by Robyn Bolton | Sep 2, 2025 | Leading Through Uncertainty, Strategy
In September 2011, the English language officially died. That was the month that the Oxford English Dictionary, long regarded as the accepted authority on the English language published an update in which “literally” also meant figuratively. By 2016, every other major dictionary had followed suit.
The justification was simple: “literally” has been used to mean “figuratively” since 1769. Citing examples from Louisa May Alcott’s Little Women, Charles Dickens’ David Copperfield, Charlotte Bronte’s Jane Eyre, and F. Scott Fitzgerald’s The Great Gatsby, they claimed they were simply reflecting the evolution of a living language.
What utter twaddle.
Without a common understanding of a word’s meaning, we create our own definitions which lead to secret expectations, and eventually chaos.
And not just interpersonally. It can affect entire economies.
Maybe the state of the US economy is just a misunderstanding
Uncertainty.
We’re hearing and saying that word a lot lately. Whether it’s in reference to tariffs, interest rates, immigration, or customer spending, it’s hard to go a single day without “uncertainty” popping up somewhere in your life.
But are we really talking about “uncertainty?”
Uncertainty and Risk are not the same.
The notion of risk and uncertainty was first formally introduced into economics in 1921 when Frank Knight, one of the founders of the Chicago school of economics, published his dissertation Risk, Uncertainty and Profit. In the 114 since, economists and academics continued to enhance, refine, and debate his definitions and their implications.
Out here in the real world, most businesspeople use them as synonyms meaning “bad things to be avoided at all costs.”
But they’re not synonyms. They have distinct meanings, different paths to resolution, and dramatically different outcomes.
Risk can be measured and/or calculated.
Uncertainty cannot be measured or calculated
The impact of tariffs, interest rates, changes in visa availability, and customer spending can all be modeled and quantified.
So it’s NOT uncertainty that’s “paralyzing” employers. It’s risk!
Not so fast my friend.
Not all Uncertainties are the same
According to Knight, Uncertainty drives profit because it connects “with the exercise of judgment or the formation of those opinions as to the future course of events, which…actually guide most of our conduct.”
So while we can model, calculate, and measure tariffs, interest rates, and other market dynamics, the probability of each outcome is unknown. Thus, our response requires judgment.
Sometimes.
Because not all uncertainties are the same.
The Unknown (also known as “uncertainty based on ignorance”) exists when there is a “lack of information which would be necessary to make decisions with certain outcomes.”
The Unknowable (“uncertainty based on ambiguity”) exists when “an ongoing stream [of information] supports several different meanings at the same time.”
Put simply, if getting more data makes the answer obvious, we’re facing the Unknown and waiting, learning, or modeling different outcomes can move us closer to resolution. If more data isn’t helpful because it will continue to point to different, equally plausible, solutions, you’re facing the Unknowable.
So what (and why did you drag us through your literally/figuratively rant)?
If you want to get unstuck – whether it’s a project, a proposal, a team, or an entire business, you first need to be clear about what you’re facing.
If it’s a Risk, model it, measure it, make a decision, move forward.
If it’s an uncertainty, what kind is it?
If it’s Unknown, decide when to decide, ask questions, gather data, then, when the time comes, decide and move forward
If it’s Unknowable, decide how to decide then put your big kid pants on, have the honest and tough conversations, negotiate, make a decision, and move on.
I mean that literally.
by Robyn Bolton | Aug 27, 2025 | Leadership
Imagine that you are the CEO working with your CHRO on a succession plan. Both the CFO and COO are natural candidates, and both are, on paper, equally qualified and effective.
The CFO distinguishes herself by consistently working with colleagues to find creative solutions to business issues, even if it isn’t the optimal solution financially, and inspiring them with her vision of the future. She attracts top talent and builds strong relationships with investors who trust her strategic judgment. However, she sometimes struggles with day-to-day details and can be inconsistent in her communication with direct reports.
The COO inspires deep loyalty from his team through consistent execution and reliability. People turn down better offers to stay because they trust his systematic approach, flawless delivery, and deep commitment to developing people. However, his vision rarely extends beyond “do things better,” rigidly adhering to established processes and shutting down difficult conversations with peers when change is needed.
Who so you choose?
The COO feels like the safer bet, especially in uncertain times, given his track record of proven execution, loyal teams, and predictable results. While the CFO feels riskier because she’s brilliant but inconsistent, visionary but scattered.
It’s not an easy question to answer.
Most people default to “It depends.”
