Agentic AI Readiness: The Stories Companies Tell vs. What They Actually Build
The AI Market Problem
High-tech customers demand precision, speed, and trust. Companies in the software, semiconductor, and cybersecurity sectors are expected to lead the AI wave, not lag behind it. That makes agentic AI readiness mandatory, not optional. However, the AI hype cycle has left many high-tech companies asking the wrong question: “What AI features should we adopt?” instead of “What foundations do we need to prepare for agentic AI success?”
This brief outlook from Alvarez & Marsal and Lucidworks provides leaders with a clear, actionable framework to assess AI readiness—and showcases prime examples of high-tech leaders facing these questions
What the Data Says
95%
of generative AI projects fail to deliver ROI
35%
of companies meet minimum requirements for agentic AI
83%
of AI leaders report “major” or “extreme” concern about AI implementation
These signals point to one takeaway: scaling agentic AI requires enterprise-grade foundations, not feature velocity.
Enterprise-Grade Agentic AI Starts with the Right Foundations
Agentic AI is a step change from traditional automation and assistive AI. It introduces real autonomy, with agents that can reason, plan, and act across interconnected systems. To deploy that autonomy safely and reliably, organizations need more than advanced models. They need an enterprise ecosystem designed for unpredictable workloads, multi-agent coordination, and consistent performance and accuracy.
A structured approach helps leaders move past feature velocity and focus on what drives scalable, trusted outcomes. The building blocks below are grouped into three pillars of readiness:
Includes digital experience foundations, data quality and integration, governance and security, and scalability and infrastructure.
This pillar represents the technical foundation that enables AI to deliver customer value on a scale. Companies must establish robust data pipelines and secure governance frameworks and scalable infrastructure before deploying customer-facing AI capabilities.
Encompasses automation vs. AI distinction, agentic capabilities for autonomous problem-solving, and customer journey mapping to identify high-value intervention points.
This pillar focuses on developing real intelligence that has a measurable effect on customers, rather than merely using automation to keep up with market trends.
Includes partner ecosystem readiness, comprehensive measurement and ROI frameworks, and organizational alignment and skills development.
This pillar ensures AI initiatives translate into sustainable business outcomes through proper implementation and management.
Together, these pillars help high-tech leaders close the gap between perceived readiness and real infrastructure, and move from experimentation to enterprise-grade deployment.
Read the full outlook for the framework, building blocks, and execution steps that help tech leaders deliver enterprise-grade agentic AI.
