Clear, data-driven KPIs are essential for measuring AI’s impact
If AI doesn’t move the needle on outcomes that matter, then it’s just a high-cost experiment. To get real ROI, you have to start with precision. Clear goals. Measurable performance. If you’re not doing that, then you’re either guessing or hoping. Both are terrible strategies.
Matt Sanchez, VP of Product at IBM’s watsonx Orchestrate, says it best, AI effectiveness starts with what you’re trying to achieve. It’s not about adopting the coolest tech or building something that looks impressive. It’s about alignment, between your company’s goals and your AI’s output. You can’t improve what you don’t measure, and you can’t measure what you didn’t plan for.
This means defining KPIs that go beyond vanity metrics. Look at productivity, operational costs, customer satisfaction, whatever directly supports your business model. Set those up before deployment. Otherwise, when the board asks for results, you’re left trying to reverse-engineer value. That’s the wrong way around.
A lot of companies jump into AI because it feels urgent, which is fine. But if you’re serious about it driving value at scale, you need measurable goals tied to strategy, before a line of code is written or a dollar is spent.
The importance of high-quality data to measuring AI efficiency
There’s no AI without data. But not just any data, the kind that’s structured, reliable, and representative. AI systems that rely on weak or incomplete data are basically running blind. And if your data is weak, your output will be even weaker. You make worse decisions, and you don’t even know it until the damage is done.
Tim Gaus, who leads smart manufacturing at Deloitte Consulting, points out a key tension that most companies miss: you need good data to justify AI deployment, but you need AI to get better at using your data. It’s circular, and it forces leadership to solve the data problem early. Not later.
Think of data as infrastructure. You don’t build high-speed rail on cracked tracks. Same logic. Companies need to invest in data collection, hygiene, and governance upfront. Otherwise, AI becomes a cosmetic tool with no underlying strength. And if your data systems aren’t connected or don’t talk to each other, that’s where transformation efforts tend to die.
It’s also worth tracking how the quality of your data evolves. You’ll find that better data not only improves AI performance but also makes it easier to measure what that performance looks like, which is the whole point. So if you want to know what AI is doing for you, start by looking at how strong your data foundation is. That’s where most organizations are either winning, or fooling themselves.
AI metrics must vary according to use cases and industry applications
One-size metrics don’t make sense for AI. They’ll give you false signals and outdated benchmarks. The type of AI you deploy matters, so does the problem you’re solving. If you’re in manufacturing running predictive maintenance, then track things like reduction in machine breakdowns or fewer defects on the line. Straightforward metrics. Clear results.
But when you’re dealing with generative AI, training employees, preserving knowledge, or enhancing internal workflows, the effects are less obvious. That doesn’t mean there’s no value. It just means measurement needs to evolve. Traditional KPIs won’t always cover the layer of human-AI interaction. In those cases, look at knowledge distribution rates, time saved in onboarding, or internal NPS. You need to track what actually changed.
Tim Gaus from Deloitte makes this point clearly. He sees clients oversimplify measurement, trying to force complex AI into narrow dashboards. That doesn’t work. Leadership needs to approve measurement tailored to both the industry and the function. Machine learning used in quality control won’t have the same impact profile as natural language models used by HR or customer service.
C-Suites should push their teams to define value from the start, specific to how their AI will be used. Otherwise, they’ll end up asking broad questions at the end like, “Did it work?” when they never decided what success looked like to begin with.
Real-world impact tracking should span financial and operational dimensions
There’s a tendency to only chase return on investment in dollar signs. That’s flawed thinking, especially with AI. The full impact isn’t just about costs avoided or revenues generated, it’s also about operations made faster, customers made happier, and employees made more efficient. Ignoring these leads to underestimating real gains.
Tim Gaus stresses that successful measurement includes both forecasts and results. You start by estimating: what kind of lift in output, speed, or satisfaction can this AI create? Then you go back after implementation and measure outcomes against that. Not once, but over time. That’s how you track if the system continues delivering.
Measurable factors like reduced downtime, increased throughput, higher resolution speed, and improved satisfaction scores show you whether AI is solving the problems it was meant to solve. You’ll still care about margins, but if AI reduced customer churn by even 2%, that’s a recurring impact, not just a line item in a quarterly report.
If your board still wants a number, fine, just make it one that includes everything you actually gained. Real measurement should fuel better decisions. Not just reporting. Leaders who combine operational metrics with broader financial outcomes don’t just get better AI insights, they get a better view of the business overall.
Pre-established measurement frameworks mitigate biases in AI evaluation
If you’re going to measure success, you need to do it right from the start. That means locking in your measurement frameworks before you launch the AI, not once you’ve already committed resources. Otherwise, you’re making room for bias, and that’s where objectivity falls apart.
Dan Spurling, SVP of Product Management at Teradata, underlines this clearly. He recommends applying proven, structured frameworks rather than inventing new ones during or after the deployment. When you implement AI without a defined evaluation model, you’re likely to justify bad outcomes using sunk cost reasoning or fall into confirmation bias.
