Developing your GenAI strategy on your own: is it the right decision?

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In an era marked by rapid technological change, businesses are forced to explore new horizons in AI, especially as generative AI (GenAI) reshapes competitive landscapes.

A recent BCG study revealed that out of 1,400 executives surveyed, 89% cite GenAI as one of the top three organizational priorities. Yet, surprisingly, 90% are still in the observation phase: watching, analyzing, and waiting to see what happens.

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This warning is not entirely surprising. We have rcs data lebanon a similar hesitation in the adoption of Open Talent strategies. The rise of GenAI, like the dawn of the Internet and the smartphone, represents a strategic rather than a mere tactical innovation, requiring a significant rethinking of organizational structures and operations.

The iPhone, the gig economy, and even COVID-19 brought about change, but not enough to prompt widespread organizational restructuring. Now, GenAI has sounded the alarm and demands a response. But should companies radically reinvent themselves to adapt to AI, or should they take a more gradual approach?

According to experts, companies don’t need to completely overhaul their systems. Instead, they could leverage the advanced capabilities of their vendors that embrace AI in their platforms. The message? “Don’t make your own AI strategy.” By leveraging these partners, companies can experience the benefits of GenAI while gradually evolving their strategies. Leaders can report generative AI integration to their boards, meet productivity goals, and improve the bottom line without risking a high-stakes reinvention.

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Why the “Don’t Do It Yourself” Strategy Works for Generative AI Adoption
Collaborating with external vendors rather than going all-in on a DIY AI strategy isn’t just about avoiding risk. This approach leverages proven AI systems built by teams whose sole focus is AI refinement and improvement, meaning companies can avoid some of the common problems of DIY AI: incomplete understanding of model limitations, lack of internal AI talent, and the significant costs of building an AI system from scratch.