How to Choose an Enterprise AI Implementation Partner

Start with a business decision, not an AI demo

A fluent chatbot demo does not show whether a system finds the right document, preserves access boundaries, handles exceptions or saves time in a real workflow. Before meeting a supplier, choose work with a named owner, meaningful frequency, observable inputs and testable outcomes. Record a baseline for time, rework, errors, cost and current satisfaction.

Ask the partner to describe the use case as a boundary: who uses it, which data is permitted, whether AI suggests or acts, when a person must take over and what happens when it is wrong. When a proposal starts with models, agents and tools without these answers, the company is buying technology before understanding the decision to improve.

• ✓A process owner able to change the workflow and accept the result.
• ✓A baseline that supports a before-and-after comparison on equivalent work.
• ✓Defined inputs, outputs, exceptions and consequences.
• ✓Agreed conditions to continue, adjust or stop the pilot.

A strong AI partner does not sell one model for every problem. It helps the business produce enough evidence to expand, adjust or stop.

Require an evaluation system, not a few favourable examples

Generative AI output varies, so acceptance cannot rely on impression alone. The partner should help create an evaluation set representing ordinary work, missing data, out-of-scope requests, sensitive content and high-consequence errors. Each use case needs suitable measures such as field accuracy, grounded citation rate, edit rate, handling time, cost and exceptions.

Evaluation must rerun when prompts, models, retrieved data or workflow change. Ask how versions, regression and failure analysis are managed. A RAG assistant should cite sources and respect document permissions; an AI agent needs separate tests for tool choice, parameters, action limits and approval behaviour.

• ✓Who creates the test set, and does it represent real data and exceptions?
• ✓Which quality threshold applies to each consequence class?
• ✓How are failures found, corrected and added to future evaluation?
• ✓Are cost, latency and human-review time measured alongside quality?

Examine data, integration and deployment choices

Enterprise AI usually depends more on data, APIs and permissions than on the model itself. Require a map of sources, purpose, ownership, retention, deployment region and data minimisation. Contradictory knowledge, missing version control and weak permissions will create unreliable output even with a capable model.

The partner should explain which requirement a cloud, private-cloud or on-premise choice addresses and the trade-offs in cost, performance and operations. Integrations need API contracts, retry, timeout, idempotency, audit logs and recovery. The business should control appropriate accounts, repositories, configuration, evaluation sets and log exports to avoid opaque dependency.

• ✓A data map, access model and source of truth for important fields.
• ✓A clear boundary around changing models or providers without rebuilding the workflow.
• ✓Handling for timeouts, API failures, stale data and discontinued dependencies.
• ✓Ownership of repositories, model accounts, vector stores, prompts, evaluations and logs.

Evaluate governance and security across the lifecycle

The NIST AI RMF organises risk management through Govern, Map, Measure and Manage. Ask who owns policy and accountability; how context, affected people and risks are mapped; how quality and risk are measured; and who operates mitigation, monitoring and response after launch.

The OWASP Top 10 for LLM Applications covers risks including prompt injection, sensitive information disclosure, supply-chain weaknesses, improper output handling and excessive agency. A sound proposal connects relevant risks to the actual architecture through authentication, authorisation, data filtering, least-privilege tools, action approval, rate limits, logs and tests. Saying only that data is encrypted does not demonstrate a secure system.

• ✓Who may change production prompts, models, tools and knowledge sources?
• ✓What may AI read, write or trigger for each role?
• ✓How are incidents, incorrect content and sensitive data detected and handled?
• ✓Are a kill switch, rollback, audit trail and response owner available?

Read the proposal through total operating cost

An AI proposal should separate discovery, data preparation, prototype, integration, evaluation, production hardening and support. Model fees are only one part; total cost includes vector storage, observability, infrastructure, moderation, human review, data maintenance and regression handling. Ask for an estimate per business unit such as document, conversation or completed task.

Milestones should correspond to evidence: an accepted baseline and test set; a pilot on one workflow; a quality, cost and exception report; a production decision; and operational handover. Avoid absolute ROI promises without data. A credible partner accepts that a pilot may recommend narrowing or stopping when value does not justify risk and operating cost.

• ✓Separate fixed, variable and third-party cost.
• ✓State assumptions for volume, context, tokens, storage and human review.
• ✓Define quality, cost and adoption thresholds for production.
• ✓Include support, monitoring, model changes and regression evaluation after launch.

Twelve questions before choosing an AI partner

Score every candidate on one matrix covering problem understanding, data, evaluation, integration, security, operations, cost and handover. Provide a cleaned sample with representative exceptions and ask candidates to design the evaluation approach before presenting a solution. How a team handles uncertainty is a stronger signal than the model name on a slide.

Friday Works begins an AI engagement with the use case, baseline, data and validation criteria, then runs a narrow pilot with human review before expansion. A business can use these questions to assess Friday Works like any other partner and require every assumption, limit and decision to be documented in the proposal.

• ✓1–3. What are the problem, owner and current baseline?
• ✓4–5. Which data is used, who may access it and where is it retained?
• ✓6–7. How are the test set, metrics and acceptance thresholds designed?
• ✓8–9. How does AI integrate, seek approval, log and recover?
• ✓10–12. What are total cost, ownership and conditions to expand or stop?