The Moment of Clarity



03.  The Moment of Clarity

It’s about time we engineered getting to “a-ha”


The exact moment when something suddenly comes into focus and we truly “get it” is priceless. We love that feeling. Everything makes perfect sense. It’s exhilarating when it happens and is usually accompanied by a temporary feeling of smugness for being so clever. It’s true – you do feel smarter in that instant.

Historically, we call that the moment of clarity. It’s nothing new. The problem is, we have never been very good at getting people there in a predictable way (unless you happen to be very articulate or demonstrative). Maybe the material wasn’t compelling. Maybe it was bad design. Or maybe it was you. There are a gazillion reasons why people don’t quite understand what was being presented or communicated to them. And in a nutshell, that’s exactly the problem. There’s no one-size-fits-all solution or magic bullet to use every time to fix this. We need to rely on our own ability to figure out what makes sense to other people.

As natural communicators and storytellers, we excel at talking in person. Our goal when face-to-face is to look for some kind of positive visual feedback indicating we’re on the right track with our approach (this doesn’t work at all with the written word – as authors, we just choose to believe we’re making sense. In person, we look at body language and listen for cues to help us make in-flight corrections to land our points. We may need to adjust our approach or tactics to make that connection we’re after and be sure people are getting it. Takes practice, but it’s something we all do pretty well to some degree or another.

Yet, despite all our whizzy tech and big brains, producing the moment of clarity on-demand remains an elusive and somewhat mysterious achievement. The conditions for getting there vary so much from person to person, situation to situation, there doesn’t seem to be a repeatable pattern. Even though we all experience these special moments continuously throughout our lives, they are far from predictable, nor are they easy to create at will. Being able to produce a message, passage, or diagram that everyone else will immediate understand is rare because our brains are wired so differently. Our hearts respond with different emotions. Our minds see only what they want. And the fact is, people are just random sometimes. Every situation differs by individual. What emotional state are you in when you’re looking at this? What is your background and life experience? How deep have you been exposed to any of the information before?

As Designers (or anyone who does a lot of presentations and talks), we try our best to be clear in our communications, unless we’re just going for effect or artistic merit. We often try and improve the odds by simplifying the message. It doesn’t pay to make things overly complicated or try too hard for a sophisticated design.


Clarity is easy to recognize. You just get it. Brilliant.


Over my career, I’ve tried to practice what I call radical simplification for communication pieces, where we get rid of as many extraneous things as possible to focus on the primary messages and key aspects. An example of that is removing all color completely from a diagram or PowerPoint slide deck to reduce distractions. Try this sometime – create something entirely in black text on white backdrop, or vice versa – no color, no fancy templates, no colorful clipart. It trains you to see what’s important. Then try it out on someone. Once the message resonates, add some color back in for very specific spots to emphasize something or deliver an aspect of the message more clearly. Re-test on someone. Did it help? I be willing to bet it did. This approach doesn’t work for every situation, but it’s a useful exercise, nonetheless.

It’s a long and challenging process to get something clear enough that most people understand it right off. The truth is, getting to the moment of clarity is just hard, even for the skilled. We need a better way to get people there – and Smart Information is it.


Engineering Clarity

Given the difficultly and effort required, it’s about time for us to engineer a way of getting people to the moment of clarity reliably. Been long enough. Most of us aren’t that good at it anyway left to our own devices. Add to that the potential saving of how many hours, days, weeks, and even years of people’s lives that could be spent on tasks (other than the highly manual and tedious job of creating effective messaging and communication). Doesn’t this all seem like it could be done by the machine now? Well, it can. Turns out AI is really good at analyzing large bodies of examples to learn how to replicate successful outcomes. Great place to start.

Smart Information, powered by AI, will help get people to the moment of clarity better than any medium that’s come before it, with music perhaps as the exception (we tend to be hardwired to divine emotional clarity from music). By having so many different versions of a message or piece of information available at any time, it gets easier to find the right altitude to tell the story from and medium to convey it. Quantifying the science of how to synthesize the moment of clarity is our quest now.

I’ve had this hypothesis for many years that we could engineer the moment of clarity if we combined talented designers, coders, data scientists, psychologists, and marketers together and focused them specifically on the issue of creating clarity. We talked earlier about how information will come in different forms with multiple levels of detail depending on your situation, so the trick for this group of people is figuring out which level is right for you and then how to present it as clearly as possible to get you to the moment of clarity quickly (unless you are going for a more theatrical experience where a slow reveal is more appropriate).

Let’s start by reverse engineering the moment of clarity. By engaging from a brain science and psychological perspective first, we stay human-centric and empathetic from the beginning. Let’s figure out how the wetware part of this equation works so the tech side knows what problem to solve for. Easier said than done, but there are decades of great research to mine and new techniques in brain imaging and impulse measurement to isolate the key psychological factors that contribute to finally getting to the point of understanding something clearly.


Emotions are a critically important part of crafting a clear signal.


From the physical side, can we identify regions of the brain that light up when we “get it”? Can we map out a way to get there reliably and predictably? Great questions – we’ll see if it is possible. On the experiential side, we’re looking for a few presentation and interaction methods that reliably get us to understanding. From a tech perspective we are identifying ways to recognize clarity in the mind or body of the recipient. If it were possible to nail down the factors that directly contribute to the light bulb going on from a psychological standpoint, we could then turn our attention to the emotional – which in many ways is the more difficult subject.

