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 Governance8 months ago
LinkedIn Content Strategy & Writing Style
Transform your AI & data ambition into action | xQuantumBlack, xMcKinsey | Global top 100 Innovators in Data & Analytics | AI & data strategy, governance and capability building
1 person tracking this creator on ViralBrain
Clare Kitching positions herself as a high-level strategic translator who bridges the gap between complex AI technicalities and executive-level decision-making. Leveraging her McKinsey and QuantumBlack pedigree, her content strategy centers on demystifying the "AI hype cycle" by focusing on operational readiness and governance rather than just model performance. She is notable for her ability to use sharp metaphors—like comparing AI strategies to Winter Olympic sports—to expose organizational dysfunction and the "backstage chaos" of poor data foundations. Her work sits at a critical intersection of technical architecture and corporate accountability, where she argues that the true challenge of the agentic era is not building intelligence, but designing systems for human trust and clear decision ownership.
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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…
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7.8 posts/week
Posts / Week
1 days
Days Between Posts
1
Total Posts Analyzed
HIGH
Posting Frequency
914.2%
Avg Engagement Rate
STABLE
Performance Trend
320
Avg Length (Words)
HIGH
Depth Level
ADVANCED
Expertise Level
0.84/10
Uniqueness Score
YES
Question Usage
0.3%
Response Rate
Writing style breakdown
<start of post>
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.
▶️ 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.
💎 Get more from me with my free newsletter here: https://lnkd.in/ghBtk6jR
<end of post>
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