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.
Fernando Bello writes about product management, leadership, and practical uses of AI at work.
In four LinkedIn posts, they argue that useful AI adoption depends on redesigning work, not simply adding tools; explain AI news in beginner-friendly terms; and treat delegation and emotional regulation as skills people can practise.
They also identify as the author of Find the Best — A Full Guide for Product Management.
Each post turns a broad idea into concrete actions, such as mapping a workflow before changing it or handing someone a decision rather than only a task.
Bello connects management ideas to specific behaviors readers can try.
In the AI workflow post, the advice moves from mapping work as it happens to removing steps, redefining “done,” and measuring business outcomes rather than time saved.
In the delegation post, a hiking story makes the point tangible: a manager must let someone else choose the route, even when that person may initially choose poorly.
A post on emotional regulation adds a human-skills strand, offering practices such as pausing before responding and preparing for pressure rather than relying on willpower.
The beginner AI-news post mainly translates and comments on external announcements rather than developing a detailed implementation method.
Across these examples, their work is most useful to readers seeking accessible, action-oriented explanations of product work and leadership, not original technical research.
Fernando Bello's posts return to one argument: capability is not the same as outcome.
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Tools, licenses, talent and temperament are inputs; results depend on where the work is pointed and whether it was redesigned around the new capability.
Bello applies this to AI rollout (redesign the workflow, don't just adopt the tool), leadership (hand over the decision, not the task), and personal performance (composure is trained, not given).
A second recurring priority is that willpower is the wrong lever: conditions, environment and preparation do more than individual grit, and most bad reactions stem from unmanaged conditions rather than character.
Each argument draws on named research — McKinsey, Gallup, Microsoft's Work Trend Index and Yale's RULER framework — translated into numbered actions and closed with a question to the reader.
Bello signs posts as the author of "Find the Best — A Full Guide for Product Management" and a LinkedIn Top Product Management Voice.
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.
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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 post pairs McKinsey figures — 80% of respondents saying AI improved their own productivity, only 37% reporting enterprise-level EBIT impact, and the 6% of high performers being roughly three times more likely to have fundamentally redesigned workflows — with Gallup data showing that 30% of US employees use AI weekly but only 25% say their organization communicated a clear plan. It ends by asking readers to name one step in their week that exists only because a human used to have to do it.
Bello argues that AI is moving into the products people already open, so early access is not a durable edge.
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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.
The post gives three news stories a plain-language nuance, a single action and a one-line takeaway: Google's Gemma 4 model running directly on the Pixel 10's chip, so conversation works offline and stays on the device; Google Images turning 25 with image generation built into Search; and OpenAI's five practices for managing AI investment.
Takeaways include "The most useful AI is the kind you stop noticing you're using" and "Speed is not value.
Useful work is."
The post closes by telling readers they do not need to be an expert to begin.
Bello argues that delegation fails when managers hand over the work but keep the decisions.
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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.
On a familiar trail, Bello gives a friend who has never led the map.
The friend stops at a fork, waiting for direction; Bello stays quiet.
The friend chooses wrong and loses twenty minutes — and never gets a fork wrong again.
Supporting data includes McKinsey's finding that only just over a quarter of respondents said their organizations made good delegated decisions, and that leaders who empower others through coaching are nearly four times more likely to make good decisions and outperform industry peers financially.
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.
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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.
Microsoft figures cited in the post: 45% of workers say it feels safer to focus on current goals than to redesign how they work; only 13% say they are rewarded for reinvention if the results do not land; and organizational factors account for 67% of AI impact versus 32% for individual mindset, across 20,000 workers in 10 countries. On the personal side, Bello advises regulating the input — sleep, prep, margin — rather than trying to suppress the outward reaction.
Composure is trainable, and suppressing a feeling is not the same as regulating it
Bello challenges the idea that calm colleagues simply are calm.
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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.
A study of 180 participants watching emotional films, half instructed to suppress their expressions, found that suppression produced more cardiovascular activation than letting the feeling show.
The post also cites workplace results from emotional-competence training for supervisors: at one manufacturing plant, lost-time accidents fell 50%, formal grievances dropped from 15 a year to 3, and the plant beat productivity goals by $250,000; at a second plant, production rose 17%.
Bello's point: nobody hired new personalities — they trained the ones they had.
This answers what Fernando Bello thinks AI adoption should change inside a company.
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It moves from cited adoption statistics to a practical product-management method.
Fernando Bello argues that the AI gap in 2026 is not about access to models or licenses.
The post cites McKinsey figures: 80% of respondents say AI improved their own productivity, and about half say it helps them make better decisions, but only 37% report enterprise-level EBIT impact.
The 6% identified as AI high performers are nearly three times more likely to have fundamentally redesigned workflows around AI than about one quarter of everyone else.
Bello offers a product-manager sequence: pick one workflow, map how it runs today, identify steps that exist only because of a human limit AI has removed, cut those steps instead of speeding them up, rewrite the definition of done, and measure business outcomes rather than hours saved.
The closing line is direct: adoption is not the outcome, redesign is.
For brands planning a campaign
For a B2B SaaS company assessing Fernando Bello for an advisory or keynote engagement, this post offers an argument and method for an AI workflow redesign session aimed at product and operations leaders. The underlying numbers come from McKinsey and Gallup, not Bello's original research.
This shows how Fernando Bello explains AI news to people who do not follow technical releases, making current developments clear and practical.
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Fernando Bello presents three AI stories in plain language.
First, Google's Gemma 4 runs on the Pixel 10's own chip: an on-device model works