LinkedIn’s CEO has quietly lifted the curtain on a practice many executives already suspected: he leans on artificial intelligence to write the majority of his emails — even the ones sent to his own boss, Microsoft CEO Satya Nadella. Ryan Roslansky disclosed during a fireside chat at LinkedIn’s San Francisco office that Microsoft’s Copilot is part drafting tool, part iterative collaborator — a “second brain” he uses to shape almost every important message he sends.
LinkedIn sits at the intersection of careers, talent markets, and enterprise software, and its corporate choices ripple widely through the way professionals use digital tools. Over the past five years under Roslansky’s stewardship, LinkedIn’s commercial footprint has grown materially: Microsoft’s financial reporting shows LinkedIn generating roughly $16.4 billion in fiscal 2024, and the platform publicly announced it had passed one billion members in late 2023. Those two facts help explain why how LinkedIn’s leaders use AI — and how LinkedIn itself deploys AI features — matters to recruiters, HR teams, and knowledge workers around the world.
At the same time, macro projections about AI’s economic impact are frequently cited in corporate conversations. A widely referenced PwC analysis estimated AI could contribute about $15.7 trillion to global GDP by 2030 — a framing that underpins many boards’ urgency to adopt AI tools. While that study dates back to earlier industry modeling, it remains part of the shorthand executives use when justifying AI investments.
He was blunt about frequency: “without a doubt, almost every email that I send these days is being sent with the help of Copilot,” he said, and added that he uses the tool “for every important email, without a doubt, on a daily basis.” Those are first-person statements of practice — not technical claims about Copilot’s inner workings or efficacy in controlled studies. The admission is notable because it normalizes executive-level dependency on generative assistants for high-stakes communication.
This tension exposes a paradox: LinkedIn the company is embedding AI to help members present themselves better, yet many members resist using those tools publicly because doing so might undermine perceived authenticity. The result is a bifurcated adoption pattern:
But the admission also raises questions for Microsoft’s compliance, given that many organizations must account for how corporate data is processed by third-party AI services. If senior leaders routinely draft sensitive messages with Copilot, enterprises will want explicit answers about data retention, model training, and internal access controls.
The upshot is that many users will adopt AI privately (emails, drafts, notes) while publicly curating their output to signal human authorship — a modal split that will shape product design and regulatory conversations for years.
That said, the practice is not without material risk. Hallucinations, inadvertent disclosure of sensitive information, diminished authorial accountability, and auditing gaps are practical problems that organizations must solve before AI becomes a default for sensitive or legally consequential correspondence.
In short: Roslansky’s approach is an early model for executive productivity in the AI era — one to study, not simply emulate blindfolded. Companies should treat executive AI use as both a productivity initiative and a governance program: amplify the positives, but build controls, logs, and training to manage the trade-offs.
Roslansky’s statement is both a practical confession and a leadership signal: AI is now a standard part of some executives’ workflows, and companies must decide whether to follow, adapt, or regulate that choice. The conversation is no longer theoretical — it’s a lived operational question for every organization that depends on precise communications, robust recordkeeping, and reputational integrity.
Source: Dataconomy LinkedIn CEO Roslansky admids using AI to draft almost every email
Background
LinkedIn sits at the intersection of careers, talent markets, and enterprise software, and its corporate choices ripple widely through the way professionals use digital tools. Over the past five years under Roslansky’s stewardship, LinkedIn’s commercial footprint has grown materially: Microsoft’s financial reporting shows LinkedIn generating roughly $16.4 billion in fiscal 2024, and the platform publicly announced it had passed one billion members in late 2023. Those two facts help explain why how LinkedIn’s leaders use AI — and how LinkedIn itself deploys AI features — matters to recruiters, HR teams, and knowledge workers around the world. At the same time, macro projections about AI’s economic impact are frequently cited in corporate conversations. A widely referenced PwC analysis estimated AI could contribute about $15.7 trillion to global GDP by 2030 — a framing that underpins many boards’ urgency to adopt AI tools. While that study dates back to earlier industry modeling, it remains part of the shorthand executives use when justifying AI investments.
What Roslansky actually said — and what he didn’t
The admission in context
Roslansky’s comments were reported after details of the fireside chat were leaked to the press. He said Copilot doesn’t blindly draft complete messages for him; rather, it works interactively — prompting questions, suggesting directions, and helping him refine tone and clarity before he hits send. That framing is critical: it places Copilot as a compositional partner that amplifies Roslansky’s judgment rather than a black-box autopilot that substitutes for it.He was blunt about frequency: “without a doubt, almost every email that I send these days is being sent with the help of Copilot,” he said, and added that he uses the tool “for every important email, without a doubt, on a daily basis.” Those are first-person statements of practice — not technical claims about Copilot’s inner workings or efficacy in controlled studies. The admission is notable because it normalizes executive-level dependency on generative assistants for high-stakes communication.
