Most AI agents haven’t delivered bottom-line value—yet. Here’s how to use this lull to prepare for scale.
AI agents are evolving fast, but most enterprises haven’t seen meaningful ROI. The hype cycle has cooled. Many deployments remain stuck in pilot mode, and few have made it into core business systems. That’s not a failure—it’s a signal. The technology is maturing, but the enterprise isn’t yet ready to absorb it at scale.
This “trough of disillusionment” is a rare window. It gives leaders time to think clearly, without pressure to chase headlines or overcommit. The pace of change is real, but so is the opportunity to shape how AI agents will operate across the business. The decisions made now—about architecture, accountability, and governance—will determine whether AI agents become a source of leverage or a source of risk.
1. Stop Treating AI Agents as Add-Ons
Most AI agents are deployed as bolt-ons—task-specific helpers layered onto existing systems. This limits their impact. Agents are not just tools; they are actors. They make decisions, trigger actions, and interact with enterprise data. Treating them as add-ons creates fragmentation and governance blind spots.
To realize value, agents must be integrated into core workflows. That means aligning them with business logic, data access policies, and accountability structures. Without this, agents remain peripheral—useful in demos, but disconnected from enterprise outcomes.
Design agents as part of the system, not outside it.
2. Use the Lull to Build Agent Governance Foundations
Agentic AI introduces new governance challenges. These systems don’t just analyze—they act. That means enterprises must rethink oversight, logging, and escalation. Most organizations haven’t built the infrastructure to track agent decisions, audit outcomes, or intervene when needed.
This is the moment to build that foundation. Define what agents can and cannot do. Establish decision boundaries. Create escalation paths. Build logging systems that capture agent actions in context. These steps are harder to retrofit later.
Build governance before scale—retroactive oversight rarely works.
3. Rethink Enterprise Architecture for Agent-Oriented Workflows
Traditional enterprise architecture assumes human decision-makers. AI agents change that. They operate continuously, across systems, without waiting for human input. That creates new demands on data pipelines, orchestration layers, and system interoperability.
Most current architectures aren’t built for agentic workflows. They lack the real-time responsiveness, modularity, and observability needed to support autonomous agents. This is the time to assess architectural gaps and redesign for agent-native operations.
Use this phase to align architecture with agentic workflows—not just human ones.
4. Clarify the Business Logic Agents Will Execute
AI agents don’t invent business logic—they execute it. But many enterprises haven’t documented their decision rules clearly enough for agents to follow. This creates ambiguity, drift, and inconsistent outcomes.
Clarifying business logic isn’t just a technical task—it’s a business one. It requires mapping decisions, defining acceptable tradeoffs, and codifying rules. In financial services, for example, agents assisting with credit risk evaluation must operate within tightly defined thresholds and escalation criteria. Without this clarity, agents either underperform or overreach.
Codify business logic now—agents can’t execute what isn’t defined.
5. Avoid Premature Scaling Based on Vendor Promises
Many vendors promise agent scalability out of the box. That’s rarely true. Scaling agents requires enterprise-specific adaptation—data integration, workflow alignment, and governance layering. Premature scaling leads to inconsistent performance, fragmented oversight, and wasted investment.
Use this phase to test agents in controlled environments. Validate performance against real business metrics. Identify failure modes. Build internal expertise. Scaling should be deliberate, not reactive.
Treat vendor claims as starting points, not endpoints.
6. Build Cross-Functional Agent Readiness
AI agents touch multiple domains—IT, data, compliance, operations. But most enterprises still treat them as isolated initiatives. That creates friction, slows adoption, and increases risk. Readiness must be cross-functional.
This means aligning stakeholders early. Clarify roles. Define shared success metrics. Build common language around agent capabilities and limitations. In healthcare, for instance, agents assisting with clinical documentation must align with both IT and medical governance teams to ensure accuracy and compliance.
Prepare the organization—not just the tech—for agentic scale.
7. Use the Trough to Shape Competitive Differentiation
The lull in AI agent adoption is temporary. When the next wave of maturity hits, the gap between prepared and unprepared enterprises will widen quickly. Those that used this phase to build foundations will scale faster, with less friction and more control.
This is not about being first—it’s about being ready. The decisions made now will determine whether agents become a source of differentiation or a source of disruption.
Use this phase to build readiness that competitors will struggle to match.
Agentic AI is not a feature—it’s a shift in how enterprises operate. Most organizations are still watching, waiting, or experimenting. That’s fine. But the ones that use this phase to build clarity, governance, and architectural readiness will be the ones that extract real value when the next wave arrives.
What’s one foundational principle you believe matters most when introducing or scaling AI agents across business units? Examples: Starting with clear boundaries, aligning autonomy with risk tolerance, embedding agents into existing workflows, designing for modular reuse.