Too many possible AI use cases, no defensible priority
Leadership sees opportunities across support, reporting, documents, sales, engineering, or operations, but cannot compare business value, readiness, and implementation effort consistently.
AI readiness assessment and model selection consulting
Vendor-neutral decision supportChoose where AI creates value, which model fits, and what to implement first.
Datrick provides AI readiness assessment consulting and AI model selection consulting services for leaders who need an evidence-backed roadmap before funding a pilot or committing to a platform.
Decision path
Evidence before implementation
1
Business valueRank use cases by consequence, frequency, effort, and measurable return.
Value
2
Model fitCompare suitable model routes against representative work and constraints.
Evidence
3
Readiness and riskInspect data, integration, security, governance, and operating ownership.
Control
4
Pilot roadmapDefine scope, success criteria, investment, owners, and decision gates.
Act
When to use an assessment
Use evidence to make the AI decision before implementation cost and risk compound.
The assessment combines generative AI readiness, AI use-case ROI, vendor-neutral model selection, and implementation roadmap consulting in one decision package.
Claude, OpenAI, Gemini, or an open-weight model could fit
Vendor claims are difficult to translate into your own quality, privacy, integration, latency, and cost requirements. You need representative evidence, not a generic leaderboard.
An experiment works, but production value remains unclear
A prototype exists, yet acceptance criteria, operating ownership, data access, failure handling, adoption, and measurable return have not been resolved.
A board, client, or executive sponsor needs a credible AI roadmap
The decision requires a written business case, risk boundaries, investment sequence, pilot charter, and decision gates that technical and business stakeholders can review.
Datrick AI Decision-to-Operations Framework
An AI readiness assessment resolves the framework's first four decision gates.
Each gate produces reviewable evidence and an explicit decision. Pilot evidence, production controls, and managed operations continue the framework after the assessment.
- 1
Value
Identify workflows where quality, speed, capacity, or decision support can create measurable value without hiding critical risk.
- 2
Model fit
Compare capable options using representative tasks, quality thresholds, context, privacy, integration, latency, and total operating cost.
- 3
Readiness and risk
Expose gaps in data, access, architecture, evaluation, security, governance, process ownership, and team adoption.
- 4
Pilot evidence
Define a bounded pilot, success criteria, roles, budget range, controls, timeline, and explicit continue, revise, or stop gates.
Assessment method
Business value, model evidence, and operating readiness are assessed together.
OpportunityBusiness case
Workflow inventoryTrigger, owner, users, frequency, current effort, consequences, and expected outcome.
Value modelCapacity, cycle time, quality, revenue, risk, adoption, and the baseline needed to measure change.
EvidenceModel selection
Representative scenariosRealistic tasks, expected outputs, edge cases, review criteria, and unacceptable failure modes.
Model routesClaude, OpenAI, Gemini, open-weight, retrieval, deterministic automation, or a hybrid where appropriate.
ReadinessDelivery constraints
Technical readinessData quality, integrations, identity, permissions, environments, observability, and support ownership.
Governance readinessPrivacy, security, retention, human review, auditability, vendor exposure, and approval authority.
RoadmapDecision package
Pilot charterScope, success criteria, owners, controls, timeline, investment, and operating assumptions.
Implementation sequenceImmediate actions, dependencies, decision gates, responsible teams, and scale conditions.
What you receive
A reviewable decision package, not a generic AI strategy deck.
Use-case and ROI scorecard
Prioritized opportunities with assumptions, baseline measures, expected value, dependencies, and reasons to defer unsuitable work.
Model selection evidence
Shortlisted routes, evaluation criteria, representative results, tradeoffs, cost drivers, and the basis for the recommendation.
Readiness and risk register
Data, integration, security, privacy, governance, adoption, support, and ownership gaps with practical mitigation actions.
Pilot charter and roadmap
Scope, architecture direction, success thresholds, roles, timeline, investment sequence, and continue, revise, or stop gates.
Fixed-scope options
Choose the smallest assessment that can support the decision.
