Not a note-taking app, which captures nuggets of information in isolated containers but doesn’t help you see how they fit together.

Not a mind map, which is better at showing relationships between bits of information, but is only a static snapshot.

Something more dynamic and context-aware.

Something like iXnote.

I recently had an opportunity to interview iXnote developer Stephen Peacock to learn more about the thinking behind this unique visual thinking tool, how it works and who can benefit from it.

Why does iXnote exist?

Chuck Frey: Please walk me through the beginnings of iXnote. What convinced you that a tool like this needed to exist?

Stephen Peacock: I’d be in a meeting and I’d need one buried detail, a figure, a name, a thing someone said three weeks ago, and I’d need it NOW, in this moment. Every note tool made me scroll through pages of linear notes, tons of padding, lots of repetition. I needed just one nugget, and I needed it now. So I built the thing that gives me the nugget.

Frey: Were there some problems with the existing tools you were using? If so, what problems were you running into?

Peacock: Plenty, and I want to be fair here, because I still use some of them. I was all-in on Evernote from 2008, tens of thousands of notes over about ten years, and one day I deleted the account outright. Ten years of notes, gone. And I barely missed them. That taught me something uncomfortable: I’d been hoarding.

Then came the desperate experimental years. Paper, Apple Notes, and a long, deep love affair with Obsidian. Obsidian is a wonderful tool and I learned a lot from it, but no matter how cleverly I set it up, it kept failing at scale, and in a predictable way. Wiki-links breed duplication. You type a colleague’s name into fifty different notes and now that one person is smeared across fifty places with no single home. Combine that with the chronic context bloat linear notes demand and you get what I call catastrophic note collapse. The system that was meant to hold your thinking together falls apart, and the more you write, the worse it gets. I wanted the opposite. A tool that gets better with scale, not worse.

Frey: It’s a well-documented issue with note-taking tools: people spend so much time working on and refining their systems that they don’t spend enough time nurturing their ideas. Is that what you’ve seen?

Peacock: Yes, and I’ve been that person. There’s a particular state my personality is drawn to, where the system becomes the hobby: the folders, the tags, the weekly review, the plugins. It feels like work because it is work. It’s just not productive thinking.

So I made a decision early on to take the maintenance away. No folders to keep tidy, no tags to prune, no weekly review to fall behind on. In iXnote the only thing you do is capture a small card and decide what it links to. That’s it. The linking is the thinking, and I wanted every bit of effort you spend to go into that one high-value act, not into grooming a filing cabinet you’ll abandon in March.

The thinking behind iXnote

Frey: What were your goals in designing iXnote?

Peacock: Three, really. Capture less, link more, zero maintenance. Only ever write your colleague’s name once. Link them to anything relevant (giving them context), and that’s it.

Underneath all of that is one belief: the tool should make you do as little thinking as possible, so you can do the right thinking AND listen to the actual conversation.

Frey: Can you walk us through the interface, how notes are arranged on screen, the meaning of each column and the role that AI plays when using iXnote?

Peacock: The iPhone UI is tab-based, fine on the go but not optimal. On a wider screen you get three columns, and the whole thing re-centres every time you tap a card.

The left is your master list, every card you have, newest first. The middle column is the first degree: the cards directly connected to whatever card you’ve selected. The right column is the second degree: the cards that are one more hop out, connected to your first-degree cards. So you never view a card out of context, and this lets you write notes without context. You read notes surrounded by their neighbours and their neighbours’ neighbours, laid out as efficient, readable columns rather than a dot-cloud diagram you have to squint at.

Despite the fact that I have spent nearly 10 years designing AI products, or perhaps because of it, AI has a deliberately narrow role. AI can help you find notes, and it will suggest notes, but it won’t help you link notes, that’s the thinking, that’s the important bit.

Frey: You emphasize that you’ve taken a minimalist approach to notes in iXnote. It’s not designed for lengthy notes or for embellishments like tagging and linking. What’s the benefit of keeping it simple?

Peacock: Small is the point, not a limitation. Each card is about the size of a tweet, one idea, a sentence or two, with the empty space taken out: you write a person, a project, an idea once, and then you link to it everywhere it’s relevant, instead of retyping it into fifty documents. That one discipline is what stops the catastrophic note collapse that is inevitable in other systems.

iXnote is simple to use but it takes a little while to get used to it. Writing small and linking well is a skill. Most people have spent their whole lives writing long, so there’s a short adjustment. It’s worth it, but it’s real.

Frey: What are the differences between first, second and third level topics?

Peacock: iXnote works in degrees of connection like a mind map. Imagine your selected card is the centre of the mind map. Its 1st degree connections are the cards you’ve linked to it directly. In a typical mind map they surround it. Its second-degree connections are one hop further out.

That 2nd degree column is where the magic happens. There actually isn’t a 3rd level, I tried it but by the 3rd degree half of any well-linked graph is visible and that’s no help.

Frey: What is the graph memory system in iXnote? How does that work? How does it assign weights to your notes?

Peacock: Weighting is how a graph with thousands of notes, or a note with hundreds of connections, does not become overwhelming. A note’s weight is dynamic, degrading, reactive to attention. Every time you select a card, you effectively upvote it, its immediate neighbours, and the edges between them, by different amounts. Over time each card accumulates a weight, each edge also, and those combine into a “relevance score” that decides sort order. So on a busy card with a hundred connections, you see the ones that matter to you based on your recent activity.

Frey: You’ve described iXnote as having “built-in forgetting.” How does that work?

Peacock: The word I’d use is fade rather than forget, because nothing is ever deleted. Cards you stop using lose weight and sink out of view. They don’t clutter the place up, they don’t demand a tidy-up, they just quietly drop below the waterline. But they’re never gone. A faded card is still there the instant you search for it, still surfaces when you type something similar, still reachable through the things it links to. Out of view is not lost.

I think this is one of the most important design choices in the whole app. Memory is supposed to forget. If you exhaustively capture everything and rank them all equally important, you’ve really captured nothing, because you can’t find the thing that matters under the mountain of things that don’t. Just as Google’s PageRank made internet search usable, better even at scale, note and connection ranking does the same in iXnote.

