7 Types of AI Integration & What You Should Know Before Integrating It In Your Business

07 Jun, 2024
Contributor - Guido, CTO, Hafiz, Developer, & Nadiy, Content Writer

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Key takeaways:

  • What are the 7 types of AI Integration?
  • 12 things you should consider before integrating AI in your business
  • How is AI integrated in Mobile App and Web App development at Lizard Global?

In recent years, we’ve seen the rise of AI integration in businesses and how impactful it has been. The incorporation of AI has aided in the improvement of efficiency and innovation. With the various types of implementations available, businesses are able to make data-driven decisions, automate tasks, and improve their customer experience by introducing intelligent chatbots. AI integration has proven to be a game-changer in many industries enabling businesses to stay ahead of the competition.

In our pervious blog What Is AI Integration in Mobile App and Web App Development & How Does It Benefit Businesses we shared with you the vast benefits that come with AI integration, also touching on the importance of businesses to integrate it. In this blog we’ll be covering the types of AI integration, and as a business what you need to consider before integrating it.

What Are The Types Of AI Integration

Since the term AI is thrown around so much in a general context with the most common being robots, the mass public still aren't aware of the various types of AI integrations that exist. Here we help you understand better the types of AI integrations that exist today. Of course there are too many for us to cover here, so we’ve narrowed it down to the 7 most popular types of AI integrations.

1. Assistive AI

This type of AI integration, as the name suggests, is designed to assist users with tasks. However, here AI does not take full control, it merely works as a support and enhancement tool for human actions. This type of integration spans to a wide range of applications, from something as complex as assisting individuals with disabilities to something as simple as improving workplace efficiency.

Simple examples of this type of AI integration are spell checkers, recommendation systems, and personal virtual assistants that suggest actions based on user behavior and preferences. A more complex example would be Babylon Health that is AI-powered to provide medical advice to patients based on the symptoms reported, and helping them decide when to seek medical attention.

2. Augmentative AI

Augmentative AI, also known as Augmented Intelligence, refers to AI systems designed to enhance human capabilities and not replace them. Working alongside humans, these systems help to improve decision-making, productivity, and overall performance across various domains.

Let's dive into the field of Business and Finance - there are AI tools that help provide analysts with insights into large datasets while helping them identify trends in a short amount of time. This enables analysts to make informed decisions in a timely manner, eliminating human errors, and allows professionals to focus their attention on bettering their strategic planning. For example, in order to analyze customer data, predict sales trends, and recommend actions to improve customer engagement, Salesforce Einstein uses AI.

3. Autonomous AI

Autonomous AI involves integrations of systems that are able to independently operate without humans. The AI is able to perform tasks and make decisions on its own, learning from its environment and adapting to any new situations. This is done through advanced algorithms and machine learning. This type of integration is most commonly known by the masses in the form of self-driving cars. However, it can be found across an array of industries from manufacturing robots to environmental data.

For example, Siemens’ Digital Factory integrated AI that manages and optimizes assembly lines. What this means is that the system (AI) can adapt to the different types of products that are being manufactured. From adjusting settings to automatic workflows that allows the factory to move back and forth between manufacturing various products quickly and efficiently.

4. Embedded AI

Embedded AI is when the AI technology is installed directly into the hardware or software of the device or application you’re using to perform tasks without needing to rely on external sources like cloud-based computing. A simple example of this are AI chips in smartphones or other devices that allow these devices to run at a faster speed with better processing capabilities even when offline.

Take the Apple Watch for example, embedded AI is used to enhance the health monitoring capabilities that makes it a great tool for personal health management. The Apple Watch has an ECG sensor that is able to monitor heart rate and rhythms, and it is able to detect falls through the advanced accelerometer and gyroscope sensors that detect any changes in motion and orientation.

5. Conversational AI

This is perhaps the most popular type of AI integration known by the masses. This is when AI is integrated in the form of chatbots, virtual assistants, or interactive voice response systems. Most people use these in the form of Siri, Alexa, Google Assistance, the three most famous virtual assistants in the world. Conversational AI is built to step in as a customer service agent handling customer queries, raising support tickets to human agents only when the issue gets a little more complex.

Another example of conversational AI is Facebook Messenger Bots that businesses use to interact with their customers to answer questions, book appointments, and even promote their products. Here Facebook uses natural language processing or NLP to train the AI to understand what the user inputs and what are the appropriate responses.

6. AI-Driven Automation

AI-driven automation uses AI to automate tasks, processes, and decision-making. It is used across all industries to help improve efficiency, accuracy and the scalability that helps reduce the need for human intervention allowing them to focus on more important areas, as well as reduce the human errors that could occur.

Here’s an example for you - BMW uses a computer vision system that is powered by AI to suss out and inspect the car parts for defects during the process of production. Here the AI is trained with a large number of high precision images (dataset) that show defects, which enables the AI to then scan the car parts and identify these flaws.

7. Predictive Analytics

This type of AI integration uses historical data to predict future outcomes. It is very useful and very much in use in today's world of retail. It is also used in other fields such as finance to carry out risk assessment, or health care to predict diagnostics or even to forecast energy consumption.

