AI Cultural Readiness Diagnostic

You can't see your AI culture from the inside.

Mirmara offers independent Culture Diagnostics, grounded in Organisational Psychology and Social Network Analysis. Mirmara measures how your workforce actually responds to the pressure of change that AI brings.

Turn the lights on See what becomes visible
A 30-minute diagnostic conversation.
Hover over the screen to discover employee voices

AI adoption can be messy.

There are adoption asymmetries. People move at a different pace.

It is hard to see how employees really stand on AI in their company:

what is their attitude toward AI technologies, which emotions dominate? And what are the underlying motives behind AI-related decisions?

01 Mirmara brings light to the dark 02 The new vocabulary 03 Why it stays hidden 04 Methodology & report 05 Positioning 06 The next step
01Mirmara brings light to the dark

Every decision you make about AI rests on an assumption about your people. Checking it takes two things that rarely come together: someone they'll tell the truth to, and a method built to measure how people actually respond to AI.

The value lies in finding clarity.

Every company has an AI culture. Employees take a stance toward AI technologies, whether implicitly or explicitly. Mirmara's value lies in making the process of AI adoption visible.

Whom do employees trust, where does knowledge flow, and where does it stall? What do employees think but never say out loud?

Mirmara delivers a comprehensive diagnostic report that examines these questions in depth and makes them discussable. The report is a structured, data-backed answer to the question: where do our people really stand on AI? And how “culturally ready” are we as an organisation for a world in which everyone has to take a position on AI? Among other things, the report supports leadership in recognising and naming AI adoption patterns and prioritising interventions.

Questions Mirmara sheds light on include:
01

Why do individual employees and teams use AI so differently, even though they theoretically have access to the same resources, such as licences and training?

02

Who shapes AI behaviour informally, beneath the org chart?

03

What are the barriers to AI use for my team? Capabilities? Trust? A lack of clarity on strategy, or something else entirely?

04

How should I prioritise interventions?

05

How do I shape messaging around AI strategy if I am not sure about the general sentiment?

Challenges Mirmara helps frame and tackle are:
01

Leaders feel a disconnect between AI adoption expectations from upper management and where their teams are in reality.

02

Leaders want to deliberately develop the culture around AI and foster knowledge exchange. But how?

03

Teams lack a shared language for dealing with AI technology. Where trust (psychological safety) is missing, sensitive topics get handled out of sight.

No matter where your team stands with AI adoption, Mirmara provides orientation.

02The new vocabulary

We see a whole new set of vocabulary emerging that captures employees’ experience with AI at work.

Especially expensive phenomena for organisations are:
/ / /

The language around AI adoption tends to be framed negatively. But these patterns aren't failures to eliminate. They're signals: evidence of how people are already adapting to AI, and where it can work better.

What can look like workarounds, hesitation or resistance, Mirmara reads as a valuable signal: of emerging practices, informal expertise and pressure points in how work is changing. All of it points to your AI culture taking shape, a culture that empirical data makes strategically shapeable.

03Why it stays hidden

Speaking openly about how you use AI can be expensive for employees, psychologically, but also literally.

An independent, outside perspective frees employees from that perceived risk.

Trust between employees and Mirmara is built through a scientific approach. All data is anonymised.

04Methodology & report

The tools most companies have for gathering data, such as performance reviews, engagement surveys, staff questionnaires and licence activations, measure exactly what they were designed to measure. Mirmara's methodology is built specifically to capture how employees experience and behave around AI, and asks not only “what and how much was used?” but why that choice was made.

Mirmara turns the data into a comprehensive diagnostic report.

Leaders receive an AI Culture Readiness score across five dimensions.

Mirmara developed the concept of AI Culture Readiness: the collective capacity of your workforce to embrace AI and integrate it sustainably into everyday work. AI Culture Readiness is the decisive difference between a workforce that has AI tools and one that knows how to use them best.

Leaders receive an AI Culture Readiness score composed of five dimensions.

  • Affective · how people feel about AI. Captures dominant emotions, attitudes and sentiment.
  • Cognitive · what employees believe it can and cannot do. Captures mental models and technical understanding of the technology.
  • Behavioural · what employees actually do with it. Captures day-to-day behaviour around AI tools.
  • Relational · captures the relationship layer: whom employees trust, whom they turn to for advice, and so on. Reveals influencers who are central to knowledge exchange, and those at the periphery.
  • Values · measures how far employees see AI use as legitimate and fair. How people think about AI responsibility and ethics, and where policies may clash with personal values.

Mirmara works with a mixed-method approach made up of semi-structured interviews, questionnaires and Social Network Analysis.

The methods are complementary. Each captures an important angle of AI adoption: the questionnaire provides the wealth of data, interviews the depth, and sheds light on the relationship layers.

Employee voices stay protected throughout by anonymity. Responses are treated confidentially and results are presented only at group level. The entire process is GDPR-compliant.

Mirmara diagnoses the conditions your AI strategy depends on.

Sequence matters. Most organisations buy “the solution” first, usually training, coaches or consulting, before they have any clarity on where employees stand on AI. If the barrier to adoption is, say, a moral unease, training will not increase use, because training closes a knowledge gap. Mirmara puts the diagnosis first, so investment can be placed strategically.

The diagnostic report is built from three pillars:

  • Leverage points. Shows where measures bring the greatest return. Internal influencers who carry knowledge exchange, for example, can be deliberately involved in workshops and focus groups. With the data at hand, leaders can develop a feel for the tone their communication needs to strike to reach their team.
  • A gap analysis. Where the team should be, compared with where it stands. It reveals acute pain points, opportunities and quick wins. The gap analysis helps shift resources to where they do the most, and backs those decisions with data.
  • An AI Culture Readiness score across the five dimensions. Corporate intelligence at the cultural level: you know where your people stand, and gain a valuable edge over the competition when it comes to AI culture.

Every diagnostic is tailor-made. Mirmara adapts to your organisation: data can be gathered for selected teams, within individual teams, or across the whole organisation. Scope and approach we develop together in a first conversation.

05Positioning
Anna Steinkamp, painted portrait

Anna Steinkamp · Founder,
Organisational Psychologist

Mirmara does not sell the intervention; it diagnoses the conditions your AI strategy depends on.

Mirmara works independently. No strategy is implemented, no change programme set up, no training delivered. Mirmara is the diagnostic layer before such measures. The mission: to help leaders see the cultural dynamics of AI adoption more clearly, before investments flow.

Who is behind Mirmara

Mirmara was founded by Anna Steinkamp. Anna is an organisational psychologist working at the intersection of culture, AI adoption and network science.

Her work with Ethical Intelligence, a consultancy for the responsible use of AI, included a full diagnostic of internal culture through SNA. That work shaped her interest in how influence, trust, collaboration and informal knowledge flows determine whether organisations can adapt to emerging technologies in practice.

She earned a BSc in Organisational Psychology at the University of Europe (Berlin) and an MSc in Performance Psychology at the University of Edinburgh, where she specialised in psychological safety, employee voice and team dynamics.

06The next step

Want to find out what your AI culture looks like from the inside?

A 30-minute conversation. Together we talk through a possible diagnostic approach and what you are seeing in your team or organisation.

Talk to Mirmara