Frey: How does this arrangement bring an element of serendipity to the thoughts, ideas and notes you have captured in your iXnote workspace?

Peacock: Through that 2nd degree column, mostly. It shows you connections you hadn’t made, or had forgotten. You link a meeting to the people who were in it; the app shows you another project, sitting there as a second-degree connection, because it shares two of those people. You never linked the two projects directly. The graph did the joining, from links you’d already made for other reasons, and now there it is in front of you at the exact moment it’s useful.

And because old-but-relevant cards resurface when the context calls for them rather than staying buried by date, you get these small, useful triangulation collisions. You went looking for one thing and the graph handed you a second thing you’d forgotten you knew. That’s the serendipity. It falls out of the structure.

Who is iXnote for?

Frey: What types of people can benefit the most from iXnote and why?

Peacock: The fastest payoff is for people who live in meetings: sales, business development, product, anyone doing fifteen conversations a day where a detail from three weeks ago decides the next five minutes. Import a meeting, and the graph starts connecting people, projects and decisions for you.

Beyond that, researchers, students and writers who are trying to stay oriented in their own thinking. It’s for people who hate note systems but need them. If you’ve bounced off every tool because the upkeep defeated you, that’s the person I built this for. It’s a tool in your toolbox, by the way, not one tool to rule them all. I use Apple Notes every single day alongside it, and collaborate with Claude in Obsidian.

Frey: You’ve described iXnote as not for people who think by writing. Why is that?

Peacock: Because for some people the writing is the thinking. They work an idea out by drafting long prose, reordering paragraphs, rewriting, and the sheer act of writing at length is how they get to clarity. They are actually linking their thinking, sentence 1, to sentence 2, insert 1a, actually, move #1 to the end. They are creating a chain of linked ideas, a one-dimensional graph. I can’t think well like that, wish I could sometimes, but there it is.

Frey: How is the daily notes option useful?

Peacock: If you switch it on, iXnote creates a card for today automatically, and new cards you make during the day get linked to it. Over time that builds a timeline of your days without you doing anything, so you can find things the way you actually remember them: not by topic but by when. “That was the Tuesday we met the vendor.” Tap the day, and there’s everything from it. It leans on the same association-by-hopping that the rest of the app runs on.

Frey: If you had to summarize the value of iXnote in a single sentence, how would you describe it?

Peacock: Context is all you need.

Frey: What are you hearing from users of iXnote? What are they telling you about the value it’s providing to them? What are the most intriguing stories you’re hearing?

Peacock: iXnote is privacy first, no tracking, no cost, no signup. That’s by design, so it can be used in even the most restrictive enterprise IT environments. Because of that I only know how many people have downloaded the app, that’s it. So I have almost no user stories. I get and cherish the odd email of thanks. One user was using iXnote to learn a new language, linking English and Polish words and phrases together. I would never have thought of using it like that. Another was doing genealogy research. Most are simply knowledge workers trying to manage the firehose of information that blasts at them every day.

Frey: You have described the right column as “listening” to what’s going on in the other two. How does that work and what types of information does it display? Is it doing a semantic analysis of the information and the other two columns?

Peacock: The FollowAlong view. It’s an on-device AI companion that follows a live conversation, a meeting or a lecture, and as it does it surfaces related cards from your graph, and occasionally a suggested new card. In an hour-long lecture it might make thirty or forty suggestions. It’s private, free, it works in airplane mode, and, this is the important bit, it never puts anything into your graph. It suggests; you decide. So there is real intelligence in the app, it’s just carefully fenced: it does the low-value text and surfacing, and it leaves the high-value thinking to you.

Frey: What’s next for iXnote?

Peacock: I have just released a new version with greatly extended Apple Shortcuts support. This opens the graph up to an almost unlimited world of workflow possibilities for the workflow hackers out there. I don’t have new features planned yet, so if you use iXnote and have ideas for what should be next, reach out.

Frey: Where can my readers learn more about iXnote?

The website contains the story behind iXnote and a blog where I write about the thinking behind it. The app is free on the App Store for iPhone, iPad and Mac, search iXnote. And they can reach me through the contact page on the site; I read everything.

The post How iXnote’s AI-enhanced visual space can help you be more productive and effective appeared first on Mind Mapping Software Blog.

Her boss asked one question and none of it helped.

What happened in that Thursday review didn’t cost Priya Raghavan her job. It cost her something slower and harder to win back: the assumption, held quietly by everyone above her, that her work could be trusted without checking.

But it didn’t have to happen this way.

A big opportunity

Priya Raghavan is a senior strategy analyst at a 600-person industrial software company. Six years in. The person you hand the hard question to.

In February, the exec team gave her the biggest question on the roadmap: should the company enter the European market next year, starting with Germany, or double down on the domestic mid-market where it already wins? A recommendation was due to the leadership team in April.

She did what any capable analyst does now. She opened a chat window and started working.

And the work went well. Genuinely well. She pulled competitive landscape analysis, regulatory requirements, channel partner structures, pricing benchmarks, localization costs, three different market sizing approaches. When a thread got long she started a fresh one so the context stayed clean. She saved everything. She built a naming convention by week two.

Forty-seven threads. A hundred and eighty pages. She synthesized it into a forty-page recommendation with an executive summary, and it was, by any standard she’d been taught to apply, the most thorough piece of research she had ever produced.

She sent it to her SVP of Corporate Development on a Tuesday for a Thursday review, one week ahead of the exec presentation.

The day it all fell apart

He’d read the whole thing. He started by saying so, and he meant it as a compliment.

Then he asked what she thought they should do.

Priya started describing what the analysis pointed toward. He stopped her and said no — not what the report says. What do you think. She heard the distinction and reached for an answer and found, with a lurch she still remembers, that she didn’t have one. She had a recommendation. She did not have a position. She had never noticed the difference before, because for five weeks nothing had required her to.

She recovered enough to argue the case. It went fine for a few minutes.

Then he pointed at a number on page eleven: the eighteen-month certification and channel-readiness timeline that the entire go-to-market sequence rested on. Where did that come from?

She said she’d pull the source.

He asked if she was confident in it.

And Priya said the sentence that ended the meeting, though it took her until the drive home to understand that it had. She said it had come up consistently across her research.

