Governance breaks down the moment no one owns the decision. A couple of weeks ago I shared a simple way to think about data governance vs AI governance. Governing inputs vs governing outcomes. Ther…

1.1K43711685 viral

AI Governanceabout 2 months ago

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Data isn't the hard part. Understanding each other is. Ontology. Lineage. Semantic layers. Vector databases. I've been in data for over 15 years, and sometimes even I feel like I'm decoding a forei…

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Data Management for AIabout 2 months ago

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Every project starts with hope. Every timeline ends with a sigh. Why? Because AI & data projects are the perfect storm of: → Optimism bias (a.k.a. the planning fallacy) → Fuzzy scope (“we think we wa…

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AI Project Managementabout 2 months ago

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2026 is the year AI stopped being a feature. It became part of the operating system of the enterprise. Across the latest 2026 flagship reports, one message is clear: this is the inflection point for…

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Enterprise AI Strategyabout 2 months ago

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Everyone talks about AI models. Very few talk about AI systems. When you look under the hood of most AI today, you rarely find just a large language model. You find layers. Context. Memory. Retrieva…

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AI Systemsabout 2 months ago

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If AI feels both everywhere and strangely unclear, you’re not behind. You’re normal. Most execs I speak to aren’t confused about AI. They’re confused by the language. The same terms get thrown aroun…

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AI Literacyabout 2 months ago

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Topics & Content Focus

Primary Topics

Enterprise AI governance as decision accountability (ownership, defendability, human-in-the-loop)AI systems thinking beyond models (LLM → RAG → agents → multi-agent/agentic orchestration)Executive AI literacy and shared language (de-jargonizing vendor/boardroom terminology)AI value architecture as a portfolio (rules + predictive + deep learning + GenAI + agents across workflows)Delivery reality of data/AI programs (scope, unknowns, compliance, adoption as the hidden failure mode)

Secondary Themes

Data quality and retrieval design as the true constraintOperational risk, monitoring, and governance operating modelsTool use, autonomy, and trust boundariesChange management and adoption enablementStrategy reframing from “cool demos” to measurable business outcomes

Industry Focus

Enterprise and regulated organisations adopting AI (board/executive-level decision contexts)Cross-functional data/AI leadership (product, risk, compliance, operations)B2B operational workflows where AI decisions can create customer/regulatory impact

Content Categories

Framework-led explainers (layered models, stacks, portfolios)Plain-English glossaries and translation posts for executivesGovernance and risk leadership takes (accountability, ownership, guardrails)Project delivery realism / post-mortem pattern recognitionAudience-led discussion prompts (diagnostic questions)

Performance Insights

914.2%

Avg Engagement Rate

STABLE

Performance Trend

Best Performing Topics

Governance accountability and decision ownership ("who owns the call?")Project delivery realism and planning fallacy (war-story patterns)Plain-English AI terminology for execs (shared language as strategy)

Virality Signals

High shareability via evergreen reference formats (glossary, stack, portfolio map)Authority through governance inevitability framing ("the room goes quiet")Comment amplification through direct questions and “add your term / your twist” promptsStrong repost CTA tied to helping others (prosocial sharing cue)

Structure & Quality

320

Avg Length (Words)

HIGH

Depth Level

ADVANCED

Expertise Level

0.84/10

Uniqueness Score

Common Hooks

Contrarian reframing ("everyone talks about X, very few talk about Y")Normalization hook ("you’re not behind") to lower anxiety and invite executives inProblem inevitability hook ("every project starts with hope…")Undermining the default solution ("not a bigger model / not more data")High-stakes governance premise ("the moment something goes wrong…")

Common Endings

Diagnostic question that forces self-assessment ("Which layer are you ready for?")Open loop for comments ("What terms would you add?" / "What’s the twist you’ve seen?")Share-trigger framing ("Repost to help someone…")Follow/newsletter CTA positioned as continued learning

Value Delivery Methods

Turns ambiguous AI hype into decision-ready mental modelsBridges technical concepts to operating-model consequences (ownership, monitoring, trust)Creates practical executive alignment tools (shared vocabulary, investment gap-mapping)Highlights non-obvious failure points (adoption, compliance timing, retrieval design)

Formatting Style

Skimmable micro-paragraphs with strong line breaksArrow bullets (→) for cause/effect and practical implicationsSection headers with ▶️ to create a guided walkthroughContrast blocks (Expectation vs Reality) to create narrative tensionEmphatic short sentences to land key claims ("Intelligence is the easy part.")

Audience & Tone

YES

Question Usage

0.3%

Response Rate

Detected Tone

Executive translator (de-jargonizing without dumbing down)Pragmatic systems thinker (models → systems → operating consequences)Risk-and-trust strategist (governance as ownership, not bureaucracy)Smart-casual authority (boardroom topics in human, readable language)Constructive contrarian (redirects attention from hype to fundamentals)semi-formalsecond-person

Interaction Style

Invites peer examples and lived experience (project twists, term confusion)Self-diagnosis prompts that encourage public reflection (readiness, gaps, accountability)Low-ego teaching stance that reduces intimidation while maintaining authority

Community Building Signals

Recurring rituals (repost + follow + newsletter) to convert readers into returning learnersCreates a shared executive language community around AI (terms, definitions, governance norms)Positions comments as co-creation ("what would you add?") rather than debate

Writing Style Patterns

Content Strategy

Hook: Contrarian reframing ("everyone talks about X, very few talkTone: semi-formalCTA: Repost/share altruism (help someone understand / g

Writing style breakdown

Strategy is a choice.

Execution is a discipline.

Most leaders treat AI like a shopping list.

A bit of this model, a dash of that tool, and a pilot in every department.

But a collection of tools is not a strategy.

It’s a distraction.

When you look at the organisations actually moving the needle, they aren't doing more.

They are doing less, but with more intent.

They focus on three specific pillars

▶️ The Problem Anchor

They don't start with "What can this tool do?"

They start with "What is the most expensive bottleneck in our customer journey?"

If the AI doesn't solve that, it doesn't get funded.

▶️ The Data Foundation

They stop chasing the newest model and start fixing their legacy mess.

Clean, accessible, and governed data is the only thing that makes AI perform.

Without it, you are just automating errors.

▶️ The Human Bridge

They invest as much in change management as they do in compute.

If the team doesn't trust the output, the system will be bypassed.

Adoption is the only metric that matters.

Moving from "AI interest" to "AI impact" requires a shift in mindset.

It’s not about being the first to use a tool.

It’s about being the first to solve a problem at scale.

Is your current AI roadmap a list of tools, or a list of solutions?

♻️ Repost to help someone focus their AI strategy.

🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

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Clare Kitching

Warm Analysis

Performance Overview

Top Posts by Engagement

Posting Patterns & Frequency

Best Performing Days

Best Performing Times To Post

Topics & Content Focus

Primary Topics

Secondary Themes

Industry Focus

Content Categories

Performance Insights

Best Performing Topics

Virality Signals

Structure & Quality

Common Hooks

Common Endings

Value Delivery Methods

Formatting Style

Audience & Tone

Detected Tone

Interaction Style

Community Building Signals

Writing Style Patterns

Content Strategy

They focus on three specific pillars