March 9, 2023

How To Develop An AI Solution For Your Enterprise?

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The world around us is changing at a rapid pace with major technological advancements. The old models of business and generalized products & services are no longer effective.

The time calls for advanced solutions that will help the business get an edge over its competitors. What can businesses do to boost productivity and revenue? 

One of the ways businesses can enhance their processes and offer better services is through enterprise AI solutions.

Enterprise AI solutions help businesses increase their productivity by automating the repetitive process, reducing human error and increasing their operational efficiency. That’s not all! AI solutions also help businesses in making informed decisions with accurate data analysis. 

Further, enterprise AI solutions also assist businesses in improving their customer experience. They do so by offering personalized services and products. Moreover, enterprise AI software solutions also help in reducing operational and maintenance costs through predictive analysis.

Overall enterprise AI solutions help in boosting the efficiency, productivity and profitability of businesses. 

Did you know that 67% of top executives believe that AI can help to improve their operations? 

Further, 74% of decision makers believe that AI in the long term will contribute to their business success.

Do you also want to join the AI revolution in business?

Looking to build an AI solution for your enterprise? But has no idea how to get started with AI?

Read this blog to find out how to implement and build enterprise AI solutions for your business.

Let’s get started!

What Is An Enterprise AI Solution?

An enterprise AI solution is an AI based software solution that aims to resolve business inefficiencies. You can build an enterprise AI application using machine learning, deep learning, natural language processing and other AI techniques. 

An enterprise AI solution streamlines business processes by automating repetitive tasks and reducing human errors. By automating workflows, businesses can cut down costs, enhance performance and boost productivity. 

Further, AI solutions help businesses take informed decisions. It does so by analysing data to provide accurate insights and prediction reports. Predictive analysis helps businesses plan their actions to scale business growth and value as per the market trends.

You can also build customized AI solutions to enhance your customer experience. An enterprise AI application can process large chunks of data to explore customer trends, market sentiment, product demands and feedback. These data are crucial for businesses to formulate their product and marketing strategies. 

You can implement enterprise AI solutions across major industries like finance, manufacturing, healthcare, retail and more. 

Benefits Of Building An Enterprise AI Solution

The benefits of building an enterprise AI solution for your business are as follows:

Increased efficiency: 

  • AI can help you automate your routine task. This will free up the time of your employees so that they can focus on high value work. 
  • Further, automated workflow increases the efficiency of the processes by reducing human error. 
  • Moreover, AI solutions can process large amounts of data quickly and accurately. Thus, it will help you make better business decisions with its analysis reports.

Improved customer experience: 

  • You can offer personalized customer service with smart AI bots. 
  • AI solutions also help you in curating custom product recommendations and services as per the preferences of your customers.

Cost Savings:

  • By automating tasks, AI solutions reduce labour costs.

Enhanced Decision Making:

  • It is not possible for you or your team to analyze crucial data accurately and quickly.
  • The predictive analysis reports and other data reports assist you in planning business strategies for success.

Competitive advantage: 

  • Implementing AI solutions will give you a competitive edge by improving efficiency, reducing costs, and improving the customer experience.


  • AI-powered solutions can scale quickly and easily, allowing you to expand your business operations without increasing their workforce.

Predictive capabilities: 

  • AI can use data to predict future trends, enabling you to proactively respond to market changes and customer needs.

How To Implement An AI Enterprise Solution?

Did you know that 87% of AI projects never make it to production? 

Most AI projects fail to move forward through the ideation phase. Some of the projects which make it to the development phase don’t come out as expected. While the rest of them fail to give the expected output after their implementation.

Here are 6 ways you can successfully implement enterprise AI solutions in your business.

Establish Clear Project Goals

The first thing you need to do is to identify and define your business needs. You need to find out the problems your business is facing and analyze how AI can resolve those. 

Next, you should have a clear set of goals for your project. Your project goals must align with your business goals and market trends.

Ask yourself these questions to identify your business challenge and a suitable AI solution for it.

  • What is the main problem in your existing business model?
  • How AI can benefit your business?
  • Can AI solutions resolve the current business challenges you are facing?
  • What outcomes you are expecting with an AI solution?
  • What are the main hurdles in achieving these outcomes?
  • How will you measure your business success with AI?

These questions will help you understand your business needs better.

Develop Structured Use Cases

Developing use cases will ensure that your AI solution development is applicable and achievable. Moreover, it will also ensure that your project ideation caters to your core problem.

Here is how you can build a strong AI use case for your business.

