by Robyn Bolton | Aug 24, 2026 | Leading Through Uncertainty, Tips, Tricks, & Tools
By now you (hopefully) know that uncertainty and ambiguity are different things caused by different combinations of knowns. But, as GI Joe taught every 80s kid, knowing is half the battle.
Doing is the other half.
Knowing about knowing
Whether it’s diagnosis, decision-making, or quantification, there are literally hundreds of different frameworks designed to manage uncertainty. With so many choices in breadth, depth, and application, choosing which one to use is an exercise in navigating uncertainty.
The Knowability Matrix (below) is rooted in the distinction that the US Army War College draws between uncertainty and ambiguity, specifically the knowability about the situation (question) and solution (answer). It’s further supported by neurological studies into the brain’s chemical reactions to different states of not-knowing.
Diagnosing the state of not-knowing you’re in is a critically important first step to “solving” the situation. Often referred to as “name it to tame it,” decades of psychological research on the theory is increasingly backed up by neurological studies that show that by simply naming or labeling a negative emotion (even silently) significantly reduces activity in the amygdala (our brain’s fight or flight center).
But that doesn’t change the actual situation.
Doing because of your knowing
To change the situation, you need to do something. And what you need to do varies by situation.
Risk
- Knowing state: Question and answer are clear
- Felt state: Tension, anticipation, caution
- Tools for resolution: Expected value, probability and actuarial tables, Monte Carlo Simulation, Kelly Criterion, portfolio hedging and diversification, insurance, Value-at-Risk, Six Sigma
- You might be a RISK expert if you are a: Professional poker player, casino operator, actuary, insurance and reinsurance underwriter, credit-risk officer, reliability engineer
Uncertainty
- Knowing state: Question is clear, but answer is not
- Felt state: Suspense and unease
- Tools for resolution: Bayesian updating, scenario planning, portfolio/diversification of bets, experiments, calibrated forecasting, real options and hedging
- You might be an UNCERTAINTY expert if you are a: Research scientist, intelligence analyst, meteorologist, epidemiologist, venture capitalist, clinical-trial researcher, detective
Ambiguity
- Knowing state: Question is unclear, but answer becomes clear when question is determined
- Felt state: Doubt and unease (especially when trying to clarify the question)
- Tools for resolution: Problem framing (How might we?), abductive reasoning, prototyping-to-learn, Jobs to be Done, sensemaking,red team/devil’s advocate
- You might be an AMBIGUITY expert if you are a: Founder or entrepreneur, strategist or design researcher, judge or appellate lawyer, diplomat or negotiator, therapist, anthropologist
Opacity
- Knowing state: Both question and answer are unclear
- Felt state: Disorientation or overwhelm
- Tools for resolution: Impose structure (incident command, triage, checklists), shrink to the next move, OODA loop, act-sense-respond, rely on muscle memory/drilled pattern
- You might be an OPACITY expert if you are a: First responder, incident commander, ER professional in a mass casualty event, war correspondent, wildfire and disaster response pro
The difficulty of doing
As anyone who has ever struggled to lose weight, quit a bad habit, or start a healthy one knows, there’s a huge gap between knowing what you should do and actually doing it. This “Knowing-Doing Gap” isn’t just a reality individuals face when trying to change. It’s baked into how companies operate.
For centuries, businesses followed relatively predictable cycles. Sure, a war, depression, or radical technology popped up every now and then, but eventually everything settled back down.
As a result, companies got really good at managing Risk. They knew the questions and the answers so they could rely on actuarial and probability tables, Monte Carlo analyses, portfolios, hedging, and insurance to get them through uncertainty.
Now we live in an unpredictable world and risk management tools don’t work in Uncertainty, Ambiguity, and Opacity. But, because they’re the tools we know, they’re the tools that get used.
Until we have the courage to let go of the familiar but wrong tool and learn the right one, the call to “embrace uncertainty” will continue to be as productive as hugging a cactus.
Is knowing which state of uncertainty you're in enough to move forward?
No. Diagnosis tells you which tool to use. It doesn’t guarantee the organization uses it. Even with the right tool in hand, growth decisions can still stall on reasonable-looking resistance: one more signoff, a handoff with no owner, a priority that quietly shifts.
