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AI needs data to provide insights, and the augmented data must be transferred from the AI system to where it can create value for the business. So, outside of AI, the integration architecture predicts the success or failure of AI projects and an integration strategy for AI becomes crucial.
The better way to create an intelligent enterprise uses the well-established the API-led approach to integration and AI. This API-led AI solution allows us to leverage the benefits of API-led for AI implementation, creating a future-proof foundation that accelerates development through reuse and abstraction.
MuleSoft’s Anypoint Platform is the perfect solution for your API-led integration strategy needs. MuleSoft has pioneered the API-led connectivity architectural paradigm, which now has universal acceptance.
In a conversation with Gustavo Leitão, our Integration Lead at Stellaxius, we asked him some questions regarding this topic.
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1. What is AI integration, and how does it work?
AI integration is a discipline focused on integrating artificial intelligence with existing systems, to add functionalities that will improve their results. This could mean better performance, improved customer experience, and increased efficiency.
It’s a strategy centred on data that needs to be gathered from different sources, which is then processed using other models depending on your goal, and ultimately allows you to take action using the results given by these models. Integration is an enabler for an AI project implementation, as it makes data available and allows AI model outputs to be shared with other systems that can transform them into some business value. In other words, integration provides the pipes for data flows, and AI augments data to provide valuable insights.
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What are the advantages of an integration strategy for AI?
The advantages of having an integration strategy for AI intersect with the benefits of having an integration strategy in your organisation. To name a few, and to focus on AI integration, I would say:
- Improve data transfer efficiency
- Accelerate project delivery
- Increased project success rate
In our article, “MuleSoft: System Integration and its Benefits“, by Bruno Costa, Salesforce Developer at Stellaxius, find out more about the benefits of having an integration strategy for your company.
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Why is it essential to have a well-defined integration strategy specifically for AI implementation?
It is essential to have a well-defined integration strategy because…
When discussing AI, we have to talk about meaningful, valuable data. And it is very rarely the case that all data needed has only one source.
Let’s say you need to train an AI model, and for that you have to feed it with data generated and stored in different systems. Some are modern systems, others are legacy, and sometimes they work in real-time, or maybe in batch, and you have to find the best way to integrate these systems with your AI engine, where models will be applied to the gathered data.
Integration is fundamental to transfer data to and from the AI system, which means that an AI project is, in fact, an integration project. The success of that project is dependent on a solid integration strategy.
This leads us to an API-led AI approach, which makes use of the advantages of API-led for AI implementation. This results in a simplified and standardised way of looking at AI, and makes it available to the whole organisation by breaking down systems in to smaller, reusable, building blocks.
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What challenges or issues can arise if an organization neglects to develop an integration strategy for AI?
It always depends on your use cases, AI is a broad field to master with many different scenarios depending on what you want to achieve.
When it comes to integrate with AI, you need to be aware of the of data you need to process, how time sensitive the use case is, and what you want to do with the result.
For instance, you can have an AI use case that uses a lot of data, that needs to be transported from different systems to a central repository, to make decisions from historical data, using a previously trained model, and the result takes its time to be calculated. On the other hand, you might need to implement a use case that uses real time stimuli, meaning real time data, to make quick, time sensitive, decisions.
How you integrate relevant data, the AI engine, and your systems will vary depending on what you need to achieve.
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What makes the MuleSoft Anypoint Platform the perfect solution for an API-led integration strategy?
The MuleSoft Anypoint Platform is a fast-evolving, flexible and adaptable solution for integration, automation and universal API management.
These capabilities give us the possibility to design and easily implement future-proof integration solutions and quickly adopt new integration strategies when needed.
It’s a platform that has been growing around the API-Led integration approach, so it provides the needed tools to implement this strategy efficiently, whilst also allowing us to implement other integration strategies whenever needed.
To me, the MuleSoft Anypoint Platform means efficient adaptability.
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What are the best practices when choosing a partner for an integration strategy for AI?
Integrating systems with AI requires that you be mindful that this area of expertise is always evolving, and maybe self-evolving soon, which can have a major impact on your business processes and IT capabilities.
So it’s not just about integrating point A with point B. It’s about being capable of understanding the business and processes involved, being innovative and adaptable from both technical and process points of view, and deliver your project with expertise and in partnership.
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How can Stellaxius help with your AI integration strategy?
Stellaxius is a Salesforce Consulting Boutique, and Salesforce is leading the way in CRM + Data + AI with an innovative approach based on solid values like trust and ethics. We’re experts in all that is Salesforce, which includes Salesforce AI integration.
With our business analysis expertise aligned with our technical expertise, we can cover the whole project life cycle, from requirement gathering, analysis and implementation, and project delivery. And since we’re also experts in Systems Integration, we can understand the end-to-end process, giving us a holistic point-of-view on the best way to implement an integration strategy for the project.
Joining all these capabilities in multi-disciplined teams is a step forward in implementing a successful AI project.
