With Generative AI promising intelligent agents in enterprise software there is a significant acceleration in the desire of businesses to automate processes and augment, or even replace, human decision-making. As companies race to implement agents with their promise of speed, scale, and efficiency, there's a risk of prioritizing problems based on their ability to be solved with this technology, rather than on their impact on the business.

Design pundits have been selling design short for years. Don’t take my word, a your favorite genAI tool to create a description for “Design Strategy”. I will wait. Keep in mind these generated descriptions are based on based on what is in the world puling from articles, papers, books and posts. Its no wonder design struggles; we are our own worst biographers.

"People don’t buy technology; they buy what it enables them to do." This simple truth highlights why GenAI, like many technologies before it, has such high failure rates. In fact, a Rand report estimates up to 80% of GenAI projects fail largely due companies focus on the latest tech rather than solving real problems for their users. I examine the five root causes of AI project failures and how design can mitigate these risks, accelerating adoption and long-term success.