Agentic AI Redefines Enterprise: Builders vs. Users

The relentless march of artificial intelligence (AI) has profoundly reshaped the corporate landscape, infiltrating every facet of how modern enterprises strategize, operate, and engage with their clientele. This evolution, keenly observed during recent earnings seasons, underscores AI's integral role in driving business transformation. Moving beyond mere analytical capabilities, the next frontier—agentic AI—ushers in an era of autonomous execution, promising a paradigm shift in operational efficacy and innovation.

The Evolving Landscape of Enterprise AI Adoption

The opportunity presented by agentic AI is undeniably vast, yet its successful integration hinges on a critical factor: the precise alignment of AI initiatives with tangible, measurable business outcomes. Research from PYMNTS Intelligence highlights that while the potential is immense, strategies for leveraging AI diverge significantly across different industrial sectors, specifically distinguishing between firms that produce goods, provide services, or power the underlying technology for both. The transition to agentic AI, characterized by its capacity for autonomous decision-making and execution, necessitates a strategic re-evaluation of internal capabilities and, crucially, the cultivation of robust relationships with external vendors.

Divergent Strategies: Goods, Services, and Technology Sectors

The adoption patterns of agentic AI reveal distinct priorities and applications across industries, reflecting unique business imperatives and operational structures. A detailed survey of 60 enterprises by PYMNTS Intelligence offers granular insights into these varying approaches.

Goods Producers: Innovating at the Core

Companies engaged in the production of physical goods, including a wide array of merchants, are at the forefront of pioneering creative applications for agentic AI. A significant one-third (33.3%) of firms within the goods sector reported utilizing agentic AI primarily for product idea generation, design, and innovation. This statistic underscores a clear strategic imperative: leveraging autonomous AI to reimagine product development cycles, foster creativity, and accelerate the path from concept to market-ready offerings.

Services Firms: Enhancing Operational Efficiency

In contrast, services firms tend to prioritize operational efficiencies when deploying agentic AI. Approximately one-third (33.3%) of these companies rely on agentic AI for the automated generation of reports and deliverables, streamlining documentation and information dissemination. Furthermore, 20% utilize agentic AI for user and accessibility testing, enhancing the quality and inclusivity of their digital services. For services providers, AI functions primarily as a tool for automation, aiming to boost efficiency, reduce manual overhead, and optimize client interactions rather than fundamentally reinventing product offerings.

Technology Companies: Integrating Across the Lifecycle

Technology firms exhibit a more distributed application of agentic AI throughout their development processes. A third of these companies focus on user testing, another third on fostering innovation, and the remaining third on comprehensive product lifecycle management. This even distribution reflects the deep integration of AI into software development and testing environments, demonstrating how agentic AI becomes an embedded component of their core value proposition.

Currently, competitive analysis and customer experience research remain secondary priorities, cited by fewer than one in five companies as leading use cases. This suggests that enterprises are initially focusing on functions that yield immediate operational payoffs before expanding into more sophisticated intelligence gathering and customer insight initiatives.

The Indispensable Role of Vendor Partnerships in Agentic AI

The PYMNTS Intelligence data unequivocally illustrates a crucial dynamic: enterprises are not embarking on their agentic AI journeys in isolation. The vast majority depend heavily on external vendors to navigate the complexities of AI deployment. These partnerships are instrumental in managing critical aspects such as model training, ensuring data quality, and seamlessly integrating new AI tools with existing IT infrastructures.

• For goods producers, vendor collaborations are pivotal for supporting generative design processes, facilitating rapid prototype testing, and providing enhanced visibility across the entire product lifecycle.
• Services firms leverage vendors for robust workflow automation, sophisticated reporting engines, and advanced AI-driven analytics. This strategic outsourcing frees internal staff to concentrate on core client relationships and strategic initiatives.
• Technology companies benefit from vendors offering scalable frameworks that enable the seamless embedding of autonomous decision-making capabilities directly into their software platforms and solutions.

This growing reliance on vendor expertise reflects the inherent complexity of advanced AI implementation. Moreover, vendor collaboration plays a vital role in addressing one of the most significant barriers to effective AI: data readiness. Companies require consistent, well-governed, and high-quality data to train agentic models that can operate reliably without constant human intervention. The findings underscore that an effective data strategy is no longer separable from a successful AI strategy.

Real-World Impact: Insights from Big Tech Earnings

The pervasive impact of AI and agentic AI is evident in the recent third-quarter earnings reports from major technology and payments companies, showcasing how these dynamics are unfolding at scale for their enterprise clients.

• Amazon's quarterly results highlighted AI as a central pillar of its operations and customer experience. The company emphasized its Bedrock and Q platforms as essential tools empowering customers and partners to develop generative and agentic AI applications, focusing on enhancing logistics, search functionalities, and advertising performance.
• Mastercard's Q3 2025 results revealed the expansion of AI utilization across critical areas such as fraud detection, authorization decisioning, and network efficiency. AI underpins Mastercard's advanced risk management and analytics capabilities, significantly improving the accuracy and speed of transaction authorizations.
• Alphabet's Q3 2025 earnings demonstrated continued strategic investments in AI across Google Cloud and Workspace. Executives underscored how AI is being deployed to automate workflows and deliver faster, more actionable insights for their enterprise customer base.
• Visa's most recent report detailed AI's supportive role in authorization processes, comprehensive fraud management, and overall network performance. AI-driven models are noted for improving transaction accuracy and efficiency within the company's extensive global payment network.

Charting the Future of Agentic AI in the Enterprise

The forthcoming phase of enterprise AI adoption will serve as a crucial test of how effectively firms can bridge the gap between creative ideation and autonomous execution. The initial focus on product design and deliverable generation is poised to broaden, encompassing sophisticated applications such as in-depth market analysis, intricate lifecycle management, and dynamic competitive intelligence. As these advanced use cases emerge and mature, the demand for specialized vendor expertise will only intensify.

Enterprises destined for success in this evolving landscape will likely be those that meticulously define clear objectives, strategically select vendor partners capable of aligning cutting-edge technology with those objectives, and consistently measure the returns on their AI investments. The next wave of agentic AI adoption will ultimately be judged not merely by the sophistication of its models but by its tangible ability to fundamentally improve daily business operations and deliver measurable value.