# Fernando Bello

Fernando Bello writes about product management, leadership, and practical uses of AI at work. Bello's posts explain ideas such as redesigning workflows around AI and coaching teams to make better decisions. He identifies himself as the author of *Find the Best — A Full Guide for Product Management*.

- Profile: https://b2binfluencer.marketing/creators/fernando-bello/
- LinkedIn: https://www.linkedin.com/in/fcbello
- LinkedIn followers: 15,696
- Topics: artificial-intelligence, leadership, product-management
- Based on 10 dated posts, latest 2026-10-07

## Summary

Useful for accessible, action-oriented explanations of AI workflow redesign and leadership coaching for product and operations leaders.

- Best for: A B2B SaaS AI workflow product or leadership coaching campaign for product and operations leaders.
- Not for: Developer tools technical education for machine learning engineers, code walkthroughs, benchmarks, or production architecture.
- Check first: Confirm audience breakdown, comparable results, and commercial terms before contacting them.

### Takeaways

- AI advantage has moved from access to tools to redesigning work around them.
- Delegate the decision, not just the task, and do not take it back.
- Composure is trainable; suppressing a feeling is not the same as regulating it.

## Main ideas

- **The AI gap is a redesign problem, not an adoption problem**: In 2026, Bello argues, the AI question is no longer about access: the models and licenses are broadly available, while the remaining gap lies in work nobody has redesigned. The post contrasts widespread personal productivity gains with flat enterprise profit impact, noting that the small group of high performers is far more likely to have rebuilt workflows around AI rather than layered it on top. Bello's method for product managers is a sequence: pick one workflow, map how it actually runs today, find the steps that exist only because a human had to do them, cut those steps instead of speeding them up, rewrite the definition of done for the new shape, and measure the business outcome rather than the hours saved. The advantage, the post concludes, belongs to whoever is willing to change the work around the tools everyone now has.
- **The advantage was never access to the technology**: Bello argues that AI is moving into the products people already open, so early access is not a durable edge. What matters is curiosity, judgment and the willingness to adapt. The practical advice: check whether an existing tool has quietly gained a feature before downloading another app, and give the assistant on your phone one real task rather than a test question. Bello also prioritizes usefulness over speed. Citing OpenAI's guidance, they argue that cheap output nobody uses is not a saving, fast work nobody needed is not progress, and the only score that counts is useful work per dollar.
- **Delegate the decision, not the task**: Bello argues that delegation fails when managers hand over the work but keep the decisions. Handing someone the map while still pointing at every fork leaves a team unable to navigate; people start deciding only after they see a leader not punish the first wrong turn. Bello asks managers to build a specific behavior: name the decision that now belongs to someone else, set the guardrails, then do not take the decision back — especially when the person pauses and looks at you. The post presents this as rarer and costlier than most leaders assume, and as a coaching skill rather than a personality trait.
- **Fix the conditions, not the individual's willpower**: Across two posts, Bello returns to the same lever: environment, preparation and organizational design explain outcomes better than individual grit. In the delegation post, citing Microsoft's 2026 Work Trend Index, they argue that people hesitate to reinvent how they work because it feels safer to focus on current goals and reinvention is not rewarded when results miss. Organizational factors, the post notes, carry more than twice the AI impact of individual mindset. In the composure post, the same logic applies at the personal scale: sleep, preparation and margin do more for composure than willpower, and most bad reactions stem from unmanaged conditions rather than character.
- **Composure is trainable, and suppressing a feeling is not the same as regulating it**: Bello challenges the idea that calm colleagues simply are calm. Citing Yale's RULER framework, where regulating comes last after recognizing, understanding, labeling and expressing, they argue that emotional regulation is a trainable skill and that the calm people admire is more likely rehearsal than temperament. The post distinguishes regulating an emotion from hiding it: suppression does not create calm; it costs the body more. Bello offers four practices — buy the pause before spending the reaction, regulate the input through sleep and preparation, treat regulation as trainable, and sometimes regulate out loud by saying you want to sit with a decision before answering.

