AI + FinOps: How to automate cloud cost optimization

Hyper-automation to reduce your cloud costs. It’s not on the horizon – it’s already here.

Artificial intelligence is being infused into FinOps practices and cloud cost management platforms. The result? Fewer manual tasks and more cost savings. Research shows companies that activate their FinOps model using AI are 53% more likely to achieve a cost savings of greater than 20% — compared to less than 10% without AI.

Here are some best practices for applying AI to the three key phases of FinOps, allowing you to turn cloud financial management into an efficient ongoing practice.

AI for FinOps Phase 1: Inform

The first phase in the FinOps Framework is INFORM. At this stage, companies explore their costs and define how efficiently (or inefficiently) they’re using their cloud infrastructure and applications. But analyzing this data is no simple task. Complex usage data comes from multiple providers and invoices can contain thousands of line items to untangle. This level of number crunching is only fit for AI.

5 AI best practices to inform FinOps programs

The primary goal of an AI-powered FinOps program should be to drive visibility into multiple sources of data, continually evaluating it as business needs change.

How to apply AI for FinOps insights:

1. Vast and frequent data ingestion for visibility: Observe client-side and vendor-side data, analyzing it frequently hourly if possible.
2. Data normalization for cost comparisons: If you want to compare unit costs using apples-to-apples comparisons across different providers, pricing models, and discounting structures, you’ll need AI to deconstruct data and normalize unstructured information.
3. Tracking dynamic prices and new features: AI should monitor daily fluctuations in service prices, keeping tabs on any newly released capabilities that can help you cut costs.
4. Data analysis for swift savings discovery: AI should understand existing cloud usage data and current infrastructure configurations, comparing your current state with millions of other cost-optimized pricing schemas. Running what-if scenarios shows you where cost optimizations can be made and how much money a particular change can save you.
5. Recommendations that get smarter with time: Quarter over quarter, AI should be able to compare historical information, making more informed recommendations based on usage patterns and habits.

AI for FinOps Phase 2: Optimize

AI is also helpful in reaching the second Phase of FinOps, OPTIMIZE. This is when things get real — action takes place to implement any identified cost savings recommendations coming out of Phase 1. For example, companies might adjust their consumption habits, right-size to reduce unused resources or modify their cloud infrastructure configurations.

AI is a critical enabler for turning potential savings ideas into real dollars saved.

Automation: Using AI to capitalize faster on savings opportunities

AI-generated cost-saving recommendations are only as good as your ability to act on them rapidly, and companies can leverage AI to automate the implementation of any suggested changes. In fact, companies not exploiting this advantage will quickly force their human IT engineers to pick up wherever their automation leaves off.

Advanced solutions handle more of the work. AI tools ask clients to approve the recommendation and then make the modifications for them. This way you can capitalize faster on the financial returns of your FinOps program.

For example, AI can:

• suspend or pause an unused IaaS service,
• change the type of service plan,
• edit a long-term commitment discount or savings plan, or
• even help you manage infrastructure upgrades (and downgrades) based on workloads.

This is an advanced capability because it requires investments in integration, which allows the AI engine to manipulate settings inside the cloud provider’s administrative management portal. This is the little-known secret that can also be used to accelerate ROI on cloud investments.

AI for FinOps Phase 3: Operate

Now let’s explore how AI streamlines FinOps Phase 3, OPERATE. We all know that the work of a FinOps program is never one-and-done. Nor is it a siloed effort confined to just one department. In fact, the FinOps Foundation says organizations should build a cross-functional, collaborative approach to continuously measure and improve cloud management.

But how does one do that exactly?

AI automation and robotic process automation (RPA) can usher in productivity for IT, financial, and procurement teams looking to align and digitize repetitive tasks needed to execute FinOps. When AI and RPA are applied not just to individual steps but to expansive workflows across multiple departments, it offers broader value. We call this hyper-automation for FinOps.

Hyper-automation: Creating a unified ecosystem for FinOps

Hyper-automation occurs when all necessary FinOps processes and workflows are aligned and then streamlined for all stakeholders. This spawns the synthesis that the FinOps strategy is now famous for. As an example, various stakeholders become synergistic in their FinOps practices when the program can address the needs of both IT and Finance teams together as one AI-driven motion. Only when companies can automate and unify the full ecosystem of tasks are they able to drive savings and process efficiencies simultaneously, which can create a multiplier effect on results.

This is important because IT, finance, and procurement teams have very different FinOps needs (see image). Each of them must be met in a way that unites all information, tasks, and stakeholders rather than fragmenting them. Integrations and centralized systems enabling unified management are key. Having one source of truth is paramount.

Challenges in this area can be overcome by understanding three prerequisites for building hyper-automated FinOps platform. 

Final note: AI pivotal to data-driven optimization

AI plays a pivotal role in FinOps. Advanced analytics help companies control cloud expenditures more effectively. Automating the implementation of cost-saving measures speeds success and building hyper-automated workflows for all involved drives a culture of cloud cost containment. Any company trying to manually govern multi-cloud estates will find that complexity quickly stymies them from winning the end game — data-driven cost optimization.

Accelerate your time to insights and savings with the smartest AI-powered FinOps solution honed with 14 patented AI capabilities for cloud cost optimization. Get started with Tangoe One Cloud.