not only increase efficiency and accuracy but also pave the way for innovative solutions that redefine the way businesses function. AIs ability to learn, predict, and automate makes it a powerful tool that can revolutionize various sectors, making operations smoother and more cost-effective.

Artificial intelligence can help identify problems before they occur, make informed decisions backed by data, and offer personalized, high-quality services to customers. It is clear that the potential applications of AI are vast and transformative, capable of reshaping the entire business landscape.

Custom solutions or AI platforms?

Your choice depends on your objective. If you aim to detect specific actions in the factory or implement smart technologies in your retail chain, the training of AI models will likely be indispensable.

One way or another, you will likely use some service providers. For instance, IBM is an excellent tool for machine learning, or in other words, for analyzing large amounts of data. Amazon or Microsoft Azure AI are among other popular services. However, even with these tools, youll need specialists to train the models and achieve the desired results. The service providers compatibility with your existing systems merits consideration as well.

The primary requirement that the enterprise AI system must meet is its compatibility with your enterprises existing system. This ensures scalability, reliability, integrity, and sustainability.

Use cases and tech stacks

Let's now look at the technologies potentially useful for specific use cases within enterprises. Different technologies and various tools might be of great help in such cases.

Use case 1: Smart scanning

Smart scanning is based on image recognition technology, machine learning, and other AI instruments that are needed for interpreting and digitizing data from physical items. This AI technology can be applied in various ways, from scanning barcodes to recognizing physical items.

Smart scanning can significantly boost the efficiency of data entry tasks, perform high-speed reading of codes, and automatically categorize and store data. Not only can it save time by automating manual tasks, but it also reduces errors associated with manual data entry. One of the well-known companies offering ready solutions in this field is Scandit. They use CV, ML, and AR to develop scanning software for all sorts of gadgets.

The technology can be applied in various fields, from scanning someone's ID for providing access to information to scanning products in retail stores and parcels in logistics.

For building a similar smart scanning AI, apart from mobile app development tools for the customer role, your tech stack could include:

• Machine-learning platforms and tools for image recognition, such as (but not limited to) TensorFlow, OpenCV, or PyTorch for the development and training of the AI model.
• APIs such as Google Cloud Vision or Amazon Rekognition could also be utilized for more complex tasks such as optical character recognition (OCR) and object detection.

Use case 2: Predictive maintenance analytics

We've discussed a broad range of technologies needed to intertwine all the systems within an enterprise, but let's give a more specific example of predictive maintenance analytics.

Predictive maintenance refers to the use of data-driven, proactive maintenance strategies to predict when equipment failure might occur. Companies can thereby conduct necessary maintenance before an actual failure happens, saving downtime and costs.

Let's consider a manufacturing company that wants to build an enterprise AI to predict equipment breakdowns and minimize downtime. This involves collecting and analyzing vast amounts of data from machinery and equipment, including temperature readings, vibration levels, and operational hours.

The tech stack for this use case may involve:

• IoT sensors. Equipment for capturing real-time data from machines.
• Apache Kafka. An open-source streaming platform that can process the real-time data coming in from the IoT sensors.
• Apache Cassandra. A highly scalable, high-performance distributed database system that can handle large amounts of data across many commodity servers. This can be used to store the processed data.
• Hadoop/Spark. These tools could be used to process and analyze massive volumes of historical and real-time data.
• Python/Java/Scala. Programming languages that are often used in data processing and machine learning. They can be used to build predictive models.
• TensorFlow/PyTorch. Two of the popular machine learning libraries that can be used to design and train predictive models.th
• Microsoft Azure AI/Machine Learning. The cloud platforms can be used to deploy the predictive models and also to leverage their built-in ML capabilities. Azure AI, in particular, has multiple tools and services to support the development and deployment of ML models.
• Tableau/Power BI. These data visualization tools can help in representing the analytical results in a visually understandable manner. These can be used to display the health of equipment, highlight potentially problematic machines, and show historical trends in machine performance.

