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A field guide to how people orient to AI - and what each pattern tends to miss.
Everyone has a mental model about AI. Not what they know about AI - how they think about it. Their default assumptions, gut reactions, and implicit beliefs about what AI is, what it does, and what it means for their work.
We identified six distinct patterns in how people think about AI. Most people show a primary orientation signal and secondary/tertiary patterns they may draw on situationally.
No profile is inherently better or worse. Each tends to see certain things clearly and miss others. That is the scotoma - the blind spot worth investigating.
"You wield AI with deliberate skill, like a master using precision instruments. You pick it up when needed and set it down when done - but the line between using a tool and being shaped by it is thinner than it appears."
You treat AI as an instrument under human control. You maintain agency, choose when to engage AI, and frame adoption as skill development. This preserves autonomy but can blind you to how AI is quietly reshaping your cognitive patterns.
Key insight: Tomorrow, choose one task where you habitually use AI and perform it entirely manually. Not to prove capability but to notice: do you approach the problem the same way? The tool metaphor assumes neutrality. This experiment tests that assumption.
"You and AI form a working system - each bringing distinct capabilities to shared work. The integration feels natural, even powerful. But symbiosis can shade into dependency if you lose track of where you end and it begins."
You see AI as forming a genuine working system with human capability. You invest in interaction design, complementarity, and seamless integration. This produces remarkable performance but can make the boundary between human and AI capability invisible.
Key insight: Next time you 'collaborate' with AI, write down your approach before seeing AI's output. If they align perfectly, the symbiosis may have already taught you its worldview. If they diverge, will you explore your approach or follow its lead?
"You see AI's promise clearly: more output, faster cycles, higher efficiency. The mathematics of optimization are compelling. What the spreadsheet doesn't show is the tacit learning that lived inside those 'routine' tasks you automated away."
You focus on productivity gains, efficiency improvements, and scaling through AI. Your business cases are sharp and your ROI calculations pristine. But optimizing for speed can mean inadvertently eliminating the slow learning processes that build deep expertise.
Key insight: Choose one task you've automated. Find the person who used to do it. Ask: 'What did you learn while doing this that wasn't the explicit goal?' Listen carefully. Routine work often carries invisible learning functions.
"You see risks others miss - the dependency forming, the skills eroding, the boundaries blurring. Your vigilance is often wiser than people think. The challenge is channeling that protective instinct into strategy rather than resistance."
You prioritize governance, boundaries, and risk awareness around AI. You see threats to professional identity, skill erosion, and unchecked dependency. This vigilance provides genuine value - but pure defense without adaptation can leave you shaping nothing.
Key insight: Write down specifically what feels threatened. Not 'AI is a threat' but the particular: tasks, roles, identity, income, professional authority. Can you defend each with reasoning beyond tradition? That distinction determines whether your defense strengthens or weakens your position.
"You see what most miss: AI isn't just changing what we do - it's changing what expertise means. You think at the system level, designing for human-AI futures. That vision is rare and valuable. Just make sure reflection leads to action."
You think at the system level about how AI transforms expertise, learning, and professional identity. You use AI to understand your own organization and design thoughtful integration. This is the most reflective and often healthiest orientation.
Key insight: Next time AI surfaces a pattern about your work, don't act on it immediately. Ask: 'Why might this pattern exist?' and 'What does finding it teach us about how we work?' The value isn't in the pattern itself but in what reflecting on it reveals.
"AI isn't something you 'use' - it's the medium you work in, as fundamental as electricity. That deep integration delivers power, but what's invisible can't be governed. Can you still think without the water you swim in?"
AI has become infrastructure in your work - so deeply embedded it's no longer perceived as a separate system. This produces maximum integration but minimum visibility. When AI shapes every decision invisibly, expertise becomes inseparable from the algorithmic environment.
Key insight: Pick any decision from last week. Trace it through every AI system that touched it: email sorting, calendar AI, search ranking, autocomplete, analysis tools. Count honestly. Many people discover more algorithmic touchpoints than they expected in what felt like an independent decision.
The most effective teams aren't homogeneous - they have profile diversity. A team of all Accelerators will move fast but miss risks. A team of all Sentinels will be cautious but slow to adopt.
The key is knowing your team's distribution and ensuring critical perspectives aren't missing. If no one on your leadership team has Sentinel tendencies, risk analysis will be systematically underweighted in decisions.
Scotoma's team assessment visualizes this distribution and flags gaps - not to label people, but to make the invisible visible.
Take the assessment to see your primary, secondary, and tertiary orientation signals.
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