- Top 3 AI Integration Strategies
- Top Reasons for Integrating ML and AI Into Your Software Applications
- Advanced Search
- Predicting User Behavior
- More Relevant Ads
- Improved Security Level
- User Engagement
- Data Mining
- Fraud Detection
- Object and Facial Recognition
- How Much Does it Cost to Integrate AI?
- Data Availability
- Development Time
- Platform and Technologies
- Upkeep and Support
- Development Team
- Best Platforms to Develop AI Implementation Solutions
- 1. Azure
- 2. IBM Watson
- 3. Tensorflow
- 4. Api.ai
- 5. Wit.ai
- 6. Amazon AI
- 7. Clarifai
- How to Initiate the AI Implementation Process into Applications?
- Identify the Issue to Solve Through AI
- Know Your Data
- Understand that APIs are Not Enough
- Set Metrics that Would Help Gauge AI’s Effectiveness
- Employ Data Scientists
- Data Integration and Security
- Use Strong Supporting Technological Aids
- 4 Common AI Integration Challenges and Solutions to Overcome Those
- Data Availability and Quality
- Legal Considerations
- Integration with Legacy Systems
- Why Choose Appinventiv for Your AI Integration Needs
As technology rapidly advances, it’s no surprise that user expectations are also rising. Today, users demand more than just basic application functionality. They expect intelligence and innovation. This is where AI integration comes in.
Whether it is about optimizing business processes or personalizing customer experiences, the strategic implementation of AI into existing workflow propels businesses to leap toward the future of intelligent automation.
AI integration empowers businesses to extract actionable insights, automate repetitive tasks, and help in the decision-making process. Consider the integration of AI into existing systems if you want to ensure scalability and enhanced efficiency in your current business workflows. Before that, let’s have a look at the adoption rate of AI in product development from 2022- 2025.
The adoption rate of AI in product development has increased in recent years. With AI ML integration into software application development frameworks, developers can leverage AI capabilities to provide intelligent features, automate tasks, and enhance user experiences.
AI-driven functionalities such as voice assistants, personalized recommendations, and predictive analytics are becoming increasingly common in mobile applications and software. The availability of AI development tools and frameworks, coupled with the demand for innovative and intelligent app experiences, has contributed to a higher rate of integration of AI in applications across various industry verticals. This has driven the evolution of smarter and more sophisticated applications.
Top 3 AI Integration Strategies
Integration of AI in software and mobile apps can make them more efficient, sound, and intelligent. But how can you do it? Here are three simple ways to add AI and ML to your app:
AI and ML are two proficient technologies that imbibe the power of reasoning for solving problems. Apps like Uber and Google Maps use AI to provide the best possible route for their users. This feature allows AI to outperform humans in tasks like chess and helps Uber optimize routes to get users to their destinations faster. With real-time decision-making capabilities, AI is the key to providing top-notch customer service.
OTT platforms like Netflix and Amazon Prime are the best AI integration examples whose streaming features attracted many customers with high user trust and retention rates.
Both Netflix and Amazon Prime have implemented AI and ML into their mobile and desktop applications that examine the customer’s decision based on age, gender, location, and preferences. Based on the customer’s choices, the technology suggests the most popular alternatives in their watch playlist or that individuals with similar tastes have watched.
Giving users insight into what they would require next has turned out to be the secret to the success of some of the top brands in the world – Amazon, Flipkart, and Netflix. This is an amazingly popular technology for streaming services and is currently being executed in numerous other applications.
Learning how the user behaves in the app can help artificial intelligence set a new border in the world of security. And this is where the implementation of AI in business comes in. Whenever someone tries to take your data and attempt to impersonate any online transaction without your knowledge, the AI system can track the uncommon behavior and stop the transaction there and then.
These three AI integration best practices enable your app to offer a better customer experience.
And now that we have looked into the top 3 ways of AI business integration, let us answer why you should go for AI-enabled application development.
