AI ROI Secrets: Why 70% of Leaders Prioritize KPIs

What is the ROI of AI? McKinsey predicts that AI ROI will total as much as $4.4 trillion in annual revenue. To put that figure into perspective, the total GDP of the United Kingdom in 2023 was $2.8 trillion.

The global impact of artificial intelligence is unignorable. Considering the vast array of potential use cases, with major industries like tech, finance, healthcare, and customer services all set to see rampant gains, AI won’t be going anywhere anytime soon.

Although AI holds incredible potential, businesses must understand how to implement it effectively to achieve true success. Organizations can measure AI ROI in their operations by using several AI KPIs (Key Performance Indicators).

By pairing AI's explosive potential with a steady and measured KPI-first approach, businesses can hit the ground running, integrating and generating ROI from AI as soon as possible. 

Defining and Measuring AI ROI

Key performance indicators (KPIs) are a core aspect of any business deployment. They allow teams to track progress, prove ROI, and monitor efficacy. AI KPIs are no different, providing critical insight into the success of AI integrations.

When surveyed by MIT Sloan in their Global Executive AI Survey, 70% of business leaders state that clear KPIs are vital for sustained business success. 

AI KPIs won’t look the same for every organization. Your AI KPIs should reflect your main business objectives. Here are some potential KPIs you could focus on:

• Total operational efficiency improvement
• Overall revenue growth
• Rate change of customer engagement

However, avoiding creating an overly long list to measure AI ROI is crucial. Just like with overall business KPIs, focus on quality over quantity. According to Parth Kulkarni, a Finance Leader at Adobe, businesses should focus on quality over quantity when defining their KPIs. After making a short list of potential options, organizations can then apply the SMART Framework to find the most fitting KPIs.

Following the SMART framework (Specific, Measurable, Achievable, Relevant, and Time-bound) will ensure your chosen KPIs are clear, actionable, and directly tied to your strategic goals. 

Another aspect that Kulkarni discusses is the identification of leading and lagging factors when creating goals. Leading indicators, like employee satisfaction with a new AI tool, can predict future performance, while lagging indicators, like cost savings from the AI implementation, measure past success. A balanced approach incorporating both is ideal and will unlock highly effective KPIs for your business.

Connect each potential goal to your overarching company strategy to determine which AI KPIs prove AI ROI in your organization. AI KPIs that monitor revenue growth make the most sense if you're in an era of expansion. Alternatively, the operational efficiency improvements will work well if you’re looking to refine your processes.

Case Studies: Demonstrating AI ROI

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Realizing AI ROI rests on a company’s ability to create clear KPIs and exceed targets. Structured goals and expectations for AI integration streamline the integration phase and progress toward reaching objectives. 

Enterprises in the AI space create central AI KPIs at the beginning of their project integration to give their company a straightforward journey toward success. 

Over the past few years, several enterprises have successfully incorporated AI, each centered its integration on different KPIs. 

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IBM and ABO Wind: Automating Tasks to Enhance Operational Efficiency

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ABO Wind is a sustainable energy company that operates solar and wind farms across 16 countries. As the company expanded internationally, the need to align with local compliance laws, political agreements, and technical constraints led to a bottleneck in production.

ABO Wind’s Director of Development, Beniot Clouet, stated that:

“We needed to cross-reference the interests and the constraints of a large scale of topics. Regarding the territories we are working on, we need to have some political agreement, we need to check all the environmental, technical and acoustical constraints, and we need to find agreements with all the landowners of each area.”

Manually tracking all of these processes and gaining authorization documents become a primary task for the organization, occupying worker hours and reducing new build efficiency. To overcome this, ABO Wind partnered with IBM to integrate an AI tool to streamline documentation and compliance.

The project had one clear AI KPI: Improve the efficiency of proposal submission, tracking, monitoring, and organization. 

Leveraging IBM’s AI technology, ABO Wind was able to automate the vast majority of these processes. In line with their central AI KPI, the integration resulted in an 80% improvement in efficiency by reducing manual tasks.

This study demonstrates a clear AI ROI, with ABO Wind identifying a problem, leveraging AI as the solution, and reaping a demonstrable benefit that aligned with their desired KPI.

