Simply put, your organization can only confidently predict future outcomes if you have historical data showing similar patterns. Don’t try to predict situations with no precedent. Instead, use available data to drive decisions based on what you know—not what you think you know.

How much data do you need?

Data scientists typically say, “the more, the better.” You need a solid data strategy to collect, manage and retain critical information across your organization.

Generative AI can help overcome data limitations by creating synthetic data to fill the gaps. Traditional models typically rely heavily on historical data, which can limit their effectiveness in complex or rapidly changing situations.

Generative AI, however, can create data samples based on existing patterns, helping you make better predictions even when you don’t have massive datasets. This artificially generated information statistically resembles your real data but doesn't contain sensitive details. This capability is valuable when you don't have enough historical examples, need to test rare scenarios or want to protect customer privacy while still training effective models.

Make sure your data represents the problem you’re trying to solve. Biased data leads to biased predictions. Data scientists look for clean datasets they understand well, and they use feedback loops to continuously improve accuracy.

Generative AI can transform many aspects of your business. By understanding its potential applications, you can direct your leadership team to leverage it for competitive advantage—whether it’s using AI to spot unusual patterns in operations or to make better revenue decisions.

Think of generative AI like a really smart assistant. It’s great at creating content, answering questions and generating text or images based on what it’s learned. It’s reactive—you ask, it responds. It’s like having an incredibly knowledgeable intern who can draft reports, create presentations and brainstorm ideas when you give it specific instructions.

Agentic AI, on the other hand, is more like a proactive team member who can take initiative. Agentic AI is an evolution of generative AI that’s integrated with other systems, so it can:

• Set its own goals
• Break down complex tasks into steps
• Make decisions independently
• Learn and adapt as it works
• Operate with minimal human supervision

For CEOs, the strategy isn’t about choosing between generative AI and agentic AI, but understanding how each can complement your business objectives. Generative AI excels at content and creative tasks, while agentic AI shows promise in complex problem-solving and autonomous operations.

The key is to start experimenting. Test small pilots, understand the capabilities and gradually integrate these technologies where they can drive the most value for your specific business challenges.

Generative AI isn’t about applying AI indiscriminately—it's about using the right tools for the right challenges. While AI experts might argue that almost every problem has an AI solution, not every problem requires generative AI. The key is knowing when and where it delivers real business value.

In marketing, companies like Delta Air Lines have utilized AI platforms like Alembic to connect advertising efforts directly to sales outcomes. By analyzing data from various sources, Delta attributed $30 million in sales to its Olympic sponsorship, demonstrating AI’s capability to enhance marketing effectiveness and ROI.

In manufacturing, generative AI is being used to streamline technical documentation and training. BACA Systems leveraged AI to restructure extensive manuals into bite-sized AI-powered knowledge bases, enabling customer service chatbots to provide more accurate responses in real-time, improving support efficiency and customer satisfaction.

The key is strategic implementation. Generative AI excels in areas like content generation, data analysis and automation, but it’s not the right solution for every business challenge. The most successful companies pinpoint specific, high-impact use cases where generative AI drives measurable improvements, whether in customer experience, product innovation or operational efficiency.

By focusing on targeted, high-impact applications, you can transform generative AI from hype into a genuine competitive advantage.

When exploring generative AI, plan for bumps in the road and view challenges as learning opportunities that can drive innovation and growth.

Having backup plans will help manage risks and ensure smooth integration of AI solutions into your existing workflows. Common challenges include:

• Unreliable AI models
• Regulatory hurdles
• Existing processes that can’t adapt to new approaches
• Insufficient data
• Performance or scaling issues
• Unclear business case

Trial and error are part of adopting new technologies. Create a culture that encourages experimentation, where “failures” are seen as steps toward success.

In practice, this means preparing for failure and creating a culture of experimentation where your organization tests hypotheses rather than sticking with ‘the way we’ve always done things.’

Remember when it seemed to appear overnight? Generative AI has followed a similar trajectory, emerging as a transformative force. Just as early skeptics questioned its staying power, many organizations remain cautious of AI's long-term impact.

“[During the internet bubble], there were a lot of businesses that looked like the one today, where they’re like, ‘We’re not sure about AI. We’re not sure if this is going to deliver immediate value. We’re not sure exactly what value it delivers. We understand that AI is important. That’s the phase we’re in,” says Nigel Vaz, CEO of Publicis Sapient.

AI will likely follow the internet’s successful path—Bloomberg Intelligence predicts generative AI alone will grow to a market value of $1.3 trillion by 2032.

So, how can businesses take the long view in the age of AI? Organizations must develop strategies that not only enable AI transformation but also ensure they’re making the right investments today to stay ahead tomorrow.

However, most AI proof-of-concepts (POCs) never make it to production—often due to unclear business cases, data limitations or a lack of integration into existing workflows. To bridge this gap, businesses need an AI strategy that moves beyond experimentation and scales effectively. This means aligning AI initiatives with business objectives, ensuring access to high-quality data and integrating AI seamlessly into operations.

As a CEO, you play a critical role in understanding generative AI and its guiding impact on your organization. This technology offers an opportunity to gain competitive edge, drive innovation and fuel business growth.

By approaching generative AI strategically, you can position your company for success in this era of AI-driven transformation.