Building Analytics Teams has just hit the highest quarterly sales numbers since publishing, nearly 6 years ago. I am so very grateful to all of you on LinkedIn and in the broader data, analytics, a…

701905 viral

Data & Analytics8 days ago

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I thought I had the focus of my next (6th) book. I had signed a contract, but it just never felt like it was locked in. Yesterday, I decided to change topics. Thanks to Steve Hoberman, DMC for work…

591003 viral

Writing & Publishing21 days ago

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I read Rob's Sunday morning missives with great interest. I like this issue and agree with the premise of the hollowing out of white collar work. An additional point that I will add is this... Whil…

321003 viral

AI Leadershipabout 1 month ago

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I have been involved in several educational initiatives related to STEM and AI. One of the most impactful has been the Mark Cuban Foundation AI Bootcamps and related programs. Over the past 5 year…

24512 viral

AI Educationabout 1 month ago

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I am no fan of Palantir, but I do respect what they have built, how intelligent they are and their unwavering focus on execution. They are a force, and they, and maybe they alone, have seen where t…

23502 viral

AI Infrastructure6 days ago

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Governance for AI Agents and Agentic Workflows. Say that in a leadership meeting if you want the conversation to stop dead in its tracks. How to govern agents and agentic workflows is not hard to un…

19712 viral

AI Governance10 days ago

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Topics & Content Focus

Primary Topics

Enterprise AI adoption and infrastructure pragmatics (what actually drives production impact)Data/analytics org building and cross-functional collaboration (teams, managers, executives)Professional network curation and platform norms (depth over reach; pruning; contribution standards)

Secondary Themes

Vendor/market appraisal through execution lens (respecting builders while staying skeptical)AI governance and assurance readiness (audit preparedness)Occasional cultural detours to humanize the feed (food/media as palate cleanser)

Industry Focus

Enterprise AI & analytics leadershipAI infrastructure/software for adoption (platform, tooling, implementation ecosystem)Data management, governance, and assurance/audit-adjacent stakeholders

Content Categories

Industry analysis + curated recommendationsOperator/author credibility and milestonesCommunity-building norms and professional principlesLight personal/cultural share to diversify context

Performance Insights

21.2%

Avg Engagement Rate

STABLE

Performance Trend

Best Performing Topics

Author/product milestone framed as community gratitude + validationPragmatic enterprise AI infrastructure commentary anchored to credible third-party analysisProfessional norms about connection quality and conversational depth

Virality Signals

Low share behavior suggests a discussion/relationship feed more than viral broadcastingEngagement lifts when credibility artifacts are present (book sales milestone, respected analyst link)Tagging acts as distribution trigger but still converts primarily to likes/comments, not shares

Structure & Quality

Avg Length (Words)

HIGH

Depth Level

ADVANCED

Expertise Level

0.72/10

Uniqueness Score

Common Hooks

Direct opinionated assertion (clear stance upfront)Contrarian/qualified take ("I’m no fan, but…")Meta-commentary on the platform/community (rules for better discourse)Pattern interrupt disclaimer ("non-AI and different")

Common Endings

Explicit endorsement or gratitude ("Well done", "Thank you")Actionable norm/standard ("If it helps no one, discard it")Reference/link-out to an external artifact (newsletter/article/book)

Value Delivery Methods

Curates high-signal reads and points to where enterprise AI is realistically headingTranslates professional principles into explicit posting/connection heuristicsBuilds trust through measured skepticism + respect for executionReinforces applied leadership expertise (teams, production outcomes, collaboration)

Formatting Style

Short paragraphs with line breaks for scanabilityOccasional single-line post for punch/controversyFrequent proper-noun specificity (companies, executives, authors)Links placed standalone or near the endMinimal emojis; professional punctuation; emphasis via phrasing not styling

Audience & Tone

YES

Question Usage

0.35%

Response Rate

Detected Tone

Principled operatorMeasured skeptic with builder-respectCurator of high-quality thinkingCommunity-first professionalsemi-formalfirst-person

Interaction Style

Credibility-led discussion (people respond to recommendations and strong stances)Reciprocity loops (gratitude + tagging prompts comments from peers)Norm-setting prompts reflective agreement rather than debate

Community Building Signals

Aggressive network curation as a stated brand mechanic (signals exclusivity/quality)Public appreciation of peers and influencers to strengthen professional graphPositions the feed as a practitioner forum ("practical topics that make us better")

Writing Style Patterns

Content Strategy

Hook: Direct opinionated assertion (clear stance upfront)Tone: semi-formalCTA: Soft directive to read/upgrade/consume referenced

Writing style breakdown

The debate around AI productivity often misses the most critical point. It is not about how much faster we can work, but how much better we can think.

I recently reviewed a series of reports on automated coding assistants. While the speed gains are undeniable, the quality of the underlying logic remains a human responsibility. If we outsource the thinking, we outsource the value.

This is why I advocate for a disciplined approach to implementation. You cannot simply 'bolt on' AI to a broken process and expect a miracle.

Think to yourself: Is this tool augmenting my reasoning or replacing it? If the answer is the latter, you are building on sand.

We must remain accountable for every output, regardless of the tool used to generate it. No kidding.

Adam Russell

John Thompson

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