What to take literally — and what to treat cautiously
- Literal: Roslansky says he personally uses Copilot as part of his email composition workflow; that statement is a direct disclosure about his behavior.
- Qualitative: He characterizes the tool as a “second brain” — a subjective assessment of productivity and personalization rather than a measurable claim.
- Unverified technical claims: Any implication that Copilot guarantees correctness, eliminates reputational risk, or is immune to hallucinations is not validated by Roslansky’s remarks and should be treated as aspirational rather than factual.
Why an executive would use AI for email — incentives and affordances
Speed, consistency and “sounding right”
For executives who run large organizations and communicate with other C-suite leaders, diplomats, investors, and regulators, the marginal cost of a tonal mishap can be high. AI assistants offer three immediate benefits that explain Roslansky’s behavior:- Efficiency: AI trims drafting time, turning sketch ideas into polished copy.
- Consistency: Copilot can help maintain a coherent organizational voice and avoid accidental mismatches of tone across high-profile threads.
- Confidence: For “super high-stakes” messages, an extra layer of stylistic and structural review helps executives feel that they’ve said what they mean, the way they want it said.
The interactive model vs. “Draft reply” buttons
Roslansky emphasized that Copilot’s current value proposition for him is interactive rather than generative in an end-to-end sense. He contrasted this with earlier tools that would simply offer a one-shot draft, making too many unilateral decisions. The interactive approach — where the AI asks clarifying questions and iterates with the user — aligns with research on human-AI teaming that stresses centaur-like workflows: humans retain final judgment while offloading repetitive or structural tasks to models.Corporate ripples: leadership behavior shapes adoption
When a CEO publicly acknowledges daily AI use, it creates both a permissive signal and a governance challenge.- Permissive signal: Teams interpret a leader’s open use of AI as tacit permission to integrate similar tools into everyday workflows, accelerating adoption across product, sales, legal, and people teams. That can be positive when it raises productivity and upskilling.
- Governance challenge: More widespread use escalates the need for explicit policies around data governance, privacy, regulatory risk, and recordkeeping — especially when emails touch on competitive strategy, M&A, or regulated industries.
LinkedIn’s own AI features and the authenticity paradox
LinkedIn offers AI-assisted features for members — from profile polishing to post-suggested edits — but not all of those features have seen enthusiastic uptake. Roslansky told Bloomberg that the company’s AI writing assistant for posts has been less popular than expected, attributing the muted response to users’ fear of reputational backlash when content reads as obviously AI-generated. On a platform where posts are tightly linked to professional identity, authenticity is a high bar.This tension exposes a paradox: LinkedIn the company is embedding AI to help members present themselves better, yet many members resist using those tools publicly because doing so might undermine perceived authenticity. The result is a bifurcated adoption pattern:
- Private adoption: Professionals increasingly use AI privately to draft and refine emails, resumes, and applications.
- Public restraint: Users are cautious about publishing AI-flavored posts that could be called out and harm credibility.
Cross-industry context: executives are using AI, publicly and privately
Roslansky’s admission did not occur in isolation. Several other high-profile CEOs have described routine AI use in public forums:- Google CEO Sundar Pichai has spoken about “vibe coding” and using tools like Replit and Cursor to rapidly prototype websites — an example of leaders embracing AI to lower the friction of creative or technical work.
- Nvidia CEO Jensen Huang has described using AI as a personal tutor, encouraging everyone to adopt AI tutors to learn new concepts faster. Those accounts show a pattern: leaders project AI as both a productivity booster and an intellectual multiplier.
The benefits: what corporate leaders gain from AI-assisted communication
- Faster decision cycles. AI reduces the time to craft clear, on-message updates and approvals.
- Higher signal-to-noise in executive correspondence. Copilot-style tools can help eliminate filler language and prioritize key asks.
- Scaled best practices. A leader’s favored phrasing, negotiation framing, and FAQ answers can be codified into reusable prompts and templates.
- Onboarding and continuity. New executives or interim leaders can inherit curated prompt sets and style preferences, accelerating continuity.
The risks: missteps, hallucinations, and reputational exposure
Relying on AI for sensitive correspondence introduces several risk vectors:- Hallucinations: Generative models sometimes assert false facts or invent citations — an especially dangerous failure mode in executive emails that reference data, contracts, or commitments.
