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Google CloudNext 2026: Observability and AIOps Announcements Expand the Operation Scope of the Platform

Shannon Kalvar — Wed, 06 May 2026 04:00:00 GMT

Microsoft Customer Insights and Agent 365: An Emerging Architecture for Agentic Mesh for CX

Gerry Murray — Wed, 06 May 2026 04:00:00 GMT

Oracle Applications Analyst Summit 2026: Hitting it of the Park with Agentic Applications

Mickey North Rizza — Wed, 06 May 2026 04:00:00 GMT

Oracle held its Applications Analyst Summit 2026 April 28^th at Oracle Park in San Fracisco. While the San Francisco Giants were away, the technology analysts took over learning more about Agentic Applications. Oracle unveiled its next step in its AI in Fusion Applications moving from hundreds of Oracle-built AI agents, the AI Agents Studio for customer and partner-built AI agents, AI workflows and the AI Agent Marketplace with pre-built AI Agents to Agentic Applications.

Oracle’s movement to Agentic Applications is the natural next step for Oracle beyond embedding AI in the standard workflows. The AI Agents are driven by outcomes but based on specific business objectives such as close the books faster or reduce attrition that have dynamic workflows that can adjust to changing conditions to solve problems and achieve objectives. The Agentic Applications are built for enterprise execution within data governance, security, and approval frameworks, along with full auditability. The Agentic Applications are powered by teams of specialized agents that continuously advance work based on the organization’s objectives, such as ledger agent and consolidation agent that work towards accounting model objectives.

These new forward-looking Agentic Applications bring about a continuous review, an ability for the employee to review what is going on and look at areas that may need additional help, changes, or updates. Employees can quickly drill in, review and update as needed right then or make changes later on as it relates to their organizational standard operating procedures and policies. Oracle just recently introduced 22 new Fusion Agentic Applications, a part of Fusion Applications, including a few that captured my attention around Maintenance Operations Workspace, Design-to-Source Workspace, and Workforce Operations Command Center, that bring more business information across more functions while also bringing in more workflows beyond just task-based Agents.

While these new Agentic Applications are critical for creating faster decisions and more economic value, they are also creating the pathway towards the autonomous organization. The organization can use the Agentic Applications as little or as much as possible, which creates less autonomy with humans in the loop where the agent assists in tasks and workflows while the human decides. For organizations that need to understand AI better and build trust it does so with low risk. While the human in the loop brings out much it also has immediate productivity gains. The middle of the road towards autonomous is more about the human in the lead with Agents handling routine work and the human handling the exceptions while scaling operations quickly. As organizations gain trust and want to adopt more with the Agent driving the processes with the human owning the outcome, they move towards autonomous execution. Autonomous execution leads to end-to-end execution with policy, human only for true exceptions, continuous real-time optimization and maximum capacity and speed which is really about business transformation.

Oracle’s customers in attendance had much to say about the Agentic Applications though all was under NDA. The great news is everyone is working on AI and finding value in Oracle’s approach overall. And many are finding new methods of working, how to prioritize the use cases for adoption, and how to calculate their ROI (throughput, productivity, efficiency, etc.)

AI's Impact on Discovery, Advertising, and Brand

Roger Beharry Lall — Tue, 05 May 2026 04:00:00 GMT

This IDC Tech Buyer Presentation, delivered at IDC Directions 2026, provides buyers with insights regarding the impact of AI on discovery, advertising, and brand.

A tectonic shift is underway in how brands are discovered, evaluated, and chosen, as generative AI reshapes the rules of marketing and commerce. Traditional search is giving way to AI-generated answers, zero-click journeys, and agent-driven buying, where both consumers and B2B buyers increasingly rely on AI to research, compare, and even make decisions on their behalf. This transformation is redefining visibility, forcing brands to move beyond SEO toward becoming machine-readable, contextually relevant, and continuously optimized across a fragmented content and technology landscape.

In addition, new advertising models are emerging within AI environments, creating both disruption and opportunity. In this new era, success will depend on how well brands align content, data, and systems to influence AI-driven discovery — and ultimately earn a place in the decisions machines make on behalf of customers.

