... a Gartner client? Delivering on the AI strategy is a top priority for software engineering leaders. Modernizing to AI-native architectures, AI-ready APIs and data, and building secure AI engineering platforms are essential to avoid costly failures and to scale AI value. Without these strong foundations ...
Analyst(s): Brian Minning | Brad Dayley | Jim Scheibmeir
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... This guide provides insights on the MDM market and solution providers to help CIOs’ digital optimization and transformation initiatives. Market Definition Market Description Market Direction Market Analysis MDM as a Consumption-Data-Analytics-Enabling Platform Impact of the IoT on the MDM Market Representative ...
... Access Insights Already a Gartner client? Decision intelligence platforms (DIPs) combine decision modeling, analytics, and AI to augment and automate decision making and drive business outcomes. Leaders responsible for decision intelligence can use these insights to invest in DIPs and boost their organization ...
Analyst(s): Kevin Quinn | Kjell Carlsson | David Pidsley | Carlie Idoine | Gareth Herschel
... practical and accessible for employees and establishing a role-specific AI literacy strategy that builds skills across AI foundations, value, engineering, and governance.” According to the Gartner survey of 100 CFOs, taken from January through February 2026, respondents identified building AI talent in the function ...
... CAEs can use low-code AI agents to reduce manual work and improve audit quality. To realize these benefits safely, they should standardize agent creation, validation and monitoring and require teams to start with small, narrow use cases and specific behavioral limits. Insights at a Glance Issue Impact ...
... Access Insights Already a Gartner client? An easy-to-read primer for CFOs on what AI costs and AI tokens are and what they can do to reduce or better control them — explaining what tokens are, how input and output costs work, and how contextualization works; explaining how token costs can be optimized ...
... Self-hosting open-weight models is enterprises’ execution path to private AI. This research offers a decision framework and four-pillar stack for “where to consider” and “how to prepare” for self-hosted open-weight model deployment. Insights at a Glance Strategic Planning Assumption Issue Impact Implications ...
Physical AI is moving from pilots to enterprise deployment. Learn how to evaluate providers and navigate the rapidly evolving physical AI services market.