Enterprise AI built into CRM for business

Salesforce Artificial Intelligence

Salesforce AI delivers trusted, extensible AI grounded in the fabric of our Salesforce Platform. Utilize our AI in your customer data to create customizable, predictive, and generative AI experiences to fit all your business needs safely. Bring conversational AI to any workflow, user, department, and industry with Einstein.

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AI Built for Business

Enterprise AI built directly into your CRM. Maximize productivity across your entire organization by bringing business AI to every app, user, and workflow. Empower users to deliver more impactful customer experiences in sales, service, commerce, and more with personalized AI assistance.

45 %
of executives
believe AI can negatively impact their organization's trust without appropriate risk management.*
73 %
of employees
believe generative AI introduces new security risks for their company.**

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AI Strategy FAQ

This AI strategy guide provides a framework and practical steps for organizations to plan, implement, and scale artificial intelligence initiatives effectively.

An AI strategy ensures that AI investments align with business objectives, mitigates risks, optimizes resource allocation, and fosters a data-driven culture for sustainable growth.

Key components include defining AI goals, assessing current capabilities, building a data foundation, selecting appropriate technologies, developing talent, and establishing governance and ethics.

A robust data strategy focusing on data quality, accessibility, integration, and governance is foundational, as AI models rely heavily on clean and relevant data for training and performance.

Leadership is critical for setting the vision, allocating resources, fostering cross-functional collaboration, driving cultural change, and championing responsible AI adoption.

A comprehensive AI strategy integrates ethical guidelines, fairness principles, transparency mechanisms, and accountability frameworks to ensure AI systems are developed and used responsibly.

Avoid common pitfalls such as siloed AI initiatives, lack of executive buy-in, ignoring data quality, underestimating change management needs, and failing to define clear success metrics.