This isn’t about adding process for the sake of it. It’s about reducing noise. Set your success metrics before your team writes code, trains a model, or spends a dollar. Use measurable inputs like time saved, resolution rate, or output quality improvement, and define how you’ll collect that data. That way, when you review AI performance, you’re seeing reality, not what someone hopes is true.
Executives should make this a non-negotiable. There’s no value in dashboards that show what you want to see. There’s real value in knowing what actually happened, even if it challenges the initial assumptions. It’s the only way you get a cycle of learning and improvement.
Key productivity metrics include worker output, scalability, and user friendliness
If AI is going to matter at scale, three things need to happen: it has to improve what people get done, operate across the business with minimal friction, and be accessible to users without technical backgrounds.
Dan Spurling from Teradata breaks it down well. He points to three metrics that cut through the noise: productivity, scalability, and user friendliness. Productivity means tracking how AI helps people get more done, whether that’s resolving issues faster, improving collaboration, or producing work of higher quality. You don’t need a behavioral study, just measure time to complete core tasks before and after implementation.
Scalability is about rolling out AI tools across functions, not just within IT. If you’ve built something that only technical users can handle, it’s already too narrow. AI needs to support teams in real-time: marketing, sales, support, everywhere. That only happens if the tools are reliable, self-service, and efficient.
Then there’s usability. If people aren’t touching the AI system because it’s confusing or buried in complexity, it’s a failed deployment. Track access, engagement, and adoption by non-technical staff. That tells you whether your AI investment is actually working, or just exists in theory.
When senior teams assess performance, these three signals, productivity shifts, organizational reach, and usability, should take priority. They paint a better picture of actual impact than vague claims about “transformation.”
Misalignment between business and technology leadership undermines ROI accuracy
AI initiatives that fail to deliver value often have one thing in common, disconnect between business objectives and the technology strategy. When C-suite executives and tech leadership aren’t aligned around what success looks like, the result is usually weak ROI tracking and confused priorities.
Tim Gaus from Deloitte Consulting highlights a critical problem here: if strategic and technical goals are out of sync, then the metrics selected will often reflect only one side of the equation. Tech teams might report system performance or model accuracy, while business leaders are looking for growth, customer impact, or long-term value. Those are not the same thing.
This gap doesn’t just skew measurement, it undermines decision-making. According to Deloitte’s digital transformation research, up to 20% of potential digital investment returns are lost due to these alignment failures. That’s not a minor slip. It’s tangible, recurring loss that compounds with every cycle of miscommunication.
Decision-makers need to establish joint ownership of key metrics before deployment. This includes operational KPIs, revenue-based performance measures, and innovation-linked indicators, like speed of iteration or experimentation tolerance across teams. The accountability should be shared, not siloed in IT or product.
In practice, this means aligning engineers, analysts, business leads, and finance around the same performance dashboard. Without that, investments in AI will keep underdelivering, not because the technology failed, but because leadership failed to agree on what winning means.
Continuous evaluation and adaptability are vital for sustaining AI benefits
AI is not a product you deploy and forget. Its effectiveness depends on how frequently it’s monitored, updated, and aligned with current business needs. Without regular evaluation, even the best AI will stagnate, drift from its intended function, or introduce errors at scale.
Matt Sanchez of IBM puts it clearly, AI is an ongoing process. If you treat it like a fixed asset, you will miss its evolving potential and unknown limits. You need to plan for recalibration. That means watching how performance shifts over time, measuring new patterns, and adjusting the systems to respond to changes in workforce behavior, market dynamics, or customer inputs.
Continuous evaluation isn’t just a maintenance task. For senior leadership, it’s a strategic necessity. Adaptability will determine long-term value. Companies often see an initial spike in productivity after deploying AI, but few sustain it because they stop treating the system like a learning platform.
Regular review cycles, updated KPIs, and clear feedback loops transform AI from a static tool into a dynamic asset. That’s when it starts amplifying innovation, freeing teams for higher-impact work, and maintaining relevance across departments.
If you’re serious about AI, treat performance reviews as part of the system design, not as post-launch troubleshooting. The highest value comes not from the system’s first success, but from how well it’s supported, adapted, and improved after day one.
Final thoughts
AI only works at scale when it’s tied to measurable outcomes and built around real business needs. It’s not about the hype. It’s about execution. If you’re not setting the right KPIs, aligning leadership, or evaluating continuously, then your AI strategy is running off assumptions, not evidence.
Strong data, clear ownership, and the ability to adapt in real time are what separate truly impactful AI from just another tech experiment. Don’t wait to course-correct after rollout. Build measurement, alignment, and feedback loops into the foundation. That’s how you move fast without losing focus.
The opportunity is there. The edge goes to companies that treat AI not as a gadget, but as infrastructure. Make sure yours is built to last, and built to perform.
