In a world inundated with AI buzz, are you feeling overwhelmed by the incessant chatter around artificial intelligence? As the AI frenzy reaches its zenith, it’s imperative that we take a collective breath and evaluate how to get to a positive impact for our organization. Let’s pause and reevaluate the landscape.
Neil Ward-Dutton, one of our distinguished IDC analysts, once aptly noted that AI appears as an enchanting spell until we unravel its inherent limitations and complications. And limitations, there are many, regardless of how you approach the matter. A considerable portion of these limitations stem from the data that fuels AI’s algorithms: for AI systems developed internally, the scarcity of high-quality data in most organizations data often proves to be a stumbling block; and for generative AI systems that leverage pre-built “foundation models” already trained on external data sources, lack of transparency about the provenance and quality of those data sources creates a number of risks.
While generating training data is an option, recent observations indicate that excessive training can actually yield adverse outcomes. And that’s just the tip of the iceberg; the complications extend into the realms of bias and ethical quandaries.
Dispelling any illusions, let’s be clear – acquiring a software package won’t instantaneously immerse your organization into the realm of AI excellence. Remember the CIO who, a few years back, joyfully declared hiring two data scientists as the path to “getting AI”? Upon inquiry about their role and the benefits they’d bring, he confessed, “I’m not a data scientist; I don’t know.” Such anecdotes underscore the essence of the issue.
Presently, history seems to be repeating itself. When inquired about AI’s potential, responses often resemble, “I don’t know; I’ll ask the AI.” This reveals a common theme – many are intrigued by AI-enabled possibilities, but grappling with its tangible advantages remains a challenge. As the chasm between curiosity and efficacy widens, it begs the question: Is the investment in perfecting AI worth the monumental effort?
We have been defining what is needed to be successful with AI and the steps needed to make it work. We invite you to come and discuss with your peers what steps make sense and where to hold back inverstment. By the end of the session you should have insight into the value you can realisticly derive from AI over the next three horizons and what the pitfalls may be.