The problem does not lie in the quality of the models or in regulation, but in the approach. General-use tools like ChatGPT are widely adopted, yet customized enterprise systems fail in most cases when moving from pilot to production.
The good news: there are ways to close these gaps and become part of the remaining 5%.

Adoption gap
Most organizations are on the “wrong” side of the gap. This means there is high adoption of generic tools, but little structural transformation achieved through them.
The fact that over 80% of organizations have explored AI shows massive adoption. Yet disruption remains scarce: only two out of nine industries show significant change.
The gap becomes clearer in the failure rate of customized enterprise tools, which rarely move beyond the pilot stage into production.
This reflects a state of “widespread experimentation without transformation,” with companies investing in solutions that do not fit well into workflows or deliver measurable value in outcomes.
Learning gap
The main reason pilot projects stall is the “learning gap”: AI tools do not learn, integrate well, or adapt to workflows. Users prefer flexible options like ChatGPT for quick tasks, but for complex work, 90% still prefer a human colleague. Why? AI systems cannot accumulate knowledge or improve with feedback.
This structural problem stands as the biggest barrier to scaling adoption within enterprises.
The most successful creators of Generative AI systems are those betting on adaptive systems that learn from customer feedback, focus on high-value use cases instead of generic solutions, and prioritize integration with existing workflows and continuous learning.
Partnership gap
Overcoming the Generative AI gap is not a challenge to be faced alone. The key is connecting with the right strategic technology partner—one that understands the business, internal processes, and transformation goals. This kind of collaboration ensures that AI implementation goes beyond isolated solutions, integrating smoothly into workflows and evolving over time.
At Nubiral, we bring expertise in adaptation and customization. We help systems learn from real user feedback and adjust to the company’s context.
We also help prioritize the highest-value use cases, accelerate adoption across the organization, and ensure every AI project generates tangible business impact—always with cybersecurity in mind.
Ultimately, choosing the right partner turns AI investment into a sustainable competitive advantage, minimizing risks and maximizing real transformation opportunities.
Conclusions
Bridging the Generative AI gap requires partnering with technology experts capable of integrating deeply and evolving over time. This way, systems not only learn and adapt but also generate sustained value, strengthening the organization’s competitive edge. The key is not just to invest in AI, but to invest intelligently—with partners and strategies that truly transform the business.
Is your organization ready to leap across the Generative AI gaps? Our team of experts can help. Schedule your meeting today!