2024-02-09
In 2023, experts estimate enterprises worldwide spent about $19.4 billion on generative AI (GenAI) solutions. Over that same year, the term “GenAI” emerged as a buzzword in C-suites and board rooms across industries, bringing the promise of revenue, operational efficiency, productivity and competitive gains.
Next-generation artificial intelligence captured the imagination of business leaders, igniting a wave of investment, with teams driven to produce headline-worthy results.
But despite the hype, many GenAI use cases fell short of ambitious growth expectations. Data maturity was a significant stumbling block. So, can GenAI still live up to huge expectations?
The irresistible appeal of GenAI
GenAI offers an exciting vision of a future where machines have human-like abilities, unlocking unprecedented progress across a huge range of functions and sectors.
We’ve already seen this technology in action, with tools like ChatGPT and Midjourney able to produce written and visual responses and analyze information at a level that seems (on the surface) to mirror our own.
GenAI has the potential to revolutionize industries, streamline operations and unlock new avenues for growth. C-suites, eager to stay ahead in a competitive landscape, are keen to embrace this transformative technology and secure their positions as industry leaders.
The harsh reality
We’re early in the GenAI journey. The “generative” in GenAI just means that it generates content. It creates digital things. And that creation comes with a level of unpredictability.
When we explore tools like ChatGPT closely, the expectation is not accurate answers to questions (that requires a model being trained on accurate, high-quality data), but plausible answers that don’t sound like gibberish at first glance.
The quality of what is generated is a wide spectrum, and is highly dependent on users to prompt the GenAI tool in the right way. The success of GenAI tools and the quality of responses are also closely linked to an enterprise’s commitment to data governance and ethics. Prompt engineering is becoming a key skill, acting as the programming language of GenAI and a methodology to achieve desired outputs.
It’s crucial for leaders to approach GenAI with a healthy dose of realism and to prioritize data governance and considerations in their implementation plans.
This may not always mean choosing the most glamorous or flashy AI solution. But with careful planning and a focus on practical use cases, GenAI has the potential to revolutionize businesses and drive long-term success.
