By Corey Wagner
There is a moment every competitive golfer knows. You're standing over a shot with full information in front of you, yardage confirmed, wind read, lie assessed, shot shape decided. Everything your caddie has told you, everything your preparation has built toward, is sitting right there. The only thing left is to execute.
And yet the most common mistake at the highest level of the game is not making the wrong decision. It is making the right decision too late, second-guessing it on the way back, and arriving at the shot without the conviction that turns good information into a good outcome.
This week at the US Open, the best golfers in the world will navigate 72 holes of the most demanding test in the sport, not just against the course, but against the gap between what they know and what they do with it. That gap, measured in tenths of a second of hesitation or a fraction of a degree of alignment, is the difference between a major champion and someone who had the same information and couldn't convert it.
The parallels to what happens in boardrooms every quarter are not metaphorical. They are structural. And the cost of getting it wrong, in both arenas, is incalculable.
The Caddie Problem: Having intelligence is not the same as using it
Research from Insight Innovation Ventures is precise about the scale of the problem: only 1-in-10 organizations have AI fully integrated into their workflows in a way that actually connects intelligence to decisions. The other nine are sitting on research that arrived too late to change anything.
Most brand and insights teams aren't suffering from a data shortage. They're suffering from a pipeline problem. Studies get commissioned. Data comes back from competitive intelligence platforms, trends tools, survey solutions, syndicated sources, and LLMs, each one speaking a different format, each one requiring translation. The insights team synthesizes it into a finding. The finding becomes a deck. The deck goes into a meeting queue. The meeting happens. Action gets assigned.
The player has already hit the shot.
Every elite golfer at the US Open carries two things onto the course: their own physical capability, and a caddie whose entire job is to make sure that capability is applied with the best possible information at the right moment. The caddie tracks yardages, reads greens, monitors weather, remembers how the player has performed under specific conditions, and synthesizes all of it into a recommendation that arrives at the exact moment the player needs it, not before, not after.
What a caddie explicitly does not do is hand the player a binder of data and say: here is everything we know, let's schedule a meeting to go over it after the round. The intelligence is useless if it doesn't reach the decision at the moment of execution.
The US Open doesn't reward the player with the most data. It rewards the one who acts on it best.
The US Open sets up specifically to punish indecision. Narrow fairways. Thick rough. Greens that penalize the wrong quarter of the pin. The course is designed to make hesitation expensive, because at this level, every player has roughly equivalent physical capability. What separates the field is not who has better information. It is who converts information into committed execution under pressure, repeatedly, over four days.
New research from Harvard Business Review and Wharton found the same dynamic playing out inside the executive suite. Senior leaders were asked to estimate how much more valuable an AI-enabled firm would be than an otherwise identical non-AI-enabled firm three years from now. The average answer: 2.35 times. A 135% value premium. The same executives were then asked where their organizations are actually pointing their AI investment. Almost universally: efficiency. Cost reduction. Process speed. Several admitted they had never thought about AI and growth in the same sentence.
This is the equivalent of a golfer spending the off-season optimizing their practice routine and reducing their warm-up time (getting more efficient) while never working on the decision-making process that determines whether the right shot gets executed when it counts. Efficiency is real. It matters. But it has a ceiling, and the math is unambiguous. Even under generous assumptions, AI-driven cost reduction boosts firm value by around 10%. A sustained two-percentage-point lift in organic growth rate (the kind that comes from making better decisions faster, with better consumer intelligence in hand) can increase firm value by 50%. A four-point lift more than doubles it.
The course doesn't care how efficiently you warmed up. It cares whether you made the right call on 17 with the tournament on the line.
Course management Is a decision infrastructure problem.
Ask any elite caddie what separates the players who win majors from the ones who contend and flame out, and the answer is rarely about ball-striking. It is about course management, the ability to take in complex, sometimes contradictory information about conditions, risk, and opportunity, and convert it into a decision that is both correct and committed. Players who manage courses well are not making more data-driven decisions. They are making fewer, better decisions, faster, with full conviction, because their information infrastructure is designed to produce clarity at the moment of execution rather than options that have to be weighed under pressure.
The business equivalent is what Insight Innovation Ventures calls the difference between the wheat and the bake. The wheat is raw data, the yardage, the wind speed, the green slope. It is now abundant and cheap, available from a dozen sources. The bake is what human organizations can actually act on: the synthesized, contextually judged, decision-ready intelligence that arrives at the moment of the shot in a form that produces committed execution rather than hesitation.
Most organizations have plenty of wheat. What they don't have is the infrastructure that turns all of that into a clear read on the putt (the single, decision-ready output that reaches the right person at the right moment with enough lead time to actually change what they do).
The cost of that gap doesn't appear on any income statement. But it shows up everywhere: campaigns that launch on consumer assumptions that were accurate six months ago and have since shifted, pricing decisions made without current demand signal from segments that are now behaving in structurally different ways, product launches that miss the positioning window because the intelligence arrived after the go/no-go call had already been made.
The leaderboard doesn't care about your pre-round preparation. It caares about what you do with it.
One of the most instructive patterns at major championships is the relationship between pre-round preparation and in-round performance. The players who contend consistently are not the ones who did the most preparation. They are the ones whose preparation was structured to produce in-the-moment clarity rather than in-the-moment complexity. They walk onto the first tee having already made most of the decisions, knowing which pins they are attacking, which they are playing away from, where the misses have to go. The preparation reduces the decision load during execution so that when conditions shift, the adjustment is fast and committed rather than paralyzed.
