AI Strategy: A Road Map From Readiness to Implementation
AI Strategy: A Road Map From Readiness to Implementation
Publish Date: 2026-03-31 10:09:00
Source Domain: www.forvismazars.us
While some companies are just starting to adopt artificial intelligence (AI), others that have already adopted generative AI assistant tools might be evaluating their next move. These organizations don’t yet employ more advanced agentic AI systems. Agentic AI systems involve multiple AI agents working together across end-to-end workflows, coordinating tasks, decisions, handoffs, and monitoring within defined guardrails. This type of AI deployment overcomes data silos, which persist when the organizations’ AI tools don’t connect. This article will focus on steps leaders can take as they move from planning to execution in the next phase of AI.
When aiming for the appropriate use of AI in their companies, leaders should consider that:
- AI strategy works when it connects business outcomes, data foundations, operating models, and AI governance.
- If the organization is still early in adoption, this can be a time when gaps between ambition and readiness surface.
- A short, step-by-step road map can help reduce “pilot purgatory” and prioritize use cases with measurable return on investment.
Why AI Strategy & AI Maturity Should Advance Together
Technology transformation has emerged as the top strategic priority for U.S. business leaders across industries and company sizes, as organizations shift from strategy design to execution, according to the C-Suite Barometer: Executive Leadership Insights in the US from Forvis Mazars. The research also shows that C-Suite executives are increasingly spending more on AI implementation as they move from experimentation to scale. Nearly seven in 10 U.S. executives say AI is having a major impact on their company, and nine in 10 U.S. companies have restructured teams to implement AI, signaling a shift toward execution and operating model change rather than isolated experimentation.
Technology transformation is firmly in execution mode, with AI being scaled across core functions and teams being restructured to support it.
