I trust evidence over assumptions.
That’s the principle that’s guided every decision I’ve made for nearly two decades. Through team restructures, client crises, market shifts, and a company acquisition that landed weeks before a global pandemic.
When other leaders were defending their plans, I was testing mine.
When consensus felt safe, I looked for what the data actually showed.
When the world told us to wait for stability, we built systems that assumed volatility was permanent.
That operating belief saved us in 2020. And it’s the same principle reshaping how B2B tech companies build authority today.
The Illusion Finally Broke
When I acquired HUSL Digital in January 2020, I inherited a team, a client roster, and a set of assumptions about how digital marketing agencies operated.
Within weeks, every assumption shattered.
Annual roadmaps became irrelevant overnight. Eighteen-month initiatives collapsed. Funded campaigns stopped mid-flight. Every client faced the same crisis: their strategic plans assumed a stable world, and that world was gone.
What I saw in those first months wasn’t just disruption. It was clarity.
Some clients froze completely, waiting for the old plan to become viable again. Others moved immediately, re-messaging their value propositions, rebuilding funnels, re-platforming events, redefining their ideal customer profiles.
The companies that survived weren’t the ones with the best plans. They were the ones who understood that traditional strategic planning had always been broken.
We were just pretending it wasn’t.
The Signs Were Always There
The volatility didn’t start in 2020. The illusion just became impossible to maintain.
Technology cycles were shrinking every year. Marketing and sales tools reinvented themselves every 12 to 18 months. Buyer behavior was already nonlinear, with prospects bouncing between channels, self-educating, and making decisions based on peer proof rather than vendor content.
Digital competition was compounding. Every niche had new entrants. Every category was fragmenting.
Yet companies kept building annual plans as if the market would pause and wait for them to catch up.
The data told a different story. Research shows that 67% of strategies fail due to execution gaps. Leadership teams spend less than one hour per month on strategy, and half spend no time at all.
Organizations clung to stability because their operating models depended on it. Annual budgeting. Annual hiring plans. Annual campaign calendars. Rigid role definitions. Siloed teams. Slow decision models.
Acknowledging volatility meant admitting the planning machinery itself was outdated.
So people pretended stability existed because the alternative was messy. It required new skills, new operating rhythms, and new levels of cross-team alignment.
What Actually Triggers the Shift
Leadership teams don’t shift to adaptive strategy because they suddenly become enlightened.
They shift because reality corners them.
I’ve watched this pivot happen dozens of times. It comes from one of three triggers, all of them emotional before they’re operational.
The first: when historical advantages stop protecting them. Inbound leads drop despite increased spend. Websites that used to convert no longer perform. Content that once ranked disappears from AI surfaces. Competitors with inferior products suddenly own the narrative.
That’s when teams finally admit they can’t optimize their way out with old playbooks.
The second trigger hits harder. We run AI visibility audits and leadership sees how large language models describe them versus how they describe themselves. They see what competitors are claiming and winning. They see which entities they’re associated with, or more often, which ones they’re not.
Leaders expect a marketing performance report. Instead, they get a diagnosis of their perceived authority.
AI describes companies in language that’s three to five years out of date. Executives think they’re positioned around cloud governance or AI automation. But when we ask LLMs to describe them, we get “IT consulting firm” or “managed services provider.”
AI is reflecting back an older version of them. Your market thinks you are who you used to be.
The third trigger is internal friction. Too many approvals. Strategy documents no one uses. Initiatives stuck in cross-functional limbo. Stalled web rebuilds. Content delayed for months.
Eventually someone says: “We’re not losing to the market. We’re losing to our own velocity.”
That’s when a leadership team becomes willing to replace planning with adaptation.
The First Move That Changes Everything
Once a team sees the gap between who they are and who the market believes they are, we implement one operational change.
We establish a 30-day evidence loop.
Not a rebrand. Not a new website. Not a 12-month roadmap.
We identify one core claim the company believes about itself. Usually, the audit has already shown that AI doesn’t validate this claim. We build one artifact that proves that claim in a structured, machine-readable way.
An artifact is structured, grounded in entities, problem-oriented, non-promotional, and schema-supported. It’s written for human and machine comprehension.
Examples: a zero trust decision guide, a FinOps cloud cost taxonomy, an AI governance maturity model, a domain-specific glossary.
These aren’t marketing outputs. They’re evidence objects.