He didn’t say anything cutting. He didn’t need to. They both sat there for a second while the words rearranged themselves in the air: consistently across her research meant the same model had told her the same thing five times, in five slightly different sentences, in five separate threads, and she had read that repetition as corroboration. Five sources, one source. She’d never once put those five statements side by side, because they lived in five different conversations and she had no surface on which to lay them next to each other.

Nobody escalated anything. That’s not how this works.

The recommendation went to an outside consultancy for validation. It cost $120,000 and six weeks to check work she had already done. Priya was assigned to support them, which meant spending September assembling context for people being paid to second-guess her. The Europe decision slipped two quarters. When the next big open-ended question came up in the fall, it went to a colleague, and her SVP framed that as protecting her bandwidth.

The reputation that formed wasn’t her analysis was wrong. As far as anyone knows, it wasn’t. It was worse than that: she produces a lot of material. That’s a label with no deliverable attached to it, which means there’s nothing she can fix and resubmit.

The private cost was steeper. She stopped trusting her own output. Every deck she built after that carried a low hum of how would I defend this if someone pushed, and the honest answer was usually that she wouldn’t know how.

The maddening part is that most of her research was probably fine. Some of it was excellent. She had no way to tell which was which.

How mind mapping would have changed the outcome

The failure happened in the first hour, when she opened a chat window instead of a blank map.

That single choice handed the model authorship of her structure. Her first prompt was broad, the response came back organized into neat sections, and every one of the forty-six threads that followed went deeper inside a frame she had never chosen and never examined. By week three she wasn’t researching a question anymore. She was elaborating an outline that a machine had produced in nine seconds.

That’s why she couldn’t audit it. She was checking the model’s answers using the model’s own categories, which is a closed loop. Nothing can look out of place in a structure derived from the very claims you’re testing.

Map it first and the loop breaks open.

Before the first prompt, you spend twenty minutes building your first-level topics from your own question and your own domain knowledge. Six to nine branches: regulatory path, channel economics, competitive response, localization cost, pricing, internal capacity, exit conditions. It takes twenty minutes and it is the single highest-leverage thing you will do in the entire project, because that skeleton is now an independent standard. Everything the AI produces has to earn a place on a structure it did not build.

Then, as material comes in, every claim gets placed — one claim, one branch, one note behind it recording where it came from and whether a human being ever verified it. This costs seconds in the moment and is impossible to reconstruct five weeks later. Ask Priya on page eleven.

What a mind map reveals

Once it’s all on one surface, three things become visible that a hundred and eighty pages of prose will hide from you until you’re sitting across from your boss.

Redundancy stops looking like agreement. The eighteen-month timeline, appearing five times in five voices, collapses on a map into one node with five pointers to the same origin. The illusion of consensus doesn’t survive proximity. That’s not a discipline problem Priya had; it’s a format problem. Fluent, confident, sequential text is exactly the medium in which repetition impersonates evidence.

Contradiction surfaces. Thread twelve assumed a consolidating market. Thread thirty-one assumed a fragmenting one. Both were plausible, neither was flagged, and they sat a hundred and forty pages apart. Put them on adjacent branches and you can’t not see it.

Gaps become visible. And the empty branch becomes the finding. This is the one that matters most, and it’s the reason none of this can be automated. AI will never tell you what it failed to address. It has no mechanism for reporting its own gaps, and it will answer the question you asked with the same confident tone whether or not that was the right question. But a first-level topic sitting there with nothing underneath it is a visible, structural, undeniable hole. Priya’s exit conditions branch would have been bare. In forty-seven threads she never once asked what would tell them the European bet had failed. The exec team would have asked. They always do.

There’s a version of that Thursday where he points at page eleven and she can say exactly where the number came from, which parts of it she confirmed independently, which parts she’s holding loosely, and what she’d need to see to change her mind.

That’s not a better report. That’s a person who owns her own thinking, in a room full of people who can tell the difference.

AI tools have become very good at generating answers in a fraction of the time of traditional research. But they’re not very good at telling you what your answer is missing.

That job didn’t go away. It just got much bigger, and much lonelier, and no one has given you a method for doing it.

That’s about to change.

Learn about a new framework for making sense of what AI hands you – and preserve the trust of the people you serve.

The post The day Priya couldn’t defend her own research appeared first on Mind Mapping Software Blog.

Our brains tend to stop thinking creatively when we generate what we think is the first “right” idea. But there is much to be gained by generating additional alternatives. When you have more possibilities to choose from, you can select the best one to solve your problem.

It leverages the brain’s power of association. When you bump two unrelated ideas together, they often trigger additional ideas and insights. How is this valuable? It forces you to look at challenges or problems from a variety of creative perspectives. In so doing, it whacks you out of your usual, rutted mode of thinking and often yields valuable, creative ideas.

I loved SCAMPER so much that I gathered trigger words from a number of publicly available sources and combined them to create the ultimate SCAMPER mind map, with nearly 250 triggers.

But not everyone uses mind maps. For these people, it created a certain amount of friction. First, it was only available as a downloadable template. That meant they needed to own a mind mapping software program to use it. It also required them to select a branch, open it and then randomly pick a trigger. If that didn’t produce any fresh insights, they needed to repeat this process again.

I wondered if there was a way to make it accessible and useful to more people. I envisioned a simple web page tool that could serve up random SCAMPER triggers on demand.

Thanks to the amazing powers of Claude AI, I just built it. You can check it out here:

The post The ultimate SCAMPER brainstorming tool – on demand appeared first on Mind Mapping Software Blog.

Why is that a problem?

Linear tools like documents, slide decks and note-taking apps are popular because they do one thing extremely well: recording information.

But they were never designed to be tools for thinking tasks like brainstorming, organizing, synthesizing and pattern recognition.

In other words, linear tools are excellent containers but poor seedbeds – environments for nurturing ideas, exploring new possibilities and creative problem-solving.

This new infographic is designed to help you understand where linear tools fall short – and how visual tools can help you elevate your thinking.

Download a PDF of this infographic

The post The tools you use are shaping your thinking (and not in a good way) appeared first on Mind Mapping Software Blog.

He made it to slide six.

