Georgian Partners, in collaboration with NewtonX and an 11-partner global consortium, has released its AI, Applied Benchmark Report, offering a strong snapshot of how AI is transforming B2B software and enterprise corporations worldwide. This expanded second wave draws on a blind survey of 612 executives—split evenly between R&D and go-to-market leaders—across 10 countries and 15 industries, representing corporations with annual revenues starting from $5 million to over $200 million.
What sets this report apart is its global scope and strategic backing. Consortium partners include the Alberta Machine Intelligence Institute, AI Marketers Guild, FirstMark, GTM Partners, Untapped Ventures, the Vector Institute, and Tel Aviv–based Startup Nation Central and Grove Ventures, amongst others. Their involvement helped broaden participation and ensure sector-diverse, international benchmarks.
Greater than only a measure of adoption, the report captures the structural barriers, emerging AI use cases like Vibe Coding, and the evolving maturity curve of AI integration. With findings grounded in validated, executive-level input, the report offers corporations a practical framework to benchmark where they stand—and what’s holding them back.
AI Becomes a Strategic Imperative
Artificial intelligence is not any longer considered optional. The report finds that 83% of B2B and enterprise corporations now rank AI amongst their top five strategic priorities. The truth is, three of the highest five most chosen business priorities are AI-related, showing how embedded it has turn out to be across corporate agendas.
The leading motivations for AI adoption proceed to be:
- Improving internal productivity
- Making a competitive advantage
- Enhancing cost efficiency and revenue growth
What’s modified, nevertheless, is that competitive differentiation has now overtaken cost savings and revenue because the second most significant motivator. This marks a shift in mindset: AI is just not only a tool for automation—it’s a weapon for market leadership.
Vibe Coding Enters the Mainstream
A standout insight from the report is the rapid rise of Vibe Coding—a term referring to automated code generation and debugging using AI models. Vibe Coding has turn out to be the #3 R&D use case reported in production, utilized by 37% of corporations, while one other 40% are actively piloting it.
This trend is just not simply about improving developer productivity. It is also a direct response to an industry-wide challenge: the shortage of AI technical talent, which has now turn out to be the #1 barrier to scaling AI. Forty-five percent of R&D leaders cited this talent gap as their top concern—surpassing even the high cost of model development.
Vibe Coding helps fill that gap by allowing leaner engineering teams to speed up delivery timelines, debug faster, and produce cleaner, documented code with less overhead. Respondents noted measurable reductions in manual effort across QA, infrastructure, and deployment workflows.
AI Productivity Gains—and Their Limits
The usage of AI across development pipelines is showing clear advantages. In line with the report, 70% of R&D respondents report faster development velocity, 63% see improved code quality and documentation, and over half have increased deployment frequency.
Nevertheless, not all metrics have improved. Areas like mean time to revive, cycle time, and change failure rate remain weak spots. This means that while AI is accelerating the front end of development, stability and resilience remain human-dependent for now.
Infrastructure Upgrades Power the AI Stack
Supporting these gains is a dramatic shift in infrastructure investment. AI-driven teams are adopting recent tooling to maneuver from experimentation to production:
- LLM observability platforms have been integrated by 53% of corporations
- Data orchestration tools resembling Dagster and Airflow at the moment are utilized by 51%
- Vector databases, cron jobs, and durable workflow engines are being deployed to support scale and reliability
Meanwhile, corporations are sourcing more data than ever to fuel their models. The usage of owned data rose 12 percentage points to 94%, while public data use rose to 80%. Synthetic and dark data—once fringe sources—at the moment are getting used by over half and 1 / 4 of corporations, respectively.
LLM Adoption Diversifies
OpenAI stays the leading provider of enormous language models, with 85% of respondents using its models in production. Nevertheless, the landscape is evolving rapidly:
- Google Gemini saw a 17-point surge, now utilized by 41%
- Anthropic Claude rose to 31%
- Meta’s Llama 3 family is gaining traction with 28% adoption
- Reasoning-specific models like OpenAI’s o1-mini (35%) and DeepSeek (18%) are also entering production
This shift reflects a move toward multi-model AI stacks, where organizations match models to make use of cases slightly than counting on a single vendor ecosystem.
AI Maturity Gains Are Uneven
Georgian segments corporations using its Crawl, Walk, Run AI maturity model. While more organizations are progressing from beginner to intermediate levels, the highest tier of maturity stays elusive:
- “Walkers” dropped to 40%, down from 49%
- “Joggers” rose to 31%, indicating growing momentum
- “Runners” remain stagnant at 11%, suggesting a ceiling in scalability
The businesses that do reach the “Runner” stage are likely to be those that connect AI projects on to revenue or cost outcomes—a capability still underdeveloped across much of the industry.
ROI Stays Elusive
One of the persistent challenges identified within the report is the lack of clear ROI measurement. Greater than half of R&D teams admit they are usually not connecting AI projects to any concrete KPIs. Only 25% directly link AI initiatives to recent revenue, and just 24% report a positive impact on customer acquisition costs.
Still, optimism persists. Over 50% of respondents say AI has improved customer satisfaction and long-term value. But the general sense is that the financial justification of AI stays fuzzy, particularly on the mid-maturity level.
Cost Management Is Improving
While talent stays the most important obstacle, costs are slowly becoming more manageable. The report shows:
- A 9-point shift toward stable or reduced data storage costs
- Declining costs in software maintenance, labor, and operations
- Less reliance on cost-cutting measures like project restrictions
Moreover, 68% of corporations now depend on third-party AI solutions to administer cost and complexity, especially as AI becomes embedded in GTM software and internal platforms.
A Look Ahead
The implications of this benchmarking data extend far beyond dashboards and boardrooms. As AI becomes central to how software is built, deployed, and maintained, the industry is entering a brand new phase—one where productivity is not any longer nearly people, but about how intelligently teams can augment themselves with machine partners.
Vibe Coding represents a turning point. It’s not only a productivity tool; it’s becoming a foundational layer of contemporary software development. For corporations facing persistent talent shortages, it offers a approach to unlock throughput, reduce time-to-market, and improve code quality without scaling headcount at the identical rate. And for those further along the maturity curve, it creates the backbone for AI-native engineering workflows—ones that may scale with observability, reliability, and measurable business impact.
The broader message is evident: the businesses that succeed won’t just use AI—they’ll operationalize it, embed it, and evolve with it. On this recent era, automation isn’t about replacing developers. It’s about amplifying them.
Those that treat Vibe Coding and its supporting infrastructure as strategic investments—not experiments—will define the following wave of enterprise innovation.
