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How to build an enterprise AI solution?

How to build an enterprise AI solution
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In an ever-changing digital landscape, businesses need a competitive edge to stay ahead of the curve. They must automate their processes and operations to improve decision-making and increase efficiency, productivity, and profitability. Enterprise AI, a sub-domain of enterprise software helps businesses achieve that.

Enterprise AI solutions are rapidly transforming how enterprises function. With the ability to process large volumes of data and automate routine tasks, AI-based enterprise-level solutions are helping businesses enhance operational efficiency, reduce costs, improve decision-making processes and do much more. For instance, AI-powered chatbots and other customer service tools improve the customer experience, while predictive maintenance systems reduce downtime and maintenance costs. The insights generated by AI ensure better decision-making and help enterprises gain a competitive advantage.

This article discusses what an enterprise AI solution is, the four major advancements that set the stage for enterprise AI solutions, and some potential benefits of building an enterprise AI solution. Finally, we will discuss eight detailed steps to build an enterprise AI solution.

  1. What is an enterprise AI application?
  2. Major advancements that laid the road for enterprise AI applications
  3. The enterprise AI architecture: What does it entail?
  4. The potential benefits of building an enterprise AI solution
  5. How to build an enterprise AI solution?

What is an enterprise AI solution?

An enterprise AI solution is an AI-based technology that is designed and implemented to solve specific business challenges or streamline business processes within an enterprise or organization. It involves the application of machine learning, natural language processing, computer vision, and other AI techniques to develop intelligent systems that can automate tasks, analyze data, and provide insights.

Enterprise AI solutions can be customized to meet the unique needs of different organizations, and they can be used in various industries, including healthcare, finance, manufacturing, retail, and more. They can be used to improve customer experience, increase operational efficiency, reduce costs, and help organizations make data-driven decisions. Now, let’s also discuss what an enterprise AI application is.

An enterprise AI application is a software application that leverages artificial intelligence (AI) technologies to improve business processes and decision-making within an enterprise setting. Some common examples of enterprise AI applications include customer service chatbots that understand and respond to customer inquiries in real-time, fraud detection systems that analyze transaction data and identify potential fraudulent activity, predictive maintenance systems, and supply chain optimization tools that optimize inventory levels and reduce transportation costs.

Major advancements that laid the road for enterprise AI solutions

The significance of enterprise AI cannot be overemphasized. As stated earlier, businesses can improve operational efficiency, automate routine tasks, and provide better customer experiences using enterprise AI solutions. Thus, it is easy to infer that businesses that embrace it will be best positioned to succeed in the future. But what were the driving forces behind this breakthrough? Taking a deep dive into the critical technological advancements that underpin enterprise AI development will give a well-rounded perspective on the technology’s capabilities and potential impact.

The first of these advancements is the emergence of machine learning as a subfield of AI. ML enables machines to learn from data to perform certain tasks and functions without being explicitly programmed to do so. Machine learning systems don’t need a list of set rules to make decisions but instead learn patterns from past training data. ML systems are highly diverse and adapt quickly to changing conditions, business requirements, and circumstances as their underlying training data evolve. ML systems have proven to outperform rules-based software across a variety of business use cases, such as in medical diagnostics, operational reliability, customer churn detection, and demand forecasting.

The second major advancement for enterprise AI solutions is the availability of vast amounts of digital data across enterprises instead of paper-based data. The success of Enterprise AI will remain dictated by the quantity, quality, and scope of data to which firms have access. Several AI solutions rely on supervised learning, which requires accurately labeled data. In cases where we use unsupervised learning for anomaly detection, the more data we have, the more accurate our results can be. Since AI and ML systems learn from historical data, the performance of these systems increases dramatically with the availability of higher volumes and a more diverse data set. With this rapid increase in available data volumes and the dramatic expansion in the variety of data sources, AI and machine learning systems are set to succeed across enterprise-grade use cases.

The third advancement has been the widespread adoption of IoT sensors across all major industries, from energy, infrastructure, manufacturing, and telecommunications to logistics, retail and healthcare. Owing to the vast number of sensors deployed across value chains, organizations can now have real-time visibility and insights across operations, supply chains, and customer service. While it may be difficult to monitor and act on this vast amount of real-time data manually or with rules-based software, enterprise AI solutions can make this feasible for us. This feature can unlock significant benefits for organizations across use cases such as predictive maintenance, quality control, operational safety, logistics management and fraud monitoring.

