# Michele Sciabarrà

Michele Sciabarrà is a Nuvolaris founder who works on AI products, including private AI applications that connect company data and systems to business processes. Sciabarrà has described focusing on product development and taking on the CTO role.

- Profile: https://b2binfluencer.marketing/creators/michele-sciabarr/
- LinkedIn: https://www.linkedin.com/in/msciab
- LinkedIn followers: 46,018
- YouTube: https://www.youtube.com/channel/UCs8u_Cl9K7NinoiUg5dEq4A
- Topics: artificial-intelligence, leadership, startup, ai-agents, enterprise-ai
- Based on 11 dated posts, latest 2026-09-24

## Summary

Michele Sciabarrà is most useful for private AI product strategy and concrete deployment patterns.

- Best for: A B2B AI infrastructure company wanting scoped product strategy advisory on private AI and agents.
- Not for: Paid LinkedIn sponsorship with fixed deliverables, CPM or CPC pricing, and broad English small-business reach.
- Check first: Confirm recent post-level results and audience survey data for job function, seniority, company size, geography.

### Takeaways

- Private AI belongs inside business processes, not above them.
- Six private-AI patterns include wiki chat, email order intake, reporting, dashboards, database queries, conversational CRM.
- Public AI is generalist; private AI matters when agents touch email, CRM, NDA documents, industrial secrets.

## Main ideas

- **Private AI belongs inside business processes, not above them**: Sciabarrà argues that confidential, scattered company data — documents, email, databases, management systems, industrial systems — usually cannot be handled by public AI tools. What Sciabarrà calls private AI is a controlled system integrated with those systems and built around the real process. The framing is that this is application building rather than "putting a chat over the data": privacy, permissions, processes and existing integrations all have to hold. Sciabarrà adds that private AI is not a replacement for public tools — the image is an umbrella and a coat, solving different problems — and that no single public model does everything, since Sciabarrà describes ChatGPT as generalist, Claude Code as strong for coding and Gemini as strong for retrieval. The decision Sciabarrà puts to a buyer is about control: Sciabarrà describes AI agents as employees that work for the buyer and not for Google, Microsoft or Amazon. The examples Sciabarrà gives come from client work at Nuvolaris, the company Sciabarrà founded.
- **The product matters more than the title, and the company should be structured to protect it**: Sciabarrà argues that being CEO, which they call the usual life ambition, crowds out the work they do best. They describe the cost concretely: meetings with clients and partners, day-to-day operational decisions, email, and reading legal and commercial documents. Technical product decisions, they argue, require sustained study, testing, hands-on work and verification — and that becomes more important, not less, when AI helps write the code. Their conclusion: choose a focus and design roles around it. Sciabarrà handed the CEO role to Mirella Di Girolamo, who had moved from CFO to COO, so they could concentrate on product. They present this as a choice for the company, not a loss of control, and explain the governance: shareholders appoint a board, the board chooses the CEO, and Sciabarrà remains principal shareholder with a board seat and enough voting power to decide who holds the CEO role.
- **AI changes what software people do, not whether people are needed**: Sciabarrà argues that software will increasingly be generated from a plain description of what someone wants, and that programmers will shift to helping people realise those wants — like an architect drawing a house, a doctor treating a patient, a chef cooking a refined meal or a lawyer advising on rights. They compare AI to earlier tools that beat humans on one narrow measure, such as cars being faster or calculators more precise, and to the smartphone, which absorbed the music player, camera, telephone and television into one device. They also push back on posts arguing that AI is a passing thing and nothing fundamental is changing, asking whether their authors believe that or are denying that a new and unknown territory has been entered. This long-range thesis extends beyond software to physical AI and building things that are currently unthinkable; it is not a description of Nuvolaris products.