- Data leakage: Drafting emails about confidential projects inside an AI assistant could expose proprietary information if the tool’s data handling or telemetry is not tightly controlled.
- Tone and nuance errors: AI can struggle with cultural nuance, sarcasm, or legal precision. Small tone errors in diplomatic or investor-facing communications can escalate.
- Overreliance: Routine use can atrophy some writing and judgment skills, making executives less prepared to notice subtle factual or legal issues.
- Auditability and recordkeeping: When AI participates in composition, organizations must decide how to archive drafts and prompt history for compliance or eDiscovery.
Practical governance checklist for organizations adopting AI for executive comms
- Classify content sensitivity (e.g., public, internal, confidential, regulated).
- Define permitted AI use per sensitivity class (yes/no/approved-tool).
- Require human sign-off on all “super high-stakes” communications.
- Log prompts, iterations, and final outputs in a secure archive.
- Ensure AI vendors provide data residency and non-training guarantees where required.
- Provide targeted training for senior leaders on prompt engineering and model failure modes.
What this means for Microsoft, LinkedIn and Copilot
The optics are significant. Microsoft invests heavily in Copilot as a differentiator for Microsoft 365 and Azure, so the LinkedIn CEO’s vocal adoption is a marketing and product validation moment for Microsoft’s AI roadmap. Internally, it signals alignment: LinkedIn’s leadership is experimenting with coproductive workflows powered by Microsoft technology, potentially accelerating feature prioritization and tighter product integration between LinkedIn and Microsoft services.But the admission also raises questions for Microsoft’s compliance, given that many organizations must account for how corporate data is processed by third-party AI services. If senior leaders routinely draft sensitive messages with Copilot, enterprises will want explicit answers about data retention, model training, and internal access controls.
The broader cultural impulse: authenticity versus amplification
Roslansky’s candor spotlights a cultural tension: professionals want to be more productive and polished, yet they fear losing the human signal that builds trust. LinkedIn’s own AI writing tool’s tepid uptake demonstrates the limits of automation in spaces where identity and reputation are tightly coupled to content authenticity. Companies that nudge employees toward AI while failing to square the authenticity question will encounter user resistance.The upshot is that many users will adopt AI privately (emails, drafts, notes) while publicly curating their output to signal human authorship — a modal split that will shape product design and regulatory conversations for years.
What journalists, regulators and enterprises should watch next
- Product controls: Will Microsoft and other providers implement clearer “do not train on my data” toggles and enterprise-grade retention policies? Enterprise demand will push vendors to make these controls standard.
- Disclosure norms: Will boards or public companies require executives to disclose AI assistance in material communications? That’s an open governance question.
- Standardization: Industry groups may push for best-practice standards around AI-assisted drafting, particularly in regulated sectors like finance and healthcare.
- Behavioral effects: Researchers should monitor whether executives’ reliance on AI changes negotiation outcomes, tone, or the cadence of decision-making.
Final analysis: pragmatic embrace, careful guardrails
Roslansky’s admission that Copilot helps him write “almost every” important email is a useful data point about where elite workflows are heading: toward centaur-style collaboration that amplifies human judgment. The benefits are compelling — speed, tone management, and a consistent cadence of communication — and they map directly to business priorities for leaders who must orchestrate complex enterprises.That said, the practice is not without material risk. Hallucinations, inadvertent disclosure of sensitive information, diminished authorial accountability, and auditing gaps are practical problems that organizations must solve before AI becomes a default for sensitive or legally consequential correspondence.
In short: Roslansky’s approach is an early model for executive productivity in the AI era — one to study, not simply emulate blindfolded. Companies should treat executive AI use as both a productivity initiative and a governance program: amplify the positives, but build controls, logs, and training to manage the trade-offs.
Practical takeaways for IT leaders and communications teams
- Implement role-based AI policies: senior executives may be allowed broader toolsets if accompanied by stricter logging and human sign-off requirements.
- Train leaders on failure modes: brief C-suite members on hallucinations, data leakage, and the need to verify facts suggested by AI.
- Standardize prompts and templates: curated prompt libraries reduce variance and improve guardrails for public-facing messages.
- Monitor authenticity signals: if a platform’s user base values authenticity (as LinkedIn’s appears to), avoid heavy-handed downstream automation that could harm reputation.
Roslansky’s statement is both a practical confession and a leadership signal: AI is now a standard part of some executives’ workflows, and companies must decide whether to follow, adapt, or regulate that choice. The conversation is no longer theoretical — it’s a lived operational question for every organization that depends on precise communications, robust recordkeeping, and reputational integrity.
Source: Dataconomy LinkedIn CEO Roslansky admids using AI to draft almost every email