Why do people use the wrong tools to solve problems?
Because those tools are what we know. For centuries, businesses built expertise in managing Risk (clear questions, clear answers) using tools like probability tables and hedging. Now most business problems are Uncertain, Ambiguous, or Opaque, where those same tools don’t work. But because they’re familiar, they still get used. Progress requires the courage to let go of the wrong-but-known tool for the right one.
Can a situation move from one state to another over time?
Yes. The four states aren’t fixed labels, they’re diagnoses of what’s still unknown right now. As problem framing, prototyping, or sensemaking clarify a fuzzy question, an Ambiguous situation often resolves into a clear Uncertainty or even a Risk problem. When that happens, the right toolkit changes with it. What worked for framing the question won’t work for hedging the answer.
by Robyn Bolton | Aug 17, 2026 | Leading Through Uncertainty
There’s a famous PSA from the 1980s in which a dad-like figure sighs with exhausted resignation, as he holds up an egg and explains it’s your brain, points to a frying pan and explains it’s drugs, then cracks the egg into the pan and, as it sizzles, states “this is your brain on drugs.”
It’s also your brain on uncertainty.
As you’ve probably heard, we’re living in a VUCA (volatile, uncertain, complex, and ambiguous) world. You’ve no doubt seen VUCA-ness play out in your workplace and your world and felt the impact of it.
And you’ve likely heard that you simply need to “embrace uncertainty.”
That’s terrible advice.
It’s like telling someone to hug a cactus. It hurts and no one benefits.
But you can try to understand it.
VUCA describes an external situation
The US Army War College popularized the acronym VUCA when it began using it in 1987 to describe “a more complex multilateral world perceived as resulting from the end of the Cold War.” Intended to be used as a framework to understand and articulate opportunities and challenges:
- V = Volatility: fast and frequent change
- U = Uncertainty: unpredictability of events’ size, timing, and impact
- C = Complexity: the existence of many interconnected parts
- A = Ambiguity: the existence of multiple interpretations and unknown odds
While Volatility and Complexity describe two different facets of a situation: speed and interconnectedness respectively, Uncertainty and Ambiguity both refer to the knowability of an answer.
And that’s where things get complicated.
Knowability exists on a 2×2 (because, of course)
We often use Uncertainty and Ambiguity as synonyms, but there’s an important difference between the two:
- Clarity of the question: Do I know what I am trying to figure out?
- Clarity of the answer: Do I know the outcomes or odds?
Put it together, and TA DA! you get a 2×2:
This 2×2 isn’t just a neat trick to renew my consulting license (not a real thing but, if it were, 2×2 usage would be a qualifying criterion). It’s essential to understanding how our brain reacts to a situation and our resulting feelings and behaviors.
(Not) Knowing > Feeling > Acting
Each of the four states of Knowing triggers chemical changes in our brains that drive certain behaviors:
| Situation |
Brain and chemistry |
Felt state it triggers |
Resulting behavior |
| Risk
Question clear
Answer known |
- Reward center (nucleus accumbens) anticipates the payoff and releases dopamine
- Bodily-alarm area (anterior insula) braces for loss
|
Minor sense of tension, anticipation or caution |
|
| Uncertainty
Question clear
Answer unknown |
- Reward and value areas (ventral striatum, ventromedial prefrontal cortex) keep working normally
- Signaling system (basal forebrain) releases acetylcholine to mark the outcome as known-to-be-unreliable
- Predictability monitor (anterior cingulate cortex) tracks how predictable things are
|
Suspense and mild unease, especially while the answer stays open |
- Gather information
- Plan for scenarios
- Prepare for multiple outcomes
- Proceed with steady caution
|
| Ambiguity
Question unclear
Answer clear once question is determined |
- Threat-and-value areas (amygdala, orbitofrontal cortex) fire more.
- Reward hub (striatum) quiets down.
- Conflict monitor (dorsal anterior cingulate cortex) flags the clash between readings.
|
Doubt and unease, especially when you can’t decide on the question. |
- Hesitate
- Avoid
- Pursue a clear answer even if an unclear one may be more beneficial
|
| Opacity
Question unclear answer unknown |
- Arousal center (locus coeruleus) releases norepinephrine to flag the surprise of an unforeseen outcome.
