... problems and unlock diverse applications. Organizations risk falling behind competitors if they continue with ad hoc AI investments. Enterprise architecture (EA) leaders must quickly enable their organizations to strategically design, invest in, and integrate AI into their existing IT systems. Join this ...
Analyst(s): Lucas Kobat
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... accelerated, prompting a marked increase in technology investments beyond corporate IT and also creating new disruption risks. In 2022, enterprise architecture leaders need to enable distributed delivery across the enterprise, while ensuring organizational resilience. Scope Analysis Topics Suggested First ...
... physics-informed neural networks, discover how the next wave of AI innovation is moving beyond prediction and toward precision, all while relying on AI-ready data. You’ll learn: Why hallucinations happen and what they say about how AI works How multiagent systems and PINNs are changing how machines reason When ...
... The Modernizing Cybersecurity Architecture Pickup Team discussed the design and philosophy of security architecture and during recent conversations, they shared examples and lessons learned from each member’s own journey. CISOs can use this research to learn from the team’s best practices. Gartner Research ...
... beyond a destination application, sales operations leaders need to distinguish a genuinely headless architecture from AI assistants, agents or basic platform interoperability. Assess five architectural characteristics and the execution loop to determine whether governed CRM capabilities can operate beyond ...
Analyst(s): Wendy Butler-Mafuz | Adnan Zijadic | Guy Wood
... Gartner's ITScore for Enterprise Architecture and Technology Innovation provides a maturity self-assessment based on eight dimensions of an EA practice. EA and technology innovation leaders can determine current-state maturity, and plan for improvements to build a world-class EA capability. Analysis ...
... enterprise data. If our data is high quality, does that make it AI-ready? “High-quality” data — as judged by traditional data quality standards — does not equate to AI-ready data. When thinking about data in the context of analytics, for example, it’s expected to remove the outliers or cleanse the data to support ...
... Data products establish trusted, governed and AI‑ready finance data, allowing CFOs to move from siloed data efforts to reusable assets that speed insight generation, lower risk and support stronger decisions. Insights at a Glance Issue Context Impact Brief More Detail Gartner Recommended Reading Gartner ...
... •Understand how no-code agent builders are accelerating enterprise AI agent adoption and enabling citizen developers to build AI-powered workflows •Evaluate the approaches startup and established vendors are taking to support agent development, governance and interoperability •Identify the actions n ...