What happened in that strategy meeting with the executive leadership team killed Marcus Bell’s career momentum and undermined his confidence. It’s an experience many of us have lived through or personally experienced.

But it didn’t have to happen this way.

The opportunity

Marcus Bell has been marketing manager at a mid-sized B2B software company for four years. Solid performer. Never missed a deadline.

In March, the CEO asked him to present the case for a full rebrand and repositioning at the Q2 leadership offsite. Forty-five minutes in front of the exec team, plus the two board members who sit in.

This was the opportunity. Everyone knew it.

Marcus prepared for three weeks. He pulled competitive research, customer interview transcripts, win/loss data, agency quotes, a timeline, a budget model. He opened PowerPoint on day four and started building. Title slide, then market context, then competitive landscape, then customer voice, then the recommendation, then the numbers.

Thirty-four slides. Every one of them defensible.

The failure

He got to slide six.

The CFO stopped him and asked what the payback period looked like and how it compared to just doubling the demand gen budget. That question lived on slide 27, inside a model Marcus had built but not internalized, three weeks and twenty-one slides away from where he was standing.

He said he’d get there. The CFO said he’d like to understand it now, because it determined whether the rest of the conversation mattered.

Marcus started clicking forward. He passed slides out of context, apologizing as he went. He landed on the model and explained it without the setup that made it make sense. Someone asked a follow-up. He clicked back to find the supporting data. He lost the thread.

Twenty minutes in, the CEO asked a simple question: what’s the one thing you want us to decide today?

Marcus didn’t have a clean answer. He had thirty-four slides of good thinking and no spine. He said something about wanting to align on direction before committing to spend. The room heard hedging. The meeting moved on to the next agenda item eleven minutes early.

Nobody fired him. That’s not how this works.

The rebrand decision got tabled for “further analysis,” which in that company means it died. His VP started asking to review his materials before anything went to the exec team, a review layer that didn’t exist a month earlier. When the annual planning cycle kicked off in June, Marcus was asked to submit input rather than attend. In October, an outside hire came in above him as Senior Director of Marketing.

The verdict formed in a room he wasn’t in, and it wasn’t “his rebrand idea was wrong.” It was “he can’t hold a room.” That judgment is much harder to reverse than a bad strategy, because there’s no deliverable he can fix. He’d been labeled as someone who doesn’t yet think at the level of the audience.

The maddening part is that his thinking was good. His argument was sound. He just never saw it whole.

How mind mapping would have changed the outcome

The failure happened on day four, when he opened PowerPoint.

Slides are a linear medium, and building in them forces you to commit to sequence before you understand structure.

Marcus spent three weeks polishing the order of an argument he had never once looked at all at the same time. He knew every slide. He didn’t know the shape.

Map it first and you’re working in the medium your thinking actually lives in. Everything goes on one surface: the market pressure, the customer evidence, the competitive gap, the cost, the payback, the risks, the alternatives.

And once it’s all visible at once, three things surface that slides will hide from you until you’re standing in front of the room.

You see the collision points. The payback question wasn’t ambush. It was the single most predictable objection from a CFO, and it was sitting right there in the map as a thin, underdeveloped branch next to a thick, overbuilt one on brand aesthetics. Imbalance is visible spatially in a way it never is in a slide sorter.

You can present from the map itself. This is the part most people miss. A one-page visual overview lets you jump anywhere in your material without losing your place, because you’re not navigating a sequence, you’re standing in a structure.

When the CFO asks about payback in minute six, you move to that branch, answer it, and come back. You look like someone who has thought about the whole problem rather than someone reading a document he built.

The room wasn’t testing whether Marcus had done the work. They could see he had. They were testing whether he could think on his feet inside his own argument, which is exactly what senior leadership does all day.

He couldn’t, because he’d never seen it whole.

Be prepared for your next big opportunity

The post The day Marcus’ career momentum cratered appeared first on Mind Mapping Software Blog.

A free assessment that tells you exactly where you stand (and what to do about it)

Word. PowerPoint. Excel. Google Suite. These linear tools are such an integral part of your workflows that you don’t even question their effectiveness.

Are they really helping you do your best work today?

• Is it getting harder to make sense out of the growing volume of information you must manage?
• Is achieving clarity becoming a distant dream?
• Are you feeling stressed by information overload?
• Is the tsunami of AI output making the problem even worse?

Are these “good enough” tools no longer good enough?

Maybe it’s time to consider integrating visual thinking tools into your workflows.

But how do you know if you need them?

That’s the purpose of my new visual thinking tools assessment.

Take a moment to answer eight simple questions and receive a score that will help you quantify whether or not you need to adapt visual thinking tools.

You’ll also get insightful advice and what may be holding you back in your work and what you can do to resolve those limitations.

No email registration is required.

The post How much Is linear thinking costing you? Find out in 2 minutes. appeared first on Mind Mapping Software Blog.

So when I recently had the opportunity to use Venngage’s AI assistant to create an infographic, the results pleasantly exceeded my expectations.

My AI-enhanced infographic experiment

Last week, over a period of four days, I published a series of Substack notes in which I shared 28 strategies to become indispensable at work.

After the full list was published, it was time to repurpose it into an infographic. I don’t ever build them from scratch because I’m not an artist. I usually start with a template instead. Let someone else do the graphical heavy lifting. Give me a nice design I can adapt to my needs.

For many years, my go-to for infographic creation has been Venngage. I like their template designs better than Canva or Visme and its editing tools are intuitive and easy to use. I found one that had 30 blocks. Visually, it looked like a good fit for what I was trying to create. So I selected it. At this point, I was planning to build my infographic manually, using the template I selected. AI wasn’t even on my mind.

When I opened the template, an AI prompt window popped up, offering to help me I’m always up for an experiment, so I wrote a very brief prompt and copied and pasted my list of 28 ways to be indispensable into it. I also asked the AI agent to select and add appropriate icons to each strategy.

I was very impressed with the output. Except for a few font sizes that needed to be corrected, it did an excellent job translating my list into a very attractive infographic. The whole process took no more than 10 minutes. Compare that to several hours if I had to do the same thing manually.

I outputted my new infographic as a PNG for a Substack note and also as a PDF that my readers can download.

The image below summarizes the minimal modifications I made to the template I used. To view a PDF of the full infographic, please click here.

