451 IT Insider October

In a world where technological advancements dictate the pace of progress, the AI revolution stands at the forefront, challenging industries to adapt or be left behind. As businesses grapple with the complexities of integrating cutting-edge AI solutions, the stakes have never been higher. This report cuts through the noise, offering a critical examination of the AI landscape, where innovation meets reality, and the path to sustainable growth is paved with both opportunities and obstacles.

Big Picture Takeaway

Global markets have reached a critical inflection point, where the rapid evolution of AI demands not only robust investment in infrastructure and talent but also a strategic recalibration of risk management practices. As adoption accelerates across businesses of all sizes, companies must balance innovation with pragmatic operational improvements to safeguard long-term value. The fundamental challenge lies in navigating a landscape marked by disruptive opportunities alongside complex integration and security hurdles.

AI in Focus: Generative AI software market in 2025: Revenue trajectories and the bubble question and Enterprise AI demand drives tech spending amid uncertainty

All eyes are on the AI market, with significant attention on what what some view as a growing AI "bubble." The market is entering a period of intense scrutiny, which brings us to a central question: Can AI vendors build a revenue trajectory robust enough to justify AI infrastructure investments and sustain their high rates of cash burn? The October 2025 Generative AI Market Monitor & Forecast provides an opportunity to examine this question. It tracks 483 GenAI model and software vendors — specifically, companies whose core value proposition centers on GenAI capabilities, rather than those that simply incorporate GenAI features into broader products. It provides insight into vendors that offer general-purpose AI capabilities to address a wide array of use cases and customer segments, and that are already generating revenue or have an active client pipeline. The GenAI industry will likely be unable to meet its proposed infrastructure investments without a significantly accelerated revenue trajectory. Aggregate revenue in the GenAI software market is projected to reach $103 billion by 2029 — a CAGR of 39.3% from 2024 to 2029. However, these figures seem out of sync with studies that suggest trillions of dollars in annual revenue will be needed to fund the computing power to meet anticipated AI demand by 2030.

Figure 1: Global Generative AI Market Forecast

Intent to spend on technology among US businesses and consumers flattened during the third quarter of 2025, showing signs of recovering after a significant decline from Q1 to Q2, according to 451 Research's US Tech Demand Indicator, a survey-backed composite of intent to spend on technology. US trade policy continues to affect the global economy and, by extension, organizations' IT spending plans. While the impact differs by technology category and region, we expect its influence to continue well into 2026. According to 451 Research's Macroeconomic Outlook, SME Tech Trends, AI Adoption 2025 survey, global tech spending is looking up, likely driven by multiple factors including tariffs, renewals and AI disruption. Global IT spending shows a bullish outlook for early 2026, though this optimism varies significantly by region. US organizations remain notably more cautious than their international counterparts, with only 18% expecting budget increases, compared to 43% of non-US respondents. This divergence appears linked to tariff impacts, particularly affecting hardware-centric segments such as IT infrastructure, employee devices and IoT.

Figure 2: US tech spending outlook remains positive despite policy volatility

AI adoption continues to gain momentum across organization sizes, with 53% of SMEs and 61% of large enterprises reporting usage or proof-of-concept initiatives. SMEs on average plan to allocate 18% of total IT budget to AI spending in the next 12 months, while large enterprises project an average of 26%, indicating substantial commitment to these technologies. Broad interest in advanced capabilities such as GenAI and agentic AI, and expected benefits across use cases and departments, suggests organizations view these tools as important drivers of productivity and operational efficiency. However, implementation challenges around cost, skills and security remain significant barriers.

SMEs with an established digital transformation strategy appear better positioned to navigate market pressures, showing stronger spending intent and higher AI adoption rates. This suggests that strategic digital planning may help organizations balance external economic pressures with technology investment priorities.

Emerging Tech: Exploring the adoption of a data science platform for Agentic AI

The ability to enable generative and predictive AI development, deployment and management in the same platform is being pursued by data science platform providers. And the appeal of this approach is recognized by enterprises. Slightly more than one-third (34.2%) of responding organizations will use a data science platform already in use to put generative AI into production, according to 451 Research's Voice of the Enterprise: Data Analytics, Generative AI 2024 survey. Here we examine the critical functionality required in a data science platform to effectively use it as an AI agent creation, deployment and management environment.
 