Top Reasons for Integrating ML and AI Into Your Software Applications
The implementation of AI in applications can unlock powerful capabilities that will enhance user satisfaction and drive business growth. Personalized experiences, intelligent decision-making, and efficient recommendations are just a few benefits ML, and AI can bring to your application. Let’s check out the reasons in detail:
Any AI algorithm attached to your mobile or software application can analyze various sources of information, from social media activities to credit ratings, and provide recommendations. Machine learning application development can be used to learn:
- Who are your customers?
- What do they like?
- What can they afford?
- What words they’re using to talk about different products?
Based on this information, you can classify your customer behaviors and use that classification for target marketing. Simply put, AI-based app development will allow you to provide your potential customers with more relevant and enticing content. This AI system integration will give your users the impression that your mobile app technologies with AI are customized especially for them.
Let’s look at a few AI implementation examples of brands setting standards by going for an AI implementation plan, starting with Appinventiv’s success story on VYRB.
- VYRB is an innovative social media application designed for Bluetooth wearables that allow users to create and distribute audio files through voice assistants like Siri and Google Assistant.
- Taco Bell is a fast food restaurant chain that went for conversational AI integration to ensure a hassle-free ordering process for customers. TacoBot takes orders, answers questions, and recommends menu items based on your preferences.
- Uber uses ML to give its users an estimated arrival time and cost.
- ImprompDo is a time management app that employs ML to find a suitable time for you to complete your tasks and prioritize your to-do list.
- Migraine Buddy is a great healthcare app that adopts ML to forecast the possibility of a headache and recommends ways to prevent it.
- Optimize Fitness is a sports app incorporating an available sensor and genetic data to customize a highly individualized workout program.
With AI integration solutions, the search results are more intuitive and contextual for its users. The algorithms analyze different customer queries and prioritize the results based on those queries.
In fact, not only search algorithms, modern mobile and desktop applications allow you to gather all the user data, including search histories and typical actions. This data can be used with behavioral data and search requests to rank your products and services and show the best functional outcomes.
Upgrades, such as voice search or gestural search, can be incorporated for a better-performing application.
Predicting User Behavior
One of the biggest benefits of AI integration for marketers is that they understand users’ preferences and behavior patterns. This is done by inspecting different kinds of data concerning age, gender, location, search histories, app usage frequency, etc. This data is the key to improving the effectiveness of your application and marketing efforts.
Amazon’s suggestion mechanism and Netflix’s recommendation work on the same principle that ML aids in creating customized recommendations for each individual.
And not only Amazon and Netflix but mobile apps such as Youbox, JJ food service, and Qloo Entertainment adopt ML to predict user preferences and build the user profile according to that.
More Relevant Ads
Many industry experts have argued that the only way to move forward in this never-ending consumer market can be achieved by personalizing every experience for every customer.
The experts of AI integration consulting companies can create AI apps that easily consolidate data intelligently. This, in turn, saves time and money that went into inappropriate advertising and improves the brand reputation of any company.
Improved Security Level
Besides making a very effective marketing tool, AI data integration can streamline and secure authentication. Features such as image recognition or audio recognition make it possible for users to set up their biometric data as a security authentication step in their mobile or desktop devices. Machine learning also helps in establishing access rights for users as well.
Apps such as Zoom Login and BioID have invested in AI app development solutions to allow users to use their fingerprints and Face IDs to set up security locks on various websites and apps. In fact, BioID even offers periocular eye recognition for partially visible faces.
The AI implementation solutions help businesses offer balanced customer support and features. Also, not just for entertainment purposes, AI chatbot assistants help users and hold a discussion at any hour. With high-end, intuitive AI chatbot app development services, you can create user-centric applications that drive greater engagement.
Also Read: How AI Will Improve Customer Experience in 2023?
At Appinventiv, our experts developed a budget management chatbot application called Mudra with AI capabilities that solves the personal budgeting issues of millennials. The results? The app is being launched in 12+ countries.