Decision Sprint
One priority workflow and up to three model routes. Designed for a buyer who needs a defensible pilot decision quickly.
Investment$15,000 fixed scope Decision outputUse-case case, model recommendation, readiness gaps, pilot charter, and executive readout.Portfolio Assessment
Up to three workflows and up to four model routes for the priority use case. Designed to rank opportunities before funding a portfolio.
Investment$22,500 fixed scope Decision outputPortfolio scorecard, priority benchmark, risk register, implementation sequence, and executive readout.Enterprise or Partner Assessment
Up to three workflows, up to five model routes, and enhanced governance or white-label delivery planning.
Investment$30,000 fixed scope Decision outputDecision package, governance boundaries, partner or enterprise operating model, roadmap, and executive readout.Standard payment is 50% at signing and 50% before the executive readout unless an existing master services agreement defines different terms. Final scope reflects workflow count, model routes, access, evidence, and governance requirements.
Risk before rollout
Common AI concerns become explicit assessment criteria.
What buyers need resolved
- Will the model be accurate enough for the real workflow?
- Can sensitive data remain within approved boundaries?
- Will token, tooling, integration, and review costs erase the return?
- Are we creating avoidable vendor lock-in?
- Who owns output review, incidents, and model changes?
- Will the team adopt the workflow after the pilot?
How the assessment responds
- Representative tasks, quality thresholds, and unacceptable failure modes.
- Data-flow, identity, permission, retention, and human-review boundaries.
- Total operating cost assumptions tied to measurable baseline value.
- Portable architecture decisions and explicit switching constraints.
- Named owners, escalation, logging, monitoring, and decision gates.
- Workflow design, training needs, adoption signals, and rollout sequence.
Choose an assessment provider
Evaluate the provider by the decisions and evidence it can produce before AI rollout.
A maturity questionnaire or readiness tool can organize inputs. It does not replace accountable analysis of business value, representative model performance, data constraints, risk, operating cost, and ownership.
| Selection criterion | Evidence to require | Warning sign |
|---|---|---|
| Business value | Workflow baseline, measurable outcome, assumptions, value range, and reasons to defer. | A generic use-case list without operational or financial evidence. |
| Model selection | Representative tasks, quality thresholds, comparable model routes, latency, privacy, and total operating cost. | A predetermined vendor recommendation or public benchmark presented as proof of fit. |
| Data and integration readiness | Named sources, access path, data quality, identity, permissions, system dependencies, and ownership gaps. | A score that does not identify the systems, owners, or remediation required. |
| Risk and governance | Failure modes, sensitive-data boundaries, human review, audit evidence, approval authority, and residual risk. | Security and legal work deferred until after the pilot is built. |
| Decision output | Prioritized scorecard, risk register, model evidence, pilot charter, owners, investment sequence, and stop gates. | A strategy presentation without a fund, revise, or stop decision. |
| Production accountability | Monitoring, evaluation, incidents, changes, cost control, adoption, and support ownership considered before rollout. | A pilot roadmap that ends at deployment and leaves operations unnamed. |
Five-minute diagnostic
Test whether the decision is ready for an accountable assessment.
AI assessment FAQ
Questions buyers ask before commissioning an AI readiness assessment.
What is included in an AI readiness assessment?
Datrick reviews business use cases, expected value, workflow ownership, data and integration readiness, security and governance constraints, model options, evaluation criteria, operating risk, and the practical path to a pilot. The output is an evidence-backed decision package and implementation roadmap.
How do you select the right AI model for a business use case?
We define the required tasks, quality threshold, latency, context, privacy, integration, and cost constraints, then compare suitable model routes using representative scenarios. The recommendation is based on observed fit and operating requirements rather than a universal model ranking.
Is the assessment vendor-neutral?
Yes. Datrick can assess Anthropic Claude, OpenAI, Google Gemini, open-weight models, and relevant workflow tools. Existing contracts and platform standards are considered, but the recommendation is tied to the use case, evidence, risk, and total operating cost.
