Transforming Investment to Impact: Unveiling the Expanded Value of Artificial Intelligence Return on Investment.
Jo Debecker is the Managing Partner and Global Head of Wipro FullStride Cloud.
With the rapid increase in AI investments, businesses are confronted with a significant issue: assessing the return on investment (ROI) for these investments. Unfortunately, many enterprises are unable to measure the impact of AI, hindering them from fully exploiting AI's capabilities.
Why do businesses struggle to measure their AI ROI?
The Challenges of Measuring AI ROI
One of the main obstacles is the absence of clear objectives for AI projects. Without clear targets, it's challenging to connect AI outcomes to specific business outcomes, such as cost savings or revenue growth.
Consider a company implementing an AI system to enhance customer service without setting specific goals for improvement. Without clear goals, evaluating the system's impact on customer satisfaction or response times becomes challenging. Key Performance Indicators (KPIs) are crucial, too. A business might deploy AI to improve operational efficiency but fail to establish KPIs like process completion time or error rate reduction, making it difficult to evaluate the AI's success.
Another challenge is poor data quality or inadequate data tracking, which can blur understanding of AI projects.
Take, for instance, an AI solution to improve inventory management within a retail operation: The system's impact is measured through outcomes like lower inventory costs, faster stock turnover, or fewer sales lost due to stockouts.
Collecting detailed data on these outcomes can be challenging. It requires advanced IT infrastructure to track and collect data across various supply chain segments, which not all organizations have. External influences, like seasonal demand shifts and supply chain disruptions, can complicate data analysis. An AI system's performance may also evolve over time as it processes new data, requiring a flexible approach to data collection to maintain accuracy.
As a result, it's difficult to directly attribute financial improvements or efficiency gains to the AI project. Without evidence of its benefits, persuading stakeholders to continue or expand their investment in AI technology becomes challenging.
Overcoming Challenges to Measure and Maximize AI ROI
Adopting a multi-pronged strategy can help businesses overcome these challenges and enhance the accuracy of their ROI assessments. Here's an outline of the strategy:
1. Enhance IT infrastructure.
Beyond purchasing new hardware, this means integrating systems that ensure smooth data flow across all supply chain segments. Integrated systems allow for real-time monitoring and data collection, providing a comprehensive view of an AI system's impact.
2. Improve data collection capabilities.
This involves implementing modular updates, using cloud-based solutions, or deploying tools that allow for flexible scaling as the organization's AI needs grow. It's also essential to consider cloud economics and develop a framework for AI spending, including how to grow successful AI programs and cost-effectively phase out underperforming ones.
3. Develop a data analysis framework that accounts for external factors affecting performance metrics.
This could involve setting up control groups or using statistical models to separate the AI system's impact from changes in market trends or operational conditions.
4. Adopt a dynamic approach to data collection.
As AI systems evolve and learn, data collection parameters may need to be adjusted to reflect changes in performance accurately. Regular audits of data-collection processes and performance metrics can help ensure that the data remains relevant and accurately reflects the system's impact. This also involves training teams to recognize and adapt to these changes.
5. Be transparent with stakeholders about the challenges and limitations of measuring AI's impact.
By openly discussing the steps taken to address these challenges and the methodologies used to calculate ROI, businesses can build trust and set realistic expectations. Detailed documentation of the AI system's performance and its contribution to financial or efficiency gains can further strengthen stakeholder confidence.
Addressing these challenges head-on will help businesses not only improve their ability to accurately assess AI ROI but also enhance the overall effectiveness and reliability of their AI systems. This lays the groundwork for more informed decision-making and stronger justification for future AI technology investments.
Of course, enterprises must also consider the possibility that otherwise promising AI initiatives could quickly become liabilities if teams proceed without proper safeguards. Hallucinations, data breaches, and biased outcomes may result in significant reputational damage. Responsible AI practices, such as privacy and security by design, fairness in processing and outputs, and protection of copyrighted information, should not be overlooked as essential elements of achieving long-term ROI.
Managing Board-Level Expectations
Managing board-level expectations for AI implementation is another hurdle to demonstrating ROI and requires a pragmatic approach.
Providing real-time data and predictive insights can help board members understand AI's impact and make informed decisions. Emphasizing the competitive necessity of AI can motivate action and highlighting AI governance and trust can ensure scalable deployment.
For example, a financial services firm implementing AI for fraud detection should provide the board with real-time data on fraud prevention rates and predictive insights on potential future threats. This approach helps validate the AI initiative's value and aligns it with the organization's broader goals.
Establishing a New Dimension of AI ROI
Measuring and maximizing AI ROI requires a structured, strategic approach that addresses AI's unique challenges. It also demands that organizations think differently because AI affects so many use cases and roles across the business.
To successfully overcome obstacles and fully harness the capabilities of artificial intelligence, companies should set specific objectives, track key performance indicators, conduct trial runs, and manage high-level leaders' expectations through real-time information. Regular surveillance, assessing the long-term effect, and implementing communication channels are vital parts of ensuring AI projects meet their intended objectives.
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