Data mining, also known as data discovery, includes analyzing a vast set of data to gather helpful information and collect it in different areas, including data warehouses and others. ML offers data algorithms that will generally improve automatically through experience based on information. It follows the way of learning new algorithms that make it quite simple to find associations inside the data sets and gather the data effortlessly.
Fraud cases are a worry for every industry, particularly banking and finance. To solve this problem, ML utilizes data analysis to limit loan defaults, fraud checks, credit card fraud, and more.
It also assists you with determining an individual’s capability to take care of a loan and the danger related to giving the loan.
Object and Facial Recognition
With the implementation of AI in software applications, it is possible to ensure robust security through facial recognition technology.
Facial recognition is the most loved and latest feature for mobile apps. Facial recognition can help improve the security of your application while additionally making it faster to log in. It also helps in securing the data from unknown sources.
Now that we have looked at the different areas in which AI and ML can be incorporated into software applications, let us discuss the cost of AI implementation.
How Much Does it Cost to Integrate AI?
The cost of AI integration might vary significantly based on the complexity, features, platform, required resources, and development time. An average AI personal assistant software can cost between $40,000 and $100,000.
However, incorporating sophisticated AI technologies with several features and functionalities might significantly raise this anticipated cost, reaching $500,000 or more. The cost of developing an AI app is influenced by several factors:
The higher the complexity of the required AI features and algorithms, the more expensive the AI app development process will be.
The cost depends on the quantity and complexity of features, such as computer vision or natural language processing.
With data collecting, cleaning, and labeling procedures, the quantity and quality of training data might impact the cost.
The cost of developing, testing, and fine-tuning AI models and algorithms increases as development time and effort increase.
Platform and Technologies
The cost of AI implementation services also depends on the choice of platforms and technologies, type of cloud services, or AI frameworks that might impact development costs.
Upkeep and Support
The cost estimation process also includes the expense of maintaining, updating, and supporting the AI app.
The cost may be affected by the development team or AI integration agency’s location, knowledge, and experience.
To receive an exact AI application development cost estimation of your project, it’s crucial to consider these factors and consult with our experts. Let’s now have a look at the platforms to develop AI apps.
Best Platforms to Develop AI Implementation Solutions
There are a plethora of leading platforms that provide the best tools and resources to build robust AI implementation solutions. Here is the list of the top platforms widely utilized by various industries.
Azure is a Microsoft cloud solution. Azure has a large support community, high-quality multilingual documents, and many accessible tutorials. The programming languages of this platform are Ruby and Python. Because of an advanced analytical mechanism, AI app developers can create mobile applications with accurate forecasting capabilities.
2. IBM Watson
The main characteristic of using IBM Watson is that it allows the developers to process user requests comprehensively regardless of the format. Any kind of data. Including voice notes, images, or printed formats are analyzed quickly with the help of multiple approaches. This search method is not provided by any other platform than IBM Watson. Other platforms involve complex logical chains of ANN for search properties. The multitasking in IBM Watson places an upper hand in most cases since it determines the minimum risk factor.
Google’s open-source library, Tensorflow, allows AI application development companies to create multiple solutions depending upon deep machine learning, which is necessary to solve nonlinear problems. Tensorflow applications work by using the communication experience with users in their environment and gradually finding correct answers as per the requests by users. Although, this open library is not the best choice for beginners.
Created by the Google development team, this platform can be successfully used to develop AI-based virtual assistants for Android and iOS. The two fundamental concepts that Api.ai depends on are – Entities and Roles.
Entities are the central objects, and Roles are accompanying things that determine the central object’s activity. Furthermore, the creators of Api.ai have created a highly powerful database that strengthened their algorithms.
