How integration enables AI: A guide to overcoming challenges and driving innovation

Artificial intelligence (AI) is transforming industries and creating new possibilities. However, AI doesn’t achieve its potential in isolation. It requires integration with existing systems, data pipelines and workflows to deliver meaningful results. Integration is the critical enabler, bridging AI with the tools and information it needs to function effectively.

This guide explores six essential ways integration supports the development, deployment and scaling of AI, while also reviewing the challenges and future trends shaping this evolving relationship.

Key ways Integration supports effective AI implementation

To ensure artificial intelligence delivers real impact within enterprise settings, it must be closely connected to the data, processes and systems that drive day-to-day operations. Integration provides the necessary bridge, enabling AI to function reliably and effectively in context.

Below, we explore six essential ways integration supports the implementation and success of AI across industries:

1. Connecting AI to data ecosystems

AI relies on data as its foundation. Whether it’s training machine learning models, interpreting real-time inputs, or providing analytics, access to high quality, diverse data is essential. Integration is the mechanism that connects AI systems with the data sources they need.

The role of integration in data access

Integration makes it easier for AI to work with data by connecting different sources and simplifying how data is prepared for use. It brings together information from places like databases, text files, IoT devices and APIs. At the same time, it helps organise, clean and standardise data, so AI tools can analyse it effectively.

• Combining multiple data sources: Integration allows AI to gather inputs from structured databases, unstructured sources like text logs, IoT devices and external APIs.
• Simplifying data pipelines: By linking data platforms and AI tools, integration helps automate tasks like cleaning, organising and normalising data for analysis.

2. Supporting real-time decision making

AI thrives on making informed decisions in real time. Without integration, these insights often remain siloed or delayed, limiting their value. Integration ensures that AI can access the right data and systems at the right moment.

Applications of real-time AI integration

• Operational systems: AI can interact directly with tools like enterprise resource planning (ERP) systems, customer relationship management (CRM) software, or manufacturing controls.
• IoT networks: Integrated AI systems use data from IoT devices to drive immediate actions, such as optimising energy usage or detecting anomalies in equipment.

3. Bridging legacy systems with AI

Many organisations depend on legacy systems that weren’t built with AI in mind. Replacing these systems can be costly and disruptive. Integration offers a practical alternative by enabling AI to work alongside these existing technologies.

How Integration connects AI to legacy systems

Integration bridges the gap between AI and legacy systems, enabling communication and modernisation without disrupting existing workflows.

• APIs and Middleware: These tools act as translators, allowing AI systems to pull data from older platforms and provide new functionalities.
• Incremental modernisation: Organisations can integrate AI into specific workflows without overhauling the entire infrastructure, providing an efficient path forward.

4. Enabling Cross-Functional AI applications

AI becomes even more valuable when it operates across multiple departments or functions. Integration is the glue that connects these silos, allowing AI to create holistic solutions.

Benefits of Cross-Functional integration

• Unified insights: "Unified insights: By linking AI to data from sales, marketing, and customer service, organisations gain a deep understanding of customer behavior."
• End-to-end automation: Integration enables AI to handle entire processes, such as order fulfillment, from inventory management to delivery tracking.

5. Facilitating scalable AI deployment

As AI systems grow in complexity, integration ensures they can scale across platforms and environments, from the cloud to edge devices.

Strategies for Scalable Integration:

Scalable integration strategies, such as using cloud platforms for expansive AI tasks and edge integration for low-latency applications, ensure AI systems operate efficiently across diverse environments

• Cloud Integration: Cloud platforms provide the computational power and storage capacity AI systems need for large-scale tasks.
• Edge AI Integration: For applications requiring low latency, like smart cities or autonomous drones, integration enables AI to process data closer to the source.

6. Supporting continuous learning and adaptation

AI must evolve with changing conditions and integration is vital to keeping it updated with fresh data and user feedback.

How Integration enables adaptation

Integration drives adaptation by connecting AI systems to real-time data and user inputs, enabling continuous learning and refinement for improved performance.

• Data feedback loops: where integration platforms like Boomi and Azure Data Factory synchronise real-time performance data to continuously improve AI models.
• Human-AI collaboration: Systems linked to user feedback tools incorporate human input to improve accuracy and relevance.

Challenges of using Integration to enable AI

The promise of AI can only be realised when it operates within a cohesive digital ecosystem that ensures real-time data access, compatibility and scalability. However, achieving such an environment is far from straightforward. Organisations often face a number of challenges in integrating AI into their operations, stemming from complex legacy systems, fragmented data environments and the dynamic nature of technological advances.

Below, we explore the most important challenges organisations face when using integration to enable AI:

1. Data Silos: Many organisations have fragmented data environments. Integration platforms help connect these sources, ensuring AI has access to all relevant information.
2. Compatibility issues: AI often needs to work with systems built on different standards. Middleware and APIs resolve these conflicts by ensuring interoperability.
3. Scalability bottlenecks: As AI systems grow, the integration framework must support increased demands without delays or failures. Cloud-native solutions often address this need.
4. Instant data access: AI applications often rely on immediate access to data, posing challenges for conventional integration techniques.
5. Continuous updates and upkeep: Ensuring AI integrations stay compatible with fast-changing technologies and shifting business priorities requires significant effort and expertise.

The future of AI integration

The relationship between AI and integration is only set to deepen. Emerging technologies, such as federated learning, which allows AI models to learn from decentralised data, will depend heavily on advanced integration methods. Similarly, the growing adoption of AI-as-a-Service platforms will require robust integration frameworks to bring AI into diverse applications effortlessly.

As AI evolves, its ability to provide value will hinge on the strength of the integration strategies that support it. From real-time processing to long-term learning, integration ensures AI systems are ready to tackle challenges across industries.

Emerging technologies that depend on Integration

One of the most significant developments shaping AI integration is federated learning. This approach allows AI models to learn from decentralised data sources while maintaining data privacy and security, a critical requirement in industries like healthcare, finance and government. For federated learning to succeed, robust integration frameworks are needed to manage data flow, ensure consistency across distributed systems and facilitate secure communication between devices.

Similarly, the rise of AI-as-a-Service (AIaaS) platforms, such as those offered by Azure, is redefining how organisations implement AI. These platforms provide pre-trained AI models and tools that businesses can deploy for various applications without building solutions from scratch. However, to maximise the value of AIaaS, integration platforms like Boomi are essential. They bridge the gap between AIaaS platforms and an organisation's existing systems, enabling data exchange and orchestration of workflows across diverse environments.

Conclusion

Integration is essential to the success of AI, connecting it to the systems, data and workflows it needs to deliver results. By building bridges between AI and organisational tools, businesses can improve efficiency, create smarter processes and innovate with confidence. As technology advances, the ability to integrate AI effectively will be a defining factor in achieving long-term success.

As a trusted partner of Boomi and Azure, we provide expert guidance in system integration to connect legacy technologies, modern platforms and AI solutions into a cohesive ecosystem. 

Get in touch with us today to explore how our expertise can help you build an integration framework that supports your AI initiatives and delivers measurable outcomes for your business.

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Amanda Garrido Vallès

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