Leveraging enterprise artificial intelligence (AI) provides a system that can automate repetitive tasks, answer questions, and improve decision-making. The AI operates machine learning extensions and automated services to deliver satisfying customer experiences and can streamline research projects. Enterprise AI can be described as a combination of machine learning and deep learning algorithms. 

With enterprise AI, managers and staff can rely on both the efficiency created by the AI and the insights gained from the business intelligence it generates. Enterprise AI does come with its own unique challenges, however. Enterprise AI is a new software system and is still in the process of evolving. Its continued evolution is dependent on the development of new, more powerful deep learning algorithms. As it continues to evolve, it will take responsibility for collecting research and performing a wider variety of repetitive tasks.

Enterprise AI has applications that are useful for almost all business models, including:

• Health care
• Banking
• Manufacturing
• Retail

Deep learning algorithms support the AI, which in turn controls a variety of machine learning algorithms.

How Enterprise AI Is Currently Used

Some of the more popular uses of enterprise AI are sales automation, human resources, security, compliance, resource planning, and e-commerce. Here are some other examples of the services enterprise AI is currently supporting:

• Chatbots: A chatbot supported by enterprise AI can easily understand conversations and will respond in real time. Some businesses are using chatbots as representatives during the customer experience. Some are even using them for human resource purposes.

• Automated Processes: The consistency and scalability offered by enterprise AI allows many tedious and routine tasks to become fully automated, saving hours of manual labor. Enterprise AI can also be used to automate customer service programs, personalizing interactions with customers. 

• Data privacy and governance: Enterprise AI can work with Data Governance software to enhance a business’s ability to manage large amounts of sensitive data. It can also be used to deal with privacy laws (GDPR, CCPA, LGPD), registering who is covered by such laws and handling their requests for data privacy.

• Enterprise AI search software: Enterprise AI can scan and access large volumes of data, quickly and efficiently. Effective AI search software allows users to quickly track down useful information, supporting better decision-making and problem-solving. But this feature does need further development, as it has problems with unstructured data, and often does not deliver on predicting customer behavior. 

• Generating leads: Some enterprise AI platforms can provide automated marketing solutions, which convert potential customers into paying customers. They can identify sales leads and interact with the potential customers, gaining an understanding of their engagement preferences, and may use chatbots. 

• Data analytics: Enterprise AI can be used to generate customized sales recommendations using a customer’s history and preferences. This form of data analytics can be especially helpful with B2B enterprises, which are more predictable than individual humans. With the right machine learning, enterprise AI can help predict the reactions to products and services.

Developing Enterprise AI Applications

Enterprise AI applications can outperform traditional, rules-based data systems in a variety of situations, ranging from medical image diagnostics to fraud detection. At the same time, they are harder to develop and operate. Building enterprise AI requires different tools, new skill sets, a new technology stack, and an enterprise AI platform. Other considerations include:

• The use of quality data: High-quality data is more important in enterprise AI systems. As with more traditional systems, the data processed by enterprise AI is used for communications and research, but with enterprise AI, it is also used for training machine learning algorithms and the larger, overall AI system. Having and using clean, high-quality data throughout the organization is a prerequisite for developing an enterprise AI system. Training an AI with misinformation will lead to problems.

• Data integration: Some large businesses have multiple operational systems, combined with several disconnected teams and processes, and use hundreds of different data sources. Integrated data from many sources is an important feature, and some enterprise AI providers claim they can handle it.

• Developing machine learning models: Machine learning development involves acquiring data from trusted sources, and it requires selecting the most appropriate machine learning algorithm. The machine learning training process can seem intimidating, but developing the understanding needed is not that difficult. The methods used for developing machine learning models have become somewhat established. (This can be contracted out.) Building a reliable, flexible machine learning model that can streamline operations and support business planning takes a certain amount of patience and perseverance (and if a contractor is involved, those behaviors should be extended).

The Challenges of Working with AI

While enterprise AI applications offer significant benefits, they also come with more challenges than traditional software. These challenges are significant and may be expensive. Some of the more obvious challenges are:

• Staffing: As with most IT positions, it may be difficult to find staff with the appropriate training. Currently, there are relatively few people with enterprise AI training or experience. However, individuals with deep learning and/or machine learning experience can be used, with the understanding they will need some inhouse self-training and may need to take some courses.

• Maintenance: Unlike traditional enterprise software, the applications of enterprise AI need much more attention (and may require hiring another IT person). A machine learning model’s performance will decay over time (called model drift). Additionally (in a process called technical debt), an organization’s network may change and expand over time, while the limitations of the machine learning algorithm remain the same. Enterprise AI applications are dynamic and need ongoing maintenance and modifications. 

• Access to high-quality data: Enterprise AI and machine learning algorithms rely on accurate or high-quality data for their training. Data that is biased, outdated, unstructured, or contains errors can result in decisions that damage the company and cause poor customer experiences. Poor-quality data can negatively impact any deep learning or machine learning algorithm. (Integrating a Master Data Management platform and a Data Governance program prior to an enterprise AI platform could help significantly in providing high-quality data, and a good training process.)

• Data security: Although generating and processing significant amounts of data can supply greater business opportunities, it also increases storage and security weaknesses. The more data, and the more users with access, the greater the chances of a hacker accessing your systems. (On the plus side, AIs are getting better and better at detecting hackers.)

Attempting to slap an enterprise AI platform onto a data processing system that isn’t ready for it can be an incredible waste of time and money. Thorough and intelligent planning and preparation are key to preparing for this software platform. While businesses should not model their entire business strategy on enterprise AI alone, it can be a remarkably useful tool, if designed appropriately. 

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