- Self-monitoring area (prefrontal cortex) registers that you cannot even frame the situation.
|
Disorientation or overwhelm. |
- Freeze
- Withdraw
- Rush to an explanation even if it is wrong
|
Don’t embrace. Understand.
None of the feelings or behaviors above are awesome. That’s why “embracing uncertainty” feels like hugging a cactus.
Starting with your feelings and actions helps you understand the situation you’re in. Which is the first step to solving “uncertainty.”
What is the difference between uncertainty and ambiguity?
Uncertainty means the question is clear but the answer isn’t. Ambiguity means the question itself isn’t clear. Uncertainty makes you want more information. Ambiguity makes you hesitate until you know what’s actually being asked.
How do I know if I'm facing uncertainty or ambiguity?
Ask two questions. Do I know what I’m trying to figure out? Do I know the possible outcomes? Clear question, unknown answer: uncertainty. Gather information and plan scenarios. Unclear question: ambiguity. Get clarity before you act.
Why does resistance to change happen, even when a decision is the right one?
Resistance to change often isn’t stubbornness. It’s brain chemistry. When the question is unclear (ambiguity), the amygdala fires, the brain’s reward system quiets, and behavior shifts to hesitating, avoiding, or grabbing a wrong-but-clear answer. When both question and answer are unclear (opacity), people freeze or withdraw. Naming which state you’re in rather than “embracing” it is the first step to moving past it.
by Robyn Bolton | Aug 11, 2026 | AI, Metrics
“AI Adoption is a Myth,” declared the headline of a recent viral blog post.
I do love a dogmatic declaration of a controversial opinion and the article opened with a compliment (“you’re already in the top 1% of AI users”), so I kept reading. Eight minutes later, I couldn’t help thinking, “Oh boy. We’re in trouble.”
AI adoption IS a myth
The author is the CEO of a company that implements AI and builds agents for companies earning $500M or more in revenue, which gives him a unique perspective across a wide variety of organizations. In his experience, he estimates that for the average Claude Cowork, or similar, rollout:
- 5-10% of employees become power users (use Cowork daily, create and use skill files for repeated tasks, install connectors to other programs, etc.)
- 20% use it a couple of times a day and either poorly or for “check the box” purposes
- 70% don’t use it at all
Now 10% adoption may be a huge win in the organic baby food market, but it isn’t going to excite a CFO who just spent $2 million on initial deployment and integration and is looking at annual operating expenses of $1-2 million.
Spending on AI is expected to hit $2.6 trillion by the end of 2026, a 47% increase from 2025. The only way that gravy train keeps moving is if CEOs, CFOs, and CIOs believe that their investments will show ROI quickly and increased profit soon after.
Huge swaths of the economy rely on AI spending continuing to rise so it’s in “everyone’s” interest to show data that justifies the spending, namely AI adoption rates, even if the math is a little squirrely.
After all, “frequent use” mean “a few times a week” as it does in Gallup’s report that 28% of employees are “frequent” AI users. Or that adoption should be measured at the enterprise, rather than employee level, as it is in AI Business Weekly’s “AI Adoption Statistics” report that proclaims “88% of organizations now use AI in at least one business function.” (underline added).
AI adoption is NOT a myth
It’s hard to claim that AI Adoption is a myth when research shows that 60% of US adults used GenAI within 3 years of ChatGPT’s launch and by November 2024, 99% had used at least one AI-enabled product in the past week (though 64% didn’t realize they were using AI).
With such widespread adoption, it’s logical to assume that even if AI usage began outside the home, as GenAI becomes widely available with companies, more and more employees will adopt the new tools available on their employer-issued laptops and smartphones.
AI adoption DOES NOT MATTER!
Harvard Business School professor Ted Levitt, famously said “people don’t buy a quarter-inch drill. They buy a quarter-inch hole.”
Yet, with regards to AI, we’re obsessing over the number of quarter-inch holes drilled. Who cares what percentage of your employees use AI? You didn’t install it so everyone could have access to a chatbot. You installed it to get work done faster, cheaper, and/or more accurately. Measure that!
Yes, AI adoption is easy to measure (and easier to “adjust” as needed), and it’s easy to benchmark where your company falls relative to its peers, and it makes everyone feel great when your adoption rate is higher than your competition.