Engineering an efficient path to clarity isn’t the whole deal here – we need to consider people’s emotions along the way. In fact, empathy plays a big role in our approach. Just because you know how to make something clear doesn’t mean you won’t offend or anger someone. It also doesn’t mean you won’t overly stimulate them and cause an unintended side effect.

Knowing how people are going to react given their current state of mind or recent experiences is critically important. Looking at ways to determine possible outcomes or reactions given current context (and emotional state) will help to deliver the message in the right way at this particular time. You can’t expect to get the emotional side of things correct all the time because of our irrational behavior and unexpected randomness. But, we can try to show some real empathy in our engineered solutions.


Measuring Impact

When we successfully reverse engineer getting someone to the moment of clarity effectively and predictably, the next question should be “How does that compare to the old way of doing it? Did it really improve anything?” Fair question. And certainly one that engineers and researchers need to pay attention to – how do you measure the impact of this cognitive task?

In our case, measuring the impact of the moment of clarity is more than just a scientific efficiency gain, it represents a new way of approaching the experience overall. Instead of optimizing for mechanical effectiveness, we need to spend most of our effort on achieving a neural and emotional connection – very different feats. And substantially more difficult to objectively measure. Unless we have access to advanced imaging to see specific parts or pathways in the brain light up when we achieve our goal, this measurement is reliant on subjective means like standard inquiry or potentially on biometric measures like vitals and micro facial expressions using something like Microsoft Cognitive Services with cameras.

The impact to be measured is also spread across two very different task orientations – authoring and consumption. The amount of change is no less dramatic on either side of the equation – creating content that can get people to moment of clarity is a huge effort, but no bigger than finding the repeatable approaches to creating clarity on playback.

Authoring content will be hugely affected by an injection of services and platforms to create Smart Information “containers” that are capable of housing multiple representations of the same information in a multitude of forms. They also need access to on-board or Cloud-based intelligence to be contextually relevant in message delivery. That’s a lot of moving parts. Measuring a change like this is a major overhaul that cannot be looked at from a purely mechanical labor perspective – it’s not an apples to apples comparison with previous methods. We can now generate an exponentially larger amount of work in a fraction of the time using the machine. That alone represents a major shift and potential inflection point on the authoring side of the equation.


We are moving into a semi-autonomous or fully automated content creation age. Let that sync in for just a moment.


Consumption is equally affected by this transformational shift. A measure of “how much better” is completely subjective if we cannot figure out a quality metric that takes into account the dynamic nature of the form. We should look carefully at the satisfaction with on-the-fly forms vs. the familiar original delivered in a rigid (albeit well-known) state. Which did the job better?

Our measure for Smart Information consumption cannot focus on a single instance or instantiation of the content – it needs to be evaluated over time as a cumulative effect on understanding and satisfaction. Looking at just individual moments in time miss the bigger point. We have changed the fundamental nature of information. This is no longer about a single data point, it’s about all of them, over time.


Continuous Learning

When we do crack the code and figure out how to reliably engineer the moment of clarity using Smart Information constructs, it will be critical to learn what works best for each of us individually on both the consumption and authoring sides of the equation. The system needs to learn what we want or need.

“Always be closing” was Alec Baldwin’s famous line from the film Glengarry Glen Ross. Smart Information has its own version of that line – “Always be learning.”  The overall system uses AI to pay attention to what is working for you and what isn’t, whether you are creating or consuming content. This is the same kind of AI found in your smartphone or smartwatch that pays attention to your daily commute. From your patterns, it knows that telling you it will take 43 minutes to get home is probably valuable when you start driving home from work at 5:37 PM. The system is always learning.


Continuous Learning is part of Adobe’s new creative platform.


To be specific, the type of Artificial Intelligence that we’ll use to track and respond to your reactions when dealing with Smart Information is known as Reinforcement Learning. It’s a form of Machine Learning that uses knowledge of behavioral psychology to employ software agents to maximize rewards, with the goal of creating a better experience for you. It gets signals from our choices when dealing with a particular delivery medium and form during consumption tasks to inform a “model” or learning algorithm that improves over time. The net result is a system that is paying attention to our reactions and behavior to feed them back into the initial state decisions for future interactions with similar types of information. Continuous learning cycle.

You’d never really notice if Reinforcement Learning was in play during typical consumption scenarios, just as you don’t pay much attention to the prompts and notifications you get throughout your day from the smartphone. The system we build to get people to the moment of clarity needs to integrate this type of tech to achieve the very people-centric focus we have for the experiences. Some of the most advanced technology we have ever developed to address mind-bendingly complex problems will be employed regularly to solve one of the seemingly simplest of problems – how systems and devices will to talk to me so I “get it”.

.  .  .


M. Pell

Chapter 3  from “The Age of Smart Information”



Copyright © 2019  Mike Pell – Futuristic Design, Inc.  All rights reserved









Learn more about the author M. Pell at

or follow on Twitter at





Copyright © 2019   Mike Pell – Futuristic Design, Inc.   All rights reserved