The accelerating interest in Generative AI does not automatically translate into measurable business outcomes. The widening disconnect between GenAI’s potential and its actual impact is what many analysts now refer to as the GenAI Gap. In essence, organizations invest heavily in promise, but execution collapses at the point where real capabilities are required. The result is predictable: investments underperform, expectations are unmet, and only those organizations capable of closing the GenAI Gap will gain meaningful competitive advantage. Recent studies reveal a striking trend: although more than 70% of companies have launched GenAI pilots, only 10–12% have successfully scaled them.
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- Skills gap
The most common challenge is the lack of people trained to work with GenAI. Companies need not only ML engineers, but also data architects, prompt-engineering specialists, and compliance and governance consultants. The problem lies both in the shortage of talent on the market and in the inability to quickly develop these competencies internally.
Solution: investing in team training, engaging expert partners, and outsourcing secondary roles.
- Legacy architecture and technical barriers
Using GenAI requires centralized and clean data, productive infrastructure, secure access to model services, and integration with legacy IT systems. The issue is that many corporate platforms simply cannot meet GenAI requirements — and companies are forced either to modernize their architecture or build a new one.
Solution: phased GenAI implementation, modernization of data architecture, and secure AI platforms development.
- Governance and responsible AI
Implementing GenAI is a responsible decision that is impossible without rules for data use, security policies, controls, certification, and structured processes. Companies that lack this foundation cannot properly manage GenAI, achieve solid results, or scale their solutions.
Solution: establishing AI committees, implementing responsible AI practices, and forming transparent usage principles.
- Trust
Even the best model will not succeed if users do not trust it. A model must inspire confidence, be convenient to use, take into account people’s workflows, and deliver accurate results.
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Solution: clear interfaces and educational materials.
- FAIGMOE and Enterprise architecture
Recent studies propose new frameworks:
FAIGMOE — a structured approach to implementing Generative AI in medium and large companies.
Enterprise Architecture (EA) as a dynamic capability — a tool for aligning technological solutions with business strategy.
Both frameworks have in common that GenAI implementation is a systemic action requiring preparation and workforce training. Therefore, the universal approach is to develop a long-term AI strategy that considers not only the model but also cultural code, security, and data.
- Security, risks, data quality
Information security is another major factor contributing to the GenAI gap. Companies are afraid to transfer confidential data, lack control tools, and are not confident in protecting their models. At the same time, many solutions rely on low-quality data, which leads to errors, biases, and inaccuracies.
Solution: building AI platforms with access control, anonymization, model auditing, and thorough data preparation.
Among the most important reasons for the GenAI gap are the underestimation of why GenAI is needed, the absence of real implementation value (only hype), and low workforce readiness.
There is also the issue that many companies have dozens of PoCs but not a single productive AI system. The reason is simple: pilots are not tied to business processes; they are evaluated based on impressions, not results.
Solution: launching small but highly practical use cases — automation, customer support, document management, expert systems.
Thus, bridging the GenAI gap is a strategic effort that requires considering all factors, investing in skills, and modernizing data architecture. This comprehensive approach underpins the development of all AI solutions in Muteki Group. You can contact our experts, receive a detailed consultation, and understand your next steps. We are proud to be among the first to integrate AI into our company’s expertise and to have 10+ years of experience working with this technology. We will gladly discuss your project and provide a practical roadmap for implementing AI in your business.
In the end, I would like to add that you can see the GenAI gap not as a technological setback or a flaw in your planning, but as a challenge — an organizational transformation that is inevitable if you want to succeed.
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