Companies that went hunting to be the first to adopt the next big thing in generative A.I. did so on the presumption that because it was so user-friendly it would also be easy to implement. That has turned out not to be the case.

Incorporating generative A.I. for strategic purposes, in even straightforward applications, has so far proven resource- and capability-intensive. Accounting for data security, company-specific functionality, and a miniscule global talent pool, has meant only the big players have been able to even attempt meaningful pilot programs beyond basic use cases (and those use cases don’t create substantial differentiation from the competition). That’s the exact opposite of the future the technology appeared to promise.

Because the barrier to entry is higher than expected for generative A.I., it’s even more important for companies to develop a strategy to maximize its potential while not wasting valuable resources. That strategy will need to take advantage of what generative A.I. offers now, but also must take account of what it could offer in the future. Jumping on the bandwagon to secure short-term gains could lead to unintended consequences and long-term costs.

Making a strategic assessment can be difficult, given that the scale of the disruption could very well outpace our imaginations. Here are four strategic moves executives should make to define and future-proof their generative A.I. strategy.