Although AI has the potential to transform a variety of industry sectors, its financial impact remains difficult to quantify due to the unique factors that distinguish it from other IT investments.
Many sectors have identified valuable AI use cases. The IT and manufacturing industries are turning to AI tools to improve operational efficiency, and major retailers and e-commerce platforms want to use AI to improve the customer experience. Scientific researchers are using machine learning models to accelerate the development of life-saving drugs.
For organizations considering AI initiatives, regardless of industry, it is essential to define the technology's value proposition as a component of a comprehensive AI strategy or broader business strategy. This involves creating use cases for AI that address your organization's specific challenges and objectives.
Once the value proposition is defined, it is important to measure the return on investment of the AI initiative to demonstrate actionable results to stakeholders. Assessing AI ROI, a process that includes multiple steps and metrics, ensures that your AI efforts are meeting your goals and delivering value to your business.
Framework for evaluating AI ROI
Evaluating the ROI of AI initiatives involves different complexities than traditional IT implementations. For example, his ROI for cloud computing typically focuses on moving from capital expenditures such as server and data center costs to operational expenditures for ongoing services.
In contrast, measuring the ROI of AI requires going beyond simple financial metrics and considering more complex, long-term factors, including a deeper analysis of strategic and operational metrics. Cost aspects may include costs associated with data acquisition, model development, computational resources, and ongoing maintenance. Benefits, on the other hand, can include factors such as increased efficiency, more informed decision-making, and stronger market positioning.
While cloud ROI is often measured financially, AI ROI calculations focus on improved decision making, increased productivity, task automation, and enhanced customer experience. The financial impact of AI can also be quantified in terms of increased revenue, reduced costs, or competitive advantage gained from innovation. For these reasons, the following steps are the best way to measure your AI ROI.
1. Define the purpose and scope of your AI initiative
First, introduce AI to your organization using a framework that articulates how your AI efforts align with your organization's broader business goals.
The framework should also capture the scope and scale of the AI implementation and detail the specific processes targeted for AI enhancement or automation. Messages and communications from project leaders should explain how the AI framework improves employee productivity, rather than replacing them.
2. Define and capture quantitative metrics
Evaluate the cost savings potential of your AI initiatives by measuring operational cost savings through process automation and efficiency improvements, as well as the revenue generated by AI.
Understand and document operational costs, including costs associated with developing, deploying, and maintaining AI tools and systems. Keep in mind that bringing AI to the public cloud will have a noticeable impact on cloud spending and may require adjustments to your current cloud cost management processes. To assess revenue growth from AI, obtain data on sales growth and analyze new revenue streams created by AI efforts.
3. Define and capture performance metrics
Focus on collecting metrics such as reducing downtime, improving decision making, scalability within budget, and qualitative metrics. Defining and capturing qualitative metrics can help you avoid deploying AI just for the sake of it. Expect to spend some time identifying the right qualitative metrics to track.
If your organization is integrating AI into existing products and services, take a closer look at how AI can contribute to product innovation and market differentiation. Sales and marketing data can help explain this. If your AI efforts are focused solely on internal applications, it's important to get direct input from your users.
4. Align AI closely with operations
Assess how your AI projects contribute to your organization's overall strategic goals, especially the impact on key performance indicators, and optimize the analysis and use of backend data. The ability of an AI project to scale as an organization grows or shrinks indicates that it is a strategic fit.
Measuring employee satisfaction with AI can be difficult. Due to human nature and distrust of companies, some employees may worry that their jobs will be taken over by AI. By creating a documented plan that outlines how AI will enhance and improve existing workflows, you can better position AI as a tool to improve employee satisfaction.
Finally, track engagement levels with new AI systems, whether internal or external. Increased interactions indicate that AI systems are working well with business users and customers.
5. AI risk and compliance monitoring
Monitoring the risk and compliance of a company's AI efforts is a necessary part of measuring ROI. Assess whether your AI systems comply with relevant data protection regulations such as GDPR and the California Consumer Privacy Act.
AI projects can also raise ethical concerns, such as bias in decision-making processes and lack of transparency in AI operations. Determining whether an AI project has ethical concerns requires a cross-functional team. If possible, talk to customers who are voicing ethical concerns.
6. Practice continuous evaluation and iteration
Measuring the ROI of AI is not a one-and-done process. You need to continually measure, evaluate, and readjust to change. Factors such as advances in technology and changes in business operations may require recalculating his ROI for AI initiatives.
This process begins by extending the performance monitoring tools already in place to AI applications, allowing companies to measure the tools against their performance goals. We expect reporting improvements to take some time as operations teams learn more about AI application performance and reporting.
To integrate the human element of continuous assessment, establish partnerships with the stakeholders most impacted by AI applications. Their feedback is invaluable to organizations that are iterating on AI tools, processes, and frameworks.
Will Kelly is a freelance writer and content strategist who writes about cloud, DevOps, AI, and enterprise mobility.
