Vibe Coding Surges as AI Talent Shortages Challenge Enterprise Innovation

Comprehensive Global AI Benchmark Report

Georgian Partners, alongside NewtonX and a global consortium of 11 partners, published the AI, Applied Benchmark Report revealing how AI is reshaping B2B software and enterprise firms worldwide. The report is based on a blind survey of 612 executives from R&D and go-to-market roles, spanning 10 countries and 15 industries, representing companies with revenues from $5 million to over $200 million.

Strategic Consortium Collaboration

The report’s global scale and strategic support come from consortium members including the Alberta Machine Intelligence Institute, AI Marketers Guild, FirstMark, GTM Partners, Untapped Ventures, Vector Institute, Startup Nation Central, and Grove Ventures. This partnership enabled diverse sector participation and broad international benchmarking.

AI as a Business Imperative

AI has become a top strategic priority for 83% of B2B and enterprise companies. Among the top five business priorities, three are AI-related, highlighting AI’s deep integration in corporate strategies. Key motivations for AI adoption include enhancing internal productivity, gaining competitive advantage, and improving cost efficiency and revenue. Notably, competitive differentiation has overtaken cost savings and revenue growth as the second most important driver, signaling AI’s role as a market leadership tool rather than just automation.

The Rise of Vibe Coding

A significant insight is the rise of Vibe Coding—automated code generation and debugging using AI models. It ranks as the third most common R&D use case in production, with 37% of companies actively using it and another 40% piloting it. This growth addresses the critical shortage of AI technical talent, the number one barrier to scaling AI, cited by 45% of R&D leaders, surpassing even the high costs of model development.

Vibe Coding enables smaller engineering teams to speed up delivery, debug more quickly, and produce cleaner, well-documented code with less effort. Companies report measurable reductions in manual work across quality assurance, infrastructure, and deployment processes.

AI-Driven Productivity Gains and Challenges

AI adoption improves development velocity (70% of respondents), code quality and documentation (63%), and deployment frequency (over 50%). However, metrics like mean time to restore, cycle time, and change failure rate show less improvement, indicating that stability and resilience still heavily depend on human intervention.

Infrastructure Investments Boost AI Capabilities

Teams are adopting new infrastructure tools to transition AI from experimentation to production. Key adoptions include:

• 53% integrating large language model (LLM) observability platforms
• 51% using data orchestration tools like Dagster and Airflow
• Implementation of vector databases, cron jobs, and durable workflow engines to support scaling and reliability

Data sourcing is also increasing, with 94% using owned data, 80% public data, and growing use of synthetic and dark data.

Expanding Large Language Model Ecosystem

OpenAI remains the dominant LLM provider with 85% production usage. However, other models are gaining traction:

• Google Gemini increased by 17 points to 41%
• Anthropic Claude at 31%
• Meta’s Llama 3 at 28%
• Reasoning-focused models like OpenAI’s o1-mini (35%) and DeepSeek (18%) are entering production

Organizations are moving toward multi-model AI stacks, selecting models based on use cases rather than relying on a single vendor.

Uneven AI Maturity Progress

Using Georgian’s Crawl, Walk, Run AI maturity model, more companies are advancing from beginner to intermediate stages, but top-tier maturity remains limited:

• Walkers decreased to 40% (from 49%)
• Joggers increased to 31%
• Runners remained at 11%, indicating challenges in scaling AI projects

Companies reaching the "Runner" stage typically link AI initiatives directly to revenue or cost outcomes, a capability still rare across the industry.

Difficulty Measuring AI ROI

Many R&D teams struggle to connect AI projects to clear KPIs; over half do not link AI efforts to measurable outcomes. Only 25% tie AI to new revenue, and 24% report positive effects on customer acquisition costs. Nonetheless, over 50% observe improvements in customer satisfaction and long-term value.

Improving Cost Management

Talent shortages remain the biggest hurdle, but cost control shows progress:

• 9-point increase in companies reporting stable or reduced data storage costs
• Decreases in software maintenance, labor, and operations expenses
• Reduced reliance on strict project cost-cutting measures

Additionally, 68% of companies use third-party AI solutions to manage complexity and expenses, especially as AI integrates more deeply into go-to-market software and internal platforms.

The Future of AI in Enterprise

AI is transforming software development beyond automation—it's augmenting teams and becoming foundational to engineering workflows. Vibe Coding is pivotal, helping companies overcome talent shortages, accelerate time-to-market, and improve code quality without proportional headcount increases.

Successful companies will operationalize AI, embedding it strategically rather than treating it as an experiment. Automation will amplify developers rather than replace them, defining the next wave of enterprise innovation.