CB Insights Tech Trends 2025: AI Agents, Spatial Computing, and Industry Disruption

CB Insights 2025 Tech Trends: A Comprehensive Overview

CB Insights’ annual Tech Trends report for 2025 identifies 14 defining technology trends across five major industry verticals: financial services, healthcare and life sciences, enterprise, AI, and industrials. The report synthesizes data from thousands of companies, funding rounds, and market signals to map the technology landscape that will shape business strategy and investment decisions.

What makes the 2025 edition particularly compelling is the convergence of trends across sectors. AI is no longer a standalone category — it is the enabling thread connecting wealth management transformation, healthcare innovation, enterprise software evolution, and industrial automation. This cross-pollination of AI capabilities across industries creates both opportunities for technology leaders and risks for incumbents that fail to adapt.

The report also highlights a maturation in how organizations approach technology adoption. The hype cycle around generative AI is giving way to practical implementation, with companies moving from proof-of-concept experiments to production deployments that deliver measurable business outcomes. This shift from experimentation to execution represents the defining theme of the 2025 technology landscape and has direct implications for how enterprises prioritize their technology investments.

The Cyborg Wealth Advisor: AI Transforms Financial Services

CB Insights’ analysis of the financial services technology landscape reveals a striking productivity challenge: financial advisors spend only 17% of their time with clients, while 70% of their working hours are consumed by back-office administration, middle-office tasks like meeting preparation, investment research, and compliance activities. This massive inefficiency represents both a problem and an opportunity.

The emergence of the “cyborg wealth advisor” — human advisors augmented by AI at every point of the wealth value chain — promises to transform this equation. Higher-earning advisors who have already adopted productivity tools spend 26% of their time with clients, translating to approximately 200 additional one-hour client meetings per advisor per year. The financial impact is significant: more client time directly correlates with higher revenues, stronger retention, and deeper relationship penetration.

Morgan Stanley’s wealth management business provides a compelling case study. Following deeper AI integration across its advisor platform, the firm reported record net revenue of $7.3 billion in Q3 2024, a 14% year-over-year increase. While multiple factors contributed to this performance, the company’s systematic investment in AI-powered advisor tools — including research assistants, client communication generators, and portfolio analytics — has been identified as a meaningful contributor to productivity gains.

Despite these success stories, the broader wealth management industry remains cautious. Nearly half of wealth managers are still in the learning phase with AI, while another third are implementing incrementally. The primary obstacles are regulatory uncertainty (62% of firms cite lack of regulatory guidelines as the top barrier), data privacy concerns, and a shortage of technical talent. These barriers create a window of opportunity for early movers to establish competitive advantages that may be difficult for laggards to close.

The New Wealth Tech Value Chain

CB Insights maps a new wealth tech value chain where open integration orchestrates software across products and services, while AI personalizes how clients and advisors work together. Over 50 AI-focused wealth tech companies have achieved commercial maturity, with the majority in the deploying stage, signaling readiness for enterprise adoption. Personalized client engagement is the most mature AI use case, with genAI startups automating marketing tasks like copywriting and video creation for financial advisory firms.

AI Agents Given Spending Autonomy in 2025

Perhaps the most provocative trend in CB Insights’ 2025 report is the emergence of AI agents with autonomous spending authority. This represents a fundamental shift in the role of AI — from tools that analyze data and make recommendations to agents that execute financial decisions within defined parameters. The implications for enterprise procurement, financial operations, and B2B commerce are potentially transformative.

The concept of AI agents with spending power builds on the broader agentic AI trend, where systems move beyond simple query-response interactions to perform multi-step tasks independently. In financial services, this manifests as AI systems that can negotiate terms with vendors, execute recurring purchases, optimize portfolio allocations, and manage operational expenses — all without requiring human approval for each individual transaction.

The governance and risk management implications are significant. Organizations deploying spending agents must establish clear authority frameworks that define spending limits, approval escalation triggers, audit trails, and oversight mechanisms. The balance between efficiency gains from autonomous operation and the risk management requirements of financial decision-making will be a critical design consideration for these systems.

For the enterprise technology ecosystem, AI agents with spending authority create new market dynamics. Vendors must now optimize their products and pricing not just for human buyers but for AI purchasing agents that evaluate options systematically and without the cognitive biases that characterize human decision-making. This shift toward AI-mediated purchasing decisions could fundamentally reshape how B2B markets function.

Spatial Computing Goes Enterprise-Ready

CB Insights reports that spatial computing for enterprises has moved beyond the experimental phase and is establishing itself as a durable technology platform. While consumer adoption of mixed reality headsets has been slower than projected, enterprise use cases — including training simulations, remote collaboration, digital twins, and industrial design — are driving meaningful adoption and demonstrating clear return on investment.

