Page 40 - Red Hat PR REPORT - OCTOBER 2025
P. 40

Article



               By embracing Private AI, CIOs can unlock the full value of their enterprise data, reduce cost
               and risk, and deliver smarter, more personalized AI experiences. In the race to scale AI
               responsibly, the future belongs to those who build their own runway.







               Real-World Applications: Regional Case Studies

               Two very different sectors point to the same conclusion: open, private AI is delivering hard
               business wins.

               At DenizBank, Intertech standardized a private, container-based AI stack and gave 120+ data
               scientists self-serve workbenches. Time-to-market for new models dropped from about a
               week to ~10 minutes, while GPU “slicing” curbed accelerator waste. Crucially, teams now
               push core banking  models—credit-risk scoring and fraud-anomaly detection—into
               production fast enough to influence real-time decisions.


               In energy, Aramco’s Upstream Digital Center built a GenAI foundation running inside its own
               environment to centralize resources and streamline upstream workflows. Drill and plant
               engineers complete routine analysis up to 10× faster, saving roughly two hours per person
               per day,  and new models are onboarded in under an hour—time that goes back into
               subsurface modeling, drilling optimization, production monitoring, and other high-value
               tasks. The firm also applies AI to operational risks such as flaring prediction and component-
               failure detection to keep output efficient and equipment online.

               Across banking and oil & gas, the pattern is consistent: open tooling plus private deployment
               = faster model delivery, tighter governance, and measurable productivity gains.

               In Saudi Arabia, Aramco is another important government entity that is utilising AI for
               complex geological surveys.

               A Glimpse into the Future: Agentic AI


               Perhaps the most exciting frontier is  agentic AI, a paradigm shift from today’s prompt-
               response models to autonomous agents capable of interacting with enterprise applications
               to complete full workflows. Imagine an AI agent that not only answers queries but
               automatically updates Salesforce entries, reconciles invoices in SAP, or schedules meetings
   35   36   37   38   39   40   41   42   43   44   45