In a recent LinkedIn post, Clare Kitching discusses the critical need to move beyond simple adoption metrics when evaluating the success of Artificial Intelligence (AI) within businesses. While many companies track user engagement with AI tools, Kitching argues that this is only the first step and often fails to demonstrate tangible financial or strategic value to the C-suite.
Kitching highlights a common scenario where AI dashboards report high daily usage, yet the financial benefits remain elusive to leadership. She points out that relying solely on metrics like user numbers, prompt frequency, or activated licenses provides an incomplete picture of AI’s contribution.
“Lots of people using AI doesn’t automatically mean your business is improving.”
This initial observation sets the stage for Kitching’s core argument: the measurement of AI’s success must evolve alongside its application.
Shifting Focus from Usage to Value
Clare Kitching emphasizes that AI’s potential extends far beyond mere productivity gains. She asserts that AI can fundamentally alter key business areas, including customer experience, revenue generation, operational efficiency, and employee satisfaction. Therefore, the metrics used to gauge success should reflect these broader impacts.
Kitching advocates for tailored measurement strategies, suggesting that different departments should not be evaluated using the same Key Performance Indicators (KPIs). As she puts it:
“A sales team using AI shouldn’t be measured the same way as a customer service function. An operations team shouldn’t use the same KPIs as HR.”
This differentiated approach, according to Kitching, ensures that metrics are aligned with the specific value streams each AI implementation aims to impact.
Expanding the Dimensions of AI Value
The thought leader urges businesses to broaden their conversations about AI’s worth, moving past a singular focus on cost savings. Kitching proposes considering value across multiple dimensions:
- Financial: Directly impacting revenue, profitability, or cost reduction.
- Operational: Enhancing efficiency, speed, or process streamlining.
- Customer: Improving satisfaction, retention, or acquisition.
- Workforce: Boosting employee experience, enabling new capabilities, or reducing workload.
Kitching notes that the most significant impacts of AI are often not in cutting costs but in areas like increasing conversion rates, improving customer retention, or shortening the time it takes to deliver value.
“A lot of the time the biggest impact of AI isn’t reducing cost. It’s increasing conversion. Or improving retention. Or shortening time to value.”
She also points to AI’s role in empowering employees to perform tasks previously considered impossible, an advantage many users already feel.
Connecting Usage to Measurable Outcomes
Ultimately, Clare Kitching concludes that while adoption remains an important indicator, its true significance is amplified when paired with demonstrable business impact. High adoption rates combined with measurable positive outcomes paint a much more compelling story for stakeholders.
Kitching’s central call to action is for organizations to establish clear connections between AI usage and quantifiable business results. By doing so, businesses can more effectively articulate and realize the full strategic potential of their AI investments.
“So start connecting usage to measurable business outcomes.”
Her insights provide a valuable framework for leaders seeking to ensure their AI initiatives deliver not just activity, but meaningful, measurable business value.
📝 About This Content
This article is based on insights shared by Clare Kitching on LinkedIn.
📅 Originally posted on September 6, 2026 | View original post on LinkedIn →