What else can Venngage’s AI assistant do?

This capability is part of a larger suite of AI-enhanced workflows Venngage can handle for you. It can also:

• Refine your designs
• Generate custom images
• Generate icons, logos and banners
• Create new designs based on prompts
• Edit text within your designs for tone, wording or length
• Translate your text into other languages
• Accessibility: auto-check contrast, layout and font readability
• Branding: extract brand colors, fonts, and styles from existing marketing material. Auto-apply your logo and visual identity to every design.

I haven’t scratched the surface of what Venngage’s AI toolset can do – but I look forward to exploring it, thanks to the encouragement of this successful project.

Why did this experiment work so well?

One thing that made this experience a success is that I was NOT starting out with a blank AI prompt. That’s always been a challenge for AI-based image creation – the tool must interpret what you want the output to look like. Faced with a myriad of design options, it makes its best guess – which is often WAY different than your expectations. In other cases, even when you tell it exactly what you want, it just doesn’t listen very well.

In this case, my starting point was a nice-looking Venngage infographic template. So the AI agent didn’t have to guess at what I was looking for visually. All it needed to focus on was adding my list of strategies to the boxes on the page and selecting and placing an appropriate icon for each. And it did an excellent job of that.

The bottom line is that this that Venngage’s AI assistant produced a high-quality infographic, customized to my needs, in a fraction of the time that it would’ve taken me to manually modify the template to create it. This approach was so effective that I’ll probably use it again in the future!

The post A better way to build infographics – fast appeared first on Mind Mapping Software Blog.

When AI functionality was first introduced to mind mapping tools several years ago, the developers mainly promoted its ability to build an entire mind map from a simple topic-based prompt. But this scenario doesn’t match any kind of common business workflow, so I always regarded it as a capability that had limited value.

But as AI tools have become more sophisticated and powerful, I’ve questioned if AI-enhanced mind mapping tools have kept pace.

So I tried an experiment: I the risk management prompt from MockFlow to five popular mind mapping tools to see what they would do with it. The results were surprisingly diverse.

Which one did the best job building out the mind map? Read my assessment below to find out! I have included overall mind map and detailed screenshots so you can see what I’m describing below.

The prompt

Design a comprehensive risk assessment mind map categorizing risks: Strategic (market changes, competition), Operational (supply chain, technology failure), Financial (cash flow, currency), Compliance (regulatory changes, legal), Reputational (PR crisis, data breach), each risk showing probability assessment, impact severity, current controls, risk score calculation, mitigation strategies, contingency plans, responsible parties, monitoring indicators, and escalation procedures.

I love how it goes beyond just capturing information about your organization’s current risk profile. It also provides you with a simple framework for assessing each risk it has identified as well as practical application plan to mitigate them.

(Click on full maps to view them at an expanded size)

Xmind

For many years, Xmind has been a leader in this space, providing mind mapping tools that are both powerful and easy to use. This tradition has continued as its developers have added AI functionality to it.

Not surprisingly Xmind did the best job of building a detailed risk assessment map. What made it really stand out was the level of detailed provided at the individual branch level. Rather than just providing a blank template, it provided data for an imaginary company.

I loved the way Xmind drilled down into each of the risk factors, assessment criteria and strategies for mitigating them. For example, under operational risks, the mind map lists a “supply chain risk factor” topic. Subtopics are devoted to risk assessment and scoring, management and control and oversight and response.

Within those, the map contains appropriate subtopics that are detailed and easy to understand. For example, under risk assessment and scoring, the map lists a probability assessment, impact severity and risk score calculation, which is based on probability multiplied by impact.

The level and organization of detail here is truly impressive. I also like the fact that this risk assessment map contains specific risk assessment scores and action plans from an imaginary example. Why does this matter? It ives anyone who is not familiar with risk assessment practices some valuable context that they can use to edit these topics and insert their own information.

Mindmap AI

The developer of this tool has been aggressively adding AI capabilities to its relatively young mind mapping tool. It took more of a template-based approach to this prompt, providing headings, specific metrics and action plans as requested by the MockFlow prompt.

What was interesting is it actually tailored them to the type of risk described. For example, the first level topic “compliance risks” contained subtopics like regulatory changes and legal liabilities. Financial risks included subtopics like cash flow issues and currency fluctuations.

Where it fell a little short is that it placed the assessment subtopics at the same level as the symptoms. Ideally, under financial risks for example, cash flow issues and currency fluctuations should have each had their own assessment framework. Instead, they appeared at the same level in the mind map. It was a minor miss in an otherwise solid AI-generated map template.

MindMeister

MindMeister recently added AI capabilities to its popular web-based mind mapping platform. It did a decent job creating a template for risk assessment, accurately following the prompt.

Unlike Mindmap AI, it correctly parsed out risk topics with a complete set of assessment criteria. For example, under financial risks, it separated cash flow and currency into two subtopics. Each of them displayed a full set of assessment criteria (probability assessment, impact severity, current controls and risk score calculation).

GitMind

GitMind’s AI engine is designed to take a simple topic and build a mind map out of it. So when I fed it the complex prompt for the risk assessment, it replied “sorry, this is beyond my knowledge scope.”

MockFlow

As I told you earlier, this is the visual thinking tool that inspired this experiment. . So I had to include it. Next to Xmind, it did the most impressive job.

Like Xmind, it not only laid out the risk factors and examples of each, it filled in sample data for each criteria. Not as much detail, but still a very clear and concise treatment of the type of data you should apply for each one.

For example, under supply chain disruption, it listed the following data:

• Probability: high (55%)
• Impact: critical – production halt
• ontrols: dual sourcing, safety stock (30 days)
• Risk score: 18 (very high)
• Mitigation: supplier diversification, regional hubs

Considering that I had never heard of MockFlow prior to this experiment, I was very pleased with the quality of its output.

Conclusion

As you can see, the quality of map-based AI output varies widely. Keep in mind that this is just a single experiment; several of the tools that didn’t perform that well in it may provide better results in other scenarios.

If you are planning to use this type of platform in your work, I encourage you to experiment with several AI-enhanced mind mapping applications to which one is the best match for your needs.

