The AI vendor landscape is broken

Every AI vendor claims to solve every problem. Demos are impressive. Case studies describe transformational outcomes. ROI calculators project millions in value.

Yet despite the impressive vendor landscape, many enterprise AI projects stall or fail. Something is broken in how the AI vendor landscape operates.

How the landscape is broken

Capability claims exceed reality

AI vendors naturally emphasise their capabilities. Marketing materials highlight the most impressive use cases. Demos are designed to impress, showing what is possible under ideal conditions.

But enterprise environments are not ideal. Data is messier than demo data. Integration is harder. Users are less enthusiastic. The gap between demo capabilities and production reality is substantial.

Data requirements are understated

Vendors have strong incentives to minimise data requirements. Acknowledging that AI success requires substantial data preparation would slow sales cycles. Easier to claim that the platform can work with your existing data.

Buyers discover data requirements mid-project when implementation stalls.

Implementation complexity is hidden

AI implementation involves much more than deploying a platform. It requires data preparation, feature engineering, model training, integration, change management, and ongoing maintenance.

Vendor sales processes often focus on platform capabilities rather than implementation complexity.

Lock-in is engineered

AI platforms often create dependency that makes switching costly. Proprietary data formats, custom model architectures, and platform-specific integrations make it difficult to migrate.

How to navigate the landscape

Start with Use Case Requirements. Begin with a clear understanding of what you need AI to accomplish. Use requirements to evaluate vendors rather than letting vendor capabilities define your requirements.

Assess Data Requirements Explicitly. Ask vendors explicitly about data requirements for your specific use case. Push past general claims to specific criteria.

Request Relevant Reference Customers. Seek customers with similar data environments, technical capabilities, and use cases. Ask specific questions about their experiences.

Run Realistic Proofs of Concept. Before committing, run a proof of concept with your actual data. Not a demo with vendor-prepared data.

Understand Total Cost of Ownership. Platform licence fees are often the smallest component. Include data preparation, implementation, integration, change management, and ongoing maintenance.

Negotiate Risk Sharing. Consider success-based pricing, trial periods, performance guarantees, and exit clauses.

An independent read gives you a perspective vendors cannot offer. A Truth Score Read shows your real starting point before you sign, so you can set realistic expectations and choose vendors on evidence.