Gen Ai Whitepaper
Late-Stage VC: Can Europe back its tech champions? GenAI in a Bottle: Assessing Real Value Amidst the Hype White Paper by Jessica Hayes, Vice President Venture & Thomas Chauvin, Analyst Introduction In a classic tale, a man uncovers a magic lamp and makes a grand wish to a genie, only to encounter complications as the wish brings unforeseen consequences. Generative AI, much like a modern-day genie, holds immense promise for creativity and productivity but also brings the risk of unforeseen effects such as hallucinations and new attack vectors. Since the debut of ChatGPT just two years ago, expectations for GenAI have surged. Our
computing and mobile phones – a comparison we still stand by today. However, recent scrutiny has emerged regarding GenAI's enterprise value, highlighted by Goldman Sachs' June 2024 report, “Too Much Spend, Too Little Benefit” ¹. This report reveals GenAI has fallen short of ROI expectations, contrasting with last year's projection of a 7% global GDP boost. The focus has shifted to concerns about AI's long-term viability, scalability, ethics, and tangible returns. Despite these concerns, GenAI continues to attract significant investment. In Q2 2024, VC funding rose 29% to $42.9 billion, with AI representing nearly one-third of this capital, supported by major deals like xAI's $6 billion Series B. Innovation is also thriving, with Microsoft investing $19 billion in data centers and AI chips, and Meta launching its largest open-source model, Llama 3.1 405B. Amidst the noise and excitement, one question stands out: where will lasting value be realized? This paper explores this in three parts:
I. The Current State of GenAI AI is a top priority for executives, but homegrown deployment is lagging A recent BCG survey shows 85% of executives plan to boost their AI and GenAI spending², shifting from experimentation to production as expectations for value creation rise. This is mirrored by a rise in AI adoption, with organizations using AI in at least one function increasing from 55% in 2023 to 72% in 2024, and GenAI adoption nearly doubling in the same period. Majority of organizations have adopted AI in at least one business function Source: McKinsey³ However, despite growing interest, in-house GenAI deployment remains nascent. Morgan Stanley's CIO survey shows 33% of CIOs anticipate their first AI/LLM projects going live in 2H24, with 15% targeting 2025 and 31% having no immediate plans. This is echoed by IBM’s recent global study of 3,000 CEOs, which found that 71% of organizations are still in the pilot phase of GenAI projects
We at AVP have seen that most Global Fortune 2000 companies are opting for third- party GenAI solutions rather than developing their own models. Among the models in production, most are used for internal purposes by employees rather than for customer- facing applications. Majority of CIOs expect initial projects to be in production in 2H24 and beyond Source: Morgan Stanley, AlphaWise The positive: concrete use cases have already emerged Internal chatbots: A growing trend in homegrown applications is the implementation of internal chatbots for employee search and support, aimed at boosting workplace efficiency. For example, a study from HBS shows that consultants at BCG who worked with a chatbot completed 12% more tasks and achieved a significant 40% improvement in quality than those who did not use a chatbot. Coding: Since GitHub Copilot's debut in late 2021, AI code assistants have gained traction, with 63% of organizations either deploying or piloting these tools
assistants are commonly used for time-saving efficiencies and cost reduction, though ROI on more qualitative tasks such as improving code quality and reducing bugs remains unclear. Sales & marketing: AI sales tools have been rapidly adopted, with over 75% of sales leaders either using, implementing, or planning to use GenAI within the next year
giants are at the forefront, with Salesforce becoming an "AI-first company" in 2014 and deploying Einstein AI for personalized sales emails and customer journeys. Our latest sales automation report also highlights growing GenAI use cases, such as Cognism’s Revenue AI, which enhances B2B data for lead generation to help teams surpass their revenue targets. The negative: enterprise challenges in adopting GenAI Key barriers to GenAI adoption Source: ClearML
