|
| Control by Design, Not by Default In this week's newsletter, we're looking at how successful organizations structure AI deployments. In last week's newsletter I pushed back on calling AI "introspective" because imprecise language creates confusion around process and accountability. This week's lead article by AI futurist Zack Kass shows the pattern clearly: the language you use helps to determine whether your team embraces AI as a supervised tool or treats it as an autonomous decision-making engine. When something goes wrong, vague terminology creates vague accountability. The organizations successfully leveraging AI aren't deploying better models, they're building better frameworks that require human control at critical decision points. This week's case studies reveal how the flag-suggest-approve (FSA) pattern keeps humans in the loop, and why guard rails and audit trails matter more than you think. | Language-workflow mismatch creates governance blind spots: Treat language as policy infrastructure so processes, responsibilities and expectations are unambiguous. | Control lives in people and process—data labeling, review cadences, and specialty QA are ongoing budget lines (not afterthoughts) if you want reliable outcomes. | Use tools, but verify: drafts, research, and docs improve with assistants like Gemini—keep humans on facts, citations, and anything hitting customers or compliance. |
|
| Crossing Societal Thresholds: Language as the Lever for AI AdoptionZack Kass suggests that adoption moves in thresholds and that people will accept a new technology once they feel its errors are understandable, its oversight is explicit, and its process and outcome responsibilities are unambiguous. That the real constraint on AI is no longer capability, it's trust, control, and the norms around deployment. A good place to start establishing norms is the clear and intentional use of language which can shrink the expectation gap and clarify process and approval. Organizations must then deploy AI with adjusted workflows, documentation, and approval chains to match the new vocabulary. When you call something a "muse" but still treat outputs as final deliverables, the disconnect creates risk. Key Insight: The gap between what we say AI does and how we actually use it becomes a governance blind spot. Treat language as policy infrastructure and match your vocabulary to documented review steps, sign-off requirements, and QA cadences. |
The New Jobs Behind AIBehind every AI system sits a workforce of data labelers, prompt engineers, and QA specialists fine-tuning outputs and catching errors. Major AI companies rely on thousands of workers to support reinforcement learning (RLHF) -- to review model responses, flag biases, and improve accuracy. As models improve for everyone, human oversight becomes more specialized, and offers real competitive advantage. Key Takeaway: To build competitive advantage, budget for the staff needed to maintain it: reviewers who provide feedback, specialists who flag errors, and experts who validate context. View Full Article → Successful AI Process for Operations That MatterThe flag-suggest-approve (FSA) process for building AI into critical operation is: AI flags items requiring attention, offers recommendations, and your team approves or rejects each suggestion. This framework allows you to leverage AI's speed and scale without surrendering control over critical decisions. One organization avoided $120,000 in losses when their system flagged four missing clauses which the team then reviewed and corrected. The Practical Angle: Use the FSA process across finance, legal, compliance, and operations. The process succeeds because it leverages the power of AI and defines clearly where final accountability sits. View Case Study → | Quick HitsBuilding AI Guardrails That Actually Work Start before deployment: define decision thresholds, set confidence floors, and human-in-the-loop checkpoints. Successful guardrails create trust through predictable control. | AI Audit Trail Best Practices Boards, regulators, and customers will expect you to explain your AI decisions, not just defend them. Build logging from day one: capture inputs, outputs, model versions, and human overrides. | Does AI Really Need Human Oversight? A BBC study shows that 45% of the responses from ChatGPT, Copilot, Gemini, and Perplexity have sourcing failures or fabricated facts. Verify AI output when accuracy is important. |
|
| | |
|
