In the race to innovate with AI, disruptor startups may have the upper hand thanks to an influx of investment capital and a rising demand for enterprise automation.
As our collective understanding of Artificial Intelligence continues to evolve, the brightest minds in tech are rising to the challenge at hand: to discover how AI can add the most value to enterprise workflows. Generative AI, also known as GenAI, first dazzled the world with its natural language capabilities with the public launch of OpenAI’s ChatGPT in November 2022. Since then, both the number of firms and variety of services offered within the GenAI sector has skyrocketed. 2024 alone saw a 35 percent rise in companies that specialize in AI [2], with those focused on developing AI agents reigning supreme in the arena of tech startups. Funding rounds for companies innovating with GenAI have garnered impressive numbers, exceeding a total of $8.5B in capital raised as of October 2024 [1].
The surge of GenAI companies this year should come as no surprise — with pressure mounting on corporate leaders to adapt to changing times, AI has become a linchpin in enterprise modernization. A recent survey of CEOs suggests that around 64 percent of enterprise leaders indicated that AI investment is a major line-item for their corporate budgets, regardless of economic conditions [3]. However, AI implementation at the enterprise is easier said than done. Challenges like high costs (both in dollars and resources,) shortcomings in structured data, and looming regulatory hurdles spell an uphill battle for early adopters of GenAI technologies within corporate settings. However, recent trends in AI development point to smoother sailing ahead, along with major gains for those willing to invest and iterate right now.
AI Agents and the Data Problem
Automation has long been the proverbial silver bullet of enterprise tech, well before GenAI burst onto the scene. Machine-led task execution promised to reduce labor costs, improve efficiency, reduce human error, and overall result in enormous value. However, automation has historically been limited to simple tasks dependent on structured data and heavy knowledge worker involvement.
Thanks to AI, tech executives have a newfound edge to explore automation strategies across departments and workflows. The latest approach to leveraging GenAI in particular is the deployment of AI agents, which can interact with incomplete or unstructured data thanks to Large Language Models (LLMs) and computer reasoning capabilities. This is transformational given that unstructured data can consist of company guidelines, emails, customer reviews, or any other form of human-written information. As a result, through agentic reasoning that leverages last-mile domain expertise and data, AI agents can perform a wide array of functions and processes formerly delegated to human knowledge workers—a paradigm shift in how work gets done.
The Application Layer and Multi-Agent Systems
If the AI agents are the workforce, then the applications that house them are their office. The demand for enterprise-ready tech powered by AI comes with an implied contingency — a functional, seamless user experience with enterprise-grade security and control. Much of the focus on AI over the past few years honed in on improving the models themselves, often favoring an open-source approach to development [4]. As we venture further into the world of GenAI use-cases, the application layer has stolen the spotlight as an essential differentiator for all companies in 2025 and beyond.
The application layer provides an ecosystem of workflows for an exciting opportunity in AI agent technology: multi-agent systems (MAS). An emerging category within AI-based software, a MAS essentially gives users, not just one AI agent at their command, but an entire team of individual agents with unique assignments and protocols. Similar to a human workforce team structure, these agents can interact with each other using natural language inputs and outputs, making them more versatile than single agents and less susceptible to data silos than other technologies.
While the opportunities within MAS-based software have only recently begun to emerge, some organizations are already fetching major investments. One such organization making headlines is Sierra.ai. Co-founded by former Salesforce Co-CEO Bret Taylor, Sierra has already raised nearly $300M and sits at a $4.5B valuation [5]. Sierra promises enterprises custom, conversational AI multi-agents managed using its Agent OS platform, and claims to eliminate common problems with AI chatbot agents like hallucinations. Sierra, though less than two years old, has already transformed retail customer service departments for blue chip companies like SiriusXM, WeightWatchers, and other household brand names.
Other GenAI startups that earned major investment this year include several with industry-specific platforms in healthcare (Abridge.ai, $250M round), legal services (EvenUp, $135M round), and software engineering (Poolside.ai, $500M round) [1].
Looking Ahead
These and other trends over the past 12 months have only further engulfed the imaginations of corporate tech leaders with the possibilities of GenAI implementation. In terms of market opportunity, investors with deep expertise in this area anticipate up to a $4.6T potential shift in spend from human-led services and salaries to MAS-based AI applications. Kevin Finn, an industry veteran who has advised on tech deals totaling more than $100B in value, offers an inside look at how companies should think about this dynamic shift.
“The size of the overall software market will expand rapidly, as the definition of SaaS inverts from software-as-a-service to service-as-a-software thanks to AI agents”, says Finn. “In the long-term, the opportunity is two-fold: selling work, and replacing existing software. For example, startup cloud companies in the 2010s grew by simply targeting the software profit pool. Today, AI companies have the unique ability to go after the services profit pool as well, which spells major upside ahead of nearly any category in enterprise software right now.”
Finn currently serves as a Senior Partner for boutique M&A advisory firm Ignatious, which specializes in representing emerging growth companies. Their team boasts over 70 years of combined experience in the tech sector, and has advised on some of the most historic deals of the past quarter-century.
Finn adds, “it’s about creating and expanding new markets, versus solely fighting over established software markets like we saw during past paradigm shifts. That is where the disruptors will likely see the biggest gains.”
[Sources]
1. https://sapphireventures.com/blog/ai-native-applications/
2. https://springsapps.com/knowledge/how-many-generative-ai-startups-are-there-latest-statistics-in-2024
3. https://kpmg.com/xx/en/our-insights/value-creation/kpmg-global-ceo-outlook-survey-2024.html
4. https://www.zdnet.com/article/ai-in-2023-a-year-of-breakthroughs-that-left-no-human-thing-unchanged/
5. https://www.cnbc.com/2024/10/28/bret-taylors-ai-startup-sierra-valued-at-4point5-billion-in-funding.html
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