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

Predictive AI analyses 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 is powered by machine learning models and generates probabilities or ranked predictions rather than guaranteed outcomes. Use cases include predicting customer churn, creating audience look-alike models and forecasting sales trends to power 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 optimised journeys.

At Adobe, agentic-first and stand-alone 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 analyse 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 summarisation.

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 specific 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 organisations. Unlike general AI applications, such as chatbots and voice assistants, enterprise AI is built for scale, security and performance to address a variety of complex business needs. Read this guide to learn more about how enterprise-grade generative and agentic AI can help marketing teams drive growth.

Enterprise AI can:

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

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. Organisations need to regularly audit and monitor AI models to prevent misuse or bias and detect model or data drift. You can learn more about how Adobe approaches responsible and safe AI here.

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

• Finance, for fraud detection and risk modelling.
• Healthcare, for diagnostics support and patient management.
• Retail and ecommerce, for increasing personalisation and simplifying inventory forecasting.
• Manufacturing, for enhancing predictive maintenance and quality control.
• Logistics and supply chain, for route optimisation suggestions and demand planning support.

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

1. Assess. Evaluate organisation 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 your organisation, then deploy with brand or organisational guidelines in mind.
4. Monitor. Measure performance against business and responsible AI benchmarks for ongoing risk management.

Learn more in The AI Inflection Point