From measurement to development
Qulak brings together skills assessment, training programs, and development analysis in a single corporate workflow. Rather than simply reporting KPIs, define the modules that suit your organization to connect them to learning objectives.
In 2026, corporate AI literacy programs should go beyond providing basic knowledge and contribute to decision-making processes, risk management, and ethical use. But understanding a program’s effectiveness requires a robust measurement plan. Measurement should target changes in participant behavior, adoption rates, decision quality, and improvements in business outcomes.
When designing your corporate AI literacy measurement approach, explore Qulak’s skills assessment and development map approach; to connect measurement results to your training program, speak with the Qulak team.
This article explains step by step which objectives measurement should support, which metrics to use, which tools can be used to collect data, and how to report the results. You’ll also find recommended cyclical processes for continuously monitoring performance, along with examples of internal implementation.
Please note: We do not make definitive claims about legal and regulatory matters. Sources such as the EU AI Act or local regulations may contain current requirements; your organization should verify its legal compliance.
The approach generally focuses on three areas: knowledge and competency (learning outcomes), behavioral application (practical use), and business impact (ROI/operational gains).
1. Measurement Framework: Define Objectives and KPIs
Program evaluation is not possible without measurable objectives. Set objectives using SMART criteria (Specific, Measurable, Achievable, Realistic, Time-bound). Example KPIs:
- % completion rate (training modules)
- Increase in skills scores (pre-test/post-test)
- Frequency of AI-based tool use
- Rate of human-approved automation in decision-making processes
- Reduction in AI-related non-compliance findings in internal audits
2. Balance Quantitative and Qualitative Tools
Quantitative data (test scores, usage statistics, number of certificates) provides a quick indicator. However, qualitative methods (surveys, focus groups, and one-to-one feedback from managers) are needed to observe behavioral change and cultural adaptation.
Recommended tools
- LMS reports and SCORM/xAPI data
- Competency maps and 360-degree surveys
- Usage logs (tool telemetry)
- Cross-matching KPIs with business unit objectives
3. Data Collection and Privacy
If the data collected contains personal data, comply with KVKK and applicable data protection regulations. Clarify your policies on matters such as data anonymization, purpose limitation, and retention periods. Establish privacy and ethical principles from the outset when designing your measurement approach.
4. Reporting, Internal Stakeholders, and the Improvement Cycle
Tailor your reporting format for managers, HR, and technical teams. Prepare high-level KPI dashboards for managers, usage metrics for operations teams, and module performance reports for learning teams. Update training content iteratively based on measurement results.
Implementation Checklist
- Have you aligned business outcomes with learning objectives?
- Have you defined KPIs using SMART criteria?
- Have you set up pre-test and post-test mechanisms?
- Do you have surveys and leader feedback processes for behavioral data?
- Have data privacy and retention policies been defined?
- Has a reporting cycle (monthly/quarterly) been planned?
Let’s design your corporate AI literacy measurement plan together. Contact us for information, a demo, or a quote:
Frequently Asked Questions
Q: When should I start measuring?
A: Start by collecting pre-test data before the program launch; this will allow you to accurately assess learning outcomes.
Q: How often should reporting be done?
A: Plan to review operational progress monthly and conduct strategic assessments quarterly or annually.
Q: What is a simple approach for small teams?
A: A combination of pre-test/post-test, usage data, and a short feedback form from a manager may be sufficient for most SMEs.