The Future of Enterprise AI: What It Means for Your Deal Flow

Two years ago, enterprise AI meant a pilot. A team of 10 data scientists, a six-month engagement, a demo that worked in the sandbox and stalled in production. The pitch decks you're seeing today describe something entirely different — and most of them are lying about which era they're actually in.

The companies genuinely operating at the frontier of enterprise AI deployment look nothing like the experiments of 2022–2023. What's changed isn't just the models — it's the architecture, the use case maturity, and critically, the degree to which AI is load-bearing versus decorative.

What enterprise AI actually looks like now

The most mature deployments share three characteristics. First, they're multimodal — processing documents, images, voice, and structured data through unified pipelines, not bolted-together point solutions. Second, they're agentic — the system takes actions, not just suggestions. Third, they're vertical-specific — trained or fine-tuned on domain data that generic models can't replicate.

Legal firms using AI to process contract stacks. Manufacturers using vision models for quality inspection at line speed. Insurers running claims triage through agentic workflows that reduce human handling by 60–70%. These aren't experiments. They're production systems with measurable P&L impact.

The companies that built these systems made specific architectural choices early. They didn't just call the OpenAI API. They designed for model interchangeability, built proprietary data pipelines, and treated the AI layer as infrastructure — not a feature.

The gap between pitch decks and reality

Most AI claims in pitch decks describe the first era, dressed up in second-era language. 'AI-powered' means there's a GPT-4 API call somewhere in the product. 'Proprietary model' means they've fine-tuned on a small dataset with no defensible moat. 'Agentic' means there's a multi-step prompt chain with no real autonomy.

This matters because the valuation assumptions built into AI-first deals — the premium over comparable SaaS multiples — are premised on the second era. If you're pricing a company like it has a data flywheel and you later discover it's a wrapper, you've overpaid by a material amount.

Three questions that cut through the noise

• Is AI core to the product or bolted on? Core means removing the AI layer breaks the product. Bolted on means it's a feature that enhances an otherwise functional system. Both can be good businesses — but they carry different valuations and different risk profiles.
• What's the data flywheel? The sustainable AI moat is proprietary data that improves the model over time. Ask specifically: what data does the system accumulate through usage, and how does that feed back into model performance? Vague answers here are a serious signal.
• How dependent is the business on OpenAI or Anthropic pricing? Foundation model pricing has dropped roughly 90% since 2023 — which sounds good until you realise the same trajectory applies to any margin built on arbitraging those models. Companies whose economics depend on stable API pricing are exposed in ways their pitch decks don't acknowledge.

The assessment implication

Technical due diligence on AI-first companies requires a different lens than standard software DD. You're not just assessing code quality and architecture — you're assessing whether the AI layer is genuinely defensible, whether the data strategy holds up, and whether the team has the depth to navigate model transitions as the underlying technology continues to evolve at pace.

The companies worth the AI premium are the ones where the answer to all three questions above is clear and specific. If a founder can't give you a direct answer about their data flywheel in two sentences, that's your answer.