Within two to four weeks, we track how machines reinterpret the company. New entity associations. Improved AI descriptions. More accurate LLM reasoning. Stronger thematic clustering.
This is where leadership sees something they’ve never seen before. Their actions change how the market understands them immediately.
Traditional planning teaches that results take quarters. Adaptive strategy shows results in weeks.
Once one artifact reshapes perception, we realign website language, sales decks, product narratives, LinkedIn content, and leadership POV posts. Everything flows outward from evidence.
The Capability That Actually Compounds
Companies that commit to this adaptive model develop a capability traditional planning never built.
The ability to continuously generate, validate, and operationalize evidence of expertise.
Organizations that run agile transformations see 30 percent gains in efficiency, customer satisfaction, and operational performance. Agile firms grow revenue 37 percent faster and generate 30 percent higher profits than non-agile companies.
Most companies are full of expertise but empty of evidence. Experts know the answers, but nothing gets documented, structured, or published.
The evidence loop model trains teams to extract expertise from subject matter experts, turn it into artifacts, publish in a structured way, and create external proof of internal knowledge.
Over time, this becomes second nature. Traditional planning teaches promotion. Adaptive strategy teaches proof.
After a few cycles, teams organically start asking: How do we know this? What evidence supports that? Have we tested this assumption? What’s the artifact that proves this?
That reflex changes culture more than any reorganization ever could.
Evidence loops also create high-frequency alignment across functions. Marketing, product, sales, and leadership rally around one validated piece of truth at a time. This collapses months of meetings into days of movement.
Traditional planning builds silos. Adaptive strategy builds alignment.
AI Decides Who Matters First
Here’s the blind spot costing companies millions right now.
Leaders still think AI is summarizing the internet. In reality, AI is interpreting the internet and deciding who matters.
Today, 90% of B2B buyers use generative AI tools during their research process. Before a buyer hears from you, AI has already summarized who you are, whether you’re credible, what you do, how you compare, and whether you’re relevant.
The buyer meets the AI’s version of you before they meet you.
Five years ago, thought leadership meant influence. You published insights, built an audience, shaped opinions. The buyer still chose whether to read your content.
Today, AI is the first filter of interpretation.
Buyers start with ChatGPT, Gemini, Perplexity, Claude, Microsoft Copilot, and AI assistants embedded in their workflows. AI doesn’t follow you. It cites you.
Being cited means your artifacts are canonical. Your frameworks are used to explain problems. Your terminology is reflected. Your definitions anchor the category. Your data is used in reasoning. Your point of view becomes the default mental model.
That’s what “source of truth” means now. Your knowledge becomes the machine’s baseline.
Category leadership used to be narrative leadership. If your messaging was tight and your brand was polished, you could win the narrative.
Today, AI doesn’t care about messaging. It cares about structured content, machine-readable proof, entity connections, trust patterns, consistency, depth, clarity, and citations validated across the web.
You don’t get authority because you say you’re the leader. You get authority because machines can prove you’re the leader.
The New Operating System
AI reinforces the perception you already have unless you actively overwrite it.
If AI learned an outdated version of your company in 2021, it’s still using that version today. Executives assume that because they’ve evolved, the market has evolved with them.
It hasn’t.
This misunderstanding costs companies millions in mispositioned deals, weaker inbound, slower trust-building, competitor advantage, and longer sales cycles.
You can publish 100 blogs and still be invisible. You can publish one canonical artifact and rewrite the category.
Authority in the AI era is a structure game, not a production game.
By the time a prospect hits your website, they’ve already asked AI who you are, what you do, how you compare, what problems you solve, and whether you’re credible. AI didn’t use your funnel. It used your evidence footprint.
Leaders still think their website is the first impression. AI is the first impression. Your website is the confirmation step.
Executives are still trying to fix perception with messaging workshops, new taglines, category design exercises, rebrands, and narrative sprints.
But AI ignores messaging. It listens to artifacts, entities, schema, definitions, how-to frameworks, knowledge structures, and citations.
This is why brand refreshes aren’t moving the needle. They’re polishing the story instead of strengthening the source.
Your biggest competitive risk isn’t that AI will get something wrong about you. It’s that AI will get something outdated about you, and you won’t realize it until it’s already shaping your pipeline.
That’s the blind spot. And until a company confronts it, everything else sits on quicksand.
The world was always this volatile. We were just pretending it wasn’t.
The companies that win are the ones who stopped pretending and started building evidence.