Enterprises have been in a full-sprint towards AI ever since its first development a couple years ago. Major tech companies are approving AI budgets at record speed, while executives are under pressure to deploy such agents before competitors do. Across entire industries, there’s never been something more promising, more innovative, more transformative until right now.
Even beneath all this enthusiasm, there’s always been some lingering concerns with AI. Surely, it eases processes. Makes work flow better. Helps employees see what would otherwise be unrecognizable. Technologists predicted it would change work forever, and it has done just that, but only to a certain extent.
At a glance, companies feel prepared to take on AI and its capabilities fully. They have equipped their teams and have a strategy of what tools they want to pilot. But in truth, there is an AI readiness gap, or the space between buying AI and being able to use it responsibly at scale.
Experts say true AI readiness doesn’t begin with the investment alone, but with clear governance, security, and regulations around it. If companies expect to succeed, they need well-structured data, defined accountability, and operating models that support how AI systems actually function over time.
Shomron Jacob, a Silicon Valley–based AI Strategy Expert and Technology Advisor, has been watching this disparity unfold across mid-to-large enterprises in particular. According to Jacob, organizations often treat AI as an opportunity to upgrade, yet they are adopting much faster than they can be deployed safely. That’s the hidden risk that’s impacting which companies remain afloat, and which ones fall behind.
One of the most common mistakes is overconfidence and overestimation. Companies frequently believe they are well-informed with AI, assuming that heavy investment automatically translates to readiness. But in reality, AI introduces far more consequences that quietly lead to bias, poor data quality, and inadequate frameworks as a whole.
When companies deploy too fast, AI pilots begin to fail or gradually dissolve. The algorithms work. The tools perform as expected. What breaks down is compliance, where poor ownership, oversight, and judgement start to affect daily operations. Without safeguards, what first seems like experimentation eventually turns into severe risk.
Jacob points out that while this is an all-too-common reality for companies, leaders can no longer afford to ignore this. The challenge now isn’t about whether to invest in AI, but it’s a question of how to proceed once the budgets, the tools, and the plans are built in.
Instead of chasing every new upgrade or agent, proper AI adoption should force organizations to take an honest look at their current complexities, team structure, and internal responsibilities. Accomplished businesses are not just using AI, they’re integrating it in a way that is going to optimize at large. That could mean adding human input in the mix, or training employees to make thoughtful decisions right alongside it.
For CIOs and CFOs, this moment also calls for a shift in the strategy entirely. Rather than viewing AI as a race, leaders must treat AI as a long-term tool that requires in-depth collaboration, expertise, and reason. Ultimately, it’s about responsible AI use that will leave a lasting impact, guiding choices based on a proper, regulatory mindset.
When companies strengthen the AI blueprint, the difference becomes revolutionary. But when they stall, AI investments never leave the experimentation phase. In fact, an industry survey reports that about 95% of generative AI pilots fail, not because the technology underperforms, but because organizations lack the governance, the trust, and the operating structures to support them.
As AI continues to accelerate, companies won’t win if they deploy just to be the first or to prove they’ve spent the most. They will only thrive if they choose to build safeguards, align teams, and understand their own limits on their own terms.
By pushing every enterprise to acknowledge what AI readiness looks like, entire teams can finally move from reactive adoption to tangible results. In an era where automation affects nearly every corner of work, this new way of AI will be the foundation of everything.