Part 2 - The Thinking Modes: keep your brain strong in the age of AI
In Part 1 we set a few simple guardrails so AI’s speed didn’t steamroll our thinking ability. Now let’s talk about your brain, what lights up for different kinds of thinking, why those pathways get stronger with use, and how to keep them strong as new tools emerge.
We rushed into AI, first as a fancy search bar, then with smarter prompts. Humans adapt fast. The risk? We adapt in ways that can de-train our own thinking. Innovation is usually designed to remove effort. Our brains love that. We’re wired to avoid pain and conserve energy, so we grab shortcuts quickly. Great for chores, risky if they replace the reps that keep our thinking strong.
In this part of the series, we begin with a short Brain 101, then a brief tour of the thinking modes. The science is light and easy to understand, with one simple goal: AI works with you, not instead of you.
Brain 101
Your brain is a network. Roughly 86 billion neurons talk to each other through trillions of connections. A lot is happening all the time, some of it conscious, much of it not.
Use it or lose it. Pathways strengthen with practice and quiet down when we don’t use them. Reps matter, and they need to happen across different kinds of thinking, not just one.
Attention is a bottleneck. Working memory holds only a few things at once. Notifications and noise steal those precious slots.
Plasticity at any age. Small, repeated practice changes the brain. That’s our leverage: tiny, consistent reps build stronger pathways over time. You can change the way you think at any age.
Different jobs, different crews. Picture four “thinking teams” that tag-team your work (they overlap in real life):
Focus & follow-through keep details straight and move a task to done.
Imagine & connect generates the ideas and connects dots.
Store & remix saves experiences and reuses them in plans.
Spotlight & switchboard notices what matters and helps you switch modes.
They rarely work alone. Creative work, for instance, involves Imagine & Connect partnering with Focus & Follow-Through; the Spotlight & Switchboard team decides when to switch. These are modes, not fixed brain boxes. The mix changes with the job.
The brain graphic is illustrative. Real networks overlap and shift; treat it as a simple visual guide.
Skills for 2030 (why we’re doubling down)
Global outlooks keep analytical and creative thinking at the top. Close behind are flexibility, curiosity, lifelong learning, and working well with others. This is great news for humans—it means the thinking modes don’t go out of fashion, and we become more valuable. In an AI world, these skills turn speed into good decisions.
How to use that to your advantage.
Let AI do the heavy (and often mundane) work, drafts, option lists, and pattern-spotting, then layer your framing, checks, and thinking on top. That combo already shows up in medicine: when clinicians treat AI when used as a second opinion (not autopilot), performance is maintained or improves in some settings. That’s the model: AI widens the field; you make the call. But we must continue to flex our brains to make the most of it.
The Thinking modes at a glance
Our brains don’t use one “thinking centre.” Different networks switch on for different jobs, and they often work together. I focus on seven practical thinking modes you’ll use most at work: analytical, critical, creative, strategic, systems, metacognition, and cognitive flexibility. They’re not the only ways the brain thinks, but they’re the core set behind everyday decisions (and they line up with the skills global 2030 outlook).
Analytical: break a problem into parts, read the signals in the data.
Critical: test claims, spot gaps and bias, check logic.
Creative: generate useful options and new connections.
Strategic: make choices today that set up a better position tomorrow.
Systems: see how pieces interact, where constraints and ripple effects are.
Metacognition: step outside the work to plan, monitor, and adjust your thinking.
Cognitive flexibility: switch modes on purpose, fast.
A spotlight on cognitive flexibility. This is the “gear-shift” skill. It’s your ability to move smoothly from, eg, Facts → Options → Risks → Decision, without stalling. It correlates with performance, and it’s trainable. AI can help or hurt here: if it does all the switching for you, your own “gearbox” gets rusty; if you practice deliberate switching, you stay sharp.
How your brain powers each mode
I’ve laid out the Thinking Modes so they’re easy to follow: what they mean, why they matter, the key brain bits (if you’re interested in the science), what AI can help with, and what to watch out for, plus a quick drill to keep your thinking in top shape.
