Procol launches Control Tower 2.0 with predictive AI for enterprise spend intelligence

In August 2026, Procol recently launched Control Tower 2.0, which is an AI-powered spend intelligence platform that is built on Multi-Agent Systems (MAS) to help procurement and finance teams analyze enterprise spending, monitor risks and compliance, and support decision-making.

The platform brings procurement data into a single intelligence layer, thus allowing organizations to analyze spending across suppliers, contracts, categories, business units, and various geographies. It also adds capabilities, including demand forecasting, should-costing, AI-assisted discrepancy detection, and enhanced risk and compliance monitoring.

Control Tower 2.0 can help to identify pricing anomalies, off-contract spending, and invoice mismatches while monitoring supplier risks and compliance gaps across the source-to-pay lifecycle. Its conversational AI interface allows users to query procurement data in natural language and receive contextual responses and visualizations.

Unlike traditional spend analytics platforms, which are focused primarily on historical reporting, Procol stated that the Control Tower 2.0 combines spend intelligence with predictive capabilities and connects insights with its AI-native procurement platform, thus allowing users to move from analysis to procurement action through connected workflows.

Impact on the AI Market

The rising importance of data security is estimated to drive the adoption of advanced enterprise data management market solutions. With the growing complexity of cyber threats, business have shifted their focus toward data protection to ensure that sensitive information is protected from hackers. Measures that are placed to safeguard data consist of strong encryption of the flows, enforcement of strict access rights in data management platforms, and surveillance mechanisms. Companies all over the world are seeking to implement effective solutions in order to protect their data.

Competition among the companies has led to a rapid introduction of artificial intelligence in the system in order to provide the best services and experience to the consumers. The business has been automated by using artificial intelligence in the analytical processes to manage huge amounts of data without error. In order to decrease the operational cost of the company, artificial intelligence has been considered as an option, which helps to increase the revenue return of the business. 

Impact on the Artificial Intelligence (AI) Infrastructure Market

The global artificial intelligence (AI) infrastructure market size accounted for USD 72.02 billion in 2025 and is predicted to increase from USD 91.21 billion in 2026 to approximately USD 518.26 billion by 2035, expanding at a CAGR of 21.82% from 2026 to 2035.

According to Precedence Research, the market has been expanding steadily due to the rising need for AI-driven solutions in industries including healthcare, banking, retail, manufacturing, and automotive. Because of their capacity for parallel computing, graphics processing units (GPUs) are frequently employed to accelerate artificial intelligence workloads. The need for artificial intelligence (AI) infrastructure is growing as companies in a variety of sectors incorporate AI into their operations to obtain insights, automate procedures, and improve decision-making. This covers both software frameworks and tools for AI development and deployment, as well as hardware like GPUs, TPUs, and specialist AI chips.

The demand for real-time AI inference and the growth of IoT devices have also led to an increasing focus on edge computing solutions. By allowing AI inference to be done locally on devices, edge AI solutions improve privacy and security while lowering latency and bandwidth needs. The artificial intelligence (AI) infrastructure market is expanding quickly, but it still faces several obstacles, such as interoperability problems, ethical dilemmas, skill shortages, and privacy difficulties with data. Resolving these issues will be essential to maintaining the market's long-term growth.

Impact on the Enterprise Data Management Market

The global enterprise data management market size is calculated at USD 124.93 billion in 2025 and is predicted to increase from USD 140.06 billion in 2026 to approximately USD 384.56 billion by 2035, at a CAGR of 11.90% from 2026 to 2035.

According to Precedence Research, the market growth is attributed to the increasing demand for scalable and efficient data management solutions. These solutions are very important in enabling real-time data access and integration to enable organizational efficiency in an advanced business environment. Furthermore, as growing organizations insist on cloud solutions, the market is expected to grow exponentially, owing to an increasing demand for flexible, secure, and easily accessible data storage services.

AI helps businesses in the enterprise data management market capture and process huge volumes of data faster, which helps them make decisions. The application of artificial intelligence improves the stewardship of data by maintaining the quality of data to be used in the formulation of strategies. Furthermore, AI contributes to the analytical forecast, which helps companies adapt in advance to alterations in the market or industry. They bring into play increased flexibility and adaptability, thus enabling organizations to cope with the rising tide of data-related competition.

Expert Opinion

“With Control Tower 2.0, we’ve expanded the platform beyond spend visibility by introducing predictive capabilities that help procurement and finance teams work with more timely, contextual intelligence,” said Gaurav Baheti, CEO and Founder, Procol. 

“Our focus is on enabling faster, more informed decision-making across the procurement lifecycle.”