The Promise and Potential of AI Exits
Projected Multiples and Exit Strategies
This comprehensive post is packed with critical info for both startups and acquirers, and written as a one-stop reference. Don't miss out! Read-on.
AI's disruption across software segments in 2024 suggests high exit multiples for select emerging companies, with high-growth AI startups potentially attracting 5-10x revenue multiples and established companies 2-5x. The projected $15 trillion economic impact of AI, according to PWC, is expected to be a significant driving force M&A and IPOs. However, this speculative landscape, influenced by variable industry growth, shifting investor sentiment, and regulatory changes, also poses challenges like market saturation and regulatory uncertainty.
Here is an overview of the major AI business segments, corresponding exit avenues, and valuation multiples for top AI contenders:
AI Consultancy: Consulting firms offering AI strategy and support services, with notable enterprise-class established examples being PwC, Deloitte, and Accenture will be attractive to consultancies investing in AI. My favorite company in this space is Cognoscenti – a strategic AI consultancy. (Full Disclosure: I’m a partner.) Exit opportunities will include acquisitions, mergers, or management buyouts, with expected exit multiples of 3x-5x EBITDA for growing businesses and 2x-3x for mature ones. Multiples are likely to be much higher for focused, pure-play AI service companies.
AI as a Service: Cloud platforms like AWS, Google Cloud Platform (GCP), and Microsoft Azure provide AI/ML services such as machine learning and natural language processing (NLP). Exit strategies here will include acquisitions by the major cloud platforms or AI marketplaces who look to deepen their portfolio of AI capabilities. Here we expect multiples ranging from 5-10x revenue however, some AI companies like OpenAI and Anthropic have achieved high valuations in 2023 that appear disconnected from current revenue streams. While these lofty valuations have been supported by investments from Cloud platforms who see long-term strategic value despite present financials, time will tell if these bets on future AI dominance payoff. Companies – like OpenAI (GPT) and Anthropic (Claude.ai) – suggest upside potential exists despite risks around hype and execution.
AI Infrastructure: This segment focuses on cloud-based data storage and computing for AI systems, and is led by incumbents such as PureStorage and Nvidia. Exit opportunities here involve acquisitions by cloud computing or semiconductor firms, IPOs, and secondary offerings, with revenue multiples of 6-12x for high-growth companies and 4-6x for established ones.
AI Software: Offers pre-built AI tools, AI co-pilots or AI-assistants for businesses. A company to watch in this space is JigsawML (Full Disclosure: I’m an advisor). Exit strategies revolve around acquisitions and IPOs, with revenue multiples reaching 5-10x for high-growth companies and 3-5x for mature businesses.
Customized Customer Interaction: Utilizes AI for enhanced CRM and marketing automation, offering personalization in customer engagement. High-growth CRM companies can expect 5x-10x revenue multiples at exit, while established marketing and sales automation tools co's can command 4x-8x. Examples here include Amelia, Drift and Gong.
Data-Centric Solutions comprise the following sub-segments:
Data-Driven Decision-Making: This AI technology category utilizes ML, deep learning, and predictive analytics for business intelligence and consulting services. This sector, advancing in 2024, offers high exit potential for AI businesses, with acquisition by larger tech firms and opportunities for IPOs and secondary offerings. Exit multiples range from 4x-8x revenue for high-growth companies to 2x-4x for established ones. Two well-known companies of this sub-segment are Databricks and Snowflake.
Data Labeling and Annotations: Focuses on creating AI training datasets and production data through data annotation and tagging. Companies in this sub-segment are prime acquisition targets for major tech companies and automotive firms developing self-driving technology. My favorite company in this sub-segment is SmartOne.ai. (Full Disclosure: I’m a Board Member.) Acquisition multiples vary from 5x-8x revenue, with potential EBITDA multiples of 15-25x.
Data Marketplaces: Platforms leaders – like AWS Data Exchange and Snowflake Data Marketplace – process and sell data crucial for AI algorithm training. Exit opportunities include acquisitions by cloud services platforms and enterprise software companies, as well as financial acquisitions like PE firms and IPOs, with revenue multiples expected to be in the 6x-10x range and EBITDA multiples potentially exceeding 25x.
Synthetic Data Companies – These companies specialize in creating artificial data that fills-in like real-world data but doesn't contain any actual sensitive or private information. Datacebo is a startup example in this sub-segment. This type of data is often used for training machine learning models and testing systems where the use of real data might be restricted due to privacy concerns or availability issues. Acquisition multiples will be determined in the coming years but estimated to be range from 5x-8x as a revenue multiple with potential EBITDA multiples of 15x-20x.
Efficiency and Productivity Software: Includes SaaS and ERP systems enhanced by AI, with exit multiples of 4x-8x revenue for high-growth SaaS companies and 5x-10x for ERP vendors. AntWorks is one example of an AI-driven efficiency and productivity software company.
Operational Automation: Covers process automation and industrial automation, with exit multiples of 5x-10x for high-growth businesses and 2x-4x for established ones in both sub-segments. Landing AI is one example of a company in this segment.
Talent and Skill Assessment: Focuses on AI-driven skills assessment, HR-Tech, and recruitment services. My favorite player in this field is TeamLift (I’m an advisor). Exit multiples range from 4x-8x revenue for emerging companies to 3x-5x for mature ones.
Vertical AI Solutions: With deep specialization and customization, these software solutions – often SaaS – are specifically developed to address the unique challenges, workflows, and data patterns of a particular industry. Leading examples are healthcare AIs (e.g., medical or dental image analysis and diagnoses) or finance AIs (e.g., predicting market trends or detecting fraudulent new customers or transactions). My favorite company in this segment is FiVerity. (Full Disclosure: I’m a Board Member). High-growth vertical AI startups can expect 5x-12x revenue multiples at exit, while established companies will produce 4x-8x multiples.
Sources: Crunchbase, Pitchbook, CB Insights and other various articles
In 2024, the tech landscape is poised for transformation due to escalating acquisition and investor interest in AI, promising high revenue multiples at exits for fast-growing startups and robust multiples for established firms. The competition among major players for AI talent and IP will drive increased M&A activity. Despite challenges such as crowded exits and regulatory risks, the significant projected economic impact of AI and its integration into company offerings hold the promise of substantial upside for firms that effectively scale their commercialization efforts.
wonderful landscape analysis. Very helpful bud.