In its State of AI 2025 report, Bessemer Venture Partners highlights “dark matter” questions about the future of vertical AI—specifically around legacy systems, competition with incumbents, and sustainable data moats. These are valid considerations, but across industries, we already see how vertical AI is addressing them in practice. The evidence points not to uncertainty, but to a clear trajectory of measurable impact.

1. Legacy Systems of Record: From Integration to Systems of Action

BVP asks: Will vertical AI depend on legacy systems, or replace them outright?

The reality: Both trends are happening in parallel. Enterprises are layering AI on top of existing systems of record—ERP, MES, SCADA, or CRM—while gradually shifting the center of gravity toward AI-native systems of action. This hybrid approach allows organizations to unlock value without disruptive rip-and-replace strategies.

For example, in manufacturing, AI companies are connecting predictive analytics to MES and SCADA systems, reducing downtime and optimizing energy use. In financial services, AI platforms are streamlining credit memo workflows by bridging into existing core banking systems. McKinsey notes that modernization through AI can cut tech-debt costs by 40–50%, accelerating transformation without wholesale system replacement.

2. Competition from Incumbents: Outcomes Over Scale

BVP asks: Will entrenched incumbents outpace vertical AI startups by virtue of scale?

The reality: Scale matters, but measurable outcomes win. Vertical AI succeeds by embedding into workflows and delivering domain-specific gains. A retail AI system that improves demand forecasting accuracy by double digits or optimizes promotions to reduce waste provides value incumbents can’t replicate with generic solutions.

A McKinsey study shows tailored AI agents boosted banking productivity by 20–60% and cut turnaround times by 30%—proof that specialized AI is outcompeting scale-driven approaches in the metrics that matter

3. Sustainable Data Moats: Context Wins Over Volume

BVP asks: Can vertical AI companies sustain data advantages in fragmented, privacy-sensitive industries?

The reality: Data moats aren’t about having more data, but about making complex data usable. Vertical AI applies ontologies, domain models, and contextual intelligence to unify fragmented datasets across silos. In healthcare AI, this means harmonizing unstructured clinical notes and structured patient records; in government and defense, it means linking disparate intelligence sources.

Wellington reports that sector-specific AI agents are already proving their ability to integrate unstructured and legacy data in regulated industries like healthcare and government.

4. Avoiding AI Sprawl: Orchestrated Intelligence

BVP asks (implicitly): With the proliferation of AI tools, will enterprises face unmanageable sprawl?

The reality: Yes, if left unchecked—but vertical AI provides a path forward. AI orchestration frameworks ensure interoperability and governance across specialized tools, reducing redundancy. TechRadar finds that 72% of enterprises now deploy generative AI, but many struggle with inefficiency due to duplication.

Vertical AI platforms are uniquely positioned to coordinate domain-specific agents into cohesive systems of action, aligning innovation with business strategy.

5. ROI from Vertical AI: Measurable and Defensible

BVP asks: Can Vertical AI prove durable ROI versus horizontal solutions?

The reality: Industry outcomes show it already does. Gartner projects that Vertical AI delivers up to 25% higher ROI than general-purpose AI because it solves sector-defined challenges with curated data and workflows (Unite.ai summary of Gartner).

Examples abound: manufacturers cutting downtime through predictive maintenance, banks reducing compliance costs while tightening fraud controls, and retailers improving margin through targeted promotions. These aren’t abstract promises—they’re proven, measurable outcomes.

From Questions to Impact

The questions BVP highlights aren’t unknowns; they’re the roadmap for progress. Across sectors, Vertical AI is:

• Integrating with legacy systems while evolving into AI-native systems of action.
• Delivering workflow-embedded outcomes that incumbents can’t match with scale alone.
• Turning fragmented data into structured, actionable intelligence.
• Preventing AI sprawl with orchestrated, domain-aware platforms.
• Demonstrating clear ROI across industries, from retail to banking to manufacturing.

The future of Vertical AI isn’t uncertain—it’s already being written in measurable, industry-specific results.

Citations

1. McKinsey: AI-enabled modernization reduces tech-debt costs by 40–50% (McKinsey).
2. McKinsey: Vertical AI agents increased banking productivity by 20–60% and cut turnaround by 30% (McKinsey).
3. Wellington: Sector-specific agents integrate unstructured data in industries like healthcare and government (Wellington).
4. TechRadar (via McKinsey): 72% of organizations use generative AI; risk of sprawl without orchestration (TechRadar).
5. Gartner: Vertical AI produces up to 25% higher ROI vs. general-purpose AI (Unite.ai).