The Benefits of Combining AI with System Integration
System Integration ties loose ends
System Integration refers to having processes and systems interlinked seamlessly for:
- Straightforward ease of use
- Improved user experience
- Higher data and insight reliability
- Faster processes
- Enhanced consistency
When making individual components interoperable you pave the way towards business sustainability and long-term market relevance. There are many System Integration flavors out there: SaaS, ERP, API, cloud, and so on. No matter what System Integration you want to build or invest in, you need to understand the business cases where it can benefit you most.
Here are some of the most common ones, but the sky is the limit when making systems blend with each other.
Systems need extension
Let’s say you have several native systems which are correctly linked, but you purchase another system which does not provide out-of-the-box integration with your native capabilities. You would like to have your databases communicate with one other.
You are dissatisfied with the fact that you need to switch contexts, applications, and that while doing this ping-pong journey, you lack consistency and make data-related mistakes.
Invest in Integration Architecture:
- Get your native systems and your external system to communicate and relay data properly via an API, for example.
- Ensure that no matter which system provides the input data, there is a hub which centralizes it, to avoid duplication and facilitate sharing.
- Reconcile distributed data storage through the use of an integration architecture that has direct access to data or functional access via other systems.
Systems need compatibility
Let’s say that you have been working with several data formats or file types for some time. You would like to reuse the data that you already have.
You soon realize that migrating, transferring or reusing the data you have requires some fine tuning and development. You most certainly want to minimize human intervention and offline flows (download/upload docs cycles). You also feel that this is a lesson learnt for other future prospects, so you want this project to be a spring board for other contexts where some system needs to pick up the data from an existing system.
Invest in Integration Procedures and Protocols:
- Transfer data intelligently by setting up file transfer protocols or procedure calls (especially remote ones).
- Automate data transfer by ingesting a common database structure for different applications.
- Interlink processes by defining functional interfaces and interaction protocols.
Systems need consistency
Let’s say that your company has grown as a result of a merger and that the solutions in the unified suite are similar or complementary.
Since the solutions were developed by different teams, the concepts they work with are different. Before you can truly merge two or more solutions into one, before you can maximize the cumulative insight the solutions can offer, you must solve all the underlying semantic issues.
Invest in Integration Methodology:
- Ensure that you set up an equivalence matrix for the different concepts across systems.
- Automate the reconciliation process.
- Make sure your Architecture and Procedures/Protocols are aligned.
AI enhances System Integration
The examples above showed that System Integration revolves around some recurrent themes:
Artificial Intelligence can help you approach all these themes proficiently by ensuring you perform System Integration faster, accurately, and with less effort.
AI has many flavors, but the one which has the most relevance in this context is Machine Learning. It all boils down to this: AI and Machine Learning prevent the mismanagement of resources and processes.
Here are some ways in which AI and ML can help your System Integration initiatives.
Software Development Assistance
System Integration will require, in most cases, some development work.
- Detect inconsistencies with an API
- Automatically evaluate an API and perform the integration work
- Secure code and spot harmful API traffic behavior
- Combine algorithms
- Minimize DevOps work, especially when compliance is a MUST
Business operations can deliver excellence if properly integrated, but a great deal of expertise is required when thinking about the integration architecture behind them (serverless applications, microservices, for example).
AI can provide code completion suggestions and generate code based on higher-level input from users. The process of creating integrations is thus simplified.
Automation is important when it comes to reducing the effort, inconsistencies and inefficiency of repetitive tasks. Automation requires the perfect-pitched orchestration of processes, RPA, and analytics.
AI can be embedded, to learn and replicate the orchestration of all layers and components, in both the current project and future projects.
If your solutions are very similar, AI-driven automation as a means to System Integration can become a no-brainer.
Cloud API Integration
APIs are a focal point of any System Integration.
AI can be integrated via APIs. How? AI models developed by data experts can be difficult to come by. Yet, with the advent of making AI more democratic, it is expected that more and more AI models will be shared via cloud APIs.
Investing in data protocols and data interchange for one project in one thing. Replicating the experience to future projects is another.
AI can streamline data handling from separate data sources, from several native formats, and unify them in an integration data base.
Let’s say your system collects large amounts of data (from your customers, for example) and you want to analyze the data to learn what it is about.
AI can be integrated as a plug-in offering a cognitive model which facilitates understanding.
Conversational Interfaces - Chatbots
Chatbots are systems acting like conversational interfaces between your business and your users (end users or staff). Chatbots can be extremely simple or extremely complex depending on:
- The input/output type (voice, written)
- The number of DBs the answer is extracted from
- The accuracy with which intent is interpreted
To implement a chatbot, System Integration and AI are both necessary.
System Integration ensures that the architecture is built with the purpose of customer personalization (for B2B and B2C), that the endpoints are securely wired (for Retail payment security), that the input data is correctly processed and classified based on it type and meaning (all industries), that the answer is tailored for specific needs (onboarding vs. maintenance).
AI (and ML in particular) ensures that the chatbot learns to integrate and understand the questions that are not listed in its model programmatically.