When generative AI catapulted to fame in late 2022, hopes were
limitless. It was going to supercharge teams by automating drudgery.
It was going to unlock new business models, unearth revenue
streams and lead to unfettered productivity. It was going to cure
diseases and save lives. Gen AI was going to change everything.
Fast-forward to today, and gen AI has changed many things — but
some of its biggest promises are still more aspiration than reality.
While nearly two-thirds of C-suite executives say trust in AI drives
revenue, competitiveness and customer success, according to a
Salesforce survey, Harvard Business Review reports that up to 80%
of AI projects fail to make it into production.
So why the disconnect? When we examine the obstacles
organizations face in bringing their gen AI use cases into production,
it’s not hard to understand why many are falling short.
Roadblocks include:
• The cost and complexity of managing multiple AI
platforms — and then moving and duplicating data across
all these infrastructures.
• The operational burden of democratizing access to AI,
equipping the right people with access to the right tools
and the right training.
• The governance requirements to ensure that data and
large language models