The enterprise spatial computing market is differentiated from the consumer segment by several factors. Enterprise buyers evaluate spatial computing technology based on specific productivity metrics rather than entertainment value. Use cases like remote expert guidance in manufacturing, immersive training for complex procedures, and collaborative design review in architecture and engineering deliver quantifiable efficiency gains that justify the investment in hardware and software.

The technology stack supporting enterprise spatial computing is maturing rapidly. Cloud-based rendering reduces the hardware requirements for end-user devices, while AI-powered spatial mapping enables more intuitive interaction with virtual content in physical environments. Integration with enterprise systems — ERP, PLM, CRM — is improving, allowing spatial computing applications to access and manipulate real-time business data within immersive environments.

The convergence of spatial computing with AI creates particularly powerful enterprise applications. AI-powered virtual assistants that operate in spatial computing environments can guide workers through complex procedures, identify potential issues in real-time, and provide context-aware information that improves both productivity and safety. As these technologies mature, the distinction between physical and digital workspaces will continue to blur.

AI-Powered Disease Management Enters a New Phase

The healthcare section of CB Insights’ report highlights a new phase in disease management, driven by AI’s ability to process vast amounts of patient data, predict disease progression, and personalize treatment protocols. This trend extends beyond diagnostic AI — which has been commercially available for several years — to encompass the full continuum of care from prevention through treatment and long-term management.

AI-powered disease management platforms are being deployed across multiple therapeutic areas, including diabetes, cardiovascular disease, mental health, and chronic pain management. These platforms continuously analyze patient data from multiple sources — electronic health records, wearable devices, patient-reported outcomes, and social determinants of health — to generate personalized care recommendations that adapt as patient conditions evolve.

The business model implications are significant. AI disease management shifts the value proposition from episodic care delivery to continuous health optimization, aligning more closely with value-based payment models that reward outcomes rather than volume. This alignment creates opportunities for companies that can demonstrate measurable improvements in patient outcomes and healthcare cost reduction through AI-driven care delivery.

Autonomous robots eyeing caregiving represent another healthcare technology trend highlighted in the report. As populations age globally and healthcare workforce shortages intensify, robotic assistants for elder care, rehabilitation, and hospital logistics are attracting increasing investment. While fully autonomous caregiving robots remain years from widespread deployment, incremental applications in medication delivery, vital signs monitoring, and patient mobility assistance are already gaining traction.

RNA Therapeutics Investment Surge in 2025

The investment floodgates have opened for RNA therapeutics, driven by the dramatic validation of mRNA technology through COVID-19 vaccines and the expanding pipeline of RNA-based treatments across multiple therapeutic areas. CB Insights documents a surge in funding for companies developing mRNA therapies, siRNA drugs, antisense oligonucleotides, and RNA editing technologies — each targeting previously untreatable genetic conditions and chronic diseases.

The investment thesis for RNA therapeutics rests on the platform’s fundamental versatility. Unlike traditional drug development where each new therapeutic candidate requires a largely independent development process, mRNA and other RNA platforms allow companies to rapidly develop new candidates by simply changing the encoded sequence. This platform approach dramatically reduces development timelines and costs, enabling companies to pursue larger portfolios of therapeutic candidates with the same infrastructure investment.

The commercial landscape for RNA therapeutics is expanding beyond vaccines to include treatments for cancer, rare genetic diseases, cardiovascular conditions, and metabolic disorders. BioNTech and Moderna are both advancing mRNA-based cancer vaccines in clinical trials, while smaller biotechs are targeting rare diseases where the precision of RNA therapeutics offers the potential for curative treatments. The breadth of therapeutic applications creates multiple paths to commercial success for companies with robust RNA technology platforms.

For healthcare investors, the RNA therapeutics trend represents a potential paradigm shift in drug development. The combination of platform economics, expanding therapeutic applications, and improving delivery technologies creates a compelling long-term investment case, though investors must navigate the clinical development risks that remain inherent in any pharmaceutical investment strategy.

Open-Source LLMs vs. Proprietary Models in 2025

CB Insights’ analysis of the AI landscape reveals a fascinating bifurcation in the large language model ecosystem. Open-source models have ceded the top positions in frontier capability benchmarks to proprietary models from OpenAI, Anthropic, and Google, but they have come to dominate the market for smaller, specialized models that form the backbone of most enterprise AI deployments.

This bifurcation has important implications for enterprise AI strategy. Organizations that need cutting-edge reasoning capabilities, complex multi-step task completion, or state-of-the-art natural language understanding may need to rely on proprietary frontier models. However, for the vast majority of enterprise use cases — classification, extraction, summarization, translation, and domain-specific Q&A — open-source models provide competitive performance at significantly lower cost with greater control over data privacy and model behavior.

