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Technical Reviews

The Technical Review for AI-ready software

Before building AI workflows, companies need a clear technical picture of their systems, data, integrations, access control, deployment and operational gaps.

Published
  • Data Foundations
  • AI Readiness

Many AI initiatives start with a tool, a model or a promising use case. That is understandable, but it often skips the part that decides whether the work can become useful in production: the current condition of the company’s software, data, integrations and operating model.

Before building AI workflows, companies need a clear technical picture.

A Technical Review for AI-ready software is not a theoretical AI strategy exercise. It is a practical assessment of the systems the business already depends on: where data lives, how it moves, who can access it, how software is deployed, where manual workarounds exist and what needs to change before AI can safely support real workflows.

AI readiness is not only model readiness

A modern language model can summarise, classify, draft and reason over information. But business value depends on the environment around the model.

If source data is unreliable, AI output will be unreliable. If permissions are unclear, AI can expose information to the wrong people. If integrations are missing, AI cannot act inside the workflow. If logs are weak, no one can explain what happened. If ownership is unclear, the workflow will decay after launch.

This is why AI readiness should be reviewed before implementation.

What a Technical Review should inspect

A useful Technical Review should focus on the practical conditions that make AI possible, safe and maintainable.

1. Data sources

The review should identify which systems, databases, documents, spreadsheets, inboxes and third-party platforms contain the information needed for the target workflow.

The important question is not simply “Do we have data?” Most companies do. The question is whether the data is reliable, current, accessible and structured enough for the intended use.

2. Data quality and structure

AI can handle imperfect input, but it cannot fix every underlying data problem. A review should look for inconsistent fields, duplicate records, unclear ownership, missing metadata, outdated exports and business rules hidden in spreadsheets.

For reporting, automation and AI-assisted workflows, data quality is not an abstract governance topic. It determines whether the output can be trusted.

3. Integrations and APIs

Many AI ideas fail because the required systems do not connect. A workflow may need customer data from the CRM, order data from ERP, documents from storage, status data from a portal and notifications through email or chat.

A review should identify available APIs, missing APIs, brittle integrations, manual exports, vendor constraints and where an integration layer may be needed.

4. Access control and permissions

AI should not become a shortcut around access control. The review should map who can access which data today and how those permissions should apply to AI-assisted workflows.

For example, an internal knowledge assistant should not show HR, finance or customer-sensitive information to users who would not otherwise have access. An agent should not receive write permissions where read-only access is enough.

5. Deployment and environments

A prototype can run on a laptop. A production workflow needs deployment discipline.

A review should inspect environments, release process, secrets handling, CI/CD, hosting, backup assumptions and operational dependencies. This is especially important if AI will be connected to business systems.

6. Logging and monitoring

AI workflows need observability. The business should know what the workflow processed, which systems were accessed, which outputs were generated, which actions were approved and where errors occurred.

For agentic workflows, logging is also a cost and governance control. Repeated model calls, tool calls and loops can create both operational and financial risk.

7. Documentation

Documentation should cover architecture, data sources, integration points, permissions, operating assumptions and known constraints.

This matters because AI workflows often cross departments. Without documentation, responsibility becomes unclear when the workflow needs maintenance or when an exception occurs.

8. Dependency and security exposure

A review should check outdated dependencies, unsupported platforms, authentication weaknesses, exposed secrets, weak permissions and unclear patching routines. It should also consider LLM-specific risks such as prompt injection, sensitive information disclosure and excessive agency.

OWASP’s LLM guidance is useful here because it frames risks such as unchecked autonomy, insecure plugins and sensitive information disclosure as application security concerns, not only AI theory.

9. Manual operational tasks

AI should be used where it improves real work. A review should identify repeated manual tasks: document classification, report assembly, copy-paste data movement, inbox triage, approval preparation, customer support routing, reconciliation and internal knowledge lookup.

The goal is to find workflows where AI or automation has operational value, not merely novelty.

10. AI cost and usage control

Usage-based AI pricing makes cost control part of technical design. OpenAI and Anthropic publish token-based pricing structures, GitHub has moved Copilot toward usage-based billing and agentic coding tools increasingly expose usage limits or on-demand usage.

A Technical Review should therefore ask how model calls will be limited, monitored and budgeted before an AI workflow becomes dependent on them.

What the review should produce

The output should not be a long strategy deck. It should be a practical roadmap.

A useful deliverable may include:

  • a system and workflow map;
  • a data source inventory;
  • integration gaps;
  • access-control risks;
  • deployment and maintenance gaps;
  • AI use-case fit assessment;
  • security and dependency observations;
  • logging and monitoring recommendations;
  • cost-control considerations;
  • a prioritised improvement roadmap;
  • a first controlled implementation phase.

This gives the business a way to decide whether to modernise, integrate, automate, prototype or postpone.

A safer sequence for AI-ready software

A practical sequence is:

Review → Prioritise → Improve foundations → Prototype narrowly → Integrate → Operate

This avoids a common mistake: building an impressive AI demo that cannot be connected to real systems without significant rework.

Boundaries matter

A Technical Review can identify practical security risks, access-control weaknesses, dependency exposure and operational gaps. It can support AI readiness and safer implementation planning.

It is not, by itself, a formal penetration test, compliance certification, legal AI Act review or replacement for specialist cybersecurity incident response. Those may be needed in some contexts, but they should be separately scoped.

The practical takeaway

AI readiness is a software, data, integration and operation question. Before building AI workflows, companies should understand the systems they want AI to support.

The most useful AI projects usually begin with a clear technical picture, not with a disconnected demo.

Sources and inspiration

Next step

Need a practical route from article topic to working software?

Memory(One) helps organisations review, modernise and build the systems their teams depend on.

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