Artificial intelligence is now deeply embedded in modern business. About 88% of organizations use AI in at least one part of their operations, from customer service and cybersecurity to finance and supply chain management. But while companies are racing to adopt the technology, many leadership teams are struggling to keep up.
Recent surveys show that nearly two-thirds of board directors have little or no experience with AI. Even more concerning, fewer than a quarter of companies have board-approved AI governance policies. This growing disconnect is creating serious risks for enterprises investing heavily in AI without a clear strategy, experienced leadership, or long-term implementation plan.
According to Frank Palermo, COO of NewRocket, many organizations make the mistake of treating AI as a fast technology upgrade instead of a long-term business transformation that requires governance, operational planning, and company-wide alignment.
As pressure grows to adopt AI quickly, enterprises are spending millions on automation tools and AI platforms without fully understanding how those systems will scale across the organization. Some projects fail because companies lack clean data, internal expertise, or clear business goals. Others create operational confusion because employees are not properly trained to work alongside AI systems.
Without leadership teams that understand both the opportunities and the risks, companies often move forward without asking critical questions. What business problem is the AI solving? How accurate are the outputs? Who is responsible if the system fails? How will sensitive company data be protected?
When those questions are overlooked, the financial impact can be significant.
Poorly planned AI initiatives can lead to wasted technology spending, failed deployments, cybersecurity vulnerabilities, and compliance issues. In some cases, enterprises end up purchasing multiple AI tools that do not integrate with existing systems, creating inefficiencies instead of productivity gains.
The reputational risks are equally serious. AI systems have already produced biased hiring recommendations, inaccurate legal research, and misleading customer service responses. A single public AI mistake can quickly damage customer trust and investor confidence.
Palermo also points to a broader issue many enterprises underestimate: AI is no longer just a technology decision handled by IT departments. It now affects operations, security, compliance, workforce strategy, and customer experience simultaneously. Without coordination between leadership teams, AI can create disruption instead of business value.
Cybersecurity is becoming another major concern. Employees are increasingly using unauthorized AI tools in daily workflows, sometimes exposing confidential company information without realizing the risk. At the same time, cybercriminals are using AI to launch more advanced attacks, making weak governance even more dangerous.
Yet moving too slowly also carries consequences. Competitors using AI effectively are already improving efficiency, reducing costs, and accelerating product development. Companies that delay adoption entirely risk losing market share to faster-moving rivals.
This leaves enterprises facing two dangerous outcomes. They can move too quickly without proper planning, or move too slowly and fall behind.
The companies most likely to succeed will be those that approach AI with clear governance, realistic goals, and experienced leadership. Increasingly, businesses are creating AI oversight committees, developing formal policies, and investing in executive education to close the knowledge gap at the board level.
The challenge facing enterprises today is no longer whether they should adopt AI. Most already have.
The real challenge is whether leadership teams are prepared to manage it responsibly before the cost of inexperience becomes far greater than the promise of innovation.