Operations teams looking to adopt autonomous cloud management need to show business value to get buy-in from leadership even when the technology "just makes sense". Gartner called out this potential disconnect with business as an obstacle to the adoption of autonomous workload optimization in their report "Hype Cycle for Monitoring and Observability, 2023" (written by Pankaj Prasad and Padraig Byrne).

Four Areas to Consider in an Autonomous Cloud Management Business Case

Here are four areas to consider including in your autonomous business case and ROI:

1) 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝘀𝘁 𝗦𝗮𝘃𝗶𝗻𝗴𝘀

The most readily understood area of value. Business case benefits can be projected at three levels:

a) 𝗪𝗼𝗿𝗸𝗹𝗼𝗮𝗱: gains from finding the optimal resource settings (e.g., CPU, memory) that meet performance needs at least cost

b) 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: optimizing the mix of instance types (e.g., M5, C5 or R5 for AWS) based on this updated workload configuration

c) 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗶𝗻𝗴: optimizing discounts based on ML-based traffic forecasts

Projections for each of these can be established by completing a pilot exercise on a representative set of workloads (later stage business case) or using general ranges from other companies' experiences (early stage).

2) 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗚𝗮𝗶𝗻𝘀

Autonomous systems can also uncover latency gains by optimizing resources (somewhat of a mirror image to cost above). However, identifying the subset of services where latency impacts revenue is key when calculating the business value of latency gains. Examples:

a) 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗳𝗮𝗰𝗶𝗻𝗴 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 in industries like e-commerce and SaaS where customer experience and revenue are linked (over time) to page load speed.

b) 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴-𝗶𝗻𝘁𝗲𝗻𝘀𝗶𝘃𝗲 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 where lower latency improves product quality (e.g., faster security scans) or time to revenue improves (e.g., drug discovery).Projecting the revenue impact of latency is also needed and reference to benchmarks (e.g., Amazon's 100ms = 1% of revenue) or internal data is needed.

3) 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗚𝗮𝗶𝗻𝘀

Autonomous systems also optimize availability by preventing issues (e.g., out of memory) that could trigger revenue-impacting incidents (e.g. payments in e-commerce). Projecting improvements on a metric tied to customer experience (like Failed Customer Interactions (FCIs)) is ideal but errors can be used as a proxy, assuming you've started measuring availability on a request basis. As with performance, the value of reducing this metric is needed. Inhouse estimates or benchmarks can be used.

4) 𝗧𝗶𝗺𝗲 𝗦𝗮𝘃𝗶𝗻𝗴𝘀

Time savings accrue as autonomous systems act as AI copilots for operations teams (e.g., SRE, DevOps, FInOps). A simple approach is to determine the volume of potential actions that the system will take and the typical time today needed to perform them.
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Key Metrics to Build an Autonomous Cloud Management Business Case

In the chart below we show how these four areas can be used to define a set of metrics that can be used to calculate the overall benefits in an autonomous cloud management business case:

To get help on creating an autonomous business case based on this approach and tailored for your organization, please contact us via website chat or our contact us page.

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