Misinformed, ambitious strategies
Thinking that AI is for everyone and that it solves everything.
Adoption of incorrect architectures
Over-informing yourself about AI can lead to poor decisions regarding the type of development, its cost-benefit ratio, and even jeopardize the user experience.
Oversupply of packaged solutions
Both hyperscalers and software vendors offer solutions to all problems. But the reality is that “one size doesn’t fit all.” The right architectures and frameworks must be evaluated.
Poor data training and ingestion
Rash developments without data science support, with decontextualized processes, no evaluation, and constant hallucinations.