## Work to read

- [Private AI in production: six concrete deployment patterns](https://www.linkedin.com/posts/msciab_ma-che-ci-fai-con-la-private-ai-molto-activity-7470042113825497089-zzBC)
- [Why private AI is not a replacement for public AI](https://www.linkedin.com/posts/msciab_se-diciho-gi%C3%A0-lai-pubblica-non-ci-serve-activity-7415285450485280768-gwVq)
- [Founder hands over CEO role to focus on product](https://www.linkedin.com/posts/msciab_dopo-4-anni-da-ceo-di-nuvolaris-ho-passato-activity-7467917071100796928-SCoY)
- [Clarifying governance and the CEO-to-CTO decision](https://www.linkedin.com/posts/msciab_ammetto-che-mi-scoccia-un-po-doverlo-puntualizzare-activity-7489306205341999104-PjBA)
- [Thesis: AI-generated software and the human role](https://www.linkedin.com/posts/msciab_mi-chiedo-e-me-lo-chiedo-di-cuore-ma-quando-activity-7432107098630533120-PiR_)

## Brand relationships

- **Nuvolaris** (owned_business)
- **ChatGPT** (mention)
- **Claude Code** (mention)
- **Gemini** (mention)

## Partnerships

Sciabarrà's posts describe no sponsorship, affiliate, or paid-partnership format. They document first-person accounts of running Nuvolaris and building private AI products; commercial work is presented as company client engagements, not a creator offer with rates, deliverables, or availability. They are a founder who now works as CTO on product, and the AI work they write about is sold and delivered through Nuvolaris to business clients. For a B2B AI infrastructure company, the posts support a product or advisory conversation about private AI, AI agents, and enterprise data integration—not a sponsorship booking. Ask directly about partnership terms; there is no published package, rate card, or media kit to evaluate.

## Alternatives

- Ivan Canovese: Keep Michele Sciabarrà in view for decisions about what the private-AI product or agent should do; choose Canovese when the buyer's blocking question is commercial: where the margin is, which contract to renegotiate, and how to fund a pilot. Canovese's posts describe being named Senior Partner at ERA Group EMEA after passing project and revenue thresholds. They frame the method as listening before proposing, asking why a process has always been done a certain way, and building cost programmes on trust rather than templated answers. A second post reads a shipping-lane risk (Bab el-Mandeb, roughly 12% of seaborne oil and over 20% of container traffic) into purchasing decisions, contract timing, and energy sourcing, arguing that cautious companies are becoming more selective rather than stopping. For a B2B AI infrastructure buyer weighing which process to fix and what it is worth, Canovese speaks to the value case; the product-side creator speaks to the build.
- Giorgio Gnoli: Choose Gnoli when the task is customer-data consolidation, CRM hygiene, or a working pipeline system rather than private-AI product architecture. One post walks through an automotive CRM rebuild step by step: extracting scattered data from Qlik, DMS and portals; building a customer 360 on standard Salesforce objects; reconciling 105,169 records into 98,146 unique customers on strict match rules; separating private and company accounts (2,371 automatic links); defining lead conversion as quote-only (240 conversions previously invisible); setting a 60-day deal cut-off with a 'sale beyond cut-off' tag; linking vehicles, workshop spend and customer lifetime value; and exposing 682 mapped fields in a configurable console. Another post lays out an agency pipeline build—cold outreach, an offer aimed at accountants, scraped lists, an A/B tracker and a CRM—reporting about €15 in setup cost and 13 positive replies in a week. If the buyer's urgent problem is dirty data feeding an AI pilot or a lack of pipeline to sell it, Gnoli addresses that step.
- Davide Rizzoli: Choose Rizzoli when the buyer's decision shifts from enterprise AI infrastructure to consumer ecommerce: how to position a new product line and plan the peak trading calendar. One post analyses a fashion brand's failed sportswear launch, separates three launch scenarios (line extension, new line, upsell and gifts), and argues that a catalogue works like a place: products too far from the existing identity reduce the audience instead of expanding it. The other reads BFCM data—over $70 billion spent in 24 hours, +5% year on year, Shopify up 24%, and average discounts rising from 20–25% in 2023 to 25–30% in 2024—and recommends concentrating spend in stronger markets, layering offers across a longer window, and treating Cyber Monday as the bigger day. For a brand campaign focused on assortment, launch risk, and promo timing, Rizzoli's posts are closer to the decision than Sciabarrà's.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/