  • Clear project objectives & viable AI application ideation
  • Define KPIs to measure success
  • Assign a case owner to handle building, testing, and validating the use case
  • Identify necessary data for AI to achieve objectives
  • Identify edge cases or unique use cases within data
  • Evaluate the ethical and legal implications of AI solution
  • Assess existing technology and capabilities to support solution development
  • Plan for potential roadblocks

Evaluate Your Internal Capabilities

There is a huge difference between what you wish to build and what is truly achievable. You should always go for the best possible solution deliverable in a given time frame.

Therefore, after outlining your business needs and project goals, you must decide which approach works best for you.

You can build an enterprise AI solution from scratch using your internal resources, or collaborate with an enterprise AI solution development company. Moreover, you can also outsource the entire enterprise AI application development to a professional AI development company.

Data Collection

Collecting data to train your AI model is a very important aspect of the project. The core feature of Artificial Intelligence is to learn from the available dataset and mimic human-like thinking and actions.

Here is how you can prepare data for your AI application.

  • Identify essential data for your solution
  • Determine the availability of data and its source
  • Examine, analyze and create data summaries
  • Sourcing the data
  • Integrate the data
  • Cleanse the data
  • Prepare the data for your AI model to use

Hire The Right Enterprise AI Solution Development Company

Developing an AI solution isn’t similar to developing a simple software solution. You must hire a competent AI development company with exceptional expertise in AI technologies. 

Here is what you should look for in an AI development company.

  • Experience in building innovative AI solutions for enterprises
  • Certified team proficient in handling sophisticated AI technologies
  • Cost effective services
  • Offering custom AI application development solutions
  • Specialized in AI, machine learning, deep learning and language processing

Define AI Learning Model

Have you ever wondered how machines learn? Well, at the heart of artificial intelligence lies machine learning, which is a crucial process that determines how your model uses the provided data. 

And guess what? You get to decide if human intervention is required at the beginning or throughout the entire learning process of the model.

Let’s dive a little deeper into the two types of training models that can be used: supervised and unsupervised learning. With supervised learning, you get to train the machine using a sample of labelled class data to teach it the difference between right and wrong. Once the machine has reviewed thousands, if not millions, of data samples, the goal is for the model to start identifying patterns on its own.

On the other hand, unsupervised learning involves machine learning independently by trying to identify patterns based solely on the provided data. In this case, the machine is not told which data is helpful or not helpful, correct or incorrect. It’s up to the machine to figure it out on its own.

Now, according to IBM, “deep” machine learning can leverage labelled datasets (labelled by human intervention), also known as supervised learning, to inform its algorithm. But some deep machine learning algorithms don’t require labelled datasets and can work with unstructured data.

Planning Human Implementation

Let’s talk about the role of human involvement in AI development. It’s no secret that determining when and where humans should come in to support your AI model is critical to its success.

At some point during the development process, you may need the expertise of human intelligence experts. The extent of their involvement may vary depending on the nature of your solution, but one thing is for sure: it’s important to identify these points early on to stay ahead of staffing requirements and meet your project deadlines.

By understanding when human intelligence plays a role in your AI development, you can ensure that your model functions as intended and delivers the results you desire. So, don’t overlook the importance of human intelligence when it comes to AI development!

How To Build An Enterprise AI Solution?

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You can build an enterprise AI solution by defining the business needs, assessing the data, using the right technologies, training, monitoring and evaluating the AI solution.

Let us now discuss each step in detail

Step 1 – Define Business Needs

As discussed earlier, the first and foremost thing you need to do is to define your business needs and challenges. This step is crucial for the ideation of your AI solution and for defining project goals.


Step 2 – Assess The Data

Processing and assessing your data is crucial. The AI solution is entirely based on the quality of data you fed into the system. Based on the data, the AI solution will train itself and give the output.

  • The quality of data should be relevant to your business problem. It should be free of errors and discrepancies.  
  • The data should be relevant to the problem being solved. Moreover, it should be structured as per the AI algorithm. If the data is unstructured, the AI will not produce desired results.
  • Preprocessing data means refining data so that it is free of errors, missing values, irrelevant facts and inconsistencies. It is crucial to transform data into a format which is convenient for the training of AI models.

Step 3 – Choose The Right AI Technologies

After assessing the data you need to choose the right AI technologies to build your AI solution.

You must select the right AI algorithms and technologies to build a robust and efficient AI application.