It doesn’t depend.
It doesn’t “depend,” because being CEO is a leadership role and only the CFO demonstrates leadership behaviors. The COO, on the other hand, is a fantastic manager, exactly the kind of person you want and need in the COO role. But he’s not the leader a company needs, no matter how stable or uncertain the environment.
Yet we all struggle with this choice because we’ve made “leadership” and “management” synonyms. Companies no longer have “senior management teams,” they have “senior/executive leadership teams.” People moving from independent contributor roles to oversee teams are trained in “people leadership,” not “team management” (even though the curriculum is still largely the same).
But leadership and management are two fundamentally different things.
Leader OR Manager?
There are lots of definitions of both leaders and managers, so let’s go back to the “original” distinction as defined by Warren Bennis in his 1987 classic On Becoming a Leader
| Leaders |
Managers |
| · Do the right things
· Challenge the status quo
· Innovate
· Develops
· Focuses on people
· Relies on trust
· Has a long-range perspective
· Asks what and why
· Has an eye on the horizon |
· Do things right
· Accept the status quo
· Administers
· Maintains
· Focuses on systems and structures
· Relies on control
· Has a short-range view
· Asks how and when
· Has an eye on the bottom line |
In a nutshell: leaders inspire people to create change and pursue a vision while managers control systems to maintain operations and deliver results.
Leaders AND Managers!
Although the roles of leaders and managers are different, it doesn’t mean that the person who fills those roles is capable of only one or the other. I’ve worked with dozens of people who are phenomenal managers AND leaders and they are as inspiring as they are effective.
But not everyone can play both roles and it can be painful, even toxic, when we ask managers to take on leadership roles and vice versa. This is the problem with labeling everything outside of individual contributor roles as “leadership.”
When we designate something as a “people leadership” role and someone does an outstanding job of managing his team, we believe he’s a leader and promote him to a true leadership role (which rarely ends well). Conversely, when we see someone displaying leadership qualities and promote her into “people leadership,” we may be shocked and disappointed when she struggles to manage as effortlessly as she inspires.
The Bottom Line
Leadership and Management aren’t the same thing, but they are both essential to an organization’s success. They key is putting the right people in the right roles and celebrating their unique capabilities and contributions.
by Robyn Bolton | Aug 20, 2025 | AI, Metrics
Sometimes, you see a headline and just have to shake your head. Sometimes, you see a bunch of headlines and need to scream into a pillow. This week’s headlines on AI ROI were the latter:
- Companies are Pouring Billions Into A.I. It Has Yet to Pay Off – NYT
- MIT report: 95% of generative AI pilots at companies are failing – Forbes
- Nearly 8 in 10 companies report using gen AI – yet just as many report no significant bottom-line impact – McKinsey
AI has slipped into what Gartner calls the Trough of Disillusionment. But, for people working on pilots, it might as well be the Pit of Despair because executives are beginning to declare AI a fad and deny ever having fallen victim to its siren song.
Because they’re listening to the NYT, Forbes, and McKinsey.
And they’re wrong.
ROI Reality Check
In 20205, private investment in generative AI is expected to increase 94% to an estimated $62 billion. When you’re throwing that kind of money around, it’s natural to expect ROI ASAP.
But is it realistic?
Let’s assume Gen AI “started” (became sufficiently available to set buyer expectations and warrant allocating resources to) in late 2022/early 2023. That means that we’re expecting ROI within 2 years.
That’s not realistic. It’s delusional.
ERP systems “started” in the early 1990s, yet providers like SAP still recommend five-year ROI timeframes. Cloud Computing“started” in the early 2000s, and yet, in 2025, “48% of CEOs lack confidence in their ability to measure cloud ROI.” CRM systems’ claims of 1-3 years to ROI must be considered in the context of their 50-70% implementation failure rate.
That’s not to say we shouldn’t expect rapid results. We just need to set realistic expectations around results and timing.
Measure ROI by Speed and Magnitude of Learning
In the early days of any new technology or initiative, we don’t know what we don’t know. It takes time to experiment and learn our way to meaningful and sustainable financial ROI. And the learnings are coming fast and furious:
Trust, not tech, is your biggest challenge: MIT research across 9,000+ workers shows automation success depends more on whether your team feels valued and believes you’re invested in their growth than which AI platform you choose.
Workers who experience AI’s benefits first-hand are more likely to champion automation than those told, “trust us, you’ll love it.” Job satisfaction emerged as the second strongest indicator of technology acceptance, followed by feeling valued. If you don’t invest in earning your people’s trust, don’t invest in shiny new tech.
