For most of my career, I have watched the same pattern repeat. A new technology emerges. Early adopters experiment, struggle and learn in public. Everyone else waits for best practices. By the time those practices are well documented, the competitive advantage has already moved.
AI is following that exact script. Only this time, the cost of waiting is far higher.
When people talk about AI risk, they usually focus on model errors, data exposure or governance gaps. Those are real concerns. But the bigger, quieter cost is competitive erosion. Brand relevance declines. Profitability compresses. Executive credibility weakens. Entire business models become vulnerable to disruption from outside the industry.
I have seen this movie before. Many times.
Early in my career, I watched Wells Fargo become the first major bank to launch online banking at scale. At the time, most other financial institutions were still debating whether customers would even trust digital transactions. Wells Fargo did not wait for best practices. They created them.
While competitors hesitated, Wells Fargo built a reputation as a technology leader. Their brand perception shifted. Customer expectations shifted with it. And every other financial institution I worked with suddenly found themselves in a permanent state of catch-up.
Those banks were not incompetent. They were cautious. They were waiting for standards, frameworks and peer validation. By the time they moved, Wells Fargo had already claimed mindshare, trust and operational maturity.
That experience shaped how I view every technology wave since.
The same pattern repeated with:
- Hotels watching Airbnb redefine trust and access to lodging
- Banks watching Venmo and Zelle redefine money movement
- Telecom companies watching smartphones eliminate landlines
- Media companies watching streaming replace physical distribution
In each case, the organizations that waited for best practices did not just lose time. They lost positioning.
AI is no different. The only difference is speed.
The executive cost no one talks about
Most AI discussions focus on organizational risk. Few talk about executive risk.
As a CIO or CISO, your value is not measured only by stability. It is measured by relevance.
Boards, CEOs and investors increasingly associate leadership credibility with the ability to navigate AI responsibly and strategically. When peers are building internal copilots, optimizing operations and improving decision velocity, the leaders who are still “evaluating” begin to look out of step.
This is not about hype. It is about optics, influence and trust.
Executives who adopt AI early gain:
- Strategic fluency in how AI actually behaves in real environments
- Practical experience with governance tradeoffs
- Credibility in board and peer conversations
- Confidence in shaping policy instead of reacting to it
Executives who wait inherit other people’s playbooks and other people’s mistakes. Over time, that gap becomes visible. Career opportunities follow the leaders who demonstrated foresight, not the ones who demonstrated caution. AI adoption is not just a technology shift. It is an economic one.
Organizations that optimize early gain:
- Lower operating costs through automation
- Faster cycle times
- Higher employee leverage
- Better customer responsiveness
- Improved margin resilience
Competitors who delay must eventually adopt the same tools simply to remain viable. But they do so under margin pressure, not advantage. This creates a structural profitability squeeze. Early adopters improve margins. Late adopters defend margins. The difference compounds over time. McKinsey has repeatedly shown that AI leaders outperform laggards in profitability and revenue growth, not because the models are better, but because the organizations learned faster.
Once margins compress, every future investment becomes harder. Innovation slows. Risk tolerance declines. Talent migrates. Waiting does not preserve profitability. It slowly erodes it. One of the most dangerous assumptions in business is that disruption will come from within your industry.
Hotels did not expect Airbnb. Taxi companies did not expect Uber. Banks did not expect fintech wallets. Media companies did not expect YouTube. AI lowers the barrier to entry across industries. A small, AI-native company can now operate with scale, efficiency and insight that previously required massive infrastructure.
This means new competitors can emerge with:
- No legacy systems
- No cultural resistance
- No process debt
- No governance baggage
They will not ask how your industry works. They will ask how it could work. If your organization is still waiting for AI best practices, someone else is building the next version of your business model.
Best practices are a lagging indicator
Best practices are valuable. They are also backward-looking. They describe what worked after it already worked. If Wells Fargo had waited for best practices in online banking, someone else would have written them. If Airbnb had waited for best practices in peer-to-peer lodging, the industry would not exist. If Venmo had waited for best practices in consumer payments, wire transfers would still dominate. Best practices are created by organizations willing to experiment under uncertainty.
AI best practices will not protect your competitive position. They will document someone else’s success. Many leaders believe waiting is the safer path. In reality, it is simply the quieter risk. AI experimentation done responsibly creates learning. Waiting creates ignorance. I have watched organizations delay AI adoption in the name of governance, only to later deploy rushed, poorly understood implementations under competitive pressure. That path produces more risk, not less. The organizations that are safest with AI are not the ones that waited. They are the ones who learned early.
OWASP and NIST both emphasize that responsible AI maturity comes from iterative learning, not theoretical governance alone.
Frameworks help. Experience matters more. Customers may not ask if you use AI. But they feel when you do not. They feel it in response time. In personalization. In accuracy. In engagement. In product evolution. Brands that leverage AI appear modern, responsive and adaptive. Brands that do not quietly feel outdated. This does not happen overnight. It happens slowly. Then suddenly.
Blockbuster did not collapse in a single year. It declined while Netflix built trust and habit. By the time the shift was obvious, it was irreversible. Brand erosion is rarely loud. It is just permanent.
I will say this directly, because most articles will not.
Executives who avoid AI today are quietly reducing their future relevance. Not because AI replaces them. But because leaders who understand AI will replace leaders who do not. Boards do not need technical experts. They need leaders who can translate complexity into strategy. If you are not building that muscle now, someone else is.
What acting now actually means
Acting now does not mean reckless deployment. It means:
- Piloting controlled use cases
- Learning how data flows through models
- Understanding where governance breaks
- Observing human behavior with AI tools
- Building internal literacy
- Creating feedback loops
- Developing institutional intuition
This is how best practices are born. You do not need perfection. You need momentum.
The call to action
If you are a CIO or CISO waiting for best practices before acting on AI, I would challenge you to reconsider the risk you are actually taking.
You are risking:
- Competitive positioning
- Profitability trajectory
- Brand relevance
- Organizational learning
- And your own executive relevance
The leaders who will define the next decade are not waiting for permission. They are building understanding. AI is not the next software upgrade. It is the next operating model. The question is not whether best practices will emerge. They will. The question is whether your organization will help write them or quietly read them later.
If there is one lesson my career has reinforced repeatedly, it is this: The future rarely rewards those who wait to be certain.
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