Let’s look at energy consumption here. Companies utilize this type of AI integration - predictive analytics - to forecast the demand of energy. Using machine learning models through the analysis of past consumption patterns, weather forecasts and even economic indications, companies are able to then predict the amount of energy that will be needed and how they will be able to optimize the supply for it.

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What Should You Consider Before Integrating AI In Your Business

It is important to keep in mind that AI is not some miracle worker that grows into what is needed of it. There is a lot that goes into building and integrating AI in your business like training or modeling. It is best to know exactly what you need to look out for before diving deep into AI integration to get your ducks in a row then take the necessary steps to implement it.

Here based on our previous experiences, we’ve compiled 12 things you should consider before integrating AI into your business:

1. Does AI integration align with my business objectives?

When looking to integrate AI into your business, it is crucial to clearly define the goal of the project. With that, comes the alignment of the integration and your business strategy. Always remember that AI is used to aid businesses in their processes, so, hone in on your focus areas for example improving efficiency, or improving customer service. Make sure the goal is crystal clear, and everyone involved is aligned with it.

2. Does your business have a good amount of quality data?

The one thing that AI systems need in order to function at its best is data, and not just any type of data but a vast amount of high-quality data. It is important to ensure that you have access to this type of data while making sure to adhere to data privacy laws. Implementing vigorous data governance practices by incorporating data cleaning, data validation, and carrying out regular updates.

3. Is your IT infrastructure capable of supporting AI integration?

At times companies have such big ideas but are limited in their IT capabilities. Make sure that your existing IT infrastructure is able to support the AI integration you’re working towards. Double and triple check your hardware & software capabilities along with your network stability. There will be times where an upgrade of either your systems or technologies are required. But an investment in these technologies and systems will benefit your business in the long run. Better to invest now, then to incur loss due to unsupportable systems and technologies later.

4. Does your company have the right experts to maintain the AI integration?

It’s not just about integrating AI and it miraculously runs smoothly forever. You need to ensure that you have experts at the ready whether in-house or outsourced to develop, deploy, and most importantly maintain these AI systems. The best way to go about doing thisin the event you do not have the sufficient experts is to invest in training and development programs with external AI consultants.

Reach out to our AI consultancy experts today and start your journey into the endless possibilities with AI integration.

5. Do you have an ethical framework for the AI integration?

There has always been a debate surrounding AI and ethics such as bias, transparency, and accountability. It can be detrimental to your company if you don’t have a fully thought out framework that addresses these concerns. Develop a comprehensive plan addressing these concerns and ensure that your AI system is fair, ethical, and transparent.

6. Does the AI integration comply with data regulations?

This does not need much explanation, every country has its own regulation when it comes to data (i.e. GDPR for Europe, and PDPA for Malaysia) and even AI. Make sure to understand the intricacies of these regulations and that your AI system adheres to them.

7. Is the company ready to tackle the integration complexity?

AI integration can be a complex undertaking when integrating it into your existing systems. It will involve a few technical challenges, compatibility issues, and potential disruption to the way your existing operations function. It is important to understand these complexities and test out your company's readiness in overcoming them.

8. Can this AI integration help with your businesses scalability?

The ultimate goal of AI integration for businesses is to scale up, as such when considering integrating AI into your business ensure that the AI system or the solution you’re looking to build is one that is scalable in line with your business needs not requiring a complete overhaul in current structures, systems, and technologies. It is there to improve and enhance the ones that are already being used. You can also scout and use cloud-based AI services if needed as you’ll be able to leverage off of the scalable infrastructure and resources that are available.

9. How will the users react to the AI integration?

More of than not, the biggest hurdle companies face when integrating AI is resistance from their users. As the visionary behind the push towards a digital solution you need to ensure you know how your users whether internal or external will react and interact to these new systems. Ensure there is ample training and support where needed to pave the way for a smooth transition and adoption.

10. How does the AI integration impact employees and business process?

Closely related to the above, with the integration of AI, there is potential for some employees to be affected whether in job roles, required skill, workflows or even retention. Consider all of these and ensure you have enough resources in place to aid a seamless transition.

11. Does the company have a comprehensive security risk protection setup?

AI integration is not all about setting up a new system and it's all good to go. There can be vulnerabilities and unique security threats that come with AI integration such as data poisoning and model theft. It is important that you are aware of the types of vulnerabilities that will be unique to the type of AI your business is integrating and to ensure that your company has solid security measures in place to protect both the system and data. While implementing a solid security plan, make sure to focus on improved encryption, running regular security audits, and better monitoring systems.

12. Does the company have sufficient funds for the AI integration?

It is no secret that AI is not cheap. So, make sure to do a proper evaluation of the costs associated with integrating and maintaining your AI system. This covers everything from the first investments, upgrades, management, training, and maintenance. The best approach for this is the agile way of working where you start off with a small test project to gauge the value and scalability before moving on to the more complex areas. Throughout this whole process make sure to continuously measure and optimize the return on investment (ROI)

Want to find out how much it costs to build your dream app or web app?