The “LLMs’ explainability moment” is another key AI trend identified by CB Insights. As organizations deploy AI systems in regulated industries and high-stakes applications, the ability to explain model decisions is becoming a critical requirement rather than a nice-to-have feature. This trend favors open-source models where organizations have full visibility into model architecture and training data, while also driving proprietary model providers to invest in explainability tools and documentation.

The strategic implication for technology leaders is clear: a multi-model strategy that leverages both proprietary and open-source models — selecting the right model for each use case based on capability requirements, cost constraints, and governance needs — will outperform single-vendor approaches. This aligns with the broader trend toward AI orchestration platforms that abstract model selection from application development.

The Global AI Arms Race Heats Up

CB Insights documents that the United States is leading the global AI arms race — for now. American companies and research institutions dominate in foundation model development, AI chip design, and venture capital funding for AI startups. However, the report highlights that this lead is being actively contested, with China investing aggressively in sovereign AI capabilities and Europe pursuing a regulatory-first approach that could shape global AI governance standards.

The AI arms race extends beyond model development to encompass the full AI stack: semiconductor manufacturing, data center infrastructure, training data curation, and talent development. The US advantage in semiconductors — particularly through NVIDIA’s dominance in AI training chips — provides a structural advantage that competitors are working to circumvent through alternative chip architectures, custom silicon, and more efficient training methods.

AI M&A is fueling the next wave of corporate strategy, according to CB Insights. Large technology companies and non-tech corporations alike are acquiring AI startups to accelerate their capabilities, access scarce talent, and secure competitive positions in AI-driven markets. This M&A activity is reshaping competitive dynamics across industries, as companies with superior AI capabilities gain advantages in customer experience, operational efficiency, and product innovation.

For enterprises navigating this landscape, the AI arms race creates both urgency and opportunity. The companies that build strong AI capabilities early — whether through internal development, strategic partnerships, or acquisitions — will be better positioned to compete as AI increasingly determines competitive outcomes across virtually every industry. Those that delay face the risk of falling behind in ways that become progressively more difficult and expensive to remedy.

Data Centers, Space Tech, and Industrial AI Trends

CB Insights’ industrials section highlights several trends reshaping the physical infrastructure of technology. The “future data center” is arriving, driven by the massive compute demands of AI training and inference. New data center designs incorporate advanced cooling technologies, renewable energy integration, and modular construction techniques to meet the scale and sustainability requirements of AI workloads.

Cheaper access to space is sparking an investor rush, as declining launch costs — driven primarily by SpaceX’s reusable rocket technology — open up new commercial opportunities in satellite communications, Earth observation, in-orbit manufacturing, and space-based services. The convergence of lower launch costs, miniaturized satellite technology, and growing demand for global connectivity creates a compelling investment landscape that is attracting both traditional aerospace investors and technology venture capitalists.

Retail’s personalization imperative represents the consumer application of AI trends. As consumers increasingly expect personalized experiences across all touchpoints, retailers are investing in AI-powered personalization engines that customize product recommendations, pricing, marketing messages, and store layouts based on individual customer data. The gap between personalization leaders and laggards is widening, creating competitive pressure that drives further technology investment.

Compressed fintech valuations are creating opportunities for acquirers, according to the report. The valuation reset in fintech — following the exuberance of 2020-2021 — has brought many high-quality companies to more reasonable price levels. Strategic acquirers with strong balance sheets and clear integration capabilities are well positioned to acquire fintech assets that enhance their digital capabilities at valuations that would have been unthinkable two years ago, as the broader financial technology landscape continues to evolve.

Strategic Implications for Technology Leaders

CB Insights’ 2025 Tech Trends report carries several overarching strategic implications for technology leaders, investors, and policymakers. The convergence of AI across every industry vertical means that AI strategy is now inseparable from business strategy — organizations that treat AI as a standalone initiative rather than a core business capability will find themselves at a growing competitive disadvantage.

The maturation of AI from experimentation to production deployment creates new organizational challenges. Companies must develop governance frameworks for AI agents with autonomous decision-making authority, build talent pipelines that combine technical AI expertise with domain knowledge, and establish measurement frameworks that track AI’s impact on business outcomes rather than just model performance metrics.

For investors, the CB Insights trends map highlights several areas of concentrated opportunity: AI-augmented financial services, RNA therapeutics platforms, enterprise spatial computing, and AI infrastructure (data centers, chips, and development tools). The report’s data-driven approach to trend identification provides a useful framework for evaluating investment opportunities and monitoring emerging technologies that may reshape competitive dynamics across industries.

The technology landscape of 2025 is characterized by accelerating convergence, maturing AI capabilities, and the growing strategic importance of technology decisions. Organizations that approach this landscape with clear strategic priorities, disciplined investment frameworks, and a willingness to move decisively on high-conviction opportunities will be best positioned to capture value in what promises to be a transformative period for technology and business.