Key Points

• Generative AI (Gen AI) creates outputs, agentic AI delivers outcomes. Gen AI excels at generating text, images, and code, while agentic AI goes further by reasoning, making decisions, and executing tasks.
• Both offer powerful but distinct business value. Gen AI is driving creativity and efficiency across functions like marketing, service, and product development, while agentic AI is emerging as a game-changer for automation, customer support, and operations.
• The shift to agentic AI is taking place at a rapid pace. Companies should start preparing by experimenting with simple agents, mapping automation-ready workflows, and building governance to manage risks responsibly.

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A new phase in the evolution of AI has arrived, marked by a growing debate between generative AI and agentic AI, a discussion that stands to redefine the very foundation of digital transformation.

For years, generative AI (Gen AI) has captured attention with its ability to create text, code, images, and more on demand. With the right prompting, Gen AI can deliver outputs that feel original and human-like, unlocking the potential of artificial intelligence to achieve new levels of creativity and productivity, for a range of applications across industries.

Now, agentic AI is grabbing the center stage. Unlike generative AI, which focuses on outputs, agentic AI is built to reason. It makes decisions and takes action to deliver on specific goals, shifting the conversation forward from what-AI-can-create to what-it-can-achieve.

This transition is redefining how organizations think about the role of AI in work, innovation, and continuous transformation. 

With this in mind, let’s break down the difference between Gen AI and agentic AI, and what it means for the way we work, build, and solve problems. 

What Is Generative AI?

Generative AI systems create new content based on patterns learned from often large datasets.

Core capabilities include: 

• Content creation: drafting articles, marketing copy, or creative writing
• Code generation: assisting developers with snippets, debugging, or even full applications
• Image and media generation: producing visuals, designs, or synthetic voices
• Summarization and translation: condensing long texts or breaking down information across languages
• Conversational interaction: powering chatbots and virtual assistants with natural language responses

With the breadth of tasks and outputs this technology can deliver, it’s not surprising that the use of Gen AI has spiked since early 2024. Today, 71% of organizations say they regularly use Gen AI in at least one business function, up from 65 percent at the start of 2024.

Business functions that have seen the highest adoption include marketing and sales, product and service development, service operations, and software engineering. And in many areas, generative AI is already driving real-world value, showing how it can redefine and reshape entire industries. 

In healthcare, for example, generative models are advancing diagnosis, personalizing treatment plans, and driving innovation in drug discovery for faster development of medicines. In manufacturing, it’s accelerating production by generating design prototypes, while facilitating proactive maintenance by predicting equipment failure. 

However, Gen AI does come with limitations that enterprises need to be aware of. The most common include:

Hallucinations 

AI may generate confident but inaccurate or fabricated information. This makes Gen AI risky to rely on for tasks that require factual accuracy, such as legal, medical, or financial advice.

Bias and Ethics

Models can reflect or amplify biases present in their training data. This can lead to outputs that unintentionally reinforce stereotypes or exclude certain perspectives, raising reputational and ethical risks.

Lack of Reasoning 

Gen AI is strong at recognizing patterns and probabilities, weaker at logic and multi-step problem-solving. As a result, it can struggle with tasks that demand true critical thinking, such as troubleshooting or strategic planning.

Context Dependence 

Outputs rely on prompts and can vary widely in quality. A poorly phrased request can lead to irrelevant or low-value results, putting more responsibility on the user to steer the AI effectively.

No Agency 

Generative AI creates, but it does not act or make decisions toward outcomes. This limits its role to an assistant rather than an autonomous problem-solver, requiring humans to bridge the gap between ideas and execution.

What Is Agentic AI? 

Agentic AI systems, on the other hand, go beyond generating content – and the ongoing discussion around agentic AI vs generative AI highlights how this shift is redefining the value of AI in business.

Most agentic AI systems are built on top of generative AI models, adding layers of reasoning, planning, and autonomous action to require minimal human input. In this way, they operate more like an intelligent assistant or digital co-worker.

Core capabilities include:

• Autonomous action: carrying out tasks without constant human input
• Multi-step reasoning: breaking down problems, planning, and adapting strategies
• Tool use and orchestration: interacting with external systems such as APIs, software, and workflows
• Goal-oriented behavior: working toward defined outcomes rather than just producing responses
• Collaboration: working alongside humans or other agents to complete complex workflows.

