Your AI Survival Guide • Sol Rashidi & Joe Reis

Start small and be deliberate when adopting AI - avoid rushing into large initiatives without proper foundation and maturity assessment

Focus on internal use cases first before external customer-facing ones to minimize risks and build experience

Evaluate actual costs and ROI of AI workloads - many organizations aren’t properly tracking infrastructure/compute expenses

Don’t rely solely on vendor sales/marketing teams - speak directly with solution architects and engineers to validate AI capabilities

For most enterprises, leveraging managed services and pre-built models is more practical than building custom foundational models

Domain-specific AI models targeting specific business areas are likely to provide more value than general-purpose large language models

Organizational readiness, data quality, and infrastructure maturity are critical prerequisites for successful AI adoption

Open source AI tools work well for individuals/small companies but enterprises typically need commercial solutions for production

Cost consciousness and sustainability impacts of AI compute resources will become bigger considerations

Focus on solving real business problems rather than implementing AI for its own sake - identify clear pain points and use cases first