Agentic AI: How Different Sectors Are Using AI to Transform Operations

Artificial intelligence (AI) is no longer just a buzzword—it’s increasingly becoming a core component of how companies plan, operate, and serve customers. As AI adoption accelerates, businesses are realizing that the key to its success lies not only in the technology itself but in how it aligns with measurable business outcomes. This is especially true for agentic AI, which moves beyond simple analysis to autonomous execution.

A recent PYMNTS Intelligence survey reveals how businesses across industries are using agentic AI differently based on whether they are goods producers, service providers, or tech firms. The journey toward AI adoption is far from uniform, with distinct strategies emerging depending on the nature of the business.

Different Paths for Goods, Services, and Tech Firms

The way companies implement agentic AI depends on their core operations. Here’s how different sectors are leveraging AI:

Goods Producers and Merchants

Firms in the goods sector, including merchants and manufacturers, are leading the way with creative applications of agentic AI. A third of these companies (33.3%) are using agentic AI to drive product innovation. This includes AI for product idea generation, design, and testing. For example, AI helps automate generative design processes and prototype testing, making it easier to create new products.

Service Firms

In contrast, service-based companies view AI primarily as a tool for operational efficiency. One-third (33.3%) of services firms use AI for report generation, while others rely on it for user and accessibility testing. These businesses focus on streamlining internal processes, such as automating documentation and enhancing customer interactions, rather than reinventing their core offerings.

Technology Companies

For tech firms, the use of agentic AI is far more integrated into the development lifecycle. These companies spread their use of AI across user testing, innovation, and product lifecycle management. The even distribution indicates how deeply AI is embedded in their software development and testing environments. While competitive analysis and customer experience remain secondary priorities, the focus on internal processes speaks to AI’s growing role in enhancing product performance and efficiency.

Vendor Partnerships: A Key Enabler of AI Adoption

An important finding from the survey is that most enterprises rely on vendors to implement agentic AI. While AI’s potential is immense, its implementation complexity means businesses are partnering with outside firms for model training, data quality management, and systems integration.

• For goods producers, vendors help with generative design, prototype testing, and product lifecycle management.
• Services firms lean on vendors for workflow automation, reporting engines, and AI-driven analytics that free up employees to focus on higher-value tasks like client relationships.
• Technology companies depend on vendors for scalable AI frameworks, enabling the integration of autonomous decision-making within their software platforms.

Vendors provide the expertise and technology infrastructure necessary for firms to adopt AI effectively. In particular, vendor relationships are essential for addressing the data readiness challenge. Companies need well-governed, consistent data to train models and ensure that their AI solutions can operate autonomously without human oversight.

AI at Scale: How Big Firms Are Using Agentic AI

As larger firms continue to embrace AI, we’re seeing how these dynamics play out at scale. Companies like Amazon, Mastercard, Alphabet, and Visa are at the forefront of integrating AI into their operations:

• Amazon highlights its Bedrock and Q platforms for helping customers build generative and agentic AI applications. Amazon uses AI to enhance its logistics, search performance, and advertising capabilities.
• Mastercard uses AI in fraud detection, authorization decisioning, and network efficiency, improving transaction accuracy and speed.
• Alphabet (Google) is investing heavily in Google Cloud and Workspace, where AI is used to automate workflows and deliver faster insights for enterprises.
• Visa relies on AI for authorization, fraud management, and network performance, helping improve the efficiency and accuracy of global transactions.

These companies are not only leveraging AI to improve internal operations but are also using it to enhance customer experiences across their platforms. The consistent use of AI across these big firms reflects its growing importance in both back-end processes and customer-facing applications.

The Growing Role of Vendor Expertise in AI Strategy

As businesses move into the next phase of enterprise AI, the role of vendor partnerships will only grow. Enterprises that succeed in adopting agentic AI will likely be those that align technology with clear business objectives and measure outcomes consistently. However, success in this next phase will depend heavily on selecting vendors who can provide the right AI models, frameworks, and data management systems to support long-term goals.

The continued evolution of AI in enterprise settings will require businesses to bridge the gap between creativity and execution. While current applications focus on product design and report generation, the next wave of AI adoption will see more use cases in areas such as market analysis, lifecycle management, and competitive intelligence.

“The next phase of enterprise AI adoption will be judged not by model sophistication but by its ability to improve how businesses operate every day,” said Matt Madrigal, Chief Technology Officer at Pinterest.

Why AI Success Depends on Clear Objectives

Ultimately, the future of agentic AI in business will hinge on objective alignment. Enterprises need to define clear goals for what they want to achieve with AI, choose vendors capable of supporting those objectives, and consistently measure returns on investment (ROI). AI adoption will be most successful when businesses focus on improving their day-to-day operations and aligning AI’s capabilities with specific business outcomes.

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