The post How do AI-enhanced mind mapping tools manage complex prompts? appeared first on Mind Mapping Software Blog.

Your brain’s default thinking mode is quietly killing your best ideas

In an era when AI can generate mountains of mediocre ideas on demand, your ability to think divergently, to escape your brain’s default patterns and generate genuinely original, high-value ideas, has never been more important.

That’s not a theory. It’s a conclusion backed by two decades of studying creativity and confirmed by a growing body of research. And yet, for most executives, entrepreneurs, and creators, divergent thinking remains a largely untapped superpower.

Let me explain why that needs to change — and what to do about it.

Your brain is working against you

Here’s something I’ve observed after 20 years of studying and writing about creativity: our brains are profoundly habitual. Left to their own devices, they follow well-rutted paths of thinking — comfortable, predictable neural grooves worn deep by repetition and routine. The ideas your default mindset produces tend to be predictable and, frankly, of low value. They are the cognitive equivalent of reheated leftovers.

Higher-value ideas — the kind that unlock new business models, solve problems that have stumped your competitors, and accelerate your career — come from a fundamentally different mode of thinking.

That mode is divergent thinking.

Coined by psychologist J.P. Guilford in 1967, divergent thinking is the cognitive process of generating multiple, varied, and original solutions in response to an open-ended problem. As neuroscientist Dr. Andrew Huberman describes it, divergent thinking “involves taking one simple stimulus and trying to radiate out from that as many different situations, properties, characteristics, and events as possible.” Think of it as the opposite of the convergent thinking you do most of the day — solving problems by zeroing in on a single, logical answer. Divergent thinking is wide, expansive, exploratory. Convergent thinking is narrow, evaluative, decisive. You need both, but most of us are wildly out of balance.

Prompts and unique stimuli help shift your brain out of its default mode. So do creative problem-solving techniques, which often force you to think in different directions or view your challenges from perspectives you’d never naturally adopt. More on those in a moment.

Why should you care. Even if you don’t think you’re creative

“I’m not a creative person.” I hear this constantly. It’s almost always wrong.

Divergent thinking isn’t an innate gift reserved for artists and inventors. It’s a trainable cognitive skill — and its business case is hard to argue with. McKinsey research found that companies that facilitated divergent thinkers grew 2.3 times faster than those that didn’t. Sixty percent of CEOs cite creativity as the most critical factor for navigating complexity.

Creativity expert Mark Runco puts the stakes plainly: a flexible, divergent thinker “will have alternatives and choices when solving problems, and therefore solutions are likely and frustration and distress are unlikely. Inflexible individuals, on the other hand, follow routines, make assumptions, and have difficulty when problems lead to fixedness.”

In other words: divergent thinking isn’t a nice-to-have creative flourish. It’s a competitive survival skill.

Why AI makes your creativity more valuable, not less

Here’s where it gets both interesting and urgent.

A landmark 2024 University of Arkansas study published in Scientific Reports found that GPT-4 outperformed most humans on standardized divergent thinking tests, generating more original and elaborate ideas than 151 human participants. Headlines ran with it: AI is more creative than you.

But dig deeper, and a very different picture emerges. Researchers from the University of Essex found that while AI excels at what they call “mid-level novelty” — clever combinations of familiar patterns — it rarely approaches the genuinely breakthrough ideas that only the best human thinkers produce. In their 2025 study, only 0.28% of AI’s ideas reached the top tier of human creativity benchmarks, and humans were 35 times more likely to produce a truly standout, original idea. AI circles the familiar. It doesn’t leap beyond it.

Even more alarming: a 2026 study published in PNAS Nexus found that while AI chatbots can match humans on individual creative tasks, they produce strikingly similar responses to one another. Widespread reliance on AI for idea generation risks what researchers call a “homogenization effect” — a narrowing of the idea space across entire organizations, industries, and societies. Job applications, marketing campaigns, and strategic plans are already starting to sound alike because they’re being scaffolded by the same AI systems making the same probabilistic word choices.

Most alarming of all: this narrowing effect persists even after people stop using AI tools. Their creative capacity appears to atrophy, like a muscle that’s been immobilized.

My take? AI absolutely should not be used as a substitute for human thinking. When you outsource your ideation to an AI model, you don’t just produce predictable output. You gradually train yourself to think less creatively over time.

That said, as an amplifier for human thinking, AI can be genuinely valuable. Here are three approaches I find particularly effective:

Use AI as a combination engine. Brainstorm your own list of ideas first, then ask AI to identify opportunities to combine them, improve them, or make unexpected connections between them. You bring the raw material; AI helps you recombine it in ways you might not have considered.

Use AI to challenge your blind spots. After you’ve thought through a challenge, share your thinking with an AI tool and ask it directly: “What am I missing here? What other perspectives should I consider to unlock new insights?” This is AI at its best — not generating ideas for you, but revealing the gaps in your current thinking.

Use AI as a cross-industry researcher. One of the richest sources of breakthrough ideas is discovering how other industries or professions have solved analogous problems. Ask AI to research how similar challenges have been addressed in completely different domains. You may be able to adapt part or all of their solutions to your own context.

The key principle: you think first, AI amplifies second. Reverse that order, and you’ve surrendered your most valuable cognitive asset.

Divergent and convergent thinking: The creative tandem

Divergent thinking doesn’t operate in isolation. The complete creative process moves through two distinct phases that creativity experts have long recognized:

Divergence is the first stage — generating a wide, diverse variety of ideas, free from judgment and premature evaluation. You’re pushing beyond your well-rutted paths of thinking to consider new possibilities. Mind mapping software is perfectly suited to this phase: it lets you branch out in as many directions as your thinking takes you, with almost complete freedom, capturing ideas, questions, knowledge gaps, and connections all in one visual space.

Convergence follows: narrowing the field, evaluating your ideas against criteria, identifying the most promising ones, and turning them into action. In mind mapping software, this is supported by the ability to move topics around, rearrange them to test new relationships, add priority icons, and build implementation notes onto the ideas that earn further development.

All divergence and no convergence produces a beautiful cloud of unrealized potential. All convergence and no divergence produces the same safe, incremental thinking that got you to where you are now, but nowhere new. The most effective creative process alternates between them: expand, then evaluate; explore, then decide.

