AI People & Change Readiness: Hake AI Readiness Deep Dive

Most SMB AI initiatives don’t fail because the model is weak. They fail because the organization isn’t set up to change how work is done. AI shifts roles, decision rights, and habits. If you don’t manage that intentionally, adoption becomes patchy, risky, and short-lived

Research consistently shows that most value from AI comes from organizational redesign and adoption, not from the algorithm itself. In SMB terms: you win by changing workflows, building skills, and reinforcing new ways of working—not by chasing the newest tool.

Hake Digital AI Self Diagnosis People & Change Question 1 of 4: Who is currently using AI, and how confidently?

SMBs often find AI usage is uneven: a few enthusiasts use it daily, while others avoid it or quietly feel behind. This creates both a capability gap and a risk gap (unreviewed outputs, sensitive data pasted into tools, inconsistent customer communications).

Hake Digital AI analysis shows common situations include:

Best practices that work for SMBs:

• Run a short ‘AI usage pulse’ survey and interviews: who uses what, for which tasks, and with what confidence.

• Define 3 levels of usage (beginner / competent / advanced) with examples relevant to your business.

•  Create a shared prompt and workflow library: reusable templates for emails, summaries, proposals, customer replies, internal SOP lookups. 

• Establish an internal community of practice (30 minutes every 2 weeks) to share learnings and prevent tool sprawl.

Hake Digital AI Self Diagnosis People & Change Question 2 of 4: Where is AI helping—and where is it disappointing?

AI often feels ‘amazing’ for drafting and summarizing, but ‘unreliable’ for anything that needs company-specific facts, consistent tone, or decisions with consequences. That is normal: those use cases require stronger guardrails, better data, and human review.

Hake Digital AI analysis shows common patterns:

Best practices that work for SMBs:

• Match use cases to risk: start with low-risk, high-volume tasks; add human review for anything customer-facing or financial.

• Use ‘source-grounded’ approaches for factual tasks (e.g., a knowledge base or RAG) and require citations in outputs.

• Create lightweight QA checklists: accuracy, tone, compliance, and “is this safe to send?”

• Build feedback loops: capture examples of great and poor outputs and use them to improve prompts and guardrails.
  
  

Hake Digital AI Self Diagnosis People & Change Question 3 of 4: Who would need training first if AI tools were expanded?

Hake Digital AI analysis shows common training gaps include:

Best practices that work for SMBs:

• Start with managers and customer-facing teams: they set norms and touch revenue.

• Use a ‘learn → apply → share’ cadence: short training, immediate application to a real task, and a quick share-back.

• Provide approved tools and templates: fewer tools, clearer standards.

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

• Include critical thinking skills: how to verify outputs, how to avoid over-trusting AI, and when not to use it.

• Track adoption with simple metrics: weekly active users, tasks completed with AI, and quality incidents.

Hake Digital AI Self Diagnosis People & Change Question 4 of 4: Is there internal ownership for AI decisions and governance?

SMBs rarely need a big committee—but they do need named accountability for: tool selection, policy, data access decisions, training, and incident handling. Without it, teams experiment independently and the business loses control of consistency and risk.

Hake Digital AI analysis shows common situations include: 

Best practices that work for SMBs:

• Create a simple governance model: one accountable owner + a small cross-functional group (ops/IT, finance, customer) that meets monthly.

• Define decision rights: approved tools, approved data sources, and required controls by risk level.

• Adopt a basic risk workflow aligned to NIST AI RMF: map use cases, measure risks, manage controls, and review incidents.
  

• Publish a short internal policy and keep it updated as tools evolve.
  
  

Hake Digital AI Self Diagnosis People & Change Summary: Why are people & change capabilities necessary before scaling AI?

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

• Measure current AI usage and confidence across roles.
• Standardize on approved tools and publish a safe-use policy.
• Train by role on real workflows; create a shared prompt/workflow library.
• Add quality checks and human review steps where risk is higher.
• Assign ownership and set a cadence for governance and continuous improvement.