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Microsoft and Atera: Achieving 10x Productivity with AI KPIs

Atera is an all-in-one IT management platform that services over 3.5 million devices across 105 countries. The platform empowers IT professionals to focus on the most important aspects of IT management. Yet, professionals in Atera continually noted that their time was spent fixing low-level issues and finishing repetitive tasks.

Oshri Moyal, Co-Founder and CTO of Atera suggested that:

“We recognized that something wasn't right about the way IT professionals work—it was inefficient and full of repetitive tasks.”

After partnering with Microsoft Azure OpenAI service, Altera set out to enhance the efficiency of their technicians by eliminating the need to spend time on low-value tasks. Azure Co-Pilot would analyze customer requests with NLP to generate suggested technician fixes. Instead of 20 minutes for diagnostics, 15 minutes for solution selection, and two hours of writing a script to fix the problem, technicians wouldonly need to review the AI-generated fix. In this case, time saved was a primary indicator of AI ROI.

Moyal states that with this change, “Technicians can focus directly on fixing the issue. All it takes is a few clicks, and the problem is solved. This change means a single technician goes from handling seven to 70 cases per day.”

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‍Summarizing the powerful integration of AI and exceeding initial goals, Moyal continues, 

“Technicians can now focus on more high-value work. Before, our platform saved them 50 percent of the time spent managing IT. Since we started using Azure OpenAI Service, we have improved their efficiency by 10x.”

Altera and Microsoft identified a redundancy in their operations, outlined a strategy to solve it with AI, and created measurable AI KPIs to determine the AI ROI of its implementation. 

Challenges and Solutions in Measuring AI ROI

Businesses routinely encounter the same select challenges when forming AI KPIs and attempting to measure the return on investment of AI integrations. Strategically identifying these challenges (and developing solutions before full integration) will help streamline enterprise AI ROI.

Here are the three main challenges in measuring AI ROI and the strategies used by leading enterprises to overcome each of them:

• Data Quality – IBM suggests that over 45% of all AI-enabled enterprises do not understand what data they need when using and monitoring AI. To overcome poor data quality impacting AI ROI calculations, IBM recommends that enterprises first scale their data management capabilities. By creating comprehensive data architecture, businesses are well equipped to scale their cloud repositories and collect the data-at-scale needed to prove AI ROI. 

• Setting Realistic Timelines – Research by Microsoft suggests that businesses are split across 5 core stages of AI implementation. At each stage, the timeline for proving AI ROI differs greatly. Instead of setting an initial timeline, follow Microsoft’s strategy of first identifying which stage you are in. This insight will generate the insight you need to create a more realistic AI timeline.

• Indirect Benefits – Artificial intelligence integrations can produce benefits in more abstract ways, like increasing employee satisfaction. International customer experience leader SurveyMonkey leverages Employee Net Promoter Score surveys to trace employee satisfaction for their clients. Businesses can incorporate these surveys pre and post-AI integration to quantify some of these more abstract benefits to AI technology

Alt Text: A diagram shows five stages of AI implementation, as well as metrics about the number of companies at each stage, the number of employees, the average age of those organizations, the number that are cloud-first, and the percentage that sees significant value from AI. The stages are Exploring, Planning, Implementing, Scaling, and Realizing. Organizations hoping to see significant AI ROI create AI KPIs to work against.
Image Description: The five stages of AI implementation—organizations hoping to see significant AI ROI create AI KPIs to work against.

This final strategy points to one of the most important aspects of measuring AI ROI: conducting both quantitative and qualitative research. Organizational AI KPIs should be quantitative, but you can supplement that data with qualitative feedback and insight to outline why AI has a positive impact.

Future Outlook: The Place of KPIs in AI Development

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Without clear, actionable, and traceable AI KPIs, businesses cannot effectively implement and leverage AI tools. KPIs provide direction and a framework that allow organizations to implement new technology with purpose. 

As artificial intelligence continues to dominate every market, from healthcare to finance and beyond, businesses must understand the value of AI KPIs to maximize their ROI from these new integrations. As with Microsoft, IBM, and other leading AI-first enterprises, success lies in effectively planning, strategizing, and future-sight. 

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