Start small, think big: Run low-risk pilot projects focused on narrow problems so teams gain familiarity with generative AI capabilities before tackling broader initiatives.

Select appropriate tasks: Match generative AI to tasks well-suited to its strengths like content generation, data analysis, personalization, and predictions versus tasks requiring real-world knowledge or reasoning.

Supplement training data: While pre-trained models are remarkably capable out of the box, providing industry or company-specific data during fine-tuning focuses outputs better for business contexts.

Embrace rapid iteration: Generative AI allows swiftly creating and refining content. Take advantage of this to rapidly iterate on materials like marketing assets, support documents, and product copy.

Make it consumable: Generative AI often produces verbose outputs. Invest time into distilling and reformatting outputs into easily digestible assets like summaries, briefs, and highlights tailored to stakeholders.

Augment human creativity: Use generative AI to help ideate and brainstorm more diversely, then have humans curate the most relevant ideas suited for further development. This balances AI's volume with human discernment.

Automate carefully: When applying generative AI to automate workflows, carefully validate required output quality. Thoughtfully calibrate handoff points between automation and human review.

Enhance data value: Use generative AI's analytical abilities to extract more insights from data. Summarize key trends, surface non-obvious correlations, and generate personalized data perspectives.

Listen to users: Capture feedback on generative AI quality from real users, not just developers. Continuously fine-tune based on feedback to address pain points and improve user satisfaction.

Enable collaboration: Use workspaces or other access models that empower teams to efficiently share, discuss, and refine generative AI outputs relevant to their roles and objectives.

Plan for upgrades: Given rapid advances in generative AI, expect to reassess capabilities every 6-12 months. Budget for upgrades to take advantage of new features and higher performance.