By Eric Dosmann, Director, Technical Sales and Offers

The AI Adoption Gap

This is Part 1 of a 3-part series on unlocking the true value of Assistive AI tools through strategic training and adoption frameworks.

The promise of assistive AI tools like GitHub Copilot, ChatGPT, and Gemini is undeniable. Organizations worldwide are investing millions in these technologies and expecting transformative results. In fact, according to the McKinsey Global Institute’s 2025 report, 78 percent of organizations are now using AI in at least one business function. But despite this remarkable progress, many find themselves asking the same question: “Where’s the ROI?”

The disconnect is striking and reveals a fundamental challenge in enterprise AI adoption. While 92 percent of executives plan to invest more in AI over the next three years, only 1 percent of leaders say their companies are fully mature in their AI deployments. This massive gap between potential and progress isn’t about the technology itself — it’s about how organizations approach implementation, training, and measurement.

The Reality of AI Adoption Today

Assistive AI tools represent a paradigm shift in how we approach software development, testing, and business analysis. However, simply providing access to these tools without proper training and adoption frameworks is like giving someone a race car without teaching them how to drive — the potential for greatness exists, but so does the risk of spectacular failure.

The McKinsey annual report emphasizes this challenge, noting that “the big gap between potential and progress can be explained by the time required for organizations to adapt, develop complementary innovations, and reskill their workforces.”

Data supports these points:

• 46 percent of leaders cite skill gaps in their workforces as a major barrier to AI adoption.
• More than 20 percent of employees report receiving minimal AI training.
• Organizations struggle with the pace of AI evolution, where new capabilities emerge faster than training programs can adapt.

The Future We Face

This skills gap isn’t just about technical “know-how”; it encompasses understanding when and how to use AI tools effectively, interpreting their outputs critically, and integrating them into existing workflows without disrupting quality or security.

The AI landscape has evolved dramatically in recent years. As referenced by McKinsey: “The rise of natural-language-based tools has democratized programming so that both professionals and amateurs can build and prototype software faster than ever.” This acceleration brings both unprecedented opportunities and new challenges, such as managing technical debt and ensuring code quality as development velocity increases.

Organizations must recognize that we’re witnessing the convergence of multiple AI capabilities:

• Generative models for language and code creation
• Analytical engines for data-driven insights
• Agentic systems capable of autonomous decision-making and task execution

This merging is quietly reshaping how businesses operate and how individuals interact with technology, creating new possibilities while demanding new skills and frameworks.

Stay tuned for the second article in this blog series, which will explore training solutions for AI adoption. In the meantime, you can check out the eimagine AI web page for an overview of how we can help businesses like yours harness the full potential of this new technology.