Figure 3: US tech spending outlook remains positive despite policy volatility

Agentic AI gives data science platforms fresh relevance and a stake in a high-growth market. Generative AI market revenue is projected to increase at a 40% compound annual growth rate from 2024 to 2029 and reach $85 billion. However, the requisite functionality is quite expansive because it must address all phases of the AI agent development, deployment and management process. Data scientists and developers know their way around a data science platform, and with this familiarity comes expectations that the same level of functionality will be available for agentic AI. Fulfilling this expectation can be hard for some players because agentic AI is outside of their core competency in data science. Organizations considering using their existing data science platform for AI agent development, deployment and management, need the benefits to outweigh the challenges. A relationship with an existing supplier and the advantages that brings, including influencing product development, potentially being able to negotiate better pricing and a reduction in the integration burden, are appealing upsides; the potential for vendor lock-in is the main downside.

M&A In Focus

Veeam pays $1.73B for Securiti AI, aiming to securely enable enterprise adoption of GenAI. Privately held data protection vendor Veeam has acquired Securiti AI, a data security posture management provider, for $1.73 billion. The buyer intends to combine its data protection suite with Securiti AI's data security offerings to enable enterprises to better leverage their data in primary and secondary storage systems so they can safely implement generative AI. While it makes sense for two vendors like Veeam and Securiti AI to integrate their offerings to address enterprise risk, greater opportunities exist to capitalize on the rewards that enterprises are seeking to achieve by leveraging their data. With a projected compound annual growth rate of 40%, generative AI remains a massive prize for enterprises looking to generate content and code, or simply drive better automation and productivity. However, data privacy and security risks are the top current challenges that organizations face when adopting GenAI, according to 451 Research's Voice of the Enterprise (VotE): AI & Machine Learning, Use Cases 2025 survey. These are the strategic drivers behind Veeam's decision to reach for Securiti AI — the secure implementation of GenAI and safe utilization of enterprise data are becoming increasingly enticing buying triggers for their joint customer base.

10 Cool Companies

HashiCorp launches Terraform updates, reveals plan for graph-based agentic infrastructure platform. Ever since its 2020 launch of HashiCorp Cloud Platform (HCP), its suite of fully managed SaaS products, HashiCorp has worked to convince buyers that its integrated collection of tools is greater than the sum of its parts. Yet the company's flagship products — Vault (identity-based secrets management) and Terraform (infrastructure as code) — are so popular that most customers have yet to move beyond them.

Weatherford launches Industrial Intelligence Platform to unlock digital-first energy operations. The global oil & gas industry is at an inflection point where digital transformation, automation and data integration have become indispensable for maintaining competitiveness. The new portfolio seeks to unify physical operations, legacy assets and digital intelligence under a single ecosystem. It combines the company's domain expertise with a modular suite of software and hardware tools designed to deliver actionable insight from the edge of the field to enterprise control rooms. By connecting sensors, automation systems and data management architectures, Weatherford aims to create a scalable intelligence layer that supports operators at every stage of digital maturity.

Posit illuminates its data science strategy, with Positron IDE and generative AI in the mix. Posit has evolved considerably over the past few years as it has expanded beyond R to Python-based data science by building multilanguage offerings to increase its addressable market. The vendor's measured approach to generative AI is appealing because it stresses a human-in-the-loop strategy, which is critical to success. Slightly more than one-quarter (26.1%) of enterprises cite quality of generations as the greatest current barrier to adoption of GenAI for data science or analytics, according to 451 Research's Voice of the Enterprise: Data & Analytics, Generative AI 2025 survey.

Badge offers a new look at strong authentication without exchange of secrets. It has taken the more recent innovations in AI/machine learning and cryptography and applied them to underserved markets and use cases that need to improve security and productivity. Enabling thousands or tens of thousands of users to easily and strongly authenticate to any kiosk, device or terminal helps to provide a better user experience and reduce whole classes of vulnerabilities around weak authentication or stolen credentials. 

