... investments in automation, AI tools and process efficiencies, software engineering organizations are falling far short of their productivity goals. This research reveals three developer-centric strategies software engineering leaders must implement to unlock developer productivity gains beyond AI. Overview ...
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... AI-native software engineering is disrupting traditional development processes as AI evolves from optional tool to essential collaborator. Product leaders must upgrade development environments, talent strategies and architectural approaches to maintain competitiveness in this rapidly evolving landscape ...
... AI-enabled physical security technologies are essential to counter the escalating threat environment and elevate risk management to predictive risk intelligence. Product leaders must accelerate investment in enabling technologies and tune roadmaps to exploit the AI race opportunity disrupting business ...
... Traditional provider-enterprise value relationships will not drive AI adoption. It is not sufficient to sell an AI tool and expect customers to make it valuable. In the intelligence supercycle, providers and enterprises need to create mutual value by working together to deliver target business outcomes ...
... AI will replace those who cannot convert expertise into tangible business benefits, forcing consulting providers to redefine themselves and their offerings. Tech services leaders must rapidly reinvent and reengage to remain relevant in an AI-driven market or risk being replaced. Overview Strategic Planning ...
... Securing AI agents without a deployment-based IAM taxonomy guarantees failure and misaligned IAM strategies. Identity architects must overcome analysis paralysis by establishing distinct IAM strategies and IAM controls to address six commonly deployed AI agent types. Overview Analysis Recommendations ...
... Access Insights Already a Gartner client? AI is shifting competitive advantage from technology implementation to ecosystem orchestration. As enterprises struggle to manage models, agents, data, and platforms, service providers that orchestrate AI ecosystems and outcomes will lead, while traditional integrators ...
... As physical AI deployments move from pilot to production, organizations find that device suitability alone does not equate to production readiness. This research equips infrastructure architects to design the integrations needed to coordinate physical AI across industrial workflows, OT data and enterprise ...