John Edwards, Technology Journalist & AuthorMay 30, 20254 Min ReadTanapong Sungkaew via Alamy Stock PhotoAI adoption can help enterprises function more efficiently and productively in many internal and external areas. Yet to get the most value out of AI, CIOs and IT leaders need to find a way to measure their current and future gains.Measuring AI efficiency and productivity gains isn't always a straightforward process, however, observes Matt Sanchez, vice president of product for IBM's watsonx Orchestrate, a tool designed to automate tasks, focusing on the orchestration of AI assistants and AI agents."There are many factors to consider in order to gain an accurate picture of AI’s impact on your organization," Sanchez says,  in an email interview. He believes the key to measuring AI effectiveness starts with setting clear, data-driven goals. "What outcomes are you trying to achieve?" he asks. "Identifying the right key performance indicators -- KPIs -- that align with your overall strategy is a great place to start."Measuring AI efficiency is a little like a "chicken or the egg" discussion, says Tim Gaus, smart manufacturing business leader at Deloitte Consulting. "A prerequisite for AI adoption is access to quality data, but data is also needed to show the adoption’s success," he advises in an online interview.Still, with the number of organizations adopting AI rapidly increasing, C-suites and boards are now prioritizing measurable ROI.Related:"We're seeing this firsthand while working with clients in the manufacturing space specifically who are aiming to make manufacturing processes smarter and increasingly software-defined," Gaus says.Measuring AI Efficiency: The ChallengeThe challenge in measuring AI efficiency depends on the type of AI and how it's ultimately used, Gaus says. Manufacturers, for example, have long used AI for predictive maintenance and quality control. "This can be easier to measure, since you can simply look at changes in breakdown or product defect frequencies," he notes. "However, for more complex AI use cases -- including using GenAI to train workers or serve as a form of knowledge retention -- it can be harder to nail down impact metrics and how they can be obtained."AI Project Measurement MethodsOnce AI projects are underway, Gaus says measuring real-world results is key. "This includes studying factors such as actual cost reductions, revenue boosts tied directly to AI, and progress in KPIs such as customer satisfaction or operational output. "This method allows organizations to track both the anticipated and actual benefits of their AI investments over time."Related:To effectively assess AI's impact on efficiency and productivity, it's important to connect AI initiatives with broader business goals and evaluate their progress at different stages, Gaus says."In the early stages, companies should focus on estimating the potential benefits, such as enhanced efficiency, revenue growth, or strategic advantages like stronger customer loyalty or reduced operational downtime." These projections can provide a clear understanding of how AI aligns with long-term objectives, Gaus adds.Measuring any emerging technology's impact on efficiency and productivity often takes time, but impacts are always among the top priorities for business leaders when evaluating any new technology, says Dan Spurling, senior vice president of product management at multi-cloud data platform provider Teradata. "Businesses should continue to use proven frameworks for measurement rather than create net-new frameworks," he advises in an online interview. "Metrics should be set prior to any investment to maximize benefits and mitigate biases, such as sunk cost fallacies, confirmation bias, anchoring bias, and the like."Key AI Value MetricsMetrics can vary depending on the industry and technology being used, Gaus says. "In sectors like manufacturing, AI value metrics include improvements in efficiency, productivity, and cost reduction." Yet specific metrics depend on the type of AI technology implemented, such as machine learning.Related:Beyond tracking metrics, it's important to ensure high-quality data is used to minimize biases in AI decision-making, Sanchez says. The end goal is for AI to support the human workforce, freeing users to focus on strategic and creative work and removing potential bottlenecks. "It's also important to remember that AI isn't a one-and-done deal. It's an ongoing process that needs regular evaluation and process adjustment as the organization transforms.”Spurling recommends beginning by studying three key metrics:Worker productivity: Understanding the value of increased task completion or reduced effort by measuring the effect on day-to-day activities like faster issue resolution, more efficient collaboration, reduced process waste, or increased output quality.Ability to scale: Operationalizing AI-based self-service tools, typically with natural language capabilities, across the entire organization beyond IT to enable task or job completion in real-time, with no need for external support or augmentation.User friendliness: Expanding organization effectiveness with data-driven insights as measured by the ability of non-technical business users to leverage AI via no-code, low-code platforms.Final Note: Aligning Business and TechnologyDeloitte's digital transformation research reveals that misalignment between business and technology leaders often leads to inaccurate ROI assessments, Gaus says. "To address this, it's crucial for both sides to agree on key value priorities and success metrics."He adds it's also important to look beyond immediate financial returns and to incorporate innovation-driven KPIs, such as experimentation toleration and agile team adoption. "Without this broader perspective, up to 20% of digital investment returns may not yield their full potential," Gaus warns. "By addressing these alignment issues and tracking a comprehensive set of metrics, organizations can maximize the value from AI initiatives while fostering long-term innovation."About the AuthorJohn EdwardsTechnology Journalist & AuthorJohn Edwards is a veteran business technology journalist. His work has appeared in The New York Times, The Washington Post, and numerous business and technology publications, including Computerworld, CFO Magazine, IBM Data Management Magazine, RFID Journal, and Electronic Design. He has also written columns for The Economist's Business Intelligence Unit and PricewaterhouseCoopers' Communications Direct. John has authored several books on business technology topics. His work began appearing online as early as 1983. Throughout the 1980s and 90s, he wrote daily news and feature articles for both the CompuServe and Prodigy online services. His "Behind the Screens" commentaries made him the world's first known professional blogger.See more from John EdwardsWebinarsMore WebinarsReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like #how #measure #efficiency #productivity #gains