Send us Fan MailAI adoption is accelerating across the enterprise, yet most initiatives struggle to move beyond experimentation, with nearly 80% of projects failing to scale or deliver measurable business value. The reasons are remarkably consistent: fragmented data, poorly defined processes, architectural misalignment, and vendor claims that often outpace organizational readiness. AI does not correct these weaknesses—it amplifies them. When underlying processes are broken, automation accelerates the problems. When data quality is poor, predictive models simply operationalize bad assumptions. As vendors promote copilots, autonomous workflows, and predictive insights, many organizations still lack the governance discipline, data consistency, and process clarity required to support these capabilities at scale. This growing gap between vendor promises and buyer readiness is why many AI pilots stall before reaching production. A successful AI strategy therefore begins with readiness. It requires aligning operating models, strengthening data governance, redesigning workflows, and clarifying decision ownership. A structured AI-readiness framework—built around data, process, architecture, people, and governance—helps executives translate AI from hype into a practical 12–24 month roadmap focused on measurable business outcomes.Video: https://www.elevatiq.com/events-and-webinars/ai-readiness-2026-how-to-strategize-ai-initiatives/Questions for Panelists?🔗Sam Gupta: linkedin.com/in/samguptausa/🔗Shrestha Dash: https://www.linkedin.com/in/shrestha-dash-662b00204/Background Soundtrack: Away From You – Mauro SommFor more information on growth strategies for SMBs using ERP and digital transformation, visit our community at wbs. rocks or elevatiq.com. To ensure that you never miss an episode of the WBS podcast, subscribe on your favorite podcasting platform.

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