1) Analytical
What is it: breaking things down, reading patterns in numbers.
Why it matters: stops you chasing noise and keeps decisions grounded.
Brain bits: a fronto–parietal “control” network (dorsolateral prefrontal + inferior parietal) helps you hold details in mind, focus, and compare options. Think of it as your internal project manager.
AI helps: clean data, suggest cuts and charts.
AI Watch-out: if you always let AI slice the data and pick the chart, you do fewer reps of checking definitions, timeframes, and denominators. Those reps matter.
Brain reps (5 min): Number sanity check. Pick one metric that matters this week. Confirm unit, timeframe, base, and direction with a concrete example: “Is 12% churn per customer or per account? Twelve per cent this month or the last 28 days? Is the base 1,000 customers or 10,000?” Write the answers in a single line and share them in the team channel. Your control network loves clear targets.
2) Critical
What is it: testing claims and reasoning in the argument.
Why it matters: reduces bad decisions and prevents “the tool said so” mistakes.
Brain bits: prefrontal areas work with cingulate and insula to monitor, detect conflict, and adjust course. This is your internal dashboard.
AI Helps: generate counter-arguments and failure modes.
AI watch-out: suggestions feel right, so our checking drops (automation bias). Build checking back in.
Brain reps (5 min): Source trail check. For anything that touches risk or money, ask three things: what’s the source, the date, what’s missing. If the tool can’t show it, don’t use it.
3) Creative
What is it: producing many new and useful options and fresh connections
Why it matters: ideas are raw material. No ideas means no vision or strategy.
Brain bits: new idea generation often involves the default mode (“imagination”) network (medial PFC, PCC/precuneus) working with control regions (DLPFC/inferior parietal) to shape/keep ideas on brief. Together, they generate and shape.
AI helps: widening the starting field quickly
AI watch-out: AI is great for first passes, but if you always take the top suggestion, you narrow your own exploration.
Brain reps (8 min): Open brainstorm. Set a timer. Get 6–8 options before you judge anything. If you use AI, ask for three different angles first (e.g., “for time-poor owners,” “for cautious buyers,” “for new-to-digital”), then expand the best one yourself. Brain hack: A short walk right before this boosts idea creation.
4) Strategic
What is it: picking today’s moves to win the position tomorrow
Why it matters: turns activity into advantage.
Brain bits: you reuse memory machinery to simulate futures, hippocampus plus prefrontal regions help you recombine experiences and project forward.
AI Helps: pull comparables, stress-test scenarios.
AI watch-out: while models can list scenarios, only you can set appetites for risk, define position, and decide trade-offs.
Brain reps (7 min): write a future news story. It’s six months from now, and a trusted outlet runs a short piece on your success. Write five lines: headline, one metric that moved, one customer quote, one thing you stopped, one choice that made the difference. This nudges the same systems you use to remember, now pointed at the future.
5) Systems
What is it: seeing how parts interact over time - interdependencies, constraints, and ripple effects
Why it matters: prevents fixes that move the problem somewhere else.
Brain bits: complex, multi-step reasoning pulls on that front–parietal control system and attention networks that help you hold several moving parts in mind.
AI helps: draft a quick cause-and-effect map from your notes.
AI watch-out: AI can draw polished maps but can also overcomplicate. You need to pick the few loops that actually drive outcomes.
Brain reps (10 min): One-page map. Sketch the few things that truly move your outcome, draw arrows for “more makes more” or “more makes less,” and mark one place a tiny pilot would change the loop. Also list one nearby area/team that might be hit as a side effect (e.g., “faster onboarding may increase support load for week one”).
6) Metacognition
What is it: thinking about your thinking, then adjusting it.
Why it matters: it’s how you learn from choices, not just make them.
Brain bits: anterior/dorsolateral PFC + cingulate/insula help you monitor performance and switch tactics
AI helps: draft checklists; but you still run the review
AI watch-out: if AI writes the rationale, you miss the learning.
