Systems | Development | Analytics | API | Testing

Organizations with more reliable, automated data environments are nearly 2x more likely to exceed ROI expectations.

There is a scenario playing out in QA teams everywhere right now. A team adopts an AI testing tool, runs it for the first time, and gets 300 test cases in minutes. The demo worked. The ROI math looked great. But three sprints later, 60 of those test cases are validating requirements that were updated in the last sprint. Twenty more test a user flow that was deprecated. The AI performed exactly as advertised. The governance system never existed.

Every year, Dresner Advisory Services publishes some of the most closely watched research in the enterprise performance management (EPM) space. Unlike analyst firms that rely heavily on vendor briefings, Dresner’s Wisdom of Crowds methodology gathers data directly from end users — the finance leaders, FP&A professionals, and CFOs who live inside these platforms every day.

APIs are transforming how AI interacts with enterprise data. Instead of directly connecting AI to databases like MySQL, PostgreSQL, or MongoDB - which can lead to security risks, schema complexities, and high maintenance - APIs act as a secure middle layer. This approach simplifies data access, reduces risks, and ensures seamless integration with multiple databases.

Customer support data is extremely valuable. Especially when integrated with other relevant sources, it can help users gain key business insights and better focus their marketing campaigns. Let’s take a look.

If you are like most enterprise leaders, you are managing a sprawling estate of hundreds—or even thousands—of disjointed legacy applications built on outdated frameworks, consuming an estimated 55% to 80% of your IT budget just to "keep the lights on." This legacy drag stifles innovation. Yet the traditional answer—"rip-and-replace"—often makes things worse. Multi-year, high-risk projects that rewrite everything from scratch can be catastrophic.

You start a research task on your laptop, the network drops during a meeting, and when you open your phone to continue, the conversation is gone – you re-prompt, get partial duplicate results, and lose 30 minutes of work. The delivery layer dropped it. That's one of the most consistent problems teams hit when building AI applications. It's particularly acute in customer support, where a session belongs to the conversation - not to any single device, connection, or participant.

If you’re responsible for keeping storage reliable, secure, and cost-efficient, 2026 planning is shaping up to be uniquely challenging. A perfect storm of pressures like ongoing semiconductor constraints, concentrated manufacturing, and unprecedented AI-driven demand are reshaping day-to-day infrastructure operations. The challenges introduced by the global supply chain crunch, however, are especially risky.

Most teams evaluate product analytics platforms based on features, integrations, and pricing. Few evaluate the underlying deployment model. That usually works - until it doesn’t. As products scale, analytics moves from being a dashboarding tool to becoming critical infrastructure. Performance expectations increase. Compliance reviews become stricter. Internal stakeholders demand reliability. At that point, the deployment architecture behind your analytics system starts to matter.

For years, SAP leaders have been told a familiar story: Scale carefully. Don’t outgrow your infrastructure. Hope your next acquisition fits inside your existing SAP footprint. Behind the scenes, many SAP teams have been managing risk not by innovating, but by working around the limits of their storage platforms. CIOs, for example, are increasingly prioritizing platform consolidation, with 75% of organizations pursuing vendor consolidation as fragmented, aging architectures become harder to manage.