Hake AI Readiness Dimension 2 DEEP DIVE: Are Your Data & Technology Ready?

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SMBs often start an AI pilot and immediately hit a wall: the data is spread across tools, access is unclear, and the tech stack can’t connect steps end-to-end. When this happens, the AI tool is blamed—but the real constraint is the “plumbing” that feeds the AI and connects it to work.

Good AI outcomes require a simple foundation: (1) the business knows what data matters for the use case, (2) that data can be accessed with the right permissions, (3) quality is good enough to trust, and (4) the stack can integrate the AI into the workflow with guardrails.

Hake Digital AI tip: don’t start by buying new AI tools. Start by checking whether your most important data is accessible, governed, and integration-ready.

Hake Digital AI Self Diagnosis Data & Technology Question 1 of 4: Hake Digital AI Self Diagnosis Data & Technology Question 1 of 4:

SMBs typically answer this quickly (“it’s in our CRM” or “it’s in our accounting system”). But AI value usually depends on data that sits across multiple places: CRM + email + tickets + spreadsheets + documents + ERP + website analytics. If you can’t find the right data reliably, you can’t scale AI beyond demos.

Hake Digital AI analysis shows common situations include:

Best practices that work for SMBs:

• Start with a use‑case-driven data inventory: list 3–5 AI use cases and the exact fields/documents each one needs; then map sources.

• Create an “AI‑ready” single view for each key entity (customer, product, job, invoice): even a simple consolidated table/view helps.

• Keep a lightweight data catalogue: a shared page that lists the dataset, owner, definitions, refresh cadence, and quality notes.

Hake Digital AI Self Diagnosis Data & Technology Question 2 of 4: Who has access to it, and under what permissions?

AI workflows often require data to move across tools (or be queried by an AI assistant). If permissions are informal (“everyone has the shared inbox password”), you create security risk. If permissions are too tight, pilots stall because nobody can connect the pieces.

Hake Digital AI analysis shows common situations include:

Best practices that work for SMBs:

• Use role-based access control (RBAC): define 5–10 roles (sales, ops, finance, support, leadership) and what each role can read/write.

• Prefer single sign-on (SSO) where possible; avoid shared accounts.

• Maintain an access register for sensitive datasets (who has access, why, how reviewed).

• For GenAI assistants, implement “least privilege” data retrieval and log prompts/outputs for audit and learning.

Hake Digital AI Self Diagnosis Data & Technology Question 3 of 4: Are there compliance, privacy, or regulatory constraints?

Even small businesses handle regulated or sensitive data: customer personal data, employee HR data, payment data, health data, contracts, and commercially sensitive pricing. AI increases the risk surface (especially with GenAI) because data can be copied into prompts, stored by vendors, or reproduced in outputs.

Hake Digital AI analysis shows common situations include:

Best practices that work for SMBs:

• Define a simple AI usage policy: what data is allowed, what is banned, and which approved tools can be used.

• Align to a recognized risk framework: NIST AI RMF provides practical categories (Govern / Map / Measure / Manage).

• Treat GenAI apps like any other system: run vendor risk checks, configure retention settings, and enforce access controls.

• For security, use OWASP’s LLM Top 10 as a checklist (prompt injection, data leakage, insecure plugins/tools, etc.).

• If you operate in the EU, be aware of the EU AI Act’s risk-based approach and document your use cases and controls accordingly.
  
  
  

Hake Digital AI Self Diagnosis Data & Technology Question 4 of 4: How well does your current tech stack support integration and automation?

Many AI pilots stall because the AI can generate an answer, but nobody can reliably trigger actions (create a ticket, update CRM fields, draft and send an email with approvals, generate an invoice, etc.). The missing piece is integration: APIs, automation tools, data pipelines, and monitoring.

Best practices that work for SMBs:

• Build a minimal integration backbone: pick one automation platform and standardize connectors and logging.

• Design for ‘human-in-the-loop’ approvals at the right points (especially when money, customers, or compliance is involved).

• For knowledge-based use cases, prefer Retrieval-Augmented Generation (RAG): keep source documents in a governed repository, chunk and index them, and cite sources in answers.
  

• Introduce basic LLMOps: prompt/version control, evaluation sets for quality, and monitoring of usage and incidents.
  
  

Hake Digital AI Self Diagnosis Data & Technology Summary: Why is data & tech readiness necessary before scaling AI?

AAI only scales when it can reliably access trusted data, operate within clear permissions, and connect to the systems where work happens.

• Pick 1–2 use cases with clear ROI.

• Map the minimum data needed; document where it lives and who owns it.
• Fix access: RBAC, SSO where possible, and an access register for sensitive datasets
• Write an AI safe-use policy; align to NIST AI RMF and check OWASP LLM Top 10 risks.
• Build a thin integration layer and instrument it (logs, errors, approvals).
  
• Only then expand to additional workflows and datasets.