A term we use deliberately. Not personality. Not performance. Preference. This page explains what it means, where it comes from, how we measure it, and why we built Sariio to deliver it.
Performance data is everywhere. Sales figures, call metrics, quality scores, pipeline numbers. Organisations capture more individual performance data than at any point in history. What they rarely capture is how each person prefers to approach that work. How they build relationships. Whether they reach for structure or possibility. How they balance evidence and instinct when making a decision. Whether they prefer to start fast or plan thoroughly.
This is the missing layer. Not what people deliver, but how they prefer to deliver it. When that gap is closed, something shifts. People can approach their work in ways that suit them. Managers can adapt their coaching to each person. Teams can see where they naturally agree and where they will need to make room. The work stays the same. The way people approach it becomes visible.
Gallup's research consistently finds that 70% of the variance in team engagement depends on the quality of the manager relationship (Gallup, 2026). Preference data is how you improve that relationship with evidence, not intuition.
The cost of leaving that gap open is well documented. 80% of the global workforce is not engaged or is actively disengaged, an estimated $10 trillion in lost productivity each year (Gallup, 2026). One in three employees leaves within a year of starting. Replacing a single frontline employee costs an estimated $10-20K once ramp time and lost productivity are counted (McKinsey, 2024). Those numbers are the downstream effect of relationships that never quite worked, and the manager relationship is where they are decided.
Work preference intelligence is the structured measurement of how people prefer to work, applied across individuals, relationships, teams, and organisations.
It differs from personality profiling in three ways. First, it measures preferences on a continuous scale, not categories or types. Second, preferences are dynamic and coachable, not fixed traits. Third, the output is designed for action: coaching conversations, onboarding decisions, team composition, and candidate screening.
It differs from performance measurement in that it looks forward, not back. Performance data tells you what someone delivered. Preference data tells you how they prefer to approach the next piece of work. Both matter. Most organisations have one without the other.
Circles apply work preference intelligence to any group -- not only organisational teams. A training cohort, a project group, a set of coaching clients, or candidates being considered for the same role can all be grouped into a circle. The platform generates a relationship map and AI-powered pairwise insights for every connection in the group. No reporting lines or shared employer required.
Sariio's framework, MAPS, draws on two established research traditions.
The first is Adlerian psychology. Alfred Adler's work established that people are motivated by belonging, contribution, and purpose. How they pursue those drives differs from person to person. Adler's framework explains why preferences matter: because the way someone approaches their work is shaped by what gives them a sense of contribution and competence.
The second is NLP metaprogrammes. These are cognitive patterns, originally described by researchers including Charvet (1997) and Hall & Bodenhamer (2000), that influence how people process information, make decisions, and interact with others. Metaprogrammes are observable, measurable, and coachable. MAPS adapts twelve of these patterns for the modern workplace.
The methodology is not new. The non-AI precursor to Sariio, called Saviio, was developed and deployed by Sariio's founder across 40+ organisations in 16 countries, with over 12,000 assessments completed. That decade of practical application is the foundation the current platform is built on.
Today that includes BPO and customer-experience operations running the platform across entire cohorts, from individual myMAPs to organisation-wide culture mapping.
MAPS organises twelve preference pairs into four factors. Each factor represents a domain of working behaviour. Each preference pair is measured on a continuous scale: there is no right answer, no good end or bad end. The survey captures where a person sits and how consistent their responses are.
How people build trust, navigate others, and communicate. Three pairs: Wary/Trusting, Concern for Task/Concern for People, Independent/Interdependent.
Whether they reach for structure or possibility, detail or the bigger picture. Three pairs: Problem Solver/Risk Taker, Similar/Different, Procedures/Options.
Their balance between evidence and instinct, caution and momentum. Three pairs: Thinking/Doing, Rational/Intuitive, Certainty/Ambiguity.
Their preference for pace, planning, and follow-through. Three pairs: Specific/General, Finisher/Starter, Perfectionist/Pragmatist.
The Sariio survey presents 60 statements grouped into 12 preference pairs. For each statement, the respondent indicates their position along a Visual Analogue Scale, a continuous slider between two contrasting poles. This format captures nuance that tick-box or forced-choice formats miss.
Responses are stable over time but can shift as roles, teams, and circumstances change. Sariio tracks those shifts longitudinally, so individuals and organisations can see how preferences evolve across coaching engagements, role transitions, or team changes.
The survey is individual, not group-normed. Each person's results stand on their own. Comparisons between two people, or across a team, are derived from individual data, never averaged away.
Sariio uses AI selectively and with human oversight at every stage. The AI generates three types of output:
Each person receives an AI-generated narrative that describes their preference profile in plain language. The narrative responds to the combination of their preferences, not each one in isolation. Two people with different profiles receive different summaries.
When two people compare their results, the AI generates a narrative describing where they align, where they differ, and what that means for how they work together. Both sides see the same evidence.
When a candidate's myMAP is compared against a talentMAP role benchmark, the AI suggests interview questions focused on the gaps.
Every piece of AI-generated content is built from actual survey responses. The AI does not invent, generalise, or fill gaps with generic advice. All output is logged with timestamps and version numbers. Organisation administrators can review, regenerate, and track AI output across their teams.
Sariio is built with data protection at its core.
The platform is hosted on European infrastructure (Frankfurt). Data is encrypted in transit and at rest. Access is controlled through role-based permissions: individuals see their own data, managers see their team's data with consent, and organisation administrators manage access across tiers.
GDPR-aligned consent flows are built into every step of the survey and sharing process. When an individual shares their results with a colleague or manager, they choose what to share and with whom. The data belongs to the person who took the survey.
People work differently. Always have. Sariio just makes it easier to see.