Popular techniques for divergent thinking

If you’re ready to start developing this skill, here are some of the most proven approaches:

Brainstorming: The classic. Set a timer, define a specific problem, and write down every idea that comes to mind — suspending judgment entirely. Volume is the goal in the divergent phase. Evaluation comes later.

Mind mapping: Start with a central challenge or concept and branch outward, following your associations wherever they lead. Mind mapping is especially powerful because it mirrors the radial, associative way your brain actually works — unlike the linear format of a word processor, which constrains your thinking to a single track.

SCAMPER: One of the most structured and versatile divergent thinking techniques. The acronym stands for Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, and Reverse. Applied to any existing idea, product, or challenge, SCAMPER systematically dismantles your assumptions and generates new possibilities from what’s already in front of you.

Analogical thinking: Deliberately look for how seemingly unrelated domains have solved similar problems. A logistics challenge might find its solution in how a hospital manages patient flow. A product design problem might find its answer in how a forest manages resources. Cross-domain analogies are one of the richest sources of original thinking available to any thinker in any field.

Brainwriting: Like brainstorming, but done silently and in writing. Each participant generates ideas independently, then builds on others’ ideas as papers rotate. This prevents louder voices from dominating the session and typically produces a more diverse idea pool.

How visual thinking tools supercharge the process

After 20 years of working with visual thinking tools, I find them invaluable at every stage of divergent thinking — not just as a recording medium, but as an active thinking partner.

Here’s why they’re so powerful:

They externalize your thinking. Getting your ideas out of your head and onto a visual canvas frees your brain to continue generating. Once captured in a mind map, those ideas also serve as catalysts for new associations, because mind mapping supports the way your brain loves to work: by association. Think of it as thinking about your thinking.

They break the linearity trap. Word processors force your thinking into a single track. Visual tools — mind maps, whiteboards, concept diagrams — let you spread ideas across a canvas in any direction. This spatial freedom directly enables divergent thinking by removing the structural constraint that your next idea must follow logically from your last one.

They reveal patterns that linear tools hide. One of the most powerful capabilities of mind mapping software is the ability to classify ideas with colors, shapes, and icons — and then move them around to test new relationships. When you drag a topic from one branch of your map to another (a technique called refactoring), you change its context. Its new neighbors often suggest connections that were completely invisible before.

They support both phases of creativity. In the divergence phase, a mind map is an infinite, flexible canvas — you can branch out in as many directions as your thinking takes you, capturing ideas, questions, knowledge gaps, and stakeholder considerations all in one place. In the convergence phase, the same tool lets you evaluate, prioritize, rearrange, and build action plans from the ideas you’ve generated.

They make team divergence richer. In group settings, collaborative visual tools allow multiple people to contribute to a shared canvas simultaneously, wherever they are. This democratizes divergent thinking — removing the dynamic where the loudest voice dominates ideation — and produces a far more diverse pool of ideas than a traditional meeting ever could.

How to make divergent thinking a daily habit

The good news: small, consistent practices compound over time into a genuinely more flexible mind:

Challenge your automatic thoughts. At least once a day, catch a reflexive assumption and force yourself to generate three to five alternative interpretations or approaches. This small practice rewires your brain toward flexibility over time.

Read promiscuously. Expose yourself to the widest possible range of ideas and domains, including fiction, nonfiction, poetry, science journals — e. Every unfamiliar concept is a new dot your brain can connect to something you already know.

Build incubation into your schedule. Your brain solves problems subconsciously during breaks. Walks, showers, and sleep allow unconscious processing to surface insights that focused effort can’t reach. Don’t just schedule deep work. Schedule the recovery that makes deep work possible.

Ask “What am I missing?” Make this a regular practice after you’ve thought through any significant challenge. Then genuinely attempt to answer it — or bring in outside perspectives, human or AI, to help.

The bottom line

Here is what 20 years of studying creativity has made clear to me: the leaders, entrepreneurs, and creators who will thrive in the years ahead won’t necessarily be the smartest or the most experienced. They will be the ones who refuse to let their thinking calcify into habit, the ones who have developed the discipline to push beyond their well-rutted paths and explore territory that their competitors haven’t thought to visit.

Divergent thinking is that discipline. And in an age when AI is rapidly producing competent, middling, homogeneous thinking at scale, genuinely original human thought is becoming the scarcest and most valuable commodity in business.

Visual thinking tools are your best ally in developing and sustaining this skill: externalizing your ideas, revealing unexpected patterns, and creating the spatial freedom your brain needs to generate the truly unexpected.

The post How to break out of your brain’s habitual mindset with divergent thinking and visual tools appeared first on Mind Mapping Software Blog.

A landmark Fast Company article just named the human capacities AI can’t replace. Here’s how mind mapping develops every single one of them.

A Fast Company article published this week stopped me in my tracks.

Written by Alan Fleischmann, founder and CEO of the global CEO advisory firm Laurel Strategies, it’s titled The Five Quotients: What Skills Will Matter Most in the Age of AI. The premise is deceptively simple: the future will belong to people who cultivate not just intelligence (IQ) and emotional intelligence (EQ), but three additional capacities that Fleischmann believes AI cannot meaningfully replicate — TQ (Trust Quotient), WQ (Work Quotient), and VQ (Vision Quotient).

It’s a genuinely important piece of thinking, and I encourage you to read it.

But here’s what struck me most: Fleischmann’s framework is a masterful answer to the question what we need to develop. What he doesn’t address — because it’s not his territory — is how.

That’s exactly where mind mapping enters the picture.

I want to argue something that I believe is true and underappreciated: mind mapping isn’t just a tool that supports these five capacities. Practiced deliberately and consistently, it’s a training ground for developing them. There’s a meaningful difference between using a hammer and becoming a skilled carpenter. The same distinction applies here. Let me take each quotient in turn.

(click the image below to view a larger version)

IQ: From information processing to architectural thinking

Fleischmann frames IQ as the capacity to understand complexity — the analytical and synthesizing intelligence that has always been prized in leaders and thinkers.

Here’s the problem: AI is now very, very good at processing information. If raw analytical horsepower is your primary edge, that edge is eroding.

