Use gen AI economics to lap the competition

Pinpoint the factors driving up generative AI expenses—and stay ahead of the curve as your projects scale. Set clear cost guardrails and assess compute needs as early as possible in project planning to avoid expensive surprises down the line.

• Identify cost catalysts. Understand how different elements influence gen AI costs—including hardware, cloud services, model selection and training, data collection and cleaning, integration, and maintenance—and how these drivers can change as you move from pilots to projects at scale. Set clear cost control parameters to guide every gen AI decision and give your teams the tools they need to assess, monitor, and manage the cost of compute implications of gen AI at every stage.
• Recalibrate your computing resources. Conduct a full lifecycle cost assessment to preempt and anticipate compute needs. Invest in more cost-efficient infrastructure, fit-for-purpose models, and workload optimization tools to keep costs manageable as you scale. Collaborate with partners to reduce training, fine-tuning, and development costs.
• Use FinOps and cloud optimization to cut gen AI costs. Use a hybrid cloud platform as your cost of compute control tower. Deploy kubernetes to manage workloads and services in containers to roll out gen AI applications in the most consistent and efficient manner. Monitor the evolving costs that come with gen AI, from data storage to model retraining and fine-tuning to security and compliance, to avoid taking an unexpected hit to the balance sheet.

Channel the combined power of gen AI and hybrid cloud to deliver on concrete business goals. Optimize and orchestrate with hybrid by design and containerized workloads to corral compute costs and streamline operations.

• Establish your nerve center. Gain visibility into where and how the demand for compute resources increases as you scale generative AI. Find opportunities to orchestrate these resources more cost-effectively, deploying only the precise amount of compute power needed for each task. Extend hybrid cloud architectural principles across your technology estate.
• Contain cost creep. Design for modularity and flexibility. Follow architectural principles that let your organization choose the best and most cost-effective environment for each AI use case and initiative.
• Measure success with clarity. Centralize your cost of compute management and create enterprise-wide guidelines, driven by clearly defined business objectives. Create a governance structure with a defined responsibility assignment matrix and performance metrics.

Arm managers with intelligent decision support tools that slash compute costs and fuel real-time adaptability. Automate workflows and prune models to unlock a new era of efficiency, reduce costs, and unleash innovation.

• Inject generative AI into the heart of IT operations. Give IT managers gen AI tools that help them create automation scripts, document operations, and spend less time on compliance. Revolutionize mainframe management with automated problem detection and resolution, predictive capacity management, and real-time performance monitoring.
• Optimize and automate your way to efficiency. Tap gen AI for synthetic data generation, automated code generation and optimization, and dynamic resource allocation to reduce cost of compute.
• Adapt to changing market conditions in real time. Use gen AI to analyze real-time demand, market trends, and competitor pricing to optimize pricing strategies, maximize revenue, and reduce price-related losses. Assess historical spending patterns to predict budget requirements, optimize budget allocation, and reduce waste.