ADOBE AI FOR BUSINESS

Agentic AI refers to artificial intelligence systems, or AI agents, that can interact, reason, and act autonomously or semi-autonomously. Agents can evolve from single-step responses to multi-step reasoning, and from directed to automated activity. They can grow from a foundation of generic, public data to proprietary, contextually relevant data.

Generative AI focuses on creating content — like text, images, or code — based on input prompts. Generative AI models analyze the vast amount of content, databases, and platforms they have been trained on to predict the best fit, and they create an output based on a user-entered prompt.

Predictive AI analyzes historical data and uses statistical algorithms to classify, label, and make predictions on new, unseen data. It anticipates what’s next based on what’s already happened. It’s powered by machine learning models and generates probabilities or ranked predictions rather than guaranteed outcomes. Use cases include customer churn prediction, audience look-alike modeling, and forecasting future sales trends to inform marketing strategies.

Adobe has been investing in predictive AI for over a decade, initially using machine learning in Adobe Analytics and Adobe Target. Today, predictive AI is used across creative and marketing applications to provide insights-driven automation, intelligent ideation, and optimized journeys.

At Adobe, agentic-first and standalone applications use one or more Adobe Experience Platform Agents to provide immersive customer experiences, such as Adobe Brand Concierge. Agent-first tools use one or more Adobe Experience Platform Agents to provide new or enhanced capabilities in Adobe applications, such as Adobe Experience Manager Sites Optimizer.

Predictive AI is a type of artificial intelligence that builds on machine learning technology to analyze data so you can identify trends and predict outcomes.

Generative AI creates content — text, images, code, video, and audio — from prompts. It focuses on content creation, expression, and summarization.

Agentic AI is a goal-driven system that can take action autonomously with limited human interaction to achieve outcomes. It focuses on action, decision-making, and single- to multi-step execution. Agentic AI is the culmination of generative AI and may use both predictive and generative models. These models can be used to make inferences or create content while pursuing a specified goal, like launching a campaign or responding to customer feedback.

Enterprise AI refers to the integration of artificial intelligence into the operations, systems, and strategies of large organizations. Unlike general AI applications, such as chatbots or voice assistants, enterprise AI is built for scale, security, and performance to address a variety of complex business needs.

Enterprise AI can:

• Automate repetitive tasks such as invoice processing and customer support.
• Provide predictive insights such as demand forecasting and churn prediction.
• Optimize supply chains, marketing campaigns, and sales strategies.
• Enable real-time decisions based on large data sets.

Companies report increased productivity, cost savings, and better customer experiences as benefits of adopting enterprise AI solutions.

Yes, when implemented correctly. Enterprise AI platforms like Adobe are designed with data governance frameworks, role-based access controls, compliance with regulations like GDPR or HIPAA, and encrypted data pipelines. Organizations need to perform ongoing audits and AI model monitoring to prevent misuse or bias and detect model or data drift.

Every industry can benefit from enterprise AI. Some of the top adopters of enterprise AI include:

• Finance for fraud detection and risk modeling.
• Healthcare to support diagnostics and patient management.
• Retail and ecommerce to increase personalization and simplify inventory forecasting.
• Manufacturing for helping with predictive maintenance and quality control.
• Logistics and supply chain to suggest route optimizations and support demand planning.

Start with a clear business problem to solve. Then you can:

1. Assess.  Evaluate organization readiness and select AI technology that is developed responsibly.
2. Pilot.  Identify and prepare priority use cases and pilot them against business and ethical AI criteria.
3. Adopt.  Train and enable the organization, then deploy with brand or organizational guidelines in mind.
4. Monitor.  Measure performance against business and responsible AI benchmarks for ongoing risk management.

Learn more in The AI Inflection Point