Beyond AI Licenses
Independent research / Austin AI Hub
Beyond AI Licenses
How do you know whether an AI tool is actually creating value?
I coauthored Beyond AI Licenses: How to Measure Whether Your AI Is Actually Working with Hamed Alikhani and Sodik Shakirov, in collaboration with Austin AI Hub.
The article draws on a structured questionnaire, interviews and working sessions with 24 AI practitioners. Fieldwork was conducted by an Uzbekistan Business Leaders fellow at the Hub in June 2026.
From evidence to a decision
The paper proposes an AI Value Audit covering governance, workflow velocity, output quality and attributed financial impact. It connects these measures to decisions about renewing, switching, scaling or retiring an AI tool.
Published in California Management Review Insights on August 5, 2026. Small, purposive sample; findings describe the participants rather than the wider market.
My role · Coauthor
From practitioner evidence to an evaluation framework.
I am a coauthor with Hamed Alikhani and Sodik Shakirov. The study’s June 2026 fieldwork was conducted by a UBL fellow at Austin AI Hub.
How can managers assess whether AI tools create value?
Questionnaire, interviews and working sessions with 24 practitioners.
Usage and time-saving signals contrasted with less-developed workflow and financial attribution.
A proposed audit covering governance, workflow, quality and financial evidence.
Coauthored CMR Insight, August 5, 2026. The purposive sample supports directional observations about participants; the framework is author-proposed.
Research record
Question, approach and contribution
Research question
How can a manager decide whether an AI tool in use is creating value rather than merely generating activity?
Study design
A structured questionnaire supplemented by interviews and working sessions with 24 practitioners responsible for adopting or measuring AI. A UBL fellow conducted the June 2026 fieldwork at Austin AI Hub. The sample is purposive and does not represent the wider market.
Proposed framework
The AI Value Audit moves through governance, workflow velocity, output quality and attributed financial impact. It connects evidence to renewal, switching, scaling or retirement decisions.
Interpretation
The four-layer audit is an author-proposed organizing framework presented to practitioners, rather than a structure independently discovered from the survey. Coauthorship is credited to Mahboubeh (Mia) Cheraghian, Hamed Alikhani and Sodik Shakirov. Published as a CMR Insight, not described here as a peer-reviewed journal article.
Findings and implications
Usage and self-reported time savings were common signals, while workflow redesign and financial attribution were less developed. The proposed audit connects governance, workflow, quality and financial evidence to investment decisions.
The survey supports directional observations about these participants. It does not establish population-wide adoption rates.
Read the published article ↗