Cerebras raises $1.1B as it pivots toward cloud inference services. It has developed its own wafer-scale silicon accelerators along with self-contained integrated systems and — more recently — cloud-based inference services. The series G comes amid a flurry of massive investments, forward-looking contract commitments and acquisitions in the AI infrastructure sector. Cloud-based inference services could be an easier route to market, as they bypass otherwise insurmountable technology packaging, customer integration support and sales distribution channel challenges that only large, established vendors can hope to address. Cerebras believes its WSE chips will continue to outperform other inference approaches. The question is whether it can move beyond offering inference services from its own hosted cloud and start attracting the big cloud providers as customers and partners.

Cisco unveils AI-enabled Webex contact center solutions and industry integrations. he company introduced Webex AI Quality Management, enhancements to Webex AI Agent and updated Cisco AI Assistant features for Webex Contact Center deployments, and expanded integrations with Salesforce Inc., Amazon Lex and Epic Systems. These updates highlight how Cisco's previously siloed products are evolving into a single unified platform, boosting value for its enterprise customers.

Attribute offers automated, granular unit economics without tagging. It is a FinOps platform positioned as a way to "simplify the business value of cloud investments and align them with organizational priorities" without some of the baggage associated with traditional FinOps platforms. It automates the discovery and grouping of cloud costs according to business metrics; crucially, without the need for tagging. For granular visibility into cloud expenditures, Attribute uses extended Berkeley Packet Filter data (application runtime data for usage-based allocations) to provide enhanced views of exact costs. The company says this enables finance, engineering and product teams to better collaborate and optimize business processes to ensure that cloud investment supports business goals.

ProHance aims to boost productivity with WFM for contact-center and back-office operations. The vendor's strong workforce analytics foundation is underpinning its move into the customer experience management space. By integrating workforce management (WFM) features with a broad suite of customer experience (CX) tools including omnichannel communications, AI predictive analytics and real-time intraday management, ProHance seeks to offer a unified platform that bridges contact-center and back-office operations. Its integrated approach could deliver deeper insight into contact-center productivity with tools like application-based data, as well as URLs and activities done by users. This could be an important differentiation for the company as it steps into an already fragmented and intensely competitive CX space.

Verint advances CX automation, highlighting tangible business outcomes. Together with its Value Dashboards' capabilities for tracking critical key performance indicators (KPIs), these are key factors differentiating its offering. By integrating its CX Open Platform with AI-driven bots and analytics tools, Verint looks to be well positioned to meet evolving contact center automation needs. However, under new ownership, Verint must navigate challenges like high AI project attrition rates and competitive pressures to maintain its leadership in the CX space. We believe it also needs a different messaging strategy that articulates its differentiation to support its positioning as a leader in the emerging CX automation category.

Hyland's Content Innovation Cloud aims to turn content into context for the 'agentic enterprise'. It is weaving content repositories and other sources of data into its Content Innovation Cloud. By leveraging its installed base and vertical expertise, the vendor emphasizes federation, governance and prebuilt industry-specific agent meshes to ease adoption, while open-sourcing its Cloud Content Repository to accelerate integration and ecosystem growth. The vision is compelling, but execution is complex. Enterprise-scale context and orchestration demand resilient connectors, compliance-grade semantics, cost-efficient operations and high-quality data. 

What to Watch

Looking ahead, the evolving landscape promises a shift in how enterprises align their strategies with new market dynamics. As organizations recalibrate to manage emerging risks and leverage untapped opportunities, we anticipate a progressive transformation in operational models that emphasizes agility and long-term resilience. Stakeholders are expected to prioritize scalable investments and adaptable frameworks that respond to fluid market conditions, setting the stage for a future defined by innovation and strategic foresight.

These developments will play a crucial role in shaping the future of enterprise technology, offering new opportunities and challenges for businesses worldwide.  S&P Global Market Intelligence 451 Research provides essential insight into the pace and extent of digital transformation across the global technology landscape. 451 Research offers differentiated insight and data on adoption, innovation and disruption across the technology markets, backed by a global team of industry experts. As the enterprise technology landscape evolves, several key trends   Keeping an eye on these developments will be essential for organizations aiming to navigate the complexities of digital transformation successfully.