With AI as a differentiator, enterprises can leverage a handful of strategic experts to automate integration efforts that free data.
From theory into reality. Integration is a major deciding factor for the success of digital transformation because data must move from where it’s generated to where it can be mined for insight. This process can’t take weeks or months. Let’s explore why the quality of integration can determine success or failure in digital transformation.
Integration creates a foundation for all digital operations even more than tools or team member expertise. Quality integration provides
- Security and governance: The balance between freeing data for stakeholders and keeping it secure relies heavily on quality integrations. Companies that struggle with security may need to upgrade their integration strategy.
- Opportunity for scale: Instead of outgrowing rigid architectures, the right integrations allow companies to grow.
- Dynamic data: Companies need an automatic flow from data generation to data insight. Integration overcomes data as a static asset to create this loop.
- A reduction in complexity: Complexity can block even the best efforts to receive insight. One way to know that integration has worked is by measuring the complexity of getting value from data.
For many organizations, a significant skills gap is preventing true integration. However, businesses can build a better architecture despite these gaps with smart automation.
So how do companies perform this feat? They do it with intelligent automation. Specifically, what’s needed is a solution that uses artificial intelligence (AI) to take over the complexity of building pipelines.
With such an approach, natural language processing might be used to allow team members to design their ideal project without having to write complex code. They can define what they need, and an AI-powered automation solution could then use those words to recommend various integration templates for that specific project. A solution would then help to customize the steps in the integration workflows once the business team selects a template.
Other technologies, like no-code development, could then be employed to make integration processes easier to perform by the lines of business. With this approach, different teams can take ownership of their stacks and begin working with data directly to make decisions. The result is speed and simplicity.
See Also: Center for Automated Integration
Where traditional delivery can take months or years of iterations before companies get it right, a closed-loop system leverages AI’s pattern-analysis and continuous development principles. AI checks logs, runs tests, and continuously updates to produce the best system at a fraction of the time.
With AI as a differentiator, enterprises can leverage a handful of strategic experts to create dynamic integrations that free data.
- Enterprise architect: Designs connections with reusable patterns to create a repository. These templates include critical security and governance features.
- Integration developer: Analyzes templates to create solutions and stacks. As different departments use natural language selection, AI can choose from these reusable templates to build custom integrations.
- Integration operations: “Productizes” the solutions for consistency, leveraging AI tools to create a closed-loop system.
Achieving operational excellence
As companies manage a pent-up need for speed in delivering value and insight from their data, these automations can jumpstart a robust system that takes a fraction of the time. With the right team and help from AI, companies can build custom integrations that provide a competitive advantage. Applications will integrate faster with fewer barriers—a true digital transformation success.