The opportunity / 01
Enterprise AI adoption is no longer the main challenge. The harder problem is converting experimentation into measurable business impact.
Agentic AI changes the discussion from “How can employees use AI?” to “Which business processes can AI execute?”
The GenAI paradox: adoption is high, impact is not
Enterprise AI adoption has grown rapidly, but measurable business impact has been hard to achieve. McKinsey’s 2025 research found that nearly eight in ten organizations report using GenAI, yet roughly the same proportion report no significant bottom-line impact.
The gap exists because many organizations have deployed broad, horizontal tools rather than redesigning specific business workflows.
of organizations report using GenAI
report no significant bottom-line impact
Source: McKinsey QuantumBlack, Seizing the Agentic AI Advantage, 2025
From AI assistance to AI action
Traditional GenAI is responsive: a user asks a question, the system generates an answer, and a person decides what to do next. An agentic system can understand an objective, break it into tasks, access enterprise systems, execute approved actions, validate results, and escalate exceptions.
GenAI
Responds to user prompts
Agentic AI
Executes end-to-end workflows
Where enterprise value can emerge
Agentic AI is most valuable in workflows involving multiple systems, decisions, and handoffs. The common characteristic is not AI. It is workflow complexity, measurable outcomes, and a defined operating boundary.
IT Operations
- Incident triage
- Automated remediation
- Capacity analysis
Cybersecurity
- Alert investigation
- Threat correlation
- Vulnerability prioritization
Customer Operations
- Case classification
- Issue resolution
- Next-best-action recommendations
Finance
- Invoice validation
- Reconciliation
- Procurement workflows
Supply Chain
- Demand analysis
- Inventory exceptions
- Logistics coordination
Architecture for Agentic AI
A production-grade agent needs more than a model. It requires an enterprise architecture that connects business objectives, models, data, tools, and governance.
Business objective
E.g., reduce incident resolution time
Agent orchestration layer
Planning, tool use, reasoning, guardrails
Enterprise context & memory
Data, knowledge, policies
Reasoning / foundation models
LLMs and domain models
Tools & APIs
Integrations and automation
Enterprise systems
ITSM, ERP, CRM, cloud, security, monitoring
Monitoring + security + governance
Observability, audit, risk controls
IT budget implications
To capture AI ROI, enterprise IT budgets need to shift toward the capabilities that make agents useful, secure, and operable. The model is one component of a broader value chain.
Increasing investment in
- AI platforms and model services
- Cloud compute and data platforms
- API integration and automation
- Security and identity management
- AI observability and governance
- Workforce upskilling
Areas requiring scrutiny
- Low-value AI pilots
- Duplicate AI tools
- Standalone departmental copilots
- Unused cloud resources
- Legacy integrations
- Poor-quality enterprise data
A practical framework for CIOs
Evaluate potential use cases against five questions before selecting a model or platform.
Is the process economically significant?
Is the workflow repeatable?
Does it have measurable outcomes?
Can the agent access required systems?
Can autonomy be safely bounded?
The 30-day Agentic AI assessment
Start without committing to a large platform investment. Establish a baseline, select a workflow, design the guardrails, and validate the economics.
Identify 5-10 high-volume workflows.
Score value, data readiness, risk, and integration complexity.
Define agent and human responsibilities, tools, and approvals.
Measure processing time, effort, errors, cost, and experience.
Key takeaway
The organizations that capture value from agentic AI will not necessarily spend the most on AI. They will connect AI investment to specific workflows, measurable outcomes, and disciplined execution.