As enterprises prepare for 2026, several trends are emerging that will shape the landscape of enterprise technology. Leading the charge is the adoption of all-you-can-eat agentic AI pricing models and the ongoing challenges with data access and monetization. While AI agents may appear more as features than groundbreaking innovations, the significance of physical AI is set to grow, particularly in manufacturing and industrial sectors.
High confidence predictions indicate a shift toward standardized pricing models for enterprise software. In 2025, concerns over unpredictable consumption-based pricing models led Chief Information Officers (CIOs) and Chief Financial Officers (CFOs) to demand more predictable solutions. Salesforce’s Miquel Milano, president and chief revenue officer, outlined the benefits of the Agentic Enterprise License Agreement (AELA), designed for businesses ready to scale their AI initiatives. “AELA is for customers that have already experimented. They’re ready to scale. They want to go all in so we agree on a flat fee, and then it’s a shared risk,” said Milano. This model allows companies to utilize various offerings under one agreement, making it financially advantageous for both vendors and clients.
However, it’s critical to note that while such agreements may entice enterprises, SaaS providers could enter these contracts at a loss, betting on future renewals to recover costs. Milano emphasized that if a deal is unprofitable for the vendor, it suggests that the customer is satisfied and may lead to long-term financial gains. With the prevalence of these pricing structures expected to rise, SaaS providers may soon find themselves in a competitive race to lock in customers.
Another pressing concern for enterprises will be navigating the complexities of data access. The legal battle between Celonis and SAP over data access illustrates the mounting tension surrounding data ownership and monetization. A recent ruling could set the stage for similar conflicts in 2026, as companies grapple with new data tolls and potential API costs. As agentic AI technology becomes more integrated within enterprise systems, the cost of connection fees may pose significant challenges, potentially hindering the scalability of AI initiatives.
Moreover, the concept of agentic AI may increasingly be regarded as merely a feature, with the true value lying in enhancing decision velocity. Michael Ni, an industry expert, stressed the importance of automating smaller decision processes, which can lead to significant efficiency gains. “What we’re seeing is the first decisions are smaller decisions, automations on the backside of that leading to human engagement,” noted Ni. This approach aims to streamline operations and facilitate rapid decision-making, marking a pivotal shift in how enterprises leverage AI technologies to drive productivity.
Market Dynamics and Predictions
Medium-confidence predictions suggest a bifurcation in the AI market, potentially occurring as early as 2026 or 2027. As capital expenses in AI infrastructure begin to rise, enterprises may shift focus away from traditional AI solutions and look toward building custom applications tailored to their specific needs. This shift is further fueled by rising dissatisfaction with escalating SaaS costs, prompting organizations to seek alternatives to off-the-shelf solutions.
AI’s applicability is expected to broaden across the enterprise stack as software vendors demonstrate tangible revenue improvements driven by AI. Wall Street may see enterprise software companies joining the AI rally, while firms like Nvidia could face stagnation amid increasing reliance on customized silicon. Concurrently, the industrial sector is poised to harness physical AI capabilities, propelling advancements in robotics and edge AI applications.
While some predictions for 2026 remain uncertain, including Nvidia’s stock performance and the overbuilding of AI infrastructure, others stand on firmer ground. The landscape is likely to witness significant initial skepticism toward new AI investments, as well as a growing demand for in-house forward-deployed engineers. These engineers will be critical in leveraging AI to streamline data processes and enhance automation.
As the technology landscape evolves, the year 2026 is shaping up to be pivotal for enterprises navigating the challenges and opportunities posed by advancing AI. The interplay between AI innovation, data access, and operational efficiency will define the next chapter in enterprise technology, marking a transition that promises both challenges and significant potential for growth.
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