Before agentic AI: The foundation every enterprise needs

For many organizations, the first wave of AI delivered what amounted to speed upgrades: faster content, faster insights, faster answers. These early wins have been real, but they haven’t fundamentally changed the way work moves across the enterprise.

As soon as teams began trying to extend AI beyond isolated tasks — past the browser tab, outside the development environment or into workflows that cross departments — progress stalled. The models were perfectly capable, but in most cases, the enterprise wasn’t ready to support them.

AI today largely operates in silos:

• Summarizing a document in one tool
• Generating a draft in another
• Answering a question inside a chat window

Those applications are useful, yes. But transformational? No. And certainly not autonomous.

The next phase of AI will operate very differently. Agentic AI promises to reason, plan and participate in the work, not just advise on it. For any AI system to influence real business processes, the organization must first create the environment to support it.

It’s critical to build a foundation for the next decade of AI to operate with clarity, coordination and control.

Why leaders often think they’re ready

When AI experiments stall, the reflex is to look at the model.

• Should the prompt be rewritten?
• Should the model be retrained? 
• Should the team switch providers?

In fact, most AI slowdowns have nothing to do with model quality. They’re caused by the operational surface the model enters. Across enterprises, the same foundational gaps appear again and again, regardless of industry or scale.

1. Work happens in silos. AI has no shared control layer. Automations, scripts, SaaS workflows and departmental tools all run independently. This fragmentation increases the likelihood of “shadow AI” — and the blind spots in security and cost that come with it.
2. Every department uses different guardrails. Access, approvals and policies vary wildly across teams. AI simply can’t follow rules that don’t exist consistently.
3. Workflows assume predictability, but reality doesn’t. Static, rule-based logic breaks the moment conditions change. AI becomes another exception handler instead of a force multiplier.
4. Leaders lack cross-system visibility. Throughput, failures, bottlenecks and downstream impacts are scattered across tools. You can’t operationalize intelligence you can’t see.

These gaps don’t make agentic AI unrealistic, but they reveal what’s missing. To safely give AI the ability to plan and act, enterprises need coordination, governance, adaptability and visibility working together under a unified orchestration approach.

Before autonomy: The architectural fundamentals

Across enterprises making real progress toward AI readiness, one theme is clear: they’ve perfected the architecture underneath the model. These organizations are doing more than just experimenting with clever tools. They’re building the conditions for intelligent systems to operate safely and consistently.

Unification: One orchestration layer to coordinate the work

Imagine an AI system evaluating a delivery delay. It checks order data in one application, inventory in another, customer records in a third and workflow timing in a fourth. Without orchestration, those steps become disconnected guesses. With it, they become a single, synchronized, visible and aligned action path governed by business rules.

A unified layer provides the control plane that keeps all forms of work — human, automated or AI-assisted — moving in the same direction.

Boundaries: Guardrails for scaling intelligence — not risk

Guardrails vary in format, but they all answer the same question: What is safe for this system to do? Instead of a long list, the most effective enterprises keep it simple with:

• Actions that are always permitted
• Actions that require verification or approval
• Actions that are never allowed

When these rules are applied consistently across departments, intelligent behavior becomes predictable. AI stops guessing how decisions should work and starts following the same standards everyone else does.

Transparency: Governance that keeps humans in control

As soon as automation can influence workflows, visibility becomes non-negotiable. Leaders need to see how a decision unfolded, what it touched and why it behaved the way it did. That requires:

• Observability into processes
• Clear documentation of decision paths
• Audit trails that withstand scrutiny
• The ability to unwind or adjust actions when needed

Governance turns autonomy into something accountable, rather than opaque.

Coexistence: A blended environment of deterministic and dynamic automation

Enterprise leaders sometimes assume they must choose between traditional automation and AI-driven adaptability, but the highest performers do the opposite. They preserve their deterministic backbone: the scheduled workflows, validations and rule-based logic that keep operations steady. Then, they layer adaptability where variability actually occurs.

In other words, it’s reinforcement, not replacement. Rule-based processes handle what is predictable, adaptive decision loops handle what isn’t and orchestration brings the two together.

How experimentation becomes an operating model

AI experimentation is happening everywhere at once. Marketing might test a summarization tool, Finance could be exploring anomaly detection and Operations may pilot an automation assistant. The activity is high, but the impact is uneven. Some pilots work, others stall and many echo work already happening elsewhere in the organization.

What’s missing is structure. Modern AI only becomes meaningful when it’s connected, governed and repeatable. That requires shifting from scattered experimentation to an operating model that gives every team the same foundation to build upon.

Read more about building the best foundation for agentic orchestration.

A platform-first evolution in automation

The transformation underway resembles the moment when analytics matured from isolated dashboards into full data platforms. AI is undergoing a similar transition. What begins as a collection of tools eventually becomes an operational discipline shaped by shared infrastructure, shared controls and shared context.

In practice, this means we have to start thinking differently about how AI gets introduced and supported. Investment decisions move away from individual tools and toward foundational capabilities that every team can rely on, like interoperability and visibility. Talent evolves as well, with roles focused on designing supervised automation, not just building models in isolation.

Metrics also expand. Instead of measuring AI success through cost savings alone, executives are beginning to track the health of end-to-end processes: throughput, order delivery rate, consistency, service quality and customer satisfaction, for example. These are the signals that show whether the enterprise is truly becoming more adaptive.

Risk posture changes, too. Rather than waiting for AI to cause a problem, leaders establish guardrails and safety patterns before AI touches a core workflow. True autonomy starts with boundaries.

This evolution marks a larger shift: the move from experimenting with AI to preparing the enterprise for it. When you treat orchestration and governance as shared capabilities instead of departmental add-ons, innovation becomes faster, safer and easier to scale. AI moves from being something scattered teams try out to something the entire organization can trust.

What agentic orchestration will unlock (when the foundation is ready)

Agentic AI at scale remains a future capability, but the directional value is already clear. Once you have orchestration, governance and interoperability in place, you can unlock an entirely new class of capabilities:

• Systems that adapt faster than conditions can destabilize them
• Cross-system decision-making that reflects real business context
• Self-service interactions where users request outcomes, not workflows
• Operations that continue running even when inputs, timing and exceptions change
• Insight that spans applications, dependencies and data in motion

Your teams can gain a level of clarity, context and control that may be elusive today.

The advantage will go to those preparing now

Organizations making progress toward autonomous operations share a common pattern. They’re not racing toward agentic AI, but building the scaffolding that will support it.

That means they’re:

• Consolidating automation under a unified orchestration layer
• Strengthening governance to define how decisions and actions occur
• Insisting on interoperability across systems and tools
• Using AI assistance to improve deterministic workflows
• Piloting new AI patterns in controlled, low-risk environments
• Defining KPIs that reflect throughput, delivery, consistency and service quality

Preparation accelerates innovation, creating an environment where AI can be introduced safely, evaluated clearly and scaled confidently. Enterprises that begin now won’t just be ready for agentic AI. They’ll be structurally positioned to benefit from whatever comes next.

To explore the now, next and beyond of AI, read “The autonomous enterprise” and get a deeper look at how orchestration, governance and preparation shape the path to more intelligent operations.