How long does an AI readiness and model selection assessment take?
A focused decision sprint normally takes two weeks. Portfolio and enterprise assessments normally take three to four weeks, depending on the number of workflows, stakeholders, model routes, data constraints, and governance requirements.
What does an AI readiness assessment cost?
Datrick's fixed-scope options start at 15,000 USD for one workflow. A portfolio assessment is 22,500 USD, and an enterprise or partner assessment is 30,000 USD. Final scope depends on workflow count, model routes, access, evidence, and governance requirements.
What is included in an AI implementation roadmap?
An AI implementation roadmap should identify the prioritized workflow, expected value, chosen model route, data and integration dependencies, security and governance controls, evaluation criteria, pilot scope, decision gates, owners, investment sequence, production requirements, and the conditions for continuing, revising, or stopping. Datrick delivers this roadmap as part of the assessment decision package rather than as a generic technology backlog.
Do we need to provide sensitive data before scoping?
No. Initial qualification can use a written description of the workflow, risk, current tools, constraints, and timeline. Sensitive data, credentials, or confidential materials should only be shared after an approved access and confidentiality path is established.
How do I choose an AI readiness assessment provider?
Choose a provider that evaluates business value, representative workflow performance, data and integration readiness, security and governance, total operating cost, and production ownership together. Require named deliverables, explicit assumptions, measurable decision gates, vendor-neutral model comparison, and a practical implementation roadmap rather than a generic maturity score or strategy deck.
How can a business assess AI readiness before investing in models or platforms?
Start with the business workflow and measurable outcome, then evaluate data access, integration, model quality, privacy, security, governance, human review, operational ownership, cost, and adoption. Compare suitable model routes on representative tasks before committing to a platform, and fund a bounded pilot only when its success criteria and stop conditions are explicit.
Should a business buy an AI readiness tool or commission an assessment?
A readiness tool can help collect answers or calculate a repeatable score, but it cannot independently validate workflow value, model behavior, data constraints, governance decisions, or operating ownership. A consequential rollout usually needs a provider-led assessment that uses tools as evidence inputs and produces a decision package accountable stakeholders can review.
Fit and boundaries
This service is for consequential decisions, not AI theater.
A named buyer must decide what to fund
The assessment has an accountable sponsor, real workflows, a decision deadline, relevant stakeholders, and a legitimate path to operational context.
Model choice must reflect real constraints
Quality, data, privacy, security, integration, cost, latency, governance, and operating ownership matter more than a generic model ranking.
A predetermined vendor recommendation needs validation
Datrick can assess an existing preference, but will not manufacture evidence for a model or platform regardless of observed workflow fit.
Rights, access, ownership, or intended use are unclear
Confidential materials, sensitive data, credentials, and restricted systems require an approved access, identity, and data-handling path before inspection.
From decision to delivery
Continue only when the assessment supports implementation.
AI consulting pricing and implementation cost guide
Compare readiness, pilot, implementation, managed operations, hidden cost, and white-label commercial models before requesting proposals.
AI consulting RFP and vendor scorecard
Give shortlisted suppliers one comparable workflow brief and score value, model fit, evaluation, security, operations, and commercial responsibility.
AI readiness assessment vs POC vs pilot
Match assessment, technical feasibility, supervised workflow evidence, and production readiness to separate decision gates.
AI workflow automation consulting
Build one controlled production workflow with evaluation, human review, integration, and operating ownership.
Claude implementation services
Move a well-scoped Claude use case into production with context, controls, evaluation, documentation, and support.
AI model training and evaluation
Operate technical evaluation programs for coding, SQL, data, analytics, rubrics, reference answers, and model-output review.
Production-ready AI workflow checklist
Inspect workflow, context, evaluation, human review, security, monitoring, and handover before rollout.
Written scoping first
Describe the AI decision, what is at risk, and when it must be made.
A senior lead reviews every inquiry and responds within one business day with a scoping recommendation or qualifying questions. Calls follow written qualification.