Api.ai and Wit.ai have largely similar platforms. Another prominent characteristic of Wit.ai is that it converts speech files into printed texts. Wit.ai also enables a “history” feature that can analyze context-sensitive data and, therefore, generate highly accurate answers to user requests, and this is especially the case of chatbots for commercial websites. This platform is good for creating Windows, iOS, or Android mobile applications with machine learning.
6. Amazon AI
The famous AI-based platform is used to identify human speech and visual objects with the help of deep machine learning processes. The solution is completely adapted for the purpose of cloud deployment and thus allows you to develop low-complexity AI-powered apps.
The solution based on AI analyzes information with the help of complicated and capacitive algorithms. The applications made using the platform (which can be integrated in-app using REST API) can adapt to individual user experiences, making it the most preferred choice for developers who wish to invest in AI integration services to enter the world of intelligent assistants.
With this, you just learned about the top platforms that streamline your AI implementation process. The next and last, and most important part that we will discuss now is how to get started.
How to Initiate the AI Implementation Process into Applications?
Implementing AI & ML in an application calls for a monumental shift in the operation of an application that works sans intelligence. Here are the things that you will have to keep in consideration when managing an AI project:
Identify the Issue to Solve Through AI
What works in the case of applying AI in applications, as we saw in the first illustration of the blog, is applying the technology in one process instead of multiple. When the technology is applied in a single feature of the application, it is much easier to manage and exploit to the best extent.
So, identify which part of your application would benefit from intelligence – is it a recommendation? Would the technology help in giving a better ETA? – And then collect data specifically from that field.
Know Your Data
Before you look forward to AI app development, it is important to first get an understanding of where the data will come from. At the stage of data fetching and refinement, it would help to identify the platforms where the information would come from in the first place. Next, you will have to look at the refinement of the data – ensuring that the data you plan to feed in your AI module is clean, non-duplicated, and truly informative.
Understand that APIs are Not Enough
The next big thing in implementing AI in app development is understanding that the more extensively you use it, the more disintegrating the Application Programming Interfaces (APIs) will prove to be.
While the APIs mentioned above are enough to convert your app into an AI application, they are not enough to support a heavy-featured, full-fledged AI solution. The point is the more you want a model to be intelligent, the more you will have to work towards data modeling – something that APIs solely cannot solve.
Set Metrics that Would Help Gauge AI’s Effectiveness
There is hardly a point in implementing an AI or ML feature in your software application until you have the mechanism to measure its effectiveness. So, before you head out forward to build an AI app, it is important for you to understand what metrics you would like it to achieve.
Employ Data Scientists
The last and most important point to consider is employing data scientists on your payroll or investing in a mobile app development agency with data scientists in their team. Data scientists will help you with all your data refining and management needs, basically, everything that is needed on a must-have level to stand and excel in your artificial intelligence game.
Data Integration and Security
While implementing machine learning, your application will require a better information configuration model. Old data, which is composed differently, may influence the effectiveness of your ML deployment.
When it is decided what abilities and features will be added to the application, it is important to focus on data sets. Efficient and well-organized data and careful integration will help provide your app with high-quality performance in the long run.
Security is another basic issue that can’t be overlooked. To keep your application strong and secure, you need to think of the correct arrangement to integrate security implications, clinging to standards and the needs of your product.
Use Strong Supporting Technological Aids
You must pick the right technology and generative AI solutions to back your application. Your data storing space, security tools, backup software, optimizing services, and so on should be strong and secure to keep your app consistent. Without this, a drastic decrease in performance may occur.
4 Common AI Integration Challenges and Solutions to Overcome Those
Putting AI integration into practice can provide a number of challenges. Following are some typical issues with AI integration, along with examples:
It might be difficult to scale AI technologies to manage vast amounts of data and rising consumer demands. For instance, during the busiest shopping times, an e-commerce platform can find it difficult to handle an increase in client data.
By employing parallel processing, distributed computing, and cloud infrastructure, it is possible to enhance performance and handle higher workloads. Optimizing algorithms and leveraging hardware accelerators can also help you achieve the scalability goal.