The next major advancement has been the emergence of the elastic cloud. AI and ML systems imbibe and improve their decision making through training. Training an ML model is finding an optimal set of model weights and parameters that best represent the relationship between the inputs and outputs observed in the training data. While the performance of the model improves significantly with an increase in the size of the training dataset and the number of training iterations, demand for storage resources needed for training can become material. Since a single enterprise AI solution may include thousands of ML models, each requiring consistent re-training, the need for storage resources can grow rapidly. The availability of elastic, cloud-based, and distributed computing and storage systems at a minimal cost addresses the model training challenge and is a major enabler for enterprise AI solutions. The elastic cloud provides managed services for running AI applications, eliminating organizations’ need to manage the underlying infrastructure and freeing up IT resources for other tasks. Additionally, it integrates with the Elastic Stack, which provides a comprehensive set of data analysis and visualization tools, making it easier for organizations to gain valuable insights from their AI applications and make data-driven decisions.

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The enterprise AI architecture: What does it entail?

The design, structure and solutions offered by AI systems together make the enterprise AI architecture. The architecture includes components such as data storage, data processing, AI algorithms, model training, and deployment. Additionally, the architecture requires integrating AI with existing enterprise systems and processes. Enterprise AI architecture creates a robust and scalable mechanism that can support the needs of organizations, including data privacy, security, and governance. Additionally, the architecture should also be adaptable enough to accommodate new AI technologies and use cases as they emerge in the market- this can be achieved if the AI architecture employs a hybrid approach that combines cloud and on-premise solutions, as well as open-source and proprietary components.

The potential benefits of building an enterprise AI solution

Building an enterprise AI solution can have a multitude of benefits for organizations, from improving efficiency and productivity to providing a competitive edge. In today’s business landscape, where data is king, leveraging AI can help organizations harness the power of data to make informed decisions, optimize processes, and enhance the customer experience. The use of AI can also help organizations quickly respond to changes in the market and identify new opportunities. Here are some of the most important benefits of enterprise AI solutions for your business:

Increased efficiency and productivity

From automating repetitive and manual tasks to streamlining processes, AI solutions can free up time and resources for more strategic activities. By working with data and machine learning algorithms, AI systems can process vast amounts of data faster and more accurately, improving overall efficiency.

Improved decision-making

AI systems can provide real-time insights, predictions, and recommendations based on data analysis. AI can also detect patterns and identify opportunities, helping organizations make more informed and strategic decisions.

Competitive advantage in the market

AI-powered systems can provide organizations with new revenue streams, cost savings, and an improved customer experience, giving them a competitive edge in the market over organizations that don’t adopt AI technology. Now, let us look at the steps to build an effective enterprise AI solution.

How to build an enterprise AI solution?

How to build an enterprise AI solution

Step 1: Defining the business problem

The first step in building an enterprise AI solution is to identify the business problem that the AI solution will go on to solve.

  • Identifying the business problem to be solved with AI: From customer experience, operations, costs, to revenue- AI can truly offer a solution to the challenges faced by any of these departments. The goal of this step is to identify the business problem that the AI solution will solve and align it with the organization’s goals and objectives.
  • Aligning the AI solution with your business goals and objectives: This step requires a sound understanding of what the organization wants to achieve and how AI can support these goals. Aligning the AI solution with the organization’s goals and objectives can ensure the solution is topical, impact-driven, and in sync with the overall business strategy.
  • Defining the problem statement: This step can be used interchangeably with the first one. Defining the business problem helps organizations to understand the business challenges they face, categorize them, and determine how AI can help to solve them.

Step 2: Assessing the data

Gathering and assessing the data is a critical step in building an effective enterprise AI solution. The quality, quantity, relevance, structure, and the process of cleaning and preprocessing the data are key considerations.

  • Quality and quantity of data available: The data should be varied, relevant to the business problem being solved, and free of errors or discrepancies. If the data is not of the desired quality or quantity, the AI solution may not provide accurate results.
  • Relevance and structure of the data: The data must be relevant to the business problem being solved and structured appropriately for the chosen AI algorithms. If the data is unstructured, preprocessing may be required to convert it, so it can be used to train AI models.
  • Cleaning and preprocessing the data: This stage involves removing inconsistencies, handling missing values, and transforming the data into a format that can be used to train the AI models. Scaling or normalizing the data, encoding categorical variables, and splitting the data into training and testing sets may be undertaken.

Step 3: Choosing the right AI technologies

A full array of AI algorithms and technologies are available, and selecting the appropriate ones for a particular business problem is essential.

  • Types of AI algorithms and technologies: Some types of AI algorithms and technologies include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Each type is suited to solving a specific business problem.
  • Selecting the appropriate technologies for the business problem: In order to select the most appropriate technology for a problem, there must be a thorough understanding of the problem and the data available to solve it. The selection process must consider factors like the size and complexity of the data, the type of problem being solved, and the desired outcome from the troubleshooting. An introspective evaluation of the strengths and weaknesses of different AI algorithms and technologies is necessary to make an informed decision.

Step 4: Building the data pipeline

A series of processes that make the movement of data from its source to the AI models possible, the data pipeline, plays a crucial role in the success of the AI solution.