## Work to read

- [The AI gap is a workflow redesign problem, not an adoption problem](https://www.linkedin.com/posts/fcbello_productmanagement-productleadership-ai-activity-7505791802797432833-uIwN)
- [AI news translated for beginners, not specialists](https://www.linkedin.com/posts/fcbello_leadership-productmanagement-ai-activity-7483532258784677889-buuW)
- [Delegation means handing over the decision, not just the task](https://www.linkedin.com/posts/fcbello_leadership-productleadership-productmanagement-activity-7505418809390919680-MJaw)
- [Emotional regulation is trainable, and suppression is expensive](https://www.linkedin.com/posts/fcbello_emotionalintelligence-leadership-employeegrowth-activity-7484319282923810816-mwYk)

## Brand relationships

- **McKinsey & Company** (mention)
- **Microsoft** (mention)
- **Google** (mention)
- **OpenAI** (mention)

## Partnerships

Fernando Bello’s posts do not establish a partnership offer. Across four complete LinkedIn posts published between mid-July and mid-September 2026, they disclose no commercial relationships: no sponsored post, named brand client, affiliate arrangement, or engagement terms. Google, OpenAI, McKinsey, Gallup, and Microsoft appear only as news subjects or research sources being summarised, not as evidence of a partnership. The posts do show a repeatable editorial format a buyer could propose: a single practical workplace idea (AI workflow redesign, delegation, emotional regulation, or beginner-level AI news), supported by cited third-party research, a short set of steps or takeaways, and a question inviting comment. Two posts carry the sign-off "Author of 'Find the Best — A Full Guide for Product Management' · LinkedIn Top Product Management Voice." That self-description and the teaching structure are what a sponsor would be buying into; they are not a published sponsorship product. They publish no sponsor policy, rate card, media kit, or inquiry route. Before committing a budget, ask for current sponsorship terms and an official contact route, then agree in writing on scope: format, placement inside a post versus a standalone post, disclosure wording, review rights, and measurement.

## Alternatives

- Jennifer Speciale: Choose Jennifer Speciale when the buyer’s real problem is how product leaders are read in senior rooms, not how their workflows run. Jennifer Speciale’s posts describe nearly twenty years recruiting design and product executives, including recruiting for McKinsey’s Experience Design practice. One walks through a candidate told she was "too junior" for a VP role who had two VP offers within 12 weeks after changing how she entered the room. The argument: the interview is decided in the first three seconds, before the case study or the metrics. For a B2B SaaS buyer whose summit audience is product managers moving into director and VP scope, that before-and-after is closer to their pain than a workflow redesign method, and the workshop offer in the posts is a ready format.
- Mark Cole: Choose Mark Cole when the AI need sits inside regulated medical device or pharmaceutical manufacturing rather than a software product organization. Mark Cole’s posts tie a Forbes piece on human judgment in the AI age to a specific FDA warning letter about AI-generated drug specifications, citing the requirement that a quality control unit approve specifications and procedures. They also lay out ISO 13485 clauses on personnel competence and on validating automated inspection processes that are not verified by later measurement. For a buyer whose product is sold into regulated manufacturing, or whose customer audience is quality and regulatory leads, that is the language those rooms need before AI adoption is approved.
- Pierre Le Manh: Choose Pierre Le Manh when the engagement is enterprise-scale transformation rather than a single product team’s workflow. Pierre Le Manh’s posts show them addressing 500 executives at a project management association’s Asia-Pacific summit in Seoul, arguing that AI investment is delivered through projects, that the project professional becomes an orchestrator connecting technology, business value, workflows, people, risk, and accountability, and that AI cannot absorb accountability. A second post makes the macro case that de-risking US manufacturing requires roughly $2 trillion and that the binding constraints are energy, skills, and shovel-ready projects. A buyer selling to transformation offices, or whose advisory needs executive-level credibility with program and portfolio leaders, gets that frame here.

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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/