Operational reliability
Experience designing for high-stakes, real-time environments informs Behaviture’s emphasis on auditability, limitations, and safe decision support.
About Behaviture
Building a more useful, responsible, and human path to workplace AI.
Behaviture helps organizations understand the conditions behind AI adoption: whether employees see value, have the right approved capabilities, understand the rules, trust the process, and know where AI fits their work.
The company was created from a combination of enterprise software experience, management study, and research into why employees use personal or unofficial AI tools even when organizations provide approved alternatives.
- Long-term enterprise software experience
- MBA in Management and Consulting
- Spent a year researching Shadow AI and AI Governance
The problem behind the product
Organizations can buy AI access faster than they can build AI capability.
Enterprise AI programs often begin with licenses, pilots, policies, and training. Those are necessary, but they do not automatically tell leaders whether AI is useful in a specific role, whether approved tools cover real employee needs, or whether people understand how to use them safely.
At the same time, a surveillance-first response can make the problem harder. Employees who expect punishment or monitoring are less likely to provide honest information about tool limitations, unclear policies, mistakes, or unmet workflow needs.
Behaviture exists to create a better exchange: private value for employees and aggregate evidence for leaders. The objective is not to identify “bad users.” It is to identify the organizational conditions that help responsible AI adoption succeed.
work_outline
Employee needtune
Approved tool, guidance, and trust conditionsverified_user
Responsible AI capabilityFounder
Tim Van Prooyen
Founder, software engineer, and MBA graduate
Tim Van Prooyen is a software developer and product builder with more than fifteen years of experience in complex enterprise, data, operational, and AI-related systems. His work has included software supporting real-time electricity-grid operations, wholesale energy markets, reliability communication, load forecasting, data engineering, industrial time-series systems, and AI-based payment-fraud prevention.
He earned a Bachelor of Science in Computer Science from the University of Arkansas at Little Rock and an MBA in Management and Consulting from the Prague University of Economics and Business.
Behaviture combines the two sides of his career: building dependable enterprise software and understanding the organizational decisions required for technology to create real value.
Connect on LinkedIn open_in_new- Enterprise systems
- Electric-grid operations, energy markets, forecasting, and data platforms
- AI and data
- Machine learning, AI-enabled systems, data engineering, and analytical products
- Business education
- MBA in Management and Consulting
- Location
- Prague, Czechia
Why this background matters
Complex systems fail when the human workflow is treated as an afterthought.
Electric-grid and market systems cannot succeed through technology alone. They depend on clear operating procedures, reliable information, appropriate controls, usable interfaces, and people who understand how to act when conditions change.
That same lesson applies to enterprise AI. The model may be powerful, but value still depends on whether the tool fits the work, whether the employee can use it confidently, whether the organization provides an approved path, and whether the rules make sense in practice.
AI Adoption Pulse applies that systems perspective to the workforce side of AI adoption.
Data and forecasting
Experience with forecasting and analytical systems informs the separation of deterministic measurement from explanatory narrative.
Enterprise change
Experience modernizing large systems informs the product’s focus on adoption barriers, workflow fit, and practical incremental action.
Research foundation
Shadow AI in the Workplace: Balancing Productivity Gains and Organizational Risk
The thesis examined why employees use unofficial AI tools for work and how organizations can balance productivity benefits with security, policy, and governance concerns.
It explored behavioral factors including perceived usefulness, ease of use, differences between approved and outside tools, policy awareness, risk perception, self-efficacy, and the emerging challenges of agentic AI.
The research reinforced a central product insight: employees may use outside tools because those tools solve real workflow problems—not simply because employees are unaware of risk or unwilling to follow policy. Effective intervention therefore requires more than stricter warnings. Organizations must understand tool fit, role demand, confidence, policy clarity, and the employee experience together.
info
The thesis informed the product’s hypotheses, language, and initial measurement model. AI Adoption Pulse is a commercial product that still requires pilot evidence, reliability analysis, and continued validation. The university has not endorsed or certified the product.
Technology Acceptance Model
Perceived usefulness and ease of use
Protection Motivation Theory
Risk, coping confidence, and protective behavior
UTAUT and facilitating conditions
Organizational support, access, and adoption conditions
Socio-technical systems thinking
The interaction of tools, work, people, policy, and organizational context
How Behaviture approaches the work
Practical principles for people-centric AI adoption.
Enablement before policing
Outside-tool pressure often points to unmet workflow needs, unclear rules, or missing approved capability. Diagnose the condition before assigning blame.
Private value for participants
Employees should receive something useful in return for their time and honesty.
Aggregate decisions, not individual rankings
The product is designed to improve organizational systems, not evaluate individual employee performance.
Deterministic facts before generated narrative
Scores, gaps, and report facts should be versioned and reproducible. Language models may explain verified facts but should not invent the measurement.
Honest limitations
Role opportunity, literacy, and agentic readiness are decision-support signals—not legal conclusions or guarantees.
Measure change after action
A useful diagnostic should lead to a tool, policy, training, workflow, or governance action that can be reviewed in a later pulse.
Current stage
Built for structured pilots and continuous learning.
Behaviture is preparing AI Adoption Pulse for early design partners and structured customer pilots. The core product includes organization setup, a privacy-first employee survey, deterministic scoring, private employee reports, aggregate dashboards, executive reporting, approved-tool and capability analysis, and optional readiness modules.
Early pilots will be used to improve report relevance, role and capability mappings, onboarding, recommendation quality, and the semiannual action cycle.
ImplementedAvailable in the working product
Pilot-readyPrepared for controlled external use
Under validationRequires customer evidence and continued analysis
PlannedNot yet part of the standard offer
Evidence, not borrowed authority
Independent research informs the problem. Customers validate the product.
Industry research from organizations such as Gartner describes a growing gap between AI access and workforce enablement. Behaviture uses sources like these to understand the market and explain why people-centric measurement matters.
External research does not prove that Behaviture works. Product claims should advance from design, to implementation, to pilot observation, and eventually to validated evidence.
Gartner: People-Centric AI Strategy and Workforce Enablement
Gartner’s May 2026 press release predicts that enterprises without a comprehensive AI people strategy face talent risk and argues that leaders must look beyond basic adoption metrics toward workforce enablement, tool experience, targeted support, and psychological safety.
Read the Gartner press release → open_in_newReferenced as independent market context. No endorsement is implied.
See the product
Turn a people-centric AI strategy into a measurable first step.
Start with a free organization-level snapshot or discuss a structured pilot for deeper role, capability, literacy, and agentic-readiness analysis.