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DEPLOYPROAI ENGINEERING · AI TRANSFORMATION · AI READINESS ASSESSMENT

Know where AI fitsbefore you spend on it.

A structured audit of your codebase, data, team, and delivery process. You get a prioritised roadmap with measurable targets and a costed plan.

3–6 weeks

Book a Strategy CallSee the work
  • Codebase audit
  • Data readiness
  • Workflow mapping
  • Team capability
  • Use-case ranking
  • Delivery baseline

The problem we solve

Most AI budgets are spent before anyone has checked whether the process is ready.

  • A list of use cases, but no way to rank them by value, risk, or readiness.
  • The leadership team wants to know how much AI will cost and what they’ll get from it, but no one has a clear, evidence-based answer.
  • A pilot ran, but success was never defined or measured.
  • Teams often buy AI tools before they’ve clearly defined the process those tools are supposed to improve.

This is not isolated.Only 37 percent of organisations report positive EBIT from AI. Only 32 percent can measure impact.

Step 1

Goals and guardrails

Agree the outcome, the metric, the baseline, and what AI is allowed to change and what it is not.

Step 2

Understand

Identify systems, dependencies, risks, and where AI should be used.

Step 3

Rank

Score each candidate process on value, risk, and readiness, with evidence for each score.

Step 4

Decide

A clear plan with costs and targets, with a roadmap.

The Nine Foundational Categories for AI Adoption

What the assessment covers.

Business case and use-case ranking

We define each use case as an outcome with a clear metric and prioritisation.

Codebase audit

We review your architecture, dependencies, and test coverage, and flag where AI-driven changes could break things and where it can help.

Data readiness

We check what each use case needs, where the data lives, and whether the data is usable in practice: accurate, complete, and up to date.

Platform and infrastructure

We look at your environments, compute, model access, and the cost controls needed to run AI workflows in production.

Governance, security and responsible AI

We assess the risks for each use case, the guardrails required, and define the approval chain.

Team capability and operating model

Who can build with AI, who can review it, and who owns the decisions.

Operational readiness

We map how this runs day to day, including monitoring, AI observability, incident ownership, and production workflows.

Delivery baseline

We establish a delivery baseline: lead time, deployment frequency, failure rate, and recovery time.

Costed roadmap

You get a clear status for each of the nine areas, and a prioritised plan with timelines and cost.

What you get at the end

The audit leads to three clear outcomes.

Start

You can begin immediately. These use cases require minimal effort and show clear return.

Fix first

Some gaps need to be addressed before starting. We identify them and estimate the effort required.

Blocked

This will not work in its current state. We explain what needs to change before it makes sense to proceed.

How this compares to existing frameworks.

These nine checks cover the same ground as common AI readiness frameworks, and focus only on what needs to work in practice.

For governance and risk, we follow the same principles used in:

What you walk away with

Deliverables. Not promises.

Outcome, use cases and roadmap

Ranked use cases, a costed 90-day plan, and clear targets

Baseline and success metrics

Current performance baseline and agreed improvement targets

Architecture and system risks

System design, dependencies, and risks that could impact delivery

Data and operational readiness

Readiness across data, systems, and workflows, with supporting evidence

Governance and operating model

Guardrails, approvals, monitoring, and ownership defined

Executive decision pack

Recommendation, first project, budget, and go/no-go decision

Optional, priced separately

End with something running.

On top of the assessment, we build the top-ranked use case, coupled with our AI Research, in a controlled setup using representative data. You’ll have a working MVP to review before deciding whether to move to production.

Check how we run AI Research

FAQs

Frequently asked questions

In that case, DeployProAI will tell you so. We assess each step of your process and use AI only where it is reliable. A 30-minute call or a readiness assessment will establish whether AI is suitable before you make any commitment.

For details, please see the AI readiness assessment.

Yes. DeployProAI signs a non-disclosure agreement (NDA) before the first call, so that you can speak openly about your code, your plans and your business.

Read access to the repositories, pipelines and data in scope, and time with the people who own those systems and the processes we are looking at. The range depends on the size of your organisation and the number of processes in scope. We work inside your perimeter, under your security policies. The optional MVP build sits on top of that.

No. We assess the platform you already run and report what any change would cost. You make that decision afterwards, with the numbers in front of you.

See all FAQs

Further reading

Why AI Fails in ProductionAI now writes half the code in many engineering teams, yet most enterprise pilots show no measurable profit. What the 2025 and 2026 evidence says, why the gains stall before production, and how the teams that get results work.Read the article

Start with the outcome.

Bring us the process you want changed. In 3-6 weeks, you will know what to build, what it will cost, and what it will return.

Book a Strategy Call
  • NDA-first
  • Your codebase
  • Measured with DORA and SPACE
  • GDPR
  • DPDP
  • SOC 2 practices
  • PCI-DSS
  • IRDAI-aware
  • EU AI Act