How to Implement an AI solution for Enterprise Risk Management?

Here is how to implement an AI solution for enterprise risk management.

1. Define the scope of your AI-powered ERM solution development project

Onboard a competent PM with experience in AI development projects. You also need a capable IT architect and a team of knowledgeable business analysts (BAs). Lead this team through the following activities:

• Identify the areas in your ERM process that you want to transform.
• Choose the ERM process steps where you will implement an AI-powered solution.
• Determine the SDLC model for your project. Use Agile since experts find that it’s helpful for AI development. Want to learn more about Agile? Check out our guide “What is software development life cycle and what you plan for?”.
• Analyze your requirements and finalize them. For this, you need to have intense discussions with the relevant stakeholders.
• You also need to finalize your “Minimum Viable Product” (MVP) scope.

Wondering how to finalize the requirements for your ERM solution including your MVP? Our guide “5 tips to create a sleek MVP” can help.

2. Planning an AI solution for ERM

Plan your AI development project thoroughly. This requires you to do the following:

• Identify the areas in your ERM process that you want to transform. You need to onboard a competent PM with the experience of managing an AI development project. Onboard a competent IT architect and a knowledgable team of business analysts (BAs).
• Choose the ERM process steps where you will implement an AI-powered solution.
• You will need to “train” your AI solution with the help of a vast dataset. Identify this dataset.
• Identify the AI capabilities you need to implement, e.g., machine learning (ML), natural language processing (NLP), etc.
• Determine the SDLC model for your project. Use Agile since experts find that it’s helpful for AI development.
• Analyze your requirements and finalize them. You also need to finalize your “Minimum Viable Product” (MVP) scope.
• Plan your technical design, development, and testing phases.
• Finally, spend sufficient time to plan your deployment and maintenance activities.

Wondering how to plan an AI development project? Our guide “AI development life cycle: explained” can help.

3. Choose a cloud platform

You want to expedite your project, don’t you? You can do that if you use a cloud computing platform instead of managing IT infrastructure yourself.

What kind of cloud platform should you choose? Well, this depends on your project scope. If you are developing a web app with AI components, then I recommend that you use a “Platform-as-a-Service” (PaaS) platform like AWS Elastic Beanstalk.

Get a complimentary discovery call and a free ballpark estimate for your project

Get Started

Trusted by 100x of startups and companies like

PaaS platforms manage the cloud infrastructure, operating system, middleware, and runtime environment. This allows you to focus on development, moreover, PaaS platforms offer robust DevOps tools and auto-scaling solutions. I have explained their advantages in “10 top PaaS providers for 2020”.

On the other hand, are you also developing a mobile app with AI capabilities? Save time spent in building and managing the mobile backend so that you can focus on the front-end and the business logic.

I recommend that you use a “Mobile-Backend-as-a-Service” (MBaaS) platform like AWS Amplify. MBaaS platforms manage the cloud infrastructure and persistent storage, therefore, you don’t need to build and manage the mobile backend. Such platforms make it easy to integrate APIs, as I have explained in “How to choose the best Mobile Backend as a Service (MBaaS)?”.

4. Decide on the technology stack

Which technologies should you use? Thanks to the growing popularity of AI, you have quite a few options!

You could use one of the AI development platforms that offer key AI capabilities like ML and NLP on the cloud. A few examples are as follows:

• Microsoft Azure AI platform;
• Google Cloud AI platform;
• IBM Watson;

I have explained their advantages in our guide “The best Artificial Intelligence software development tools of 2020”.

On the other hand, you could choose to develop your AI software from scratch. You can cater to your customized requirements this way. Python is a popular language for AI development, and it has many libraries to help with AI/ML programming. If you want to learn more about this language, then check out our guide “Julia vs Python: Can this new programming language unseat the king?”.

If you are developing a web app, then I recommend you to use Node.js. It’s a highly popular open-source runtime environment, and you can create scalable and performant web apps with it. Read our guide “10 great tools for Node.Js software development” to learn more about its advantages.

Developing mobile apps too? I recommend that you develop native Android and iOS apps. For native Android development, consider using Java. This security-friendly language has been a mainstay for native Android development for a long time.

I recommend that you use Swift for native iOS development. This popular language enables you to code scalable and performant iOS apps, moreover, you can avoid common coding errors. Our guide “How to migrate your Objective-C project to Swift?” explains its advantages.

5. Find the right people

You need the right people to achieve success in a project like this, don’t you? Remember that AI development is a niche area, and you could find it hard to onboard competent people. Need help with this? We at DevTeam.Space can help, as I have explained in our guide “How to find a good software developer”.

While you will need to follow the right development and testing practices, does that suffice? It doesn’t! You need to implement a systematic code review process to find defects early in the cycle.

Finding experienced reviewers can be a challenge though. Once again, we at DevTeam.Space can help, and you can read about our capabilities in “Why choosing DevTeam.Space to review your code can ensure your software product is a success”.

Planning to Implement an AI Solution?

An AI development project to transform your initiate an artificial intelligence enterprise risk management feature can be challenging since AI is a niche area. It will surely be a high-visibility project. I recommend that you engage a reputed software development company for such a project. Check out our guide “How to find the best software development company?” to find one.

FAQS