More users don’t lead to more impact: Companies assume that making AI available to everyone guarantees ROI. Yet of the 70% of Fortune 500 companies deploying Microsoft 365 Copilot and similar “horizontal” tools (enterprise-wide copilots and chatbots), none have seen any financial impact.
The opposite approach of deploying “vertical” function-specific tools doesn’t fare much better. In fact, less than 10% make it past the pilot stage, despite having higher potential for economic impact.
Better results require reinvention, not optimization: McKinsey found that call centers that gave agents access to passive AI tools for finding articles, summarizing tickets, and drafting emails resulted in only a 5-10% call time reduction. Centers using AI tools to automate tasks without agent initiation reduced call time by 20-40%.
Centers reinventing processes around AI agents? 60-90% reduction in call time, with 80% automatically resolved.
How to Climb Out of the Pit
Make no mistake, despite these learnings, we are in the pit of AI despair. 42% of companies are abandoning their AI initiatives. That’s up from 17% just a year ago.
But we can escape if we set the right expectations and measure ROI on learning speed and quality.
Because the real concern isn’t AI’s lack of ROI today. It’s whether you’re willing to invest in the learning process long enough to be successful tomorrow.
by Robyn Bolton | Aug 13, 2025 | Speaking
by Robyn Bolton | Aug 11, 2025 | AI
“We have to do more with less” has become an inescapable mantra, and goodness, are you trying. You’ve slashed projects and budgets, “right-sized” teams, and tried any technology that promised efficiency and a free trial. Now, all that’s left is to replace the people you still have with AI creativity tools. Welcome to the era of the AI Innovation Team.
It sounds like a great idea. Now, everyone can be an innovator with access to an LLM. Heck, even innovation firms are “outsourcing” their traditional work to AI, promising the same radical results with less time and for far less money.
It sounds almost too good to be true.
Because it is too good to be true.
AI is eliminating the very brain processes that produce breakthrough innovations.
This isn’t hyperbole, and it’s not just one study.
MIT researchers split 54 people into three groups (ChatGPT users, search engine users, and no online/AI tools using ChatGPT) and asked them to write a series of essays. Using EEG brain monitoring, they found that the brain connectivity in networks crucial for creativity and analogous thinking dropped by 55%.
Even worse? When people stopped using AI, their brains stayed stuck in this diminished state.
University of Arkansas researchers tested AI against 3,562 humans on a series of four challenges involving finding new uses for everyday objects, like a brick or paperclip. While AI scored slightly higher on standard tests, when researchers introduced a new context, constraint, or modification to the object, AI’s performance “collapsed.” Humans stayed strong.
Why? AI relies on pattern matching and is unable to transfer its “creativity” to unexpected scenarios. Humans use analogical reasoning so are able to flex quickly and adapt.
University of Strasbourg researchers analyzed 15,000 studies of COVID-19 infections and found that teams that relied heavily on AI experts produced research that got fewer citations and less media attention. However, papers that drew from diverse knowledge sources across multiple fields became widely cited and influential.
The lesson? Breakthroughs require cross-domain thinking, which is precisely what diverse human teams provide, and, according to the MIT study, AI is unable to produce.
How to optimize for efficiency AND impact (and beat your competition)
While this seems like bad news if you’ve already cut your innovation team, the silver lining is that your competition is probably making the same mistake.
Now that you know better, you can do better, and that creates a massive opportunity.
Use AI for what it does well:
- Data analysis and synthesis
- Rapid testing and iteration to refine an advanced prototype
- Process optimization
Use humans for what we do well:
- Make meaningful connections across unrelated domains
- Recognize when discoveries from one field apply to another
- Generate the “aha moments” that redefine industries
Three Questions to Ask This Week
- Where did your most recent breakthroughs come from? How many came from connecting insights across different domains? If most of your innovations require analogical leaps, cutting creative teams could kill your pipeline.
- How are teams currently using AI tools? Are they using AI for data synthesis and rapid iteration? Good. Are they replacing human ideation entirely? Problem.
- How can you see it to believe it? Run a simple experiment: Give two teams an hour to solve a breakthrough challenge. Have one solve it with AI assistance and one without. Which solution is more surprising and potentially breakthrough?
The Hidden Competitive Advantage
As AI commoditizes pattern recognition, human analogical thinking and creativity become a competitive advantage.
The companies that figure out the right balance will eat everyone else’s lunch.
