Editor’s note: The following is a guest post from Becky Carroll, global AWS GenAI strategic partnership leader at IBM Consulting.
When generative AI became widely available, organizations felt pressure to act. Leadership teams wanted to show they were using AI, leading some businesses to quickly launch pilot projects without a clear path to value.
Though some of these pilots succeeded, many stalled because they weren’t tied to business priorities and lacked a way to measure success. Now, there’s a shift toward a more thoughtful approach, focusing on AI use cases that solve real problems, deliver measurable results and lay the groundwork for long-term success.
Choosing the right first use cases can determine whether AI delivers real value or becomes just another experiment. Here are four key factors that CIOs and other technology executives should consider:
1. Start with use cases that already drive value
The most successful AI projects enhance processes that already drive business value. If leadership is already tracking how long a process takes, how much it costs or how accurate it is, then AI’s impact is easy to measure. This makes it much simpler to show results, get leadership buy-in and scale AI across the organization.
Early AI pilots often fail because they focus on new, untested ideas that sound promising but aren’t tied to clear business metrics. Without a baseline for comparison, it’s difficult to prove whether AI is making a meaningful difference.
Organizations that start with established, high-value use cases where success could be clearly measured are able to demonstrate value faster and secure long-term investment.
2. Solve a problem that teams currently struggle with
AI is more likely to gain traction when it improves processes employees already see as a challenge. If AI speeds up a slow process, eliminates repetitive tasks, reduces errors or even reworks a process, teams will see the benefits immediately.
This makes them more likely to trust the technology and advocate for its use in other areas or processes.
For optimal adoption, AI should integrate into existing workflows without adding complexity. If a solution forces teams to completely change how they work, adoption may slow, even if the technology is effective.
The best AI use cases improve efficiency while fitting into daily operations, making it easier for teams to adopt and for the organization to expand AI over time. That said, agentic AI can help to simplify and rework processes, so an organization doesn’t just automate a poor process.
3. Make sure your organization has the right data foundation
A critical barrier I consistently observe with clients is data readiness. Fragmented data ecosystems, quality issues and outdated infrastructure severely limit AI’s potential to deliver transformative business outcomes.
AI works best when it has the right support in place. High-quality data, strong infrastructure and clear processes ensure that AI produces reliable, consistent results. If the data AI is working with is incomplete, outdated or spread across disconnected systems, even a promising use case can fail to deliver value.
Organizations that successfully scale AI take the time to get their data in order first. By addressing data quality early, companies set themselves up for AI that delivers reliable results and creates a smoother path for broad adoption.
4. Have a plan for what comes next
When AI improves a process in a measurable way, it builds confidence and momentum. Teams see its value and start looking for other ways to use it. Leaders, seeing clear results, are more willing to invest in AI at a larger scale.
To scale AI effectively, organizations need to think ahead. This includes identifying new opportunities for AI, ensuring the necessary infrastructure to support more initiatives and setting up processes to measure AI’s impact over time. Planning for growth ensures that AI can continue to add value as it expands throughout the business.
The difference between AI becoming a strategic advantage versus a costly experiment lies in starting with the right use cases. By focusing on practical, high-value use cases that are already aligned with business priorities, you set the stage for early, measurable wins.
Small wins drive excitement and build momentum across teams, creating champions who advocate for AI and help it scale across the organization
