The promise of widespread enterprise AI is held back by a fundamental problem: many companies still run on legacy, on-premise systems from the 80s and 90s. This "digital transformation" bottleneck must be solved first, as AI can't be adopted until the prerequisite move to modern cloud infrastructure is complete.

For established firms like VCs, the primary challenge in adopting AI isn't change management or model selection. It's the painstaking process of migrating and cleaning decades of financial data from outdated systems to make it accessible and useful for modern AI agents.

AI coding's true enterprise value is limited because models struggle with legacy systems. Companies run on trillions of lines of mediocre code in old languages like COBOL—a problem that requires human intervention over decades, not a simple AI solution, which limits immediate, real-world impact.

Enterprises are trapped by decades of undocumented code. Rather than ripping and replacing, agentic AI can analyze and understand these complex systems. This enables redesign from the inside out and modernizes the core of the business, bridging the gap between business and IT.

For mid-market companies outside the tech sector, AI adoption is primarily blocked not by strategy, but by fundamental realities: the absence of internal engineering teams to guide implementation and the challenge of legacy systems with siloed data.

Large enterprises operate on complex webs of legacy systems, compliance controls, and fragile integrations. Their high risk aversion and lengthy change management cycles create a powerful inertia that will significantly delay the replacement of established B2B software, regardless of how capable AI agents become. Enterprise architecture moves slower than market hype.

For industries like healthcare and finance, the primary obstacle to deploying AI isn't the technology's capability but the state of their own data. Many organizations lack the proper data formatting and security infrastructure, making it impossible to "unleash" AI on their most valuable information.

Financial institutions are at a tipping point where the risk of keeping outdated legacy systems exceeds the risk of replacing them. AI-native platforms unlock significant revenue opportunities—such as processing more insurance applications—making the cost of inaction (missed revenue) too high to ignore.

The primary obstacle for Fortune 500 companies adopting AI isn't a lack of good models, but their disorganized data. Decades of fragmented systems mean agents can't reliably find the right information, creating a massive, decade-long data cleanup and consolidation opportunity for services firms.