Inside Georgian’s AI Applied Report: Vibe Coding Rises as Talent Gaps Stall AI Progress

There’s no more waiting for the “AI future”—for most businesses, it’s already here. Artificial intelligence has shifted from interesting experiments to something boardrooms now rank among their very top priorities. In fact, according to the latest AI, Applied Benchmark Report by Georgian Partners, a remarkable 83% of B2B and enterprise organizations say AI is now central to their strategy.

AI isn’t just streamlining tasks or automating routine processes anymore. For many organizations, it’s become the secret ingredient for staying ahead of the competition. Where once AI was seen as mostly a cost-cutter, the new reality is that companies use it to carve out a clear advantage and, ideally, to lead their markets. Georgian’s report covers activity in ten countries and tracks how AI is already being used in 15 industries, highlighting how universal this shift has become.

One standout trend gets special attention: Vibe Coding. This emerging field refers to AI-powered code generation and debugging. Amid a shortage of highly specialized AI talent, more organizations are using “Vibe Coding” as a way around the bottleneck. Already, 37% of surveyed companies have adopted these tools, and another 40% are running pilots. That makes it the third most common AI use in R&D departments, right behind the perennial favorites.

AI now plays a big role in speeding up development cycles and improving software quality. But old-fashioned human oversight isn’t going away—metrics like cycle time, system stability, and system failure rates all show that AI, powerful as it is, isn’t ready to run the show solo. Experienced engineers are still needed to keep things on track.

As reliance on AI deepens, organizations are investing heavily in the infrastructure to support it. Over half the survey respondents are using observability platforms to track large language models (LLMs) in action. Tools for data orchestration and vector databases are also seeing a jump in use, alongside engines that can handle complex, durable workflows. Companies are relying more on their own data than ever, but they’re also experimenting with synthetic datasets and previously untapped, so-called “dark” data.

When it comes to choosing AI models, OpenAI might still be the biggest name in town, but there’s growing interest in alternatives like Google’s Gemini, Anthropic’s Claude, and Meta’s Llama. Some companies are even turning to specialized mini-models like o1-mini from OpenAI, or DeepSeek. This is leading to a more customized, multi-model approach—organizations are stacking different AI systems together, tailoring them to their unique needs.

Still, the journey to full-blown, revenue-linked AI maturity is not quick or easy. Georgian’s “Crawl, Walk, Run” model shows most companies are somewhere in the middle. Just a handful have fully operationalized AI at scale or are able to tie it directly to revenue. For many, connecting specific AI projects to business outcomes remains a tough nut to crack.

While AI adoption barrels forward, many organizations admit that crystal-clear ROI is elusive. More than half of R&D teams haven’t linked their AI projects to tangible KPIs yet. That said, most agree that customer satisfaction and long-term value have improved, even if the impact isn’t always showing up in the financials.

Cost barriers are still a sticking point, but there’s some relief. Storage expenses have leveled off and the price tag for maintaining and operating software is starting to drop with smarter strategies and tools in play. Increasingly, companies are leaning on third-party AI platforms, which offer agility and control without ramping up costs or complexity.

Taking all these findings together, one message rings clear: the next step is making AI a fully integrated, operational muscle for the business—not just a tech experiment in the lab. Vibe Coding, in particular, stands out as a vital tool for software teams, promising both a boost in productivity and quality, even when headcount is tight. But none of this is about robots replacing people. Instead, it’s about empowering developers and teams to do more, faster, and better. Businesses willing to invest in the right foundations and line up AI with real goals are the ones who’ll define the next era of enterprise software.

Curious to dig deeper? Check out the full report: Inside Georgian’s AI Applied Report