This is the model that brand decision-making has to move toward, and it is precisely what a fragmented intelligence stack makes impossible. When a business question requires pulling from a competitive intelligence platform, a trends tool, a survey platform, an LLM, and a syndicated data source before it can even begin to be synthesized into a finding, the decision load during execution is enormous. By the time the synthesis is complete and the finding is formatted and the deck is presented, the conditions have shifted. The player is standing over a different shot than the one the preparation was designed for.
Shifting conditions in the current market have led to an economy that has bifurcated sharply, high-income consumers operating with premium expectations on one side, value-pressured consumers re-evaluating category loyalty at every decision point on the other. A brand strategy built on a unified consumer model is making course management decisions based on a pin position that has already moved. The only way to play the hole that actually exists (rather than the one on last quarter's scorecard) is to have intelligence infrastructure that reflects the current state of the course, not the pre-round version.
And the course itself has been restructured. Adobe's 2026 Digital Trends report found that roughly a quarter of consumers now cite AI-powered platforms like ChatGPT as their top research tool for purchase decisions, ahead of brand websites and ahead of online reviews. More than three in four consumers have used AI to help with shopping or purchasing decisions in the last six months. According to Yotpo's 2026 analysis, 58% of consumers now use generative tools for product discovery, bypassing traditional search friction entirely. The consideration phase of the consumer journey is now being shaped by AI systems before a brand's own channels activate, and if a brand doesn't have a current read on how its consumer is framing their need to a chatbot, it cannot influence the hole that is actually being played.
The Caddie Every Brand Team Needs
The best caddie in professional golf does not hand the player more data. The best caddie distills everything that is known: conditions, course, history, the player's current form, the risk profile of every available shot, into a single, clear recommendation that arrives at the right moment, expressed with conviction, and gives the player the foundation to execute without hesitation.
That is exactly what Suzy was built to be for brand, product, and insights teams.
Not another tool in a fragmented stack that adds more wheat to the pile. Not a faster survey platform that shortens one step in a pipeline that still has seven more before it reaches a decision. The platform that collapses the distance between the business question and the decision-ready answer, replacing the multi-tool, multi-format, multi-meeting translation burden with a single surface that produces intelligence that actually reaches the shot.
Before a campaign launches, a brand team can test messaging against the actual target consumer and receive a clear directional, not a 40-slide deck that requires its own meeting to interpret, but the read on the putt: this frame works, that one doesn't, here is why. Before a pricing decision goes live in a bifurcated market, current demand signal from the relevant consumer segments reaches the decision-maker in time to inform the call, not confirm it after the fact. Before a product enters a market being reshaped by LLM-driven discovery, positioning gets validated against consumers who are using AI systems to form their consideration sets right now, giving the brand the intelligence it needs to show up in that answer correctly, not reactively.
72 Holes. 4 Days. No time for a binder.
The US Open will be won this week by someone who had great information and converted it. Who made the call on the difficult holes and executed it with conviction. Who had a caddie that distilled everything into clarity at the moment of the shot.
The executive teams that win over the next 18 months will be the ones who built the same thing inside their organizations. Not more data. Not more tools. The infrastructure that closes the distance between what is known about the consumer and what the business does with it, fast enough and with enough conviction to actually move the outcome.
The HBR research puts the stakes plainly: a two-percentage-point lift in organic growth rate is worth 50% more in firm value. A four-point lift more than doubles it. Those lifts don't come from efficiency gains. They come from decisions made faster, with better intelligence, at the moment they can still change the result.
The course is set up. The conditions are shifting. The pin has moved from where last quarter's preparation said it would be. The question is whether your organization has the caddie to give you a clear read, or a binder to read on the way to the green.
Ready to see what it looks like when intelligence actually reaches the decision in time to change it? Book a demo with Suzy today.
What is the insights-to-action gap in marketing? The insights-to-action gap is the delay between when a brand collects consumer intelligence and when that intelligence actually reaches a business decision. For most organizations, that gap isn't measured in hours, it's measured in weeks, and sometimes in entire product cycles. Research from Insight Innovation Ventures found that only 1-in-10 organizations have AI integrated into their workflows in a way that genuinely connects intelligence to decisions; the other nine are operating on research that arrived too late to change anything.
How does AI improve the speed of consumer insights? AI-powered consumer insights platforms collapse the distance between a business question and a decision-ready answer, replacing multi-tool, multi-format, multi-meeting translation pipelines with a single surface that produces synthesized intelligence at the moment it can still change the outcome. Rather than moving through a chain of commissioning, synthesis, deck-building, and meeting queues, brand teams receive a clear directional in time to inform the call, not confirm it after the fact.
Why are brands using AI for efficiency instead of growth? According to HBR and Wharton research, most executives have simply never thought about AI and growth in the same sentence. AI investment defaults to efficiency, cost reduction, process speed, because those gains are easier to measure and attribute. But the value math heavily favors growth: AI-driven cost reduction boosts firm value by roughly 10% under generous assumptions, while a two-percentage-point lift in organic growth rate is worth 50% more in firm value. A four-point lift more than doubles it.