Agentic AI is gaining significant traction because of these abilities. In a May 2025 PwC survey, 88% of senior executives said they plan to increase AI-related budgets in the next 12 months due to agentic AI. In addition, 79% said AI agents are already being adopted in their companies. 

Many of the enterprises deploying agentic AI solutions within their business functions are using it to optimize operations and enhance efficiencies. 

Many are also harnessing agentic AI to improve customer experience by handling complex customer queries, analyzing data to understand the root cause of issues, and using its reasoning capabilities to provide solutions on behalf of human agents. By 2029, it’s expected that agentic AI will autonomously resolve 80% of common customer service issues without human intervention.  

However, even with its vast potential to streamline customer support and other business functions, agentic AI comes with its own limitations and risks. 

The biggest ones are:

Reliability 

Outputs and actions can still be error-prone, especially in complex environments. This means that without careful monitoring, an agent could make mistakes at scale – amplifying problems instead of solving them.

Oversight Required 

Human governance is still essential to ensure that agent behavior aligns with business goals, safety standards, and compliance requirements.

Resource-intensive

Agentic systems often require more compute and integration with external tools. Building and maintaining these can be costly and technically demanding, limiting accessibility for smaller organizations.

Ethical and Safety Concerns 

When AI agents act independently, this raises questions around accountability. If an autonomous agent causes harm or makes a poor decision, responsibility is not always clear, raising legal, ethical, and trust issues.

Early-stage Maturity 

Agentic AI is emerging, and many of its use cases are still experimental. Organizations must balance the promise of innovation with the reality that many solutions are not yet stable, scalable, or proven in production.

Generative AI vs Agentic AI: What’s the Difference?

While both generative AI and agentic AI stem from the same technological foundation, they serve very different purposes. 

Here are the key differences:

Aspect	Generative AI	Agentic AI
Primary Focus	Producing outputs that inform, inspire, or assist	Driving outcomes that deliver measurable results or completed workflows
Examples	ChatGPT (text), DALL·E / Midjourney (images), GitHub Copilot (code)	AutoGPT, Devin (AI software engineer), workflow automation agents, AI-powered support bots
Benefits	Fast, creative, versatile, user-friendly, scalable across industries	Goal-oriented, proactive, capable of handling complex workflows with less human intervention
Best Use Today	Drafting, brainstorming, augmenting human creativity, speeding up routine tasks	Automating workflows, executing multi-step tasks, enabling “hands-off” problem-solving
Shift in Value	From novelty in generating what’s possible	To utility in achieving what gets done

Use Cases for Agentic AI vs Generative AI

Generative AI Use Cases

The list of Gen AI use cases continues to grow as its capabilities expand. Right now, a few of the top use cases across functions, organizations, and industries include:

Customer Service and Chatbots

• Business impact: Enables faster, more natural responses to customer inquiries, reducing wait times and improving satisfaction.

Education and Training Support

• Business impact: Provides personalized learning materials, tutoring, and simulations, making knowledge more accessible.

Multilingual Communication

• Business impact: Breaks down language barriers, making global collaboration and customer engagement more seamless.

Agentic AI Use Cases

Agentic AI use cases are gaining momentum and becoming increasingly sophisticated. Some critical examples include:  

Workflow Automation

• Business impact: Agents can complete multi-step processes such as updating systems, sending approvals, filing reports, or changing passwords without human intervention, saving time and reducing errors.

Market and Competitive Intelligence

• Business impact: Agents can continuously scan sources, track trends, and alert teams proactively, ensuring businesses stay ahead of shifting landscapes.

Supply Chain and Operations Management

• Business impact: AI agents can monitor inventory, predict demand, and trigger replenishment actions automatically, reducing costs and avoiding disruptions.

The Path Forward: Embracing Agentic AI

Agentic AI represents the next major shift in artificial intelligence. Businesses that prepare now will be best positioned to capture its value. 

Companies can start preparing by: 

• Experimenting with simple task-oriented agents 
• Mapping out workflows that could benefit from automation 
• Strengthening governance frameworks to manage oversight, ethics, and risk. 

By investing early in integration, data readiness, and responsible AI practices, organizations can move confidently into an agentic future where outcomes, not just outputs, define competitive advantage.

Sutherland Can Help Your Business Stay Ahead

Having a strategic partner with the expertise and market-leading solutions at hand will bolster this strategic edge.

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