Brain reps (1 min): decision sticky note. After any decision, write four items: what we chose, who owns it, why we believe it will work, and the review date. That “why” line builds better judgement next time.
7) Cognitive flexibility
What is it: switching modes quickly and on purpose.
Why it matters: modern work needs you to shift gears without stalling.
Brain bits: switching has a real cost, handled by control systems and basal ganglia that reconfigure “task sets.” Practice reduces the cost.
AI helps: changing views/filters quickly; summarising by lens.
AI watch-out: constant tool-hopping can look like flexibility but is often fragmentation.
Brain reps (10 min): Four-stage cycle. Take one problem, spend two minutes each on Facts, Options, Risks, Decision. No skipping, one timer, then stop. This is like the gym set for your switching muscles.
What AI boosts, and what it dulls
Good: AI lightens repetitive work, widens your starting options, and speeds early drafts.
Watch-outs:
Cognitive offloading: When information is easy to find, we remember where it is rather than the content. This is useful sometimes, but harmful if it replaces understanding.
Automation bias: smooth suggestions feel right, so our checking drops.
Lower engagement when you over-lean on AI: Early lab work hints that heavy AI use can quieten signals tied to attention/self-monitoring and produce more templated output. Treat this as emerging evidence, as studies are still being done
Bottom line:
Use AI as a power-assist. Let the tool lift; you steer.
We’ve just seen how wildly capable and complex your brain is and how those thinking modes stay strong with small, regular brain reps. We’ve also seen where AI helps (faster drafts, wider options) and where it can quietly dull the very circuits we need.
On a personal note: I hope you found this as interesting as I do. I’m always curious about how the brain works, and how a few small habits keep our best thinking switched on. I keep brain science front-of-mind when I design ways of working so people can do their sharpest thinking. It’s a deliberate approach.
Stay tuned for Part 3: brains on ways of working. Practical team rhythms, simple guardrails, and a few metrics that protect attention and lift performance
Resources & further reading:
Herculano-Houzel (2009), The human brain in numbers — Frontiers
https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/neuro.09.031.2009/fullMorra, Patella & Muscella (2024), Modelling Working Memory Capacity — Journal of Cognition
https://journalofcognition.org/articles/10.5334/joc.387Duncan (2010), The multiple-demand (MD) system — Trends in Cognitive Sciences (abstract)
https://www.sciencedirect.com/science/article/abs/pii/S1364661310000057Beaty et al. (2015), Default–Executive Network Coupling Supports Creative Idea Production — Scientific Reports
https://www.nature.com/articles/srep10964Schacter, Addis & Buckner (2007), Remembering the past to imagine the future — Nature Reviews Neuroscience
https://www.nature.com/articles/nrn2213Fleming & Dolan (2012), The neural basis of metacognitive ability — Philosophical Transactions B (PDF)
https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2011.0417Egner & Siqi-Liu (2023), Insights into control over cognitive flexibility from task-switching — Current Opinion in Behavioral Sciences
https://www.sciencedirect.com/science/article/pii/S2352154623000967Risko & Gilbert (2016), Cognitive Offloading — Trends in Cognitive Sciences (full text)
https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(16)30098-5World Economic Forum (2023), Future of Jobs Report 2023 (overview + PDF)
https://www.weforum.org/publications/the-future-of-jobs-report-2023/Oppezzo & Schwartz (2014), Give Your Ideas Some Legs: The Positive Effect of Walking on Creative Thinking — APA (PDF)
https://www.apa.org/pubs/journals/releases/xlm-a0036577.pdfLång et al. (2023), AI-supported screen reading vs. standard double reading in MASAI trial — The Lancet Oncology
https://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(23)00298-X/fulltextNakai et al. (2024), Artificial intelligence as a second reader for screening mammography — Radiology Advances
https://academic.oup.com/radadv/article/1/2/umae011/7667296