Agentic AI: Deep Integration for Operations & Efficiency

In 2026, IBM introduced AI Agent and LLM Observability, a system to improve visibility into production AI systems. This marked a new era: managing AI's internal interactions became as critical as monitoring its output. As companies rapidly embed AI agents into core operations, they must simultaneously build new systems to observe and control these increasingly autonomous entities. The true challenge lies in effectively governing, observing, and measuring the impact of these sophisticated, often opaque, AI agents.

The Shift to Agentic AI and Deep Integration

The operational landscape shifted significantly in 2026 as IBM and OpenAI formalized a partnership to embed frontier AI models into core business processes. This collaboration integrates advanced OpenAI models, including GPT-5.6, Codex, and ChatGPT Work, directly into IBM Consulting Advantage. This deep integration moves beyond standalone AI applications, creating comprehensive, agentic AI ecosystems that execute complex tasks autonomously. For example, Pearson Plc utilized IBM Enterprise Advantage to develop a customized AI platform, combining human expertise with agentic AI assistants. A fundamental shift is signified: AI now manages intricate workflows, demanding an entirely new layer of oversight and control to ensure reliability and compliance.

Quantifying AI's Value and Cost

The shift to agentic AI operations requires new methods to quantify its business impact, moving beyond traditional productivity metrics. As AI agents assume more complex roles, businesses need granular data to validate investments and understand performance.

Laurel Signal — This system measures the business impact of AI-powered legal work by analyzing usage in tools like CoCounsel Legal, providing leverage ratios and cost-of-delivery data, according to Thomson Reuters Legal Solutions. This tracking attributes specific financial outcomes directly to AI's contribution.
Laurel Time — This platform automatically captures attorney activity across applications, including CoCounsel Legal, converting it into matter-tagged time without manual entry, as reported by Thomson Reuters Legal Solutions. This automation addresses a critical challenge in accurately billing and assessing efficiency gains from agentic systems.

Operational Aspect	Previous Approach (Pre-2026)	Current Approach (2026)	Shift in Focus
AI Integration Support	General IT consulting or in-house development teams handling diverse tech needs, often lacking specialized AI deployment knowledge.	Dedicated OpenAI Practice with thousands of consultants and engineers trained on advanced OpenAI Partner Network certifications. This specialized workforce ensures sophisticated deployment and ongoing management of complex AI models.	From general tech support to highly specialized, certified AI integration expertise, capable of handling frontier models and their nuanced operational requirements.
AI Workflow Visibility	Manual tracking, siloed departmental reports, and limited insight into AI's specific impact on end-to-end processes, making ROI difficult to ascertain.	Partnerships like Thomson Reuters and Laurel, enabling law firms to see AI-powered legal work end-to-end, from work to invoice to ROI. This provides a holistic view of AI's financial and operational contribution.	From fragmented data to comprehensive, real-time, and financially linked visibility of AI-driven workflows, ensuring transparency and accountability for autonomous agents.