The rapid development and adoption of artificial intelligence (AI) has been incredible. When it comes to generative AI alone (GenAI), 65% of respondents in a recent McKinsey Global Survey said their companies regularly use the technology, doubling findings from just 10 months earlier. Moreover, three-quarters anticipate that in the years ahead, AI will bring significant or disruptive change to their industries.
Without question, AI is rapidly revolutionizing areas of business operations. However, there are still a ton of hurdles to manage when it comes to deploying and profiting from it. The highest among these is ensuring the use of AI is customized to meet specific business needs and that its return on investment (ROI) is closely followed.
A good way to look at this is through parallels that exist between cloud computing and AI adoption. Just as cloud computing lets companies scale resources on demand, AI must help businesses scale decision-making and automation to realize greater cost savings and efficiency. However, businesses should be wary, remembering lessons learned from the fast cloud adoption that resulted in out-of-control costs.
Drawing on financial operations (FinOps) principles to guide AI adoption can be the key to success.
Implementing AI with FinOps
FinOps is a management practice promoting shared responsibility for cloud computing infrastructure and expenses. Global Market Estimates noted the cloud FinOps market will climb from $832.2 million in 2023 to more than $2.75 billion by 2028. The non-profit FinOps Foundation reports that its community went from zero to 23,000+ in only five years, and 48 of the Fortune 50 are represented. It’s pretty clear that FinOps is having a positive impact.
Still, organizations are being pushed to create AI strategies on the run to generate revenue and advance employee productivity. The problem is that building a strong foundation before turning AI loose on operations is vital. Key to this is cost management, allocation of resources, and tracking return-on-investment (ROI) – all of which are focus areas of FinOps. Implementing its principles can eliminate errors and tighten processes, spurring healthy AI adoption. This can also help produce a roadmap for AI integration, best use cases, tools to use, and a formal implementation plan.
The overall gist is that FinOps goals facilitate AI adoption, driving seamless integration, capturing business objectives, and raising overall ROI.
Artificial intelligence, real control
In tandem with the greater demand for AI solutions, costs have increased. In a survey by Ernst & Young, the number of senior leaders investing $10 million or more is set to nearly double in the year ahead. Even so, the research showed that AI’s impact could be jeopardized for many because they’re not investing in the proper infrastructure. Regardless, AI will remain a major investment, making it even more critical to implement strategies that effectively manage and control costs.
To start, a budget and forecast for AI projects should be created – this is a cornerstone of FinOps. This should accurately gauge initiatives’ total costs, allowing for effective resource allocation and reducing the likelihood of billing surprises. Businesses can also utilize cost optimization to unearth opportunities and lower expenses without weakening the effectiveness of AI tools. To that end, you may find that cloud-based AI services provide better scalability and cost efficiency than in-house infrastructure. This can also help businesses gain control and assurances that their AI investments are improving operations and lowering expenses.
Keeping tabs and making (big!) gains
Analysts from Forrester have said enterprises can succeed with AI in the long run if they possess a strategy that’s in lockstep with business goals. Chief Information Officers (CIOs) in particular might be chomping at the bit to scale AI, but they also know that while it’s not easy to demonstrate ROI, it’s critical for proving its value.
Developing proper metrics and benchmarks will enable CIOs to measure the impact of an AI initiative. These should focus on operational performance and clearly illustrate results to all members of the C-suite. It’s imperative to track and analyze AI across business functions, especially in areas highlighting important gains such as increased customer engagement, sales, efficiency, and savings.
Regular performance reviews can allow businesses to leverage their data and identify areas where AI is producing positive outcomes while pinpointing places where improvement is needed. Using FinOps practices to manage and monitor related expenses can introduce additional savings and optimize usage. This will provide greater visibility into AI investments and a financial picture of initiatives, strengthening decision-making and maximizing ROI.
With AI continuing to reshape business operations, companies must be cautious when planning deployments (especially where high-cost resources are involved) – and this is where FinOps can drive success. Through strategies to control costs and effectively allocate resources, businesses can beat the complexities of AI adoption while delivering new value that is easily provable to each and every stakeholder.