  • There are various AI algorithms and technologies that can be used to solve specific business problems, such as supervised learning, unsupervised learning, reinforcement learning, and deep learning. 
  • To choose the most suitable technology for a problem, a thorough understanding of the problem and the available data is required. 
  • Factors like the size and complexity of the data, the type of problem, and the desired outcome must be considered during the selection process. 
  • It is necessary to evaluate the strengths and weaknesses of different AI algorithms and technologies to make an informed decision.

Step 4 – Build Data Pipeline

Once you have selected the AI technologies and algorithms, you need to start working on your data pipeline. The Data pipeline ensures the smooth movement of data from its source to the AI model. 

  • While designing the data pipe, you should pay attention to scalability, security and efficiency.
  • The data pipeline should meet the requirements of the AI model to solve the business problem.
  • The data implementation process includes data sources, storage and processing.
  • You should always carry out data ingestion that involves processing, cleaning, transforming and normalizing the data.
  • Lastly, you should always choose a highly secure environment to store your data.

Step 5 – Train AI Model

In this step, your proposed AI model is trained using the processed data. Training in the AI model is necessary to accurately solve business problems and provide valuable insights.

  • The data from the pipeline train the AI model for the algorithm to produce results or predictions as required.
  • The developers also adjust the algorithms and parameters of the model to make it more efficient and productive.
  • You also need to evaluate the performance of the AI model to see if it’s as per your requirement or not. It is important to analyse the accuracy and reliability of the AI model. Moreover, it’s vital to analyze if the AI model is solving your business problem or not.
  • Based on the evaluation of the AI model, developers then move forward with the AI application development.
  • Further, the developers adjust the parameters, collect more data, and improve the algorithm to boost the productivity of the AI application.

Step 6 – Deploy AI Solution

At this step, developers integrate the AI solution into your existing business model. 

  • Developers integrate or connect the AI solution with the enterprise APIs and database for the exchange of information. Integration is a key step in aligning the enterprise’s existing system with AI solutions.
  • While deploying the AI solution you must focus on its scalability, security and reliability.
  • The scalability of AI solutions refers to their capability to handle a large amount of data.
  • Security of AI solutions means fortifying the AI application with institutional grade security protocols.
  • Reliability of AI solutions refers to the potential of AI applications to produce relevant output and prove their worth in solving business problems.

Step 7 – Monitor, Evaluate & Improve

To keep your business operations, and work efficiently, it’s necessary to consistently upgrade it.

  • You should consistently monitor the performance of the AI solution. It should deliver an effective solution to your business problems with accuracy, speed and reliability.
  • Performance monitoring will help you identify areas where the AI application lacks. Based on your evaluation, you can then suggest data, algorithm, and model changes to your enterprise AI solution.
  • You should also evaluate the impact of the AI solution on your business. Analyse what benefits an AI solution is offering to your business. Is it reducing the cost? Is it successful in solving your business problems? Is It helping you achieve your targets?
  • Based on your analysis of the AI solution performance and impact, you can recommend changes to the development team.


To build an enterprise AI solution, you must have access to good quality data, large data sets, a data pipeline, and the ability to consistently train models for optimal performance. 

You can build AI solutions for enterprises by defining the business problem, gathering and evaluating data, selecting appropriate AI technologies, constructing a data pipeline, training models, deploying the solution and monitoring and evaluating performance.

As an entrepreneur, you can utilize the power of AI to boost your business productivity, efficiency and profitability. 

Blocktech Brew holds expertise and experience in developing enterprise AI applications. Our enterprise AI development services are powered by elite AI technologies such as deep learning, machine learning, computer vision, and natural language processing. 

Contact Blocktech Brew today to discuss your requirements and transform your ideas into reality!


What is an AI?

AI (Artificial Intelligence) is an advanced technology that uses machine learning and deep learning techniques to process large data to mimic human like thinking and action capabilities. It is used to automate repetitive tasks and help businesses make informed decisions through Big Data analysis and predictive analysis.

What is enterprise AI solution development?

An enterprise AI solution development refers to the development of smart AI software solutions and applications that assists businesses in boosting their efficiency, productivity and profitability.

Which is the best AI development company?

Blocktech Brew is the best blockchain development company which excels in enterprise AI solution development and AI application development services. We have the best set of developers proficient in handling machine learning, deep learning, language processing and other AI techniques.

How does enterprise AI solution help businesses?

Enterprise AI solutions help businesses by streamlining and automating workflows. Therefore, it reduces operational costs and human error while promoting efficiency. Most importantly, AI solutions assist companies with crucial and accurate insights that help them make informed decisions.

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