If AI is Table Stakes, What Makes a Winning Hand?

AI is becoming general-purpose infrastructure—like electricity or the internet. But simply having access to it won’t make you special. Using it thoughtfully, ethically, and in service of real problems might. Here’s why, and how to provide guidance.

In many domains, AI has already crossed the threshold from optional enhancement to expected capability. In areas like search, recommendation systems, fraud detection, logistics optimization, language translation, and customer support, AI is no longer a differentiator—it is the baseline. If a modern e-commerce platform cannot personalize recommendations, or a bank cannot algorithmically flag suspicious transactions, it is already behind. In these contexts, AI is table stakes.

Generative AI has accelerated this shift. Tools that draft text, write code, summarize documents, and answer questions are reshaping knowledge work. They do not replace expertise outright, but they increase leverage. A single analyst, developer, or marketer can now move faster and operate at greater scale. As these tools become cheaper, faster, and more deeply integrated into everyday software, opting out will feel less like prudence and more like refusing to adopt spreadsheets in the 1990s.

But infrastructure does not equal magic

Electricity did not transform companies that failed to redesign their factories. The internet did not automatically create competitive advantage for businesses that lacked strategy. AI follows the same pattern. Meaningful gains require process redesign, high-quality data, governance, and human adaptation. Slapping a chatbot onto a broken workflow does not fix the workflow. Training a model on poor data does not produce insight.

The organizations seeing durable returns treat AI as disciplined infrastructure rather than spectacle. They focus less on announcing initiatives and more on solving specific, measurable problems. They ask: Where does decision quality improve? Where does cycle time compress? Where does customer experience meaningfully change? The value is rarely in the novelty of the model. It is in the redesign of the system around it.

There is also a misconception that every company must build proprietary AI capabilities to compete. In reality, most organizations will consume AI much the way they consume cloud computing—through platforms, APIs, and embedded tools. The advantage will not come from owning foundational models. It will come from how effectively AI is applied to proprietary data, embedded into workflows, and aligned with strategic priorities.

So, is AI table stakes? Increasingly, yes. But table stakes does not mean uniform impact. Access is becoming commoditized. Differentiation lives in application.

The more useful framing might be this: AI is becoming general-purpose infrastructure, like electricity or the internet. Simply having access to it won’t make you special. Using it thoughtfully, ethically, and in service of real problems might.

In that sense, AI isn’t just hype—but believing it’s a silver bullet is.

How Boards and Advisors Can Help

Effective governance around AI does not mean micromanaging projects. It means ensuring clarity, capability, and accountability. Here is how boards and advisors can actively shape that journey:

1. Set the Strategic Context
Boards can help management articulate why AI matters for the business. Is it a growth driver? A risk mitigator? A way to enhance efficiency or customer retention? Framing AI within the company’s broader mission keeps initiatives focused and credible.

2. Encourage Evidence-Based Readiness Assessments
Before major investment, boards can prompt leadership to assess data quality, workflow maturity, and cultural readiness. Infrastructure works best when foundations are solid.

3. Facilitate Introductions to Qualified Experts
Boards and advisors often have deep networks—including operating partners, subject matter specialists, and transformation consultants—who can be matched to the company’s scale and needs. These introductions can compress timelines and reduce execution risk.

4. Ensure Governance and Ethics Are Embedded
AI introduces risks around bias, privacy, and transparency. Boards are responsible for ensuring governance frameworks evolve alongside technical capability. Treating AI as infrastructure means treating oversight as infrastructure as well.

5. Monitor Progress Without Overreach
The board’s role is to ensure that AI initiatives produce measurable results and remain aligned with strategy—not to manage implementation. Clear KPIs and structured reviews allow effective oversight without interference.

6. Promote Long-Term Capability Building
AI maturity should not depend on permanent outside help. Boards can encourage management to treat external partnerships as accelerators, not crutches—transferring knowledge to internal teams over time.

Final Thought

AI will not reward theatrics. It will reward discipline. Like electricity or the internet before it, its power compounds quietly in the hands of those who redesign their systems, elevate their standards, and stay anchored to real-world outcomes. Access is becoming universal. Advantage is not. The companies that win will not be the loudest adopters, but the most thoughtful ones—those who understand that AI is infrastructure, not ornament, and who use it to solve meaningful problems rather than chase headlines.

By Ken Marshall, Managing Partner