Defining AI Bill of Materials: Structure, Scope, and Role in AI Trust Services

Sakshi Grover, Grace Trinidad — Tue, 05 May 2026 04:00:00 GMT

This IDC Perspective examines the emergence of the AI bill of materials (AI BOM) as a foundational framework to manage the growing complexity of AI systems. As enterprises transition toward agentic, distributed, and continuously evolving AI architectures, traditional governance and security models are proving insufficient. The document outlines the structure, scope, and operationalization of AI BOM, highlighting its role in enabling system-level visibility, traceability, and control across models, data, pipelines, and runtime environments. It also explores enterprise adoption drivers, regulatory influences, and vendor approaches, positioning AI BOM as a critical control layer for managing risk, cost, and compliance in modern AI ecosystems.

"AI systems are evolving into distributed, stateful execution environments composed of models, data pipelines, prompt orchestration layers, APIs, and autonomous agents operating across dynamic runtime contexts. AI BOM functions as the control plane for this architecture, capturing dependency graphs, lineage, identity propagation, and runtime telemetry in a machine-readable form. Without this, enterprises cannot correlate system behavior with underlying components, resulting in blind spots across governance, security, and operational integrity as AI systems scale," says Sakshi Grover, senior research manager, Cybersecurity Products and Services, IDC.

Governance as the Backbone for AI in the U.S. Federal Civilian Government

Massimiliano Claps — Tue, 05 May 2026 04:00:00 GMT

This IDC Tech Buyer Presentation analyzes the U.S. federal government's evolving AI governance by combining a discussion on the policy and regulatory developments that were mandated over the past 12 months with a survey of 152 IT and non-IT senior leaders from U.S. federal government departments and agencies.

IDC research shows that AI adoption is progressing fast in the U.S. federal civilian government market; thus AI governance becomes the backbone of AI adoption to ensure coordination via CAIO roles, interagency collaboration, updated policies, and structured compliance frameworks.

Microsoft Customer Insights: An Emerging Architecture for Agentic Mesh for CX

Gerry Murray — Tue, 05 May 2026 04:00:00 GMT

Microsoft moved Conversational Journeys from public preview to GA and added SMS in the same release. The runtime combines Customer Insights — Journeys, Dynamics 365 Contact Center, and Copilot Studio agents in a single no-code Dataverse environment.

Additive Digital Governance: Redesigning Authority, Work, and Coordination for Machine-Scale Operations

Shannon Kalvar — Mon, 04 May 2026 04:00:00 GMT

As machine-scale digital complexity overtakes human-scale coordination models, enterprises need more than better telemetry and faster automation. Additive digital governance provides a governance model for redefining authority, redistributing work between humans and machines, and turning operational events into durable institutional learning. The result is a more governable, resilient, and sustainable digital operating model.

"When complexity exceeds human absorption capacity, the decisive advantage no longer comes from speed alone. It comes from the ability to define boundaries, legitimate trade-offs, and govern machine-scale operations without exhausting the people responsible for them," says Shannon Kalvar, research director, IDC.

IDC ProductScape: Worldwide Advanced Production Planning and Scheduling, 2026

Lorenzo Veronesi — Mon, 04 May 2026 04:00:00 GMT

The IDC ProductScape offers a comprehensive guide on the key functionalities of worldwide advanced production planning and scheduling, featuring products from Blue Yonder, Dassault Systèmes, Epicor, Eyelit, Infor, MPDV, QAD, RELEX, SAP, Siemens, and Velotic. The status of each functionality is categorized as fully supported, partially supported, partner provided, road map, or not supported, aiding technology purchasers in quickly identifying which vendors align with their changing requirements.

IDC PeerScape: Best Practices for Asset Management in Utilities

John Villali — Thu, 30 Apr 2026 04:00:00 GMT

This IDC PeerScape highlights five key best practices that distinguish successful APM implementations in utilities: adopting risk-based asset prioritization, integrating IT and OT data, transitioning to condition-based predictive maintenance, aligning APM with business and regulatory objectives, and strengthening data governance. Together, these practices enable utilities to optimize maintenance strategies, improve reliability metrics, reduce operational costs, and enhance their ability to justify investments in a highly regulated industry.

Utilities that treat APM as an enterprise transformation rather than a standalone technology deployment are better positioned to manage risk, extend asset life cycles, and deliver consistent, high-quality service to customers. As grid complexity continues to increase, APM will play a central role in enabling utilities to balance reliability, affordability, and sustainability objectives.

"Asset performance management is becoming a foundational capability for utilities navigating grid modernization, changing market dynamics, and rising reliability expectations. Organizations that integrate data, analytics, and operational decision-making into a cohesive APM strategy will be best positioned to reduce risk, optimize assets, extend asset life cycles, and deliver measurable business and regulatory outcomes," said John Villali, senior research director, IDC Energy Insights.