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How To Build AI Business Management Software

Create Artificial Intelligence Business Management Software

Interested in how to build AI business management software? 

This is an excellent market with many opportunities to be explored.

With great financial rewards as well as the chance to innovate in this industry, companies are rushing to create innovative new AI buisness management software solutions. Here’re a few amazing case studies of companies who hired DevTeam.Space to build their management products:

  1. Micro Loans Management Solution – Management Web Application
  2. eTournament Management Application – eGaming Management Web Application
  3. MyTime – Scheduling And Payment Web Application!


Artificial Intelligence: What is it?
Why should you explore AI?
The market for AI in business management
How to build an AI business management software
Planning to build an AI business management software?

Artificial Intelligence: What is it?

AI or Artificial Intelligence is an interdisciplinary branch of computer science, and you can think of it as a continuum. AI involves “training” computer systems from “experience”, which translates to a vast amount of data.

Computer systems powered by AI can perform tasks that typically require human intervention. The technology uses algorithms to “train” the computer systems, and over time the said system improves its performance.

AI is partially commercialized, e.g.: Reactive AI, which deals with the immediate tasks without a concept of the past. There is also the “Limited Memory AI”, which enables computers to go back to the immediate past. However, research and development continue on other types of AI, which are “Theory of Mind AI”, and “Self-Aware AI”. Read “A giant leap for humankind: Theory of Mind AI” to learn more about this.

Why should you explore AI?

Consider this: AI augmentation will generate business value worth $2.9 trillion in 2021! You can read this Gartner press release for more details about this projection.

Analyzing a few use cases will help you to understand why AI matters. Consider the following use cases:

  • Personal digital assistants: Siri, Google Assistant, and Alexa have already made a mark as digital assistants, as I have explained in “10 best AI apps of 2019”.
  • Medical chatbots: Medical chatbots can help patients since they offer valuable information early, even before patients visit a doctor‘s office. Read more about this in “Benefits of building medical chatbots for your healthcare business”.
  • Digital identity: AI can help with digital identity management since businesses can use it to detect forgery attempts and verify the true identity of users. AI-powered identity verification systems are faster, as I have explained in “AI solutions to digital identity”.
  • Classifying books in publishing businesses: Large publishing houses need to classify their large volume of books effectively, and Machine Learning (ML), a key AI capability can help. Read “How to build a machine learning filing system to classify books” to learn more about this use case.
  • Facial recognition software: Governments and security agencies need to keep the borders of their countries safe, and AI-powered facial recognition software can significantly help. Our guide “How to build facial recognition Software” explains this use case in detail.
  • ML in software development: As software development projects become more complex, developers and testers will benefit from computers carrying out more tasks with the help of intelligent automation. ML can bring plenty of benefits in this field, as I have earlier explained in “Machine learning in future software development”.

The above isn‘t an exhaustive list, and you can find more use cases in “The top 10 AI and machine learning use cases everyone should know about”.

The market for AI in business management

Artificial Intelligence has a growing market. The global market for AI was valued at $20.67 billion in 2018, however, it‘s expected to reach $202.57 billion by 2026, according to this Globe Newswire report.

Gartner expects that AI will shape the future of manufacturing, retail, and many more sectors. It also states that by 2020, 30% of businesses will use AI to augment their sales processes. You can read more about this projection in “Is artificial intelligence shaping the future of ERP software?”.

How to build an AI business management software

create ai software

I will now explain the steps involved in building an AI business management software, and these are as follows:

1. Induct your core team for project planning

As the first step, you need to hire a competent project manager (PM), an IT architect, and a team of business analysts. They need to have experience with developing AI solutions, and this team will need to discuss with the business stakeholders to gather the requirements. They will need to define the project scope.

2. Factor in the AI development lifecycle in your project planning

AI development is a full-blown software development project, however, it has a few unique flavors. Your project planning exercise should factor in the AI development lifecycle. The phases of this lifecycle are as follows:

  • Define why you would use AI, i.e., identify the business functions that you could positively impact with AI.
  • Select the business processes and sub-processes that you could meaningfully automate using AI.
  • Take adequate care to select data sets for “training” your proposed AI system.
  • Identify the AI capabilities you need.
  • Choose the right SDLC model for the project.
  • Analyze the requirements.
  • Design the AI solution.
  • Develop your proposed AI system.
  • Test the AI system.
  • Deploy the AI app and maintain it.

Read more about the AI development lifecycle in “AI development life cycle: explained”.

3. Ascertain the AI capabilities you need

The business requirements will determine which AI capabilities you need. Various AI capabilities are as follows:

  • Machine Learning (ML): This includes deep learning, supervised learning, and unsupervised learning.
  • Natural Language Processing (NLP): Content extraction, classification, machine translation, question answering, and text generation fall within the ambit of this capability.
  • Vision: This AI capability includes image recognition and machine vision.
  • Speech: Speech-to-text and text-to-speech are parts of this capability.
  • Other key AI capabilities are expert systems, planning, and robotics.

You can read more about these AI capabilities in “Artificial Intelligence: definition, types, examples, technologies”.

4. Prepare a cost estimate for the AI development project

As part of your project planning exercise, you will need to prepare a cost estimate for the project. You need to factor in the following cost elements while estimating:

  • Manpower cost;
  • Managed cloud services cost, assuming you won‘t manage IT infrastructure and you will sign-up with a managed cloud services provider;
  • The cost for API solutions corresponding to different AI capabilities;
  • The cost for other tools;
  • Other administrative costs.