  • Designing and implementing a data pipeline: The design and implementation of the data pipeline involve a series of decisions around data sources, storage options, and the processing steps required. Scalability, security, and efficiency are key when it comes to designing the pipeline. The pipeline must meet the requirements of the AI models and the business problem being solved.
  • Ingesting, processing, and storing the data: The data ingestion process involves extracting data from databases or other data sources, while the data processing steps may involve cleaning, transforming, and normalizing the data. Finally, the data storage process ensures that the data is protected and easily accessible for training the AI models.

Step 5: Training the AI models

The goal of this step is to create and train the AI models that can accurately solve business problems and provide valuable insights.

  • Training the models using the data pipeline and selected algorithms: The data from the pipeline trains the models, and the algorithms generate predictions. The training process is an iterative process that involves adjusting the parameters of the models to optimize their performance.
  • Evaluating the performance of the models: The evaluation of the models includes comparing the predictions generated by the models to the actual outcomes and determining the accuracy and reliability of the models. This information is used for further development of the models.
  • Making improvements and refinements as needed: Based on how the models perform, improvements and refinements may be required. This could be improving the accuracy or reliability of the model or even just adjusting the parameters of the models, collecting more data, or selecting different algorithms.

Step 6: Deploying the AI solution

Some call this the final step in the development of an enterprise AI solution. The goal here is to integrate the AI solution into the existing enterprise systems and processes. This collaboration ensures that the solution operates smoothly and provides value to businesses.

  • Integrating the AI solution with existing enterprise systems and processes: This step involves connecting the AI solution to databases, APIs, or other enterprise systems to exchange data and information. The integration process is key for the organization’s existing systems and processes, which get the opportunity to align with the AI solutions.
  • Ensuring scalability, security, and reliability: Scalability refers to the ability of the AI solution to handle large amounts of data and processing demands. Security refers to the protocols in place to protect sensitive data. Reliability refers to the ability of the AI solution to perform consistently and accurately and to provide valuable insights.

Step 7: Monitoring and evaluating

An ongoing process in the implementation of an enterprise AI solution, monitoring and evaluation involves continuous monitoring of the performance of the AI solution, evaluating its impact on the business, and making improvements and refinements as needed.

  • Performance monitoring of the AI solution: The AI solution must deliver efficiently and effectively. In order to ensure this, certain measures, such as tracking key metrics like accuracy, speed, and reliability, can be taken. Performance monitoring can help identify potential concerns with the AI solution, such as data quality problems or algorithmic inefficiencies, and make improvements as needed.
  • Evaluating the impact on the business: This is essentially the process of determining the value that the AI solution is providing to the organization. The process may involve measuring the impact on business outcomes, such as increased efficiency, reduced costs, or improved customer satisfaction.
  • Making improvements and refinements as needed: Based on the results of performance monitoring and impact evaluation, the AI solution may need improvements and refinements to ensure that it continues to provide value to businesses. Sometimes these refinements can look like making changes to the data pipeline, updating algorithms, or improving the integration with existing enterprise systems and processes.

Step 8: Plan for continuous improvement

The goal of this plan is to ensure that the AI solution remains dynamic and continues to evolve over time to meet the changing needs of businesses.

  • Staying current and relevant with new technologies and use cases: It is important for organizations to stay abreast with new technologies and use cases being developed in the field of AI. Attending conferences and workshops, conducting research, or engaging with experts in the field are all important ways to do so.
  • Making Continuous Improvements to the AI SolutionThis step involves making updates and refinements to the data pipeline, algorithms, and existing enterprise systems and processes. The AI solution must continue to better over time and meet the diverse needs of businesses.


Building an enterprise AI solution can seem like a challenging process that needs careful planning and execution. Some factors that form the backbone of a robust enterprise AI solution include having good quality data, having large data sets, having a data pipeline, and the possibility of consistent training of the models, so they perform optimally at all times. By carefully defining the business problem to be solved with AI, gathering and assessing the data, choosing the right AI technologies, building a data pipeline, training the models, deploying the solution, monitoring and evaluating performance, and fostering a data-driven culture, organizations can benefit from the power of AI to improve their operations, drive business growth, and stay ahead on the curve.

LeewayHertz boasts extensive expertise in creating enterprise AI applications leveraging AI technologies like deep learning, machine learning, computer vision and natural language. Contact LeewayHertz today to discuss your requirements and bring your vision to life!  

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Author’s Bio


Akash Takyar

Akash Takyar
CEO LeewayHertz
Akash Takyar is the founder and CEO at LeewayHertz. The experience of building over 100+ platforms for startups and enterprises allows Akash to rapidly architect and design solutions that are scalable and beautiful.
Akash's ability to build enterprise-grade technology solutions has attracted over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s.
Akash is an early adopter of new technology, a passionate technology enthusiast, and an investor in AI and IoT startups.

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