Why AI Readiness Matters
A recent AWS study revealed that only 6% of respondents said they had one or more gen AI use cases in production deployment.
Of course, there are many reasons that a proof of concept is not rolled out. One of the biggest factors is a lack of readiness in one (or more) of four key areas: Organizational Readiness, Business Readiness, Data Readiness, and Infrastructure Readiness.
The foundation of a successful AI project is readiness in all of these areas.
With AI projects, a lack of readiness can not only slow progress—it could set an organization up for expensive or potentially embarrassing misfires. To avoid these, take time to assess your readiness status before starting an AI project.
Unfortunately, many teams skip this step.
Because checking readiness means uncovering uncomfortable truths. Like:
- The necessary data isn’t ready to support that AI workflow.
- The frontline staff doesn’t trust AI or worries that AI will replace them.
- Current governance or compliance policies do not consider AI impacts.
Rather than guessing, the Global Tech readiness assessment delivers crystal-clear answers:
- Where the infrastructure could support AI—and where it can’t yet.
- Which departments are most receptive (and resistant) to adopting new tech.
- What quick wins are realistic—with quickest, visible ROI.
- Where the organization is ready—or not—to adopt AI.
How a Global Tech AI Readiness Assessment Solves Your Problem: Our framework breaks down readiness across infrastructure, skills, culture, and data flows. It’s honest, calibrated, and built to inform—not overwhelm. The results will provide a firm foundation from which to build your AI projects.
Not ready for a full assessment? Take our 10-minute quiz for a high-level look at your AI Readiness in four important categories:
- Organizational Readiness
- Business Readiness
- Data Readiness
- Infrastructure Readiness
Contact us to discuss your needs.
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