By Aaron Wright
Many companies struggle with how to scope their generative Al approach. In a recent Kyndryl and Altman Solon study on generative AI in the telecommunications sector, roughly 80% of executives surveyed reported exceeding their budgets when scaling their generative Al projects, with a third overshooting by up to 50%.
My role is to help our customers develop strategies that align their generative Al investments with their broader business outcomes. A major part of this work involves identifying possible obstacles to ROI early in the process to ensure that teams are mapping out the true costs and timelines required for these projects.
Here are some of my key takeaways from the challenges we’ve helped teams work through to scale their generative Al approach for success (and within budget).
Assess goals and data foundations
Scoping your generative Al approach starts with an honest conversation around goals and expectations.
My team often starts with questions like: What do you find to be the most intriguing aspects of generative Al? Where do you think they might apply to your business? And what have you done so far to start working towards these use cases?
Even basic lines of inquiry like these can reveal the bottom-line business outcomes that should provide the backbone for your investment, as well as the more practical obstacles standing in your team’s way.
For example, a common challenge many companies encounter early on is gaps in their data and Al foundations.
While it’s usually easy to envision the end goals of your investment — such as chatbots to personalize customer interactions or streamlined warehouse ops — it’s inevitable that before all that, your team will need to do some slightly less sexy work on your existing data infrastructure.
Our research with Altman Solon, for example, found that industry leaders who achieved greater success in scaling generative Al tended to focus on key areas such as:
- Access to quality data assets
- Scalable data infrastructure
- Efficient data integration
It is hard to overstate the value of this foundational legwork. Your data and data architecture are the driving forces behind your generative AI play and, therefore, must be capable of meeting its demands.
To borrow a metaphor from my colleagues: think of generative AI as a car, with your data architecture as the engine and your data as the fuel. Just as a car needs a reliable engine and the right fuel to perform efficiently and safely, generative AI needs a strong data architecture and clean, quality data to function successfully.