4 Classic Pitfalls in AI Vendor Selection

Introduction

Artificial intelligence is moving quickly from experimentation to execution. Across industries, enterprises are piloting AI in customer service, product development, HR, supply chain, and finance. Yet one reality is clear: the wrong AI vendor can derail even the best strategies.

Many failures stem from four predictable pitfalls. This article focuses on those pitfalls and pragmatic safeguards for CIO and CDO teams. Use them to shape your RFP questions and shortlist reviews. For a structured evaluation model and checklist, see: The Six Dimensions of AI Vendor Evaluation That Matter Most. For the business and reputational consequences, see: The Hidden Costs of Choosing the Wrong AI Vendor.

Pitfall 1: Proof-of-Concepts That Never Scale

The trap: Vendors deliver impressive pilots that collapse under enterprise conditions.

Why it happens:

• Optimizing for demos over production readiness.

• Measuring novelty instead of scalability and reliability.

• Vague pilot success criteria and no path to production.

The cost: Endless pilots, budget burn, credibility loss, and stalled adoption.

How to avoid it (at a minimum):

• Tie pilot success to measurable business KPIs and production SLOs.

• Validate performance with realistic data volumes and failure modes.

• Require production case studies from similar enterprises.

For evaluation criteria that operationalize this (benchmarks, observability, scalability requirements), use the Six Dimensions framework.

Checklist prompts:

• What are the specific production SLOs we expect?

• Which business KPIs will define a successful pilot?

• Has the vendor proven scale with a customer like us?

Pitfall 2: High-Maintenance Integrations

The trap: Heavy customization to fit core systems turns integration into an ongoing burden.

Why it happens:

• Limited native connectors and standards support.

• Siloed architectures and brittle one-offs.

• Underestimated lifecycle integration cost.

The cost: Persistent IT drain, slower time-to-value, and fragile operations.

What to check (concise):

• Mature APIs/SDKs and prebuilt connectors.

• Identity and networking patterns enterprises expect.

• Transparent total cost of integration.

For a full technical checklist and scorecard weighting of integration, see the Six Dimensions framework (Integration Ease).

Checklist prompts:

• What native connectors and APIs are available?

• Who owns integration maintenance and updates?

• How is ongoing integration cost calculated?

Pitfall 3: Vendor Lock-In

The trap: Attractive pricing and features hide closed, proprietary ecosystems.

Why it happens:

• Proprietary architectures limit portability of data, models, and workflows.

• Licensing models and quotas discourage exit.

• Short-term cost focus ignores long-term optionality.

The cost: Innovation slows, switching costs soar, and agility erodes.

Safeguards:

• Favor open standards and modular architectures.

• Negotiate portability, export, and exit assistance.

• Validate the vendor’s track record on customer flexibility.

For prevention levers (portability criteria, contractual protections), see the Six Dimensions framework (Flexibility). For the downstream impact (innovation stall, opportunity cost), see Hidden Costs.

Checklist prompts:

• What data portability and export options exist?

• Are there exit clauses and assistance in contracts?

• Does the vendor support open standards?

Pitfall 4: Overlooking Governance & Compliance

The trap: Governance is sidelined in the rush to deploy.

Why it happens (typical):

• Speed-to-market pressures overshadow risk management.

• Certifications and controls assumed, not verified.

• Risk and compliance teams engaged too late.

Minimal non-negotiables:

• Independent attestations (e.g., SOC 2, ISO 27001) and sector controls as applicable.

• Clear policies for data use, privacy, bias mitigation, and model documentation.

• Early involvement of legal, security, and compliance stakeholders.

For the complete control set and regulatory readiness guidance, use the Six Dimensions framework (Security & Compliance). For the consequences of gaps (fines, trust erosion), see Hidden Costs.

Checklist prompts:

• What certifications and attestations does the vendor hold?

• How is data usage and privacy handled?

• When are legal and security teams involved?

Bringing It Together

Treat these pitfalls as a pre-mortem for AI vendor selection. Use them as a checklist to shape pilot design, RFP questions, and shortlist reviews. Then apply the structured evaluation model to score vendors objectively:

• Framework and scorecard: The Six Dimensions of AI Vendor Evaluation That Matter Most.

• Business impact and risk narrative: The Hidden Costs of Choosing the Wrong AI Vendor.

This separation keeps your process tight: pitfalls for awareness, dimensions for decisions, and hidden costs for executive alignment.

Conclusion

AI vendor selection is a strategic choice. Avoid predictable traps, anchor pilots to production outcomes, and insist on portability and governance from day one. Use the Six Dimensions framework to evaluate rigorously, and use the Hidden Costs narrative to align stakeholders on why discipline matters.