But it’s all vanity unless you’re getting the real results that justify the investment.
Is a high AI adoption rate just innovation theater?
It can be. High adoption numbers are easy to report and easy to feel good about, especially next to a competitor’s lower number. But adoption alone doesn’t tell you if the work got faster, cheaper, or more accurate. Measure what changed in the work AI touched, not how many employees opened the app.
Why do AI adoption stats vary so wildly between reports?
Because the definitions move to fit the story. Gallup counts 28% as “frequent” users, meaning a few times a week. AI Business Weekly reports 88% adoption by counting at the company level: one business function using AI counts the whole organization as an adopter, regardless of how many employees actually use it. Check how a number was measured before you compare it to yours.
If adoption rate is the wrong number, what should I actually measure?
Not how many people opened the app. Measure whether the work AI touched actually got faster, cheaper, or more accurate. As the saying goes, people don’t buy a quarter-inch drill, they buy a quarter-inch hole. You didn’t install AI so employees could have a chatbot. You installed it to get specific work done better. Track that outcome, not usage.
by Robyn Bolton | Aug 4, 2026 | AI, Leadership
“Try AI. It will make you more efficient,” every executive at least once in the past three months.
And it always makes me laugh.
Yes, AI will make save you time and effort. Eventually. But not on the timeline you, or your boss, is comfortable with.
A story that proves the rule
A few days ago, I spent 4 hours building a single AI tool for one research task.
Some context: I use AI Daily and have built prompts, tools and agents to streamline regular tasks. The idea came from a Whop site that I use regularly, which outlined the specific tools, prompts, and steps required to successfully build an AEO (answer engine optimization) scanning tool.
That’s why I only blocked four hours for the build.
To build the tool, I created new accounts on four different websites, worked in Terminal, Claude Cowork, and Claude Code, and referred to instructions from both the originating website and Claude.
If I had a boss or an IT department, neither would be happy with me.
Why? After all, I built a tool for a task that was too complicated and time-consuming to do previously, will run automatically every week, recommends practical fixes, and measures impact against meaningful business metrics.
I’ve also had bosses that, if I told them I spent 10% of my week on this, would say, “that’s nice but where do we stand on (fill in the blank).”
The cost of efficiency
“Lean is the enemy of learning,” according to MIT professor, Ben Armstrong. That’s as true for AI in the office as it is on the manufacturing floor.
Learning is inherently inefficient. Baked into the experience are user mistakes, unexpected outcomes, and dead ends. But those mistakes, outcomes, and dead ends generate the insights required to increase the odds the next attempt will succeed.
As Thomas Edison said, “I have not failed. I’ve just found 10,000 ways that don’t work.”
When we do ultimately succeed, we almost immediately forget time, money, and effort the learning cost us. But forgetting is what makes harder to keep learning. And it results in nonsense like, “try AI. It will make you more efficient.”
The value of waste
Next time you’re tempted to say, “try AI,” stop and ask yourself these five questions:
- “Why am I saying this?” Do you really believe that AI is a solution to the problem you’re hearing or are you just saying it because the company is pushing AI? If it’s the latter, just provide a solution to the problem. If it’s the former, keep reading.
- “What does ‘efficiency’ look like?” As with all experiments, define success first. Do you expect time to completion to be cut in half? Or for it to take just as long, with fewer people? Or maybe it simply costs less. Without a goal, AI is just a toy.
- “How long am I willing to wait for efficiency?” Efficiency doesn’t magically appear the moment a prompt or tool spits out a response. Are you willing to dedicate an hour a week, even if it takes six months to be efficient. Or is success expected in a day, no matter how many hours?
- “Am I willing to pay the cost of learning?” The costs of learning go beyond trainings and tools. It’s the opportunity cost of time spent on another project, calls to customers, conversations with consumers, engagement in meetings. If you’re not willing to pay all those costs, don’t encourage people to “try AI.”
- “How will I reward and spread this learning?” Your “learning waste” can create value for your organization. But only if you reward the people who did the work and spread the learning beyond your team, enabling people to build on what’s known with less “waste.”
“Try AI” is a shortcut to avoid the hard and inefficient work of learning. But paying the cost is the only way to earn the efficiency.
