Generative artificial intelligence (GenAI), hyper-automation, and advanced algorithms all have one thing in common – they are energy-demanding technologies requiring reliable network platforms to operate efficiently. However, this can lead to unexpected and unnoticed increases in cloud infrastructure expenditures – bloating costs at a time when cloud spending is already intense:
- Cloud services command a significant portion of IT budgets and will consume more than 50% by 2025 according to Gartner.
- Ad-hoc deployments and a lack of oversight have cloud infrastructure invoices spiraling out of control. The vast majority of CIOs exceed their allotted budgets.
- The price of cloud infrastructure and applications are on the rise, forcing businesses to spend more due to “cloud-flation.”
Forward-leaning organizations and those planning to double down on GenAI need to take strategic measures to sustain innovation by staving off cloud bankruptcy.
AI: A lynchpin of digital innovation (and potential loose cannon)
AI is the golden ticket to business prosperity, but it comes with financial sustainability risks that can threaten the durability of its own use. For example:
- AI relies heavily on cloud for its storage and computing powers; however, companies can easily exceed their spending thresholds due to the sheer capacity of support needed.
- AI is revered for its ability to automate operations, but it also triggers a paradigm shift from an expense management perspective. Financial visibility, control, and predictability are paramount when investing in dynamically scalable technologies.
The Wall Street Journal recently reported on this issue of AI-driven technical debt. For CIOs, the pressure is at an all-time high to experiment with and show demonstratable financial returns from AI and GenAI investments. This means dialing up cloud spending, but companies can’t lose sight of cost control and cloud ROI.
Yet, there are two sides to every coin.
While AI drives innovation, its features can also be used to curb cloud spending. Companies can (and should) leverage AI to safeguard their financial futures from the technology’s potential threats to sustainable innovation, more adeptly navigating the challenging economics of innovation. In fact, research shows companies that use AI-enabled cloud cost management tools see +20% greater overall savings when compared to those that don’t.
Using AI to maximize cloud cost optimization and FinOps savings
Here are three ways businesses can use AI to neutralize its own negative impacts.
- Instantly detect cloud waste and course correct: AI-powered cloud cost management platforms can rapidly sniff out the root causes of overspending in multi-cloud environments and pinpoint the most cost-effective infrastructure configurations, making changes to service settings automatically. This accelerates time-to-savings.
- Find the best prices and rates to maximize cloud savings: AI can serve up side-by-side comparisons across different cloud providers, showing how their services, pricing models, and discounting structures stack up. Companies can strategically capitalize on the best market deals with a clear view of unit costs.
- Govern spending and tie expenses to returns: Strengthen cloud cost governance by implementing spending thresholds and proactive, automated alerts. Use AI to intelligently track and analyze comprehensive costs linking expenses to innovation projects and lines of business to fine tune ROI measurements and result tracking.
When leveraged correctly, AI is a strong-arm tactic for ensuring lean portfolio management – something Gartner predicts 70% of digital transformations will fail without. Technology portfolio management helps companies embrace innovation while putting financial checks in balances in place. It’s an ongoing strategy for ensuring tech spend translates into business value, and it helps IT leaders discuss technology in relation to corporate goals and business outcomes.
Cloud costs: Tensions are building between Finance and IT
It’s crucial that IT teams and finance teams work cohesively together to drive cloud cost optimization. This synergy, however, is often missing. One research report shows that CFOs are most concerned about budget overruns and gaining clearer views into IT costs while CIOs are more worried about operational efficiencies. Tensions brew between the two as siloed information and tactical challenges in reducing cloud waste create friction for IT teams already under pressure to hit deadlines and help build new products.
4 ways to cultivate a culture of cloud cost accountability
Here are four ways companies can make the pendulum swing in a positive direction:
- Encourage alignment between tech and finance leaders and their teams, recognizing that as cloud spending inevitably rises there will be an increased need for tighter collaboration, visibility, and real-time controls.
- Embrace just-in-time approaches to drive necessary mindset shifts, continuously matching cloud resources to business demands with a focus on rightsizing and waste reduction. Most start with the FinOps strategy and framework, considering if they cloud expense management software and services to support it.
- Ensure that financial responsibility is ingrained within DevOps teams while digital innovation is ingrained within finance. Frame innovation as an enabler of ongoing, widespread advancements driven by sound financial investments versus isolated projects.
- Above all, provide IT and finance teams with a single source of truth for cloud-related expenditures and use corporate financial systems and rules for cost optimization. When teams and their supportive tools all work in sync, tensions ease.
AI promises growth, but companies need to avert the risks
Hidden cloud infrastructure and application costs add zeros to AI expenditures in an already tricky cloud economy. It’s time to shake out the carpet and bring unforeseen cloud costs to light. By strategically using AI to counteract its own cost complexities and fostering a culture of accountability, organizations can capitalize on the limitless potential of AI without compromising their ability to transform reliably.
