How to Implement Artificial Intelligence and Machine Learning in an Existing App [2021]

Businesses are coming up with intelligent applications, integrated with AI and ML enabled features. Hence, it is becoming tough for existing apps to survive in their respective industries. 

Are you among those old market players, who are facing reduced user engagement or retention due to the upgraded rivals, armed with Artificial Intelligence and Machine Learning deployments?

To stay ahead in this era of cutting-edge competition, you should now focus on improving your existing application. Following are the reasons to implement AI and ML in an app:

• Startups having AI and ML implemented are getting funding easily nowadays. So, expect increased competition.
• AI-ML improves apps’ capabilities and improves user experience. 
• Using Artificial Intelligence or ML in sales and marketing operations helps in improving other statistics significantly.

Implementing AI and ML in an App

Here’s how you should update your application and integrate AI/ML enabled efficiencies in it –

1. Understand What AI Can Do

AI and its subset Machine Learning are very potent technologies. Capable of doing a lot, this technology can take your existing solution to the next step. However, it is important for you to understand what is possible through it. 

A few things you can do to understand the efficacy of AI and ML are –

• Take help of AI consultants, online resources and digital information to figure it out.
• Check the existing tools and technologies to improve your knowledge of Artificial Intelligence and Machine Learning.
• Go through the case studies from your industries, in order to understand how they’ve implemented intelligence algorithms successfully in their products.

2. Mark Areas Where AI and ML Can Improve the App

Once you’ve gained quick know-how on Artificial Intelligence and Machine learning, it becomes easy for you to introspect and identify the challenges AIML can resolve for you. 

Explore the existing application and make a list of capabilities, which can be added or improved by utilizing AI. 

To validate your ideas, you can perform a quick market analysis and check if similar implementations worked or not. You can also separately analyze the need for Artificial Intelligence, Machine Learning, Pattern Recognition, Image Processing, etc. 

Overall, in this step, you should focus on problem identification and implementation scope.

3. Estimate the Incurring and Prioritize the Additions

Planning upgrades without considering the budget will be short-sightedness. So, first of all, decide how much you want to incur of the AI-ML integration. It will be better if you get things done one by one. Or, if you’ve enough financial backup, you can go for integrating all changes at once.

As you have already identified the main additions & improvements in your app and assessed your financial capabilities, now you should prioritize what needs to be done first. 

In short - Prepare a staged plan for AIML integration at this step.

4. Feasibility and Practical Changes to Make

Till now, you must have a proper plan in mind about what needs to be done and how will your app work/look like once these changes are made. Therefore, it is the time to perform a few checks before moving forward, such as –

• Perform a quick feasibility test to understand if your future implementation is going to benefit your business, improve user experience and increase engagement. A successful upgrade is the one which could make existing users happy and attract more people towards your products. If an update is, in no way, increasing your efficiency, there is no point in putting in money for it.
• Analyze if your existing team (if have AI-ML experts) is able to deliver what is required. If you don’t have enough internal capability, do not hesitate in hiring new resources or outsourcing the work to reliable & capable resources.

5. Involve AI-ML Experts and Strategize

Having discovered your technological stance and final requirements by now, you can move to involve experts in AI and ML development finally. 

Choosing the resources, who are going to carry out the development and upgradation process, is very important. If you don't choose the right specialists, it will become difficult to accomplish what’s expected. Hence, make the selections wisely.

Your team should have consultants and development/design experts. Involve all your resources in strategizing the project so that your plan remains effective, practical and achievable.

User behavior analysis, expectations, need of personalization, etc. should not be overlooked while preparing the strategy of functional additions in the application.

6. Data Integration and Security

If implementing Machine Learning, your application will need a better data organization model. Old data, which is organized differently, may affect the efficiency of your ML deployment.

So, once the teams have planned what capabilities and features will be added in the app, do focus on databases. Well-organized data and careful integration will help in keeping your app performance-oriented and high-quality in the long-term.

Security is another critical issue, which cannot be ignored. To keep your application robust and intrusion-proof, come up with the right plan to integrate security implications, adhering to standards and need of your product.

7. The implementation Step

As most of the planning and pre-deployment assessment must have been done at your end yet, development and deployment won’t be a big task. Though, your teams will have to carefully deploy and test the implementations before making the changes live.

One important suggestion for you here is - Do consider putting a strong analytics system in place while adding AIML capabilities in your application. It will help you analyze the impact of this new integration and get amusing insights for future decision-making.

8. Use Robust Supporting Technological Aids

Choose the right technologies and digital solutions to back your application. Your data storage aids, security tools, backup software, optimization solutions, etc. need to be robust and future-proof, in order to keep your application consistent. Without this, drastic decline in performance may take place.

Conclusion

For personalized customer experience and providing advanced services, it is important for all the new and existing applications to utilize cutting-edge technologies, such as Artificial Intelligence and Machine Learning. At the same time, integrating AI and ML in your existing solution needs a lot of planning for its success.

So, always go by a pre-planned process and put qualified resources at work. Making your existing application intelligent, this upgrade will definitely multiply your revenue by manifold, improving the customer experience for your end-users.