Every enterprise will need an AI tech strategy. If we don’t adopt, we won’t keep up. Many large companies have been slow to adopt AI, choosing to wait and see as things shake out. 2025 will be a huge year for more widespread adoption as the technology matures, becomes more reliable, and high-value use cases are identified. Education and consulting will continue to boom as companies grapple to understand AI’s potential and how to appropriately apply it to their business infrastructure and workflows.
In a recent TechCrunch article by Rebecca Szkutak, the future of enterprise technology in 2025 is explored through the predictions of 20 venture capitalists. The article highlights the slow adoption of AI by enterprises, despite its potential to be a major technological advancement. While 2024 didn’t see the expected surge in AI adoption due to budget constraints and the experimental nature of the technology, 2025 might be a different story. Venture capitalists are optimistic about AI’s role in reshaping enterprise operations, with a focus on data quality, modernization of app development, and automation in traditionally high-cost sectors.
Here were the questions they were asked, and their summarized responses:
What Enterprise-Related Trends Will You Be Paying Attention to the Most in 2025?
- AI adoption and data quality are central themes, with enterprises transitioning from experimentation to large-scale implementation, creating an intensified demand for high-quality data.
- Code agents for modernizing legacy systems, such as migrating mainframe applications to the cloud, are expected to gain prominence.
- Automation is enabling traditionally high-cost sectors like accounting, legal, and revenue cycle management to achieve software-like margins.
- Another key focus is understanding enterprise sales cycles, including the duration of tool trials before adoption, and the evolution of AI pricing models, such as consumption- or outcome-based approaches.
- Additionally, time-to-first-value (TTFV) is emerging as a critical metric for ease of implementation, favoring solutions that deliver faster results.
What Areas Are You Looking to Invest In?
- Investments are centered on enterprise resilience, targeting solutions that mitigate operational failures and cyber threats.
- Data sovereignty is another major focus, driven by regulatory and geopolitical pressures, with startups enabling full control over data storage and compliance garnering interest.
- Task-specific AI models and alternatives to current architectures, like transformers, are being closely watched for their potential to reduce the computational demands of large language models.
- The infrastructure to support second-order effects of AI, such as platforms for managing and securing digital agents, is seen as a significant opportunity.
- Other promising areas include observability, IT service management, sales engagement, and vertical workflows reimagined with generative AI.
What Technologies, Sectors, or Companies Are You Finding Interesting That Aren’t AI?
- Quantum computing and cybersecurity remain highly promising, with the latter becoming increasingly complex due to AI-driven threats.
- Fintech, SaaS, and e-commerce, which slowed in recent years, are seeing renewed interest.
- The public sector is also attractive, given the significant fiscal environment and the push for modernization.
- Energy, especially nuclear, is gaining momentum due to rising power demands and grid challenges.
- Additionally, the evolution of data infrastructure, such as lakehouse architecture and multi-cloud deployments, continues to attract attention.
When It Comes to AI, How Are You Determining That a Company Has a Moat?
- Proprietary data, cutting-edge algorithms, and scalable infrastructure are the cornerstones of a strong AI moat.
- Companies with unique datasets that excel in specific verticals or workflows are particularly defensible.
- Evaluating moats includes assessing whether a company’s value increases or diminishes as AI models improve.
- The ability to acquire, clean, and utilize previously inaccessible data efficiently is also a key determinant. A strategic approach, like Sapphire Ventures’ “5D framework” (design, data, domain expertise, distribution, and dynamism), helps evaluate these factors.
What Does It Take to Raise a Series A as an Enterprise Startup in 2025?
- Strong founder-market fit, ambitious vision, and metrics like $2–$5 million ARR are critical for Series A fundraising.
- Startups must demonstrate repeatable business models and address urgent pain points with clear demand.
- Growth metrics of >100% YoY and low burn multiples are now standard expectations.
- AI-first products are enabling faster early-stage traction compared to traditional SaaS, with the time from launch to $5 million ARR significantly shortened in the AI era.
Do You Predict Enterprises Will Increase Their Tech Budgets for 2025?
- Enterprises are expected to increase their tech budgets modestly, focusing on areas that deliver measurable ROI and clear KPIs.
- AI applications, particularly those that drive operational efficiency and improve top-line growth, are priorities.
- Experimental budgets for AI are high, but sustainable product-market fit remains elusive for many startups.
- Continued investment in cybersecurity, cloud optimization, and AI-enabled tools is anticipated, with economic conditions influencing budget allocation across the year.
Will There Be More AI Adoption?
- AI adoption will accelerate, driven by improved model capabilities, enabling infrastructure, and the emergence of stronger AI-first products.
- Application vendors currently dominate adoption due to the fragmented enterprise platform tools market. Unified platform solutions to simplify AI integration are expected to gain traction as demand grows.
- Enterprises are keen to optimize workflows with AI while addressing challenges like pricing and data security.
What Kinds of Companies in Your Portfolio Are Seeing the Strongest Growth?
- AI defense tech and infrastructure tools enabling seamless AI deployment are experiencing rapid growth.
- Vertical AI agents tailored to specific workflows and companies addressing urgent enterprise pain points are scaling quickly.
- Defense applications of AI are particularly strong, driven by formal adoption strategies in national security.
- Companies focused on infrastructure layers, such as virtual cloud services for AI inference, are also thriving.
What Are Your Predictions for the Exit Environment Next Year?
- M&A activity is expected to increase as large companies acquire startups with domain-specific AI capabilities or strong data moats.
- The IPO market will remain selective, favoring companies with strong growth and profitability metrics.
- Regulatory changes and macroeconomic conditions may create more favorable exit opportunities, but challenges persist for businesses with inflated private valuations.
- Strategic acquisitions will prioritize innovative technical teams and technology over scaled businesses.
As we look towards 2025, the potential of AI in enterprises is both promising and challenging. While the technology offers the opportunity to enhance efficiency and drive growth, it also requires careful consideration of data quality, infrastructure, and security. The balance between innovation and practicality will be key as enterprises navigate this evolving landscape. As AI continues to develop, it’s important for businesses to weigh the benefits against the challenges, ensuring that they are prepared for the changes ahead.
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Image Credit: DALL-E
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