Goldman Sachs: AI Software Market to Hit $300B by 2028

Goldman Sachs has projected that the global AI software market will surge to $300 billion by 2028, representing one of the most aggressive growth forecasts from a major Wall Street institution. The projection underscores a dramatic acceleration in enterprise AI adoption, fueled by advances in generative AI, large language models, and an expanding ecosystem of AI-native applications.

The investment bank's analysis suggests the market could grow at a compound annual growth rate (CAGR) of roughly 30-35%, up from an estimated $90-100 billion in 2024. This trajectory positions AI software as one of the fastest-growing segments in technology history, outpacing the early growth curves of cloud computing, mobile, and even the internet itself.

Key Takeaways From the Goldman Sachs Forecast

• $300 billion projected market size for AI software by 2028, up from roughly $90-100 billion in 2024
• Generative AI is identified as the primary catalyst, with enterprise spending on LLM-powered tools accelerating sharply
• Cloud infrastructure providers like Microsoft Azure, Amazon Web Services (AWS), and Google Cloud stand to benefit most from rising demand
• Enterprise adoption is shifting from experimental pilots to full-scale production deployments across industries
• AI-native startups are capturing growing market share, but incumbents are fighting back with aggressive integration strategies
• Healthcare, financial services, and manufacturing are flagged as the sectors with the highest near-term AI software spending growth

Enterprise AI Spending Shifts From Experimentation to Production

The Goldman Sachs forecast reflects a critical inflection point in how businesses approach AI investment. Through much of 2023 and early 2024, enterprise AI spending was dominated by proof-of-concept projects, pilot programs, and exploratory initiatives. That dynamic is changing rapidly.

Companies are now moving AI workloads into production environments at scale. According to multiple industry surveys, the percentage of enterprises with at least 1 AI application in production jumped from approximately 20% in 2023 to over 45% by late 2024. Goldman's analysts expect this figure to exceed 75% by 2027.

This shift matters because production deployments generate significantly higher software revenue than pilot programs. When a Fortune 500 company moves from testing an AI-powered customer service chatbot to deploying it across 50,000 agents, the associated software licensing, API consumption, and infrastructure costs multiply dramatically. Goldman Sachs sees this 'pilot-to-production' transition as the single largest revenue driver behind its $300 billion estimate.

Generative AI Becomes the Market's Growth Engine

Generative AI tools — including large language models from OpenAI, Anthropic, Google, and Meta — sit at the center of Goldman's bullish outlook. The bank estimates that generative AI alone could account for $150 billion of the total $300 billion market by 2028, effectively making it the majority driver of growth.

Several factors support this projection:

• API-based pricing models from companies like OpenAI and Anthropic create recurring, usage-based revenue streams that scale with adoption
• Multimodal AI capabilities — combining text, image, video, and code generation — are expanding the addressable market far beyond simple text-based chatbots
• Fine-tuning and customization services allow enterprises to build proprietary AI solutions on top of foundation models, driving additional software revenue
• AI coding assistants like GitHub Copilot, Amazon CodeWhisperer, and Cursor are creating an entirely new software category with strong monetization potential

Compared to the traditional machine learning and analytics software market — which grew at a CAGR of roughly 15-18% over the past decade — generative AI's projected 40%+ growth rate represents a step-change in velocity. Goldman's analysts attribute this to the technology's unusually broad applicability across industries and job functions.

Big Tech Dominates, but Startups Are Gaining Ground

The forecast highlights a competitive landscape that remains fluid and contested. Microsoft, through its partnership with OpenAI and deep Copilot integration across Office 365, Teams, and Azure, is positioned as the early market leader. The company's AI-related annual revenue run rate has already surpassed $10 billion by some estimates.

Google is aggressively pursuing the market with its Gemini model family and Vertex AI platform, while Amazon is leveraging AWS Bedrock to offer enterprises access to multiple foundation models in a single environment. Salesforce, SAP, and ServiceNow are embedding AI features into their existing enterprise platforms, creating additional revenue streams from AI-powered upgrades.

