Georgian partners partnered with Newtonx and an 11-Partner Global Consortium to release their AI, benchmark reports on applications, providing a powerful snapshot of how AI can transform B2B software and enterprise companies around the world. The second wave of expansion has been drawn from 612 executives– Equal distribution between R&D and listed leaders – Off-road racing in 10 countries and 15 industries, companies representing annual revenues range from $5 million to over $200 million.
What sets the report apart is its global and strategic support. Consortium partners include Alberta Machine Intelligence Academy, AI Marketing Chamber of Commerce, Firstmark, GTM partners, Undeveloped Ventures, Vector Institute and Tel Aviv’s startup Nation Nation Nation Central and Grove Ventures, among others. Their participation helps expand engagement and ensures sectoral diversity on international benchmarks.
The report is not just a measure adopted, but also captures structural barriers that inspired AI use cases such as Vibe encoding and the evolving maturity curve of AI integration. Based on validated, execution-level opinions, the report provides companies with a practical framework to achieve their position and what is holding them back.
AI becomes a strategic priority
Artificial intelligence is no longer considered optional. The report found Now, 83% of B2B and enterprise companies rank AI as their top five strategic priorities. In fact, the five of the most selected business priorities are three related to AI, suggesting that embedding methods have become a cross-company agenda.
The main motivations for AI adoption are still:
- Improve internal productivity
- Create competitive advantage
- Improve cost efficiency and revenue growth
However, what happened is that competitive differences have now surpassed the second important driving force. This marks a shift in mindset: AI is not only a tool for automation, but also a weapon of market leadership.
Vibe encoding enters mainstream
The report’s outstanding insight is the rapid rise in Vibe encoding, a term used to automatically code generation and debug using AI models. Atmosphere coding has become #3 R&D Use Cases Report in production, by 37% of companieswhile the other one 40% are actively driving it.
This trend is not only to increase developer productivity. This is also a direct response to the challenges of the industry as a whole: A shortage of artificial intelligence technology talentsnow #1 Obstacles to Zoom AI. 45% of R&D leaders see this talent gap as their primary concern, even at the high cost of model development.
Vibe encoding helps fill this gap by allowing lean engineering teams to speed up delivery schedules, debug faster and generate cleaner, less overhead code. Respondents noted that manual efforts across quality inspections, infrastructure and deployment workflows are measurable.
Artificial intelligence productivity increases to their limits
AI usage across development pipelines shows obvious benefits. According to the report, 70% of R&D respondents reported faster development, 63% of people were looking for improved code quality and documentation, and more than half of the deployment frequency increased.
However, not all indicators have improved. area Average recovery time,,,,, cycleand Change the failure rate Keep your weakness. This shows that although AI is accelerating the front end of development, stability and resilience are still human dependent.
Power supply to infrastructure upgrade AI stack
Supporting these gains is a huge shift in infrastructure investment. AI-driven teams are using new tools to transition from experiments to production:
- LLM Observability Platform 53% of companies have been integrated
- Data Orchestration Tool 51% of Dagst and airflow are now used
- Vector database,,,,, Cron Jobsand Durable workflow engine Deployment to support scales and reliability
Meanwhile, companies are purchasing more data than ever to facilitate their models. use Data possessed 12 percentage points rose to 94%, while Public data Use rose to 80%. Synthetic and dark data (once the edge source) are used by more than half of the companies respectively.
LLM adopts diversification
Openai remains a leading provider of large language models, with 85% of respondents using their models in production. However, the landscape is developing rapidly:
- Google Gemini See a 17-point surge and now use 41%
- Claude Rises to 31%
- Meta’s Llama 3 family Gain attraction among 28% of people who adopt
- Inference-specific models O1-Mini like Openai (35%) and DeepSeek (18%) are also joining production
This shift reflects a shift to a multi-model AI stack where organizations match models to use cases rather than relying on a single vendor ecosystem.
Improperity of artificial intelligence
Georgian segment companies use their crawling, walking, and running AI mature models. While more and more organizations are ranging from beginners to intermediate levels, the maturing tops are still elusive:
- “Pacers” dropped to 40%, below 49%.
- “Jogging” rose to 31%, indicating growth momentum
- “Runners” keep stagnant at 11%, indicating the upper limit of scalability
Companies that do reach the “runner” stage are often those that connect AI projects directly to revenue or cost-cost costs, which is still underdeveloped capabilities throughout the industry.
ROI remains elusive
One of the most ongoing challenges identified in the report is Lack of clear ROI measurements. More than half of the R&D teams acknowledge that they did not connect AI projects to any specific KPIs. Only 25% directly link AI plans to new revenues, and only 24% report positive impact on customer acquisition costs.
Nevertheless, optimism remains. More than 50% of respondents said AI improves customer satisfaction and long-term value. But overall, the financial reasons for AI remain vague, especially at the medium-term level.
Cost management is improving
Although talent remains the biggest obstacle, costs are becoming increasingly easier to manage. The report shows:
- Transition to a 9-point transition to a stable or reduced data storage cost
- Software maintenance, labor and operational costs decline
- Rely less on cost-cutting measures
Additionally, 68% of companies now rely on third-party AI solutions to manage costs and complexity, especially when AI is embedded in GTM software and in-house platforms.
Look forward
The meaning of this benchmark data goes far beyond the dashboard and board of directors. With AI’s key to software construction, deployment and maintenance, the industry is entering a new phase – a new phase no longer just about people, but about how teams can enhance their intelligence with machine partners.
Vibe encoding represents a turning point. This is not only a productivity tool; it has become the fundamental layer of modern software development. For companies facing an ongoing talent shortage, it provides a way to unlock throughput, reduce market time and improve code quality without scaling up employees at the same rate. For those along the maturity curve, it creates a main chain for the AI-Native engineering workflow that can be scaled with observability, reliability, and measurable business impact.
The broader message is clear: Successful companies not only use AI, but they embed it and grow with it. In this new era, automation is not about replacing developers. It’s about amplifying them.
Those who view atmosphere coding and its support infrastructure as strategic investments (rather than experiments) will define the next wave of corporate innovation.