Building this Predictive Maintenance Analytics system starts with the collection of real-time and historical data using IoT sensors installed on the equipment. This data is then sent to an Apache Kafka cluster that processes it in real time, after which it gets stored in Apache Cassandra for future analysis and training of the predictive models. Hadoop or Spark can be used to draw insights from this data.

The actual predictive models could be built with a language like Python, Java, or Scala, using libraries such as TensorFlow or PyTorch. These models can then be deployed on a scalable cloud platform like Microsoft Azure. Finally, Tableau or Microsoft Power BI can be used to visualize the data and the machine learning model's predictions in a way that's easy for stakeholders to understand.

This Predictive Maintenance Analytics system could significantly reduce maintenance costs and machine downtime, improving the overall operational efficiency of the manufacturing process. This approach is not only limited to manufacturing. Industries like aviation, utilities, oil and gas, and any other that heavily rely on equipment and machinery can also adopt such systems and benefit from their predictive power.

Use case 3: Cybersecurity anomaly detection

In our discussion above, we mentioned that AI tools can help in monitoring various network activities and identifying unusual patterns that could signify potential threats. Let's go into more detail on that, by providing an example of a cybersecurity anomaly detection system powered by artificial intelligence and machine learning.

With growing cybersecurity concerns, it becomes critical for organizations to actively monitor their network traffic to identify and manage potential cyber threats. ML technology with anomaly detection capability can identify deviations from normal behaviors and alert system administrators about possible intrusions or attacks.

Let's consider an organization that aims to implement a cybersecurity anomaly detection system to monitor network traffic and detect any anomalies or threats at an early stage. Activities involved include but are not limited to, processing large volumes of data in real time, creating base models for detection, and implementing automated responses to perceived threats.

For enterprise applications like that, the tech stack may include:

• SciKit Learn. An open-source machine learning library used for generating the base anomaly detection models;
• Apache Kafka or Apache Storm. These platforms can be employed to process large volumes of real-time data;
• Snort or Suricata. These are intrusion detection systems used for scrutinizing network traffic and spotting unusual behavior;
• IBM's Watson for Cybersecurity or LogRhythm's Threat Lifecycle Management framework. These platforms can be used for more evolved tasks such as automatic responses to detected threats.

The construction of this cybersecurity anomaly detection system would kick off with the creation of base detection models utilizing the ML library, SciKit Learn. Streams of real-time data would then be processed with systems like Apache Kafka or Apache Storm. For closer surveillance of the network activity and to identify abnormal behavior or potential threats, intrusion detection systems like Snort or Suricata would come into play.

For a more advanced level of protection, automatic responses to these perceived risks could be implemented using platforms such as IBM's Watson for Cybersecurity or LogRhythm's Threat Lifecycle Management framework.

With the potential to identify and respond to threats at a remarkably early stage, this cybersecurity anomaly detection system could significantly enhance the security protocols and policies of the enterprise. But, this methodology isn't limited to any specific industry. Any sector that relies heavily on data and network transmissions can adopt this system, thereby drastically improving its cybersecurity measures.

All in all

Every enterprise being a complex long elaborated system has its own unique specificities and flaws that require automation. Hence, every instance requires an attentive approach. Answering the question of what value an enterprise AI solution brings. There are just some stats:

• 54% of the businesses have already implemented generative AI into their businesses. While 58% expect to invest in AI.

• 24% of companies have saved around $50,000–70,000 by implementing generative AI into their processes.

• 79% of corporate strategists find AI critical to the success of their businesses and have already started implementing or exploring the technologies.

The latest technologies are rapidly adopted and have already helped companies save resources. And there's more to come. Although implementing AI solutions may have its challenges, the potential benefits are overwhelming. From manufacturing to customer service, HR, and cybersecurity, artificial intelligence is steadily becoming a cornerstone of modern enterprise solutions.