However, the startup ecosystem is proving remarkably resilient. Companies like Anthropic (valued at over $18 billion), Cohere, Mistral AI, and Perplexity are carving out significant niches. Anthropic's Claude models have gained particular traction in enterprise settings where safety and reliability are paramount. Mistral AI, based in Paris, has emerged as Europe's leading AI challenger.

Goldman's analysis suggests that while Big Tech will capture the largest absolute dollar share, AI-native startups could command 25-30% of the total market by 2028 — a substantial figure given the projected market size.

Sector-by-Sector Breakdown Reveals Uneven Adoption

Not all industries are adopting AI software at the same pace. Goldman Sachs identifies financial services as the sector with the most aggressive near-term spending plans, driven by use cases in fraud detection, algorithmic trading, risk modeling, and customer service automation. Banks like JPMorgan Chase have already deployed thousands of AI-powered applications internally.

Healthcare represents the second-largest opportunity, with AI software spending projected to grow at over 40% annually through 2028. Drug discovery platforms, clinical documentation tools like those from Nuance (now part of Microsoft), and diagnostic imaging AI are all contributing to this surge.

Manufacturing and supply chain operations rank third, with AI-powered predictive maintenance, quality control, and demand forecasting tools seeing rapid adoption. Companies like Siemens and Honeywell are investing heavily in AI-enabled industrial software.

Other sectors showing strong growth include:

• Legal services: AI-powered contract analysis and document review tools from companies like Harvey AI
• Education: Personalized learning platforms and AI tutoring systems
• Media and entertainment: Content generation, localization, and recommendation engines
• Retail: Dynamic pricing, inventory optimization, and personalized marketing

What This Means for Developers and Businesses

For software developers, the Goldman forecast signals an unprecedented opportunity. Demand for AI engineering talent — including ML engineers, prompt engineers, and AI infrastructure specialists — will continue to outstrip supply through at least 2028. Developers who invest in skills around RAG (Retrieval-Augmented Generation), fine-tuning, and AI agent frameworks are positioning themselves for the highest-demand roles.

For businesses, the message is equally clear: AI software is transitioning from a competitive advantage to a competitive necessity. Companies that delay meaningful AI adoption risk falling behind peers who are already realizing productivity gains of 20-40% in specific workflows. The cost of inaction is growing steeper with each quarter.

For investors, the projection validates the massive capital flowing into AI infrastructure and software companies. However, Goldman also cautions that not all AI spending will translate into proportional returns. Companies that fail to demonstrate clear ROI from their AI investments may face budget scrutiny as the initial hype cycle matures.

Looking Ahead: Can the Market Actually Reach $300 Billion?

Goldman's $300 billion projection is ambitious but not without precedent. The cloud computing market grew from roughly $60 billion in 2014 to over $500 billion in 2023, following a similar adoption curve driven by enterprise digital transformation. AI software could replicate — or even exceed — that trajectory given its broader applicability.

Several risks could derail the forecast, however. Regulatory uncertainty in the EU (via the AI Act) and potential US legislation could slow adoption in certain sectors. Model performance plateaus — if foundational AI capabilities fail to improve at their current pace — could dampen enterprise enthusiasm. And data privacy concerns remain a persistent friction point for industries handling sensitive information.

Despite these headwinds, the consensus among analysts is increasingly aligned with Goldman's bullish outlook. Morgan Stanley, Bank of America, and UBS have all published similar forecasts, with estimates ranging from $250 billion to $350 billion by 2028-2029. The directional bet is clear: AI software is entering its most transformative growth phase, and the companies, developers, and institutions that position themselves now stand to capture outsized value in the years ahead.

The next 12-18 months will be critical in determining whether Goldman's projection proves conservative or optimistic. All eyes will be on enterprise AI spending reports, cloud provider earnings, and the pace of generative AI innovation to gauge whether the $300 billion target is not just achievable — but perhaps too modest.