I have earlier explained how to estimate the cost of an AI development project, and you can read it in “How much does it cost to develop an AI solution for your company?”.

5. Form the rest of the project team

You now need to form the rest of the project team, which requires you to induct the following roles:

  • UI designers:
  • AI developers covering various AI capabilities like ML, NLP, vision, speech, etc.;
  • Web developers with Node.js skills;
  • Testers;
  • DevOps engineers.

If your project scope includes developing mobile apps, then you would also need to induct Android and iOS developers. You should look for Android developers with Java skills and iOS developers with experience in Objective-C.

AI development projects can be complex and they typically have high visibility. I recommend that you induct a field expert development team for such projects, as I have explained in “Freelance app development team vs. field expert software development teams”.

6. Procure the right managed cloud services

Using managed cloud services platforms eliminates the need for you to manage IT infrastructure, therefore, I recommend that you the advantage of these. Amazon Web Services (AWS) has robust cloud capabilities, and I suggest you sign-up with it.

AWS offers various cloud services and you can choose what you need. E.g., if you only need Infrastructure-as-a-Service (IaaS), then you can use Amazon Elastic Compute Cloud (EC2).

On the other hand, if you want a Platform-as-a-Service (PaaS) for developing a web app, then AWS Elastic Beanstalk is a great option. If offers plenty of advantages, e.g.:

  • Elastic Beanstalk manages cloud infrastructure, networking, storage, operating system, middleware, and runtime environment. This enables you to focus on development.
  • You can use the application performance management (APM) and auto-scaling solutions offered by AWS to scale your web app.
  • Elastic Beanstalk makes it easy to integrate databases and 3rd party APIs.
  • Developers can use robust AWS DevOps tools.

I have earlier explained these advantages in “10 top PaaS providers”.

If your project scope includes developing mobile apps, then you will find AWS Amplify quite helpful. Amplify is the Mobile-Backend-as-a-Service (MBaaS) offering from AWS, and it offers the following advantages:

  • Amplify manages cloud infrastructure, persistent storage, etc. You don‘t need to build and manage the mobile backend, therefore, you can focus on the front-end.
  • A mobile app needs security features, moreover, it should have user management and push notification features. You will find it easy to implement these when using Amplify.
  • Programmers can easily integrate 3rd party APIs when using Amplify.
  • Scaling a mobile app is easy if you use AWS Amplify.

You can read more about these advantages in “Where to Host Mobile App Backend?” and “How to choose the best Mobile Backend as a Service (MBaaS)?”.

7. Choose the right AI/ML API solutions

There are several market-leading AI/ML API solutions covering various AI capabilities, and your choice of a solution depends on your requirements. I will describe a few API solutions so that you can make the right choice. Here we go:

Amazon Rekognition

If your proposed AI solution needs image and video analysis capabilities, then Amazon Rekognition could be a good choice. This API solution enables you to identify objects, people, text, scenes, and activities.

E.g., if you are building a facial recognition software, then Amazon Rekognition is worth considering. It offers accurate facial analysis and recognition capabilities, and this API solution is highly scalable. It‘s a fully managed API solution, moreover, Amazon continuously “trains” it with new data.

Amazon Comprehend

Are you building an AI application to discover insights and relationships in texts? You could consider Amazon Comprehend, a robust NLP solution from Amazon.

Amazon Comprehend offers many features, e.g.:

  • Keyphrase extraction;
  • Sentiment analysis;
  • Syntax analysis;
  • Entity recognition;
  • Relationship extraction;
  • Custom entities;
  • Language detection;
  • Custom classification;
  • Topic modeling;
  • Multiple language support.

Amazon Comprehend APIs and SDKs are easy to use, and you can access its extensive documentation in “Amazon Comprehend developer resources”.

Microsoft Azure AI Platform

Microsoft Azure AI Platform utilizes the impressive cloud and AI capabilities of Microsoft. It‘s a comprehensive AI development platform, which offers the following key AI capabilities:

  • Machine Learning (ML);
  • Vision, including object recognition;
  • Speech recognition and other speech capabilities;
  • Machine translation and other language capabilities;
  • Knowledge mining.

The Azure AI Platform offers many benefits, thanks to its many tools and services. E.g., its ML capabilities include services like Azure ML, Azure Databricks, etc. Similarly, the knowledge mining capabilities in the Azure AI Platform includes advanced services like Azure Search, Form Recogniser, etc. The Azure AI Platform features extensive documentation and you can find it on its website.

Google Cloud AI Platform

Google Cloud AI Platform is yet another example of robust cloud capabilities working hand-in-hand with considerable AI capabilities. This AI platform offers all the key AI capabilities, e.g.:

  • Machine learning (ML);
  • Deep learning;
  • Natural Language Processing (NLP);
  • Speech;

For each of the above capabilities, Google offers impressive tools and services. E.g., Google Cloud AI Platform includes Kubeflow, an open-source platform from Google to create portable ML pipelines. You can access the comprehensive documentation for Google Cloud AI Platform in “AI Platform documentation”.

Note: This list of AI APIs/SDKs isn‘t exhaustive, and you can find more examples in “The best Artificial Intelligence software development tools of 2019”.

8. Code, test, and deploy your app using other relevant tools and guidelines

Depending on your scope, you might need to use the following tools and guidelines:


Planning to build an AI business management software?

While this guide, platforms, APIs, and other tools can expedite your project, building an AI business management software can be complex. AI involves several niche skills, and I recommend that you take help from a reputed software development company. Our guide “How to find the best software development company?” can help you to find a reliable development partner.

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