The mind mapping practice that elevates IQ isn’t about processing more — it’s about thinking more structurally. Every time you build a map, you’re forced to make a decision that linear note-taking never requires: what is a branch, and what is a sub-branch? That seemingly simple act is hierarchical reasoning in practice. You’re not just recording ideas; you’re asserting relationships between them.

Do this regularly, and something interesting happens. You start to see those structures in problems before you open your mapping software. The practice of refactoring maps — rearranging branches, reconsidering how ideas relate — builds the habit of questioning your own first assumptions about how the world is organized. That is the kind of intelligence AI cannot optimize its way to.

EQ: Building empathy through deliberate perspective-taking

Emotional intelligence — the ability to genuinely understand and connect with other people — is one of the capacities Fleischmann says AI can simulate but not authentically possess. Machines can produce empathy-sounding language. That’s different from actually inhabiting someone else’s experience.

The mind mapping practice for EQ is specific and powerful: the empathy map. Before important conversations, negotiations, or decisions that affect others, build a dedicated map from their perspective. What are they worried about? What do they need that they haven’t said out loud? What pressures are they under that shape how they’ll hear your words?

This isn’t a warm-up exercise — it’s the practice of perspective-taking made visible and systematic. And what gets made visible gets developed.

The deeper practice is returning to these maps afterward. What did you miss? What did they actually need? Over time, this kind of reflective mapping builds the calibration that distinguishes genuinely empathetic leaders from those who are merely polite.

TQ: Creating a visual system for integrity

Of Fleischmann’s five quotients, TQ is perhaps the most demanding — and the most underestimated. He defines it not as likability or warmth, but as earned credibility under pressure. Trust is the confidence others place in you when the stakes are high and uncertainty is real. It’s built slowly and lost quickly.

What mind mapping practice builds this? I’d call it the commitment audit map.

This isn’t a to-do list. It’s a living visual record of what you’ve promised, to whom, and whether you followed through — including the uncomfortable entries. The practice of sitting with that map honestly, especially after you’ve fallen short of your own standards, is precisely the kind of self-reckoning that trust is built from.

No machine builds trust this way, because trust requires moral accountability — the willingness to look your own track record in the eye. That’s a human act. And a mind map makes it harder to look away.

WQ: Making the unglamorous work visible

Fleischmann’s description of the Work Quotient is one of the most honest things I’ve read about professional excellence in years. He defines it as the discipline to carry work all the way through to completion — long after the excitement of starting has worn off. Not hustle. Not optimization. Completion.

The mind mapping practice for WQ begins with treating your project map as a living document — one you return to repeatedly, adding the undramatic sub-tasks, the follow-ups, the finishing details that don’t make it into the initial brainstorm because they aren’t inspiring. Making that work visible makes it real. Real things get done.

But the deeper practice is what I’d call the resistance map: a dedicated branch in your project map where you honestly name the obstacles, excuses, and patterns of avoidance that pull you away from completion. Most people carry these patterns invisibly and wonder why they keep recurring. Making them explicit — naming them, looking at them — is the first step toward changing them.
Fleischmann notes that what distinguishes human work from machine work is judgment, ownership, and the willingness to take responsibility for an outcome rather than a task. A resistance map is how you practice ownership.

VQ: Training the vision muscle

This is where I think the practice potential is deepest, and where Fleischmann’s framework most directly intersects with everything I’ve been writing about on this blog.

Fleischmann calls VQ the most important quotient of all — the distinctly human capacity to perceive possibility before proof exists, to imagine futures that don’t yet exist and then summon the conviction to pursue them. He makes a point that I find both stunning and important: AI is trained on existing patterns and existing realities. Its outputs, however impressive, are extrapolations from what already is. Human vision, at its best, works by defying what is.

No algorithm envisioned democracy. No machine independently dreamed of flight. Humans did — without complete information, without consensus, and often in the face of active ridicule.
The mind mapping practice for VQ is what I’d call pre-evidence mapping: starting a map not from what you know, but from a “What if…?” at the center. What would be true if the conventional wisdom in your industry were wrong? What exists at the edge of your field that no one is paying attention to yet? What problem keeps being described as unsolvable, and what would have to be true for it not to be?

The practice of contrarian branching — deliberately taking a widely-held assumption and exploring what the opposite would mean — trains the brain to question consensus rather than extrapolate from it. Done regularly, this is how you develop the mental habit of seeing around corners.

And here is something I’ve written about before that takes on new significance in this context: the power of association. When you place two seemingly unrelated ideas in close proximity in a mind map and something sparks — that moment of unexpected connection is VQ in action. It’s the mechanism by which visionary thinking actually works. You’re not manufacturing insight. You’re creating the conditions for it to emerge.

The bigger picture: Divergent thinking is the engine behind all five

Step back and look at Fleischmann’s framework as a whole, and something important comes into focus.

Every one of these five quotients requires the ability to think differently — to see what others miss, to question what others accept, to imagine what others can’t yet picture. In other words, they all require divergent thinking. Not as a creativity technique. As a fundamental orientation toward the world.

This is the thread I’ve been pulling on throughout my work on this blog: in a world where AI can generate competent, convergent answers to almost any question at machine speed, the human advantage lies in asking questions that AI wouldn’t think to ask, making connections that consensus thinking would never form, and pursuing possibilities that the evidence hasn’t caught up to yet.
Fleischmann has given us an elegant taxonomy for why this matters. But the practice of developing it — the daily discipline of externalizing your thinking, questioning your assumptions, making unexpected connections, and seeing what’s missing — is exactly what mind mapping is designed to support.

He answered “what.” You already know the “how.”

A challenge for you

Pick one of the five quotients where you know you have room to grow. Just one.

Now design a mind mapping practice specifically for that quotient — not a one-time map, but a recurring practice. A map you’ll return to. A map that will show you your own patterns over time. A map that will make visible what you’d otherwise leave invisible.

The world is going to keep asking more of our distinctly human capacities. The people who develop those capacities deliberately — through practice, not just aspiration — are the ones who will matter most in the years ahead.

Your mind map is waiting.

Alan Fleischmann’s original article, “The Five Quotients: What Skills Will Matter Most in the Age of AI,” was published in Fast Company on May 14, 2026. I encourage you to read it in full.

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