Choosing the Right Problem for AI to Solve: A Practical Guide for Engineering Leaders

There’s no question that we’ve reached—or at the very least are approaching—the peak of the AI hype cycle. Artificial intelligence is increasingly being positioned as a silver bullet for nearly every business challenge. But while AI can be a valuable asset, adding it to your business’s tech stack won’t automatically transform your operations. Why? Because AI is most effective when used strategically to address specific problems. 

Identifying the Right Problems: Why It’s Critical

As an engineering leader, identifying which obstacles the technologies can effectively address is a crucial step toward achieving AI readiness. Taking a practical approach to AI adoption boosts your return on investment and instills greater confidence in AI across your workforce—a vital goal, considering that just 25% of employees are currently confident in their company’s AI strategy. 

Below is a brief guide to help you identify the right bottlenecks for AI to solve within your business to unlock your full potential. 

1. Locate areas of inefficiency

Start by zooming out and reviewing your operations as a whole. Identify areas where processes are running smoothly and where there is room for greater efficiency. For example, if teams spend excessive amounts of time manually inputting data or creating reports, this could be an area where AI can help you automate tasks, freeing up time for higher-value activities.

2. Identify scalability bottlenecks

Scalability challenges can inhibit growth. However, many AI tools are well-suited to streamline operations and support expansion. Consider recruitment and onboarding tasks, for example: drafting job descriptions, filtering candidates, assessing resumes, interviewing, and onboarding. These activities take hours, if not days or weeks, and limit how quickly you can grow your team. AI-powered solutions can automate many of these processes, helping you grow your workforce more efficiently and accelerate projects. 

3. Consider problem complexity

When selecting issues for AI to address, it’s essential to consider the complexity of the issue. The most impactful AI solutions tackle moderately to highly complex operational challenges, particularly those involving pattern recognition, forecasting, predictions, or process optimization. Assessing which types of AI technologies—such as basic machine learning, deep learning, natural language processing, image recognition, or large language models—will bring the most value to your company is key, as this choice determines which challenges AI can effectively address.

4. Ensure sufficient data

AI is only as effective as the data that powers it. In fact, 91% of respondents surveyed by the Harvard Business Review believe that a reliable data foundation is crucial to successfully adopting AI . If your business lacks solid data infrastructure or access to high-quality data, it will be challenging to achieve meaningful results with AI. To become AI-ready, organizations should establish data governance policies, manage the data lifecycle, and standardize data practices, while ensuring data remains accurate, accessible, and scalable.

Conclusion 

Ultimately, identifying problems that can be solved with AI is the first step toward AI readiness. It’s also essential for building employee buy-in for implementing AI and automation tools. Stay tuned for upcoming posts, where we’ll dive deeper into the topic of AI readiness in greater detail.