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Webinar: Driving AI Investment for Improved ROI
Take control of your AI investments
AI adoption is exploding – and so are the costs. Are you able to determine what you are actually getting out of your AI investments?
Costs are fragmented, value is vague, and investments are disconnected from outcomes. Few companies can confidently say whether they’re investing wisely.
Gartner anticipates that spending on generative AI software will increase by 76% year-over-year, reaching $644 billion in 2025.
Gartner
of generative AI spending is coming from existing budgets
Menlo Ventures Survey
of CIOs cite out-of-control costs as a major barrier to achieving AI success.
Gartner
Get end-to-end visibility and optimization across your entire AI investment lifecycle
To manage AI effectively, we need to recognize that it isn’t a single project or budget line – it’s a portfolio of interconnected investments across infrastructure, talent, tooling, and business outcomes. Managing AI effectively means supporting it across its full lifecycle, from upfront prioritization, to real-time operational management, to long-term ROI tracking.
Get end-to-end visibility and optimization across your entire AI investment lifecycle
To manage AI effectively, we need to recognize that it isn’t a single project or budget line – it’s a portfolio of interconnected investments across infrastructure, talent, tooling, and business outcomes. Managing AI effectively means supporting it across its full lifecycle, from upfront prioritization, to real-time operational management, to long-term ROI tracking.
Plan intelligently
Plan and prioritize AI initiatives by aligning work, resources, and budgets to strategic business goals
Model trade-offs across budget, people, and tech capacity.
Operate efficiently
Give FinOps and engineering teams the telemetry and financial lens to manage the unpredictable costs of AI workloads
Bring in token, GPU, and API usage data via FOCUS and custom connectors
Optimize for value
Calculate the full total cost of ownership of AI investments by integrating cloud, vendor, infrastructure, and labor costs to drive smarter funding decisions
Surface insights and connect cost to value for decision-making.
Features
AI-enabled TBM solutions such as IBM Apptio and IBM Cloudability help you track, optimize, and align AI investments from prioritization to ROI across the lifecycle. Each feature captures a critical piece of AI investment—from infrastructure usage to team labor to portfolio-level trade-offs.
AI TCO & Usage
AI Cost Tracking
Anomaly Detection
Get answers to critical AI investment management questions
AI infrastructure costs are difficult to control and even harder to scale. When resources, strategy, and ROI don’t align, it’s nearly impossible to know which projects to fund. Apptio gives you the clarity to make confident investment decisions and connect AI spend to real business outcomes.
- Cost mapping
- Cost tracking
- Value alignment
Answers questions like:
- How is my AI spend trending, and which models or teams are driving it?
- Which AI models are consuming the most tokens and driving cost increases?
- Which AI models are being used across my business, and how are they contributing to overall cost by department?
Answers questions like:
- How are AI solution costs distributed across the business?
- How can I track and allocate OpenAI costs across projects or business units?
- How do I understand which AI workloads are driving cloud costs the most?
Answers questions like:
- How can I evaluate and track the value, progress, and risks of my AI initiatives across teams?
- How can I compare different AI investment plans and choose the best path forward?
- How do I know our AI initiatives support our business strategy?
Get the full picture of AI investments
Everyone has a stake in AI – but does anyone have the whole picture?
Every major stakeholder in AI, including CIOs, IT Finance, Cloud leaders, and AI program owners who are under pressure to deliver value, but each one sees only part of the picture. The lack of a unified view leads to strategic misalignment, hidden costs, and disconnected outcomes. What they really need is a shared, end-to-end understanding of AI investments.
CIOs
CIOs are under pressure to drive AI strategy but often lack a clear view of how initiatives align with available resources or business readiness. They need a connected, portfolio-level lens to make smarter, more informed investment calls.
IT Finance
IT Finance is on the hook for explaining AI costs that are scattered across vendors, cloud platforms, infrastructure, and internal labor. Without an end-to-end TCO model, it’s nearly impossible to validate spend or track ROI.
Cloud CoE
Cloud Centers of Excellence are tasked with running AI workloads efficiently, but GPU-backed VMs and managed AI services are hard to track or rightsize. They need real-time telemetry to allocate spend to actual use and avoid runaway costs.
Innovation or AI Lead
AI and innovation leaders are pushing projects forward, but too often lack benchmarks or mechanisms to prove business value. They need tools to track outcomes and demonstrate that AI is delivering real, measurable impact.