Data Availability and Quality
AI business integration might be hampered by the lack of good-quality data. For instance, missing or inconsistent medical records in the healthcare industry may impact the precision and dependability of AI models developed using that data.
Investing in data cleaning and preprocessing techniques, as well as data quality checks, is essential to ensure the reliability and availability of data. By implementing these methods, you can improve the accuracy of your data and reduce the risk of errors.
AI integration presents questions about privacy, security, and legal compliance from an ethical and legal standpoint. For instance, AI algorithms used for credit scoring must adhere to fairness and transparency requirements to prevent biased results.
To handle ethical and legal issues, implement strong data protection and security measures, and abide by regulatory compliance, such as GDPR or HIPAA.
Integration with Legacy Systems
Due to compatibility difficulties or antiquated infrastructure, integrating AI with current legacy systems might be difficult. Including AI-driven chatbots in a customer care system that uses antiquated software and protocols is one example.
Legacy systems can seamlessly communicate with AI components with well-defined APIs, enabling data exchange and functionality. This approach streamlines operations and allows AI technology integration with legacy systems.
Why Choose Appinventiv for Your AI Integration Needs
By understanding the transformative potential of AI in education and knowing the reasons for implementing AI on mobile and desktop applications, it’s time to take it to the next level. The future of application development lies in the combination of AI and ML, and it is high time for you to be at the forefront of this advancement.
Appinventiv, a reputed artificial intelligence services company, has a team of highly skilled AI implementation consultants who deeply understand the intricacies of AI and machine learning. Our AI implementation strategy allows for the seamless integration of these cutting-edge technologies into your app, resulting in exceptional results.
By choosing Appinventiv for AI integration, you gain a reliable partner dedicated to transforming your app and empowering your business to stay ahead in the rapidly evolving digital landscape. Get in touch with our experts today!
Q. How are AI integration and implementation transforming industries today?
A. Here are some of the ways how AI integration in business is transforming industries today:
- Healthcare: AI in healthcare improves healthcare results through better patient monitoring, tailored treatment, and diagnostics.
- Manufacturing: AI boosts operational efficiency by optimizing production processes, predictive maintenance, and quality control.
- Agriculture: Precision farming, crop monitoring, and yield prediction are made possible by AI, which optimizes agricultural operations and boosts productivity.
- Energy: AI enables smart grid management for effective energy distribution, optimizing energy usage, and forecasting equipment breakdowns.
- Finance: AI improves risk analysis, algorithmic trading, and fraud detection, making financial processes safer and more effective.
- Transport: AI makes autonomous cars, route optimization, and predictive maintenance, enhancing the security and effectiveness of transportation networks.
- Retail: Chatbots for customer service, inventory management, and personalized suggestions are all powered by AI, improving customer experiences.
[Also Read: AI in Retail – How Artificial Intelligence is Improving the Retail Shopping Experiences]
Q. What are the benefits of integrating AI into existing systems?
A. Here are some of the top benefits of integrating AI into the existing systems:
- Cost savings
- Enhanced efficiency
- Improved decision-making
- Increased accuracy
- Competitive advantage
Q. What must you consider before hiring an AI application development company?
A. Here are the following things before hiring an AI application development company:
- Expertise: Assess the company’s experience and knowledge in developing mobile and desktop applications, AI technologies, and other relevant fields.
- Portfolio: Examine their prior work to confirm that AI has been successfully integrated into mobile apps.
- Skills: Confirm the technical proficiency of their development team, including knowledge of machine learning, data analysis, and AI algorithms.
- Data Security: Verify that the business takes strong data security and privacy procedures to safeguard user information.
- Communication: To ensure efficient project management and transparency, assess their methods for collaboration and communication.
- Read testimonials and reviews: Read customer testimonials or reviews to get a sense of their reputation and level of client satisfaction.
- Budget: Talk about and make sure the project budget meets your expectations.