Constructing connected data ecosystems for AI at scale
Modern integration platforms are helping enterprises streamline fragmented IT environments and prepare their data pipelines for AI-driven transformation.
Enterprise IT ecosystems are sometimes akin to sprawling metropolises—multi-layered environments where aging infrastructure intersects with sleek latest technologies against a backdrop of continuously ballooning traffic.
Similarly to how driving through a centuries-old city that’s been retrofitted for automobiles and skyscrapers may cause gridlock, enterprise IT systems often experience data bottlenecks. Today’s IT landscapes encompass legacy mainframes, cloud-native applications, on-premises systems, third-party SaaS tools, and a growing edge ecosystem. Information flowing through this patchwork gets caught in a tangle of connections which are costly to keep up and susceptible to snarls—kind of like emerging from a high-speed expressway to a narrow, cobblestone bridge that is continuously undergoing repairs.
Forward-looking organizations are actually turning to centralized, cloud-based integration solutions.
To create more agile systems suited to an AI-first future, forward-looking organizations are actually turning to centralized, cloud-based integration solutions that may support all the things from real-time data streaming to API management and event-driven architectures.
Within the AI era, congestion just like the scenario described above is a serious liability.
AI models rely upon clean, consistent, and enriched data; lags or inconsistencies can quickly degrade outputs. Fragmented data flows can undermine even essentially the most cutting-edge AI initiatives. And when connectivity snafus occur, systems aren’t in a position to communicate at the dimensions or speed that AI-driven processes demand.
Even essentially the most promissing AI initiatives can fail to deliver value when data connectivity is in danger.
AI’s potential to drive such outcomes hinges on an organization’s ability to maneuver clean data, at speed, across all the enterprise. At the identical time, AI itself has the potential to reshape the mixing landscape. Cloud-native integration platforms are starting to include AI-powered capabilities that automate flow design, detect anomalies, recommend optimal connections, and even self-heal broken data pipelines. This creates a virtuous cycle: integration enables AI—and AI, in turn, turbocharges integration.
Beyond the technical advantages, intelligent automation facilitated by modern integration stands to enhance overall operational efficiency and cross-functional collaboration. Business processes turn into more responsive, data is accessible across departments, and teams can adapt more quickly to changing market or customer demands. And as integration platforms handle more of the routine data-wrangling work, human teams can shift focus to higher-value priorities.
Integration platforms help unify data streams from on-prem to edge and ensure API governance across sprawling application landscapes.
Pre-built connectors enriched with knowledge graphs further speed up connectivity across diverse systems, while real-time monitoring provides predictive insights and early warnings before issues impact business operations.
We’re already seeing real-world examples of how thoughtful integration is empowering enterprises to turn into more agile and AI-ready. Listed below are three corporations using SAP Integration Suite to streamline data flows and simplify their operations.
- Siemens Healthineers: Within the healthcare sector, where data accuracy, timeliness, and security are non-negotiable, Siemens Healthineers is using integration solutions to make health services more accessible and personalized.
Siemens Healthineers operates a various business landscape spanning diagnostics, medical imaging, and therapy, each with unique data requirements and processes. To enable more autonomous decision-making, the corporate’s integration layer helps streamline core financial processes, similar to closing and reporting, while also supporting flexible planning and quick insights into operations. It also enables seamless data access across systems without the necessity for data replication, a very important consideration in a highly regulated industry. - Harrods: Luxury retailer Harrods operates a complex hybrid IT landscape that supports each its flagship London store and a growing e-commerce business; the corporate now offers 100,000 products online and processes 2 million transactions per day through digital channels. To modernize and simplify this growing footprint, Harrods leverages SAP’s pre-built B2B connectors and Event Mesh architecture to orchestrate greater than 600 integration flows across key business processes.
Since implementing the SAP solutions, Harrods has reduced integration-related process times by 30% and cut total cost of ownership by 40%. More importantly, the corporate has created a nimble data and application backbone that may adapt as customer expectations — and digital retail technologies — evolve.
- Vorwerk: German direct-sales company Vorwerk, known for products like smart kitchen appliances and cleansing systems, has undergone a sweeping digital transformation in recent times. Between 2018 and 2023, the corporate grew its digital sales from just 1% to 85%.
Vorwerk relies on SAP solutions to automate data flows across critical systems, including CRM and inventory management, payment processing, and consent management. The updated system has helped eliminate manual paperwork, significantly speed up order-to-cash cycle times, and improve the accuracy and consistency of customer data.
Using SAP solutions, retailers Harrods and Vorwerk are primed for achievement within the AI era.
Digital growth
Vorwerk’s digital
transformation boosted
digital sales
Process efficiency
Harrods data infrastructure
evolved with technology
and customer expectations
As these examples show, connectivity is crucial groundwork for AI across nearly every industry. Because the healthcare sector rapidly embraces AI, as an illustration, robust integration is a prerequisite to be used cases like diagnostic imaging and predictive care. Stringent regulatory requirements also demand accurate, transparent data handling and traceability across systems.
In retail, too, unified, event-driven integration underpins AI-driven innovations starting from dynamic pricing and personalized product recommendations to predictive inventory management—all of which require fast, accurate data flows across sales, inventory, customer, and partner systems.
And in direct-to-consumer models like Vorwerk’s, integration enables latest levels of personalization, real-time marketing, and optimized supply chains. Such capabilities will help D2C businesses stay competitive and responsive in highly dynamic markets — a necessity as greater than 70% of consumers now expect personalized experiences from the brands they buy from. Moving forward, AI (particularly generative AI) will likely play a pivotal role in scaling these personalized experiences and enabling brands to deliver tailored messages with the best tone, visual guides, and duplicate to satisfy the moment.
In accordance with a recent IDC report, nearly half of enterprises are juggling three or more integration tools, with 25% using greater than 4 across their environments.
While many corporations see value in consolidating, technical challenges and skill gaps remain barriers to simplification. One other structural issue: One-third of enterprises don’t consider integration until system implementation is already underway—limiting opportunities to design future-ready data flows from the beginning.
Sustained innovation and long-term agility rely upon whether infrastructure can evolve as quickly as an organization’s ambitions. Modern integration platforms provide the connective fabric that makes this sort of adaptability possible.
A unified integration strategy offers a path forward. An integration roadmap will help corporations shift from reactive, piecemeal efforts to a more purpose-built, scalable foundation—one which supports each current business needs and the demands of AI-driven innovation.
The cities that thrive today aren’t those that simply manage traffic flow by expanding their highways or adding in sporadic roundabouts—they’re those which have reimagined mobility entirely. In enterprise IT, the identical principle applies: Sustained innovation and long-term agility rely upon whether infrastructure can evolve as quickly as an organization’s ambitions. Modern integration platforms provide the connective fabric that makes this sort of adaptability possible.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of information for surveys. AI tools that will have been used were limited to secondary production processes that passed thorough human review.
By MIT Technology Review Insights
