Microsoft just launched Copilot Cowork – built with Anthropic. These are AI agents that run multi-step tasks across your entire M365 stack. What you see here is a shiny demo obviously. But it shows on…
17237186 viral
AI Strategy9 days ago
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Tobias Zwingmann
LinkedIn Content Strategy & Writing Style
Author: The Profitable AI Advantage. Created profits with AI from enterprise giants to 20-person teams.
Germany
10 Viral ScoreView on LinkedIn
0 people tracking this creator on Viral Brain
Performance Overview
Followers
83.3K
Connections
14.8K
Avg. Engagement
Top Posts by Engagement
View on LinkedIn
Posting Patterns & Frequency
5.7 posts/week
Posts / Week
1.4 days
Days Between Posts
1
Total Posts Analyzed
HIGH
Posting Frequency
Timezone: Europe/Berlin
Best Performing Days
MondayThursday
Best Performing Times To Post
16:30-18:0019:30-20:30
Topics & Content Focus
Primary Topics
Enterprise AI adoption with governance (analytics validation, rollout design, risk containment)Practical ChatGPT configuration for business workflows (CustomGPTs as lightweight job-specific tools)AI security and the compounding risk of autonomy (agents expanding the attack surface)LLM-native discoverability and buyer journey shifts (being recommended by ChatGPT vs traditional search)
Secondary Themes
Data readiness and 'ugly data' remediation as a prerequisite for reliable AI outputsProductivity AI limitations and the gap between efficiency gains and business transformationAI training enablement for non-technical knowledge workersModel/version change management (capability jumps enabling new workflows)
Industry Focus
Enterprise AI enablement and trainingData & analytics teams operating in Excel-heavy environmentsProfessional services/consulting and knowledge-work organizationsB2B services firms impacted by AI-driven discovery (local providers, niche industries)
Content Categories
Cautionary implementation playbooks (governance, rollout, validation)Tooling walkthroughs and templated workflows (CustomGPTs for specific jobs)Security/newsjacking analysis with leadership implicationsField stories from talks/trainings used as narrative proofThought leadership reframes (what AI is and is not good for)
Performance Insights
66%
Avg Engagement Rate
STABLE
Performance Trend
Best Performing Topics
Practical, immediately usable ChatGPT configurations (job-specific GPTs, data/forecasting workflows)Security incidents and agent risk framing (real-world breach + leadership questions)Contrarian takes on what to prioritize (configuration/governance over shiny autonomy)
Virality Signals
Share spikes correlate with organizational-risk framing (security, governance failures)Comment spikes correlate with 'how-to' assets that reduce effort for business users (templates, configurations)Specificity (named tools, constraints like instruction limits, concrete failure scenarios) outperforms general thought leadership
Structure & Quality
1200
Avg Length (Words)
HIGH
Depth Level
ADVANCED
Expertise Level
0.82/10
Uniqueness Score
Common Hooks
Contrarian reframe (everyone is chasing X, but Y is the real lever)Credibility-by-fieldwork (talk/training recap as proof of proximity to real adoption)Threat-based hook (a failure mode that feels plausible and costly)Newsjacking with implications (a public incident used to diagnose organizational gaps)Binary segmentation (two groups emerge; one is safe, one is dangerous)
Common Endings
Actionable checklist or rollout principlesRisk-based takeaway framed as a simple rule (secure/validate before scaling)Soft invitation to think ahead (teasing the next wave/version)Light CTA to read an article or reference materialGratitude/tagging the host or audience (relationship signaling)
Value Delivery Methods
Converts abstract AI hype into operational guidance (rollout in batches, validate outputs, secure what exists)Provides ready-to-apply artifacts (named GPT roles and what they do)Teaches failure modes (silent spreadsheet parsing errors, autonomy multiplying risk) rather than only benefitsTranslates model upgrades into practical capability shifts for non-technical teams
Formatting Style
Short paragraphs with heavy line breaks for scroll-stopping cadenceParenthetical asides to add voice and nuanceNumbered lists and arrow bullets for frameworks/toolsConcrete examples + specific numbers to increase believabilityPunchy single-line statements used as emphasis
Audience & Tone
YES
Question Usage
0.3%
Response Rate
Detected Tone
Pragmatic operator-teacherSmart-casual expert with a skeptical edgeRisk-aware realist (anti-hype, pro-implementation)Security-and-governance minded strategistHands-on builder (shows what was built, not just what should be done)semi-formalfirst-person
Interaction Style
Audience addressed as operators and decision-makers ("your deployment", "who owns architecture")Invites reflection through direct questions rather than debate-baitCommunity signposting via trainings/talks and tagging organizations
Community Building Signals
Recurring training/speaking narrative builds an ongoing 'in the field' series feelPublicly credits hosts/partners to deepen professional relationshipsUses shared language (agents vs. basics) that signals an in-group of practitioners
Writing Style Patterns
Content Strategy
Hook: Contrarian reframe (everyone is chasing X, but Y is the realTone: semi-formalCTA: Practical next steps (do these 3 things / roll out
Writing style breakdown
<start of post>
I spent yesterday looking at a "perfect" AI agent demo. It handled emails, booked meetings, and updated the CRM without a single error.
The audience was mesmerized.
Then I asked the founder one question
What happens when the customer sends a screenshot of a messy Excel file instead of a text request?
Silence.
Because here is the reality of AI in 2025
The models are Ferraris. But most corporate data is a dirt road full of potholes.
You can have the fastest car in the world. If the road is broken, you are still going to crash.
I see this mistake every week.
Companies spend $200k on "Agentic Workflows" before they spend $5k on cleaning their data headers.
It is the "Flying Car" problem again.
We want the magic. We ignore the plumbing.
If you want AI that actually works in production, stop looking at the shiny demos.
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