How Does Lizard Global Integrate AI into Mobile App and Web App Development?

At Lizard Global, we’ve had years of experience with AI implementation for our clients. We understand the importance and impact that AI can have on a business with AI improving efficiencies, customer experience, decision-making and many many more. We’ve worked on implementing facial recognition for identity validation, object recognition for counting and qualifying harvest, pattern recognition for recommendations on mortgage credibility, chatbots for customer support, ChatGPT integration, and machine learning integration to name a few.

Just to give you an idea of how we integrate AI, we spoke to one of our in-house full stack developers Hafiz to share his experience on how ChatGPT integration is done.

"Just like how you use ChatGPT to ask a question, this project is the same, the only difference is that instead of using the ChatGPT interface, we build a new interface that integrates ChatGPT at the backend. So, our client’s mobile app or web app becomes the face that users interact with and ChatGPT is the representative at the backend providing the answers to the queries. For the client, each query asked comes with a charge.

For example, WAPA (War Additional Premium Application) a web app that provides real-time tracking technologies, that proactively tracks vessels, voyages, and HRA’s (High Risk Areas) to estimate the exposure to theft or damages. Let’s say the user wants to summarize their journey from start to their destination and back, it’ll hold a vast amount of data to sift through. So, instead of the user manually deciphering the gist of their voyage, they can just input it in the system with the prompt “What is the summary that can be made from route A to route B”. With that the user will get an almost instant summary of their voyage generated in human language based on the prompt."

We understand that sometimes the topic of AI can get a little bit complex and lost in translation. Our skilled experts are more than happy to guide you through this process or even if you’re just testing the water. Reach out to us to get your free consultation with us today and start your digital transformation journey with the best partners in the business.

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Frequently asked questions



How to choose the right AI tools and platforms for integration?

When choosing the right AI tools and platforms makes sure to:

  1. Assess your business needs by identifying what problem you want to solve using AI
  2. Evaluate the AI capabilities by comparing features, performances, and the scalability of the different AI solutions that are best suited to your needs. 3. Consider the ease of integration in your business by ensuring that the AI tool that you’re looking to integrate is able to seamlessly integrate with your existing systems.
  3. Research the reputation and support given by the vendor by choosing vendors who have a good reputation and/or reviews, and look for one that provides comprehensive support as well as documentation.
  4. Always assess the budget/cost and ROI by analyzing how much you’ll spend for the implementations versus how much you are looking to potentially make from it.


How important is data in AI integration, and how should it be managed?

In AI integration, data is an absolute must-have as it is used as the fuel that trains and forms these systems

The effective ways to manage data is through:

  • Data collection: where relevant and high quality data is gathered from various reliable sources
  • Data preprocessing: where the data is clean, transformed, and normalized to ensure it is suitable for the AI algorithms
  • Data storage: where to store data the storage is secure and scalable
  • Data governance: where by the policies that ensure data security and privacy are properly implemented and complied


How can AI integration impact cybersecurity?

AI can enhance cybersecurity by:

  • Threat detection: Identifying and responding to cyber threats in real-time through pattern recognition and anomaly detection.
  • Fraud prevention: Analyzing transactions and user behaviors to detect fraudulent activities.
  • Automating responses: AI can automatically mitigate certain types of attacks, reducing response times.
  • Predictive analytics: Forecasting potential security breaches and vulnerabilities to take preventive measures.


How can businesses measure the success of AI integration?

Businesses can measure the success of AI integration by tracking key performance indicators (KPIs) such as:

  • Efficiency improvements: Reduction in time and cost for specific tasks.
  • Accuracy and performance: Improvement in the accuracy of predictions and decision-making.
  • Customer satisfaction: Enhanced customer experiences and feedback.
  • Return on investment (ROI): Financial gains compared to the cost of AI implementation.
  • Operational scalability: Ability to handle increased workloads and data volumes.


Are integrative ai and ai integration the same?

While these two are related concepts they are not the same.

AI integration is where artificial intelligence technologies are incorporated into systems, workflows, and business processes that already exist, while Integrative AI is the development and use of AI systems usually combined though multiple AI techniques and technologies for a more comprehensive solution.

For example, AI integration adds the predictive analytics tool to their management system for the purpose of inventory. Integrative AI developed through multiple AI techniques build a healthcare diagnostic tool that is multi-faceted.


How can AI be integrated with non AI technologies?

Integrating AI with non-AI technologies involves using artificial intelligence capabilities within existing systems, processes, and technologies that may not inherently incorporate AI. Here are some examples:

  • Application Programming Interfaces (APIs)
  • Middleware and Integration Platforms
  • Custom Development and Integration
  • Embedding AI Models
  • AI-enabled Hardware
  • Cloud Services and Platforms
  • AI-powered Automation
  • Training and Upskilling
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Hey there, can I help you?

Did you like the blog above, but do you still have some questions about the subject or related topics? No issue! You can easily contact one of our Lizard specialists on these specific topics, and they gladly tell you more about it. This way, you’ll never leave with uncertainties.


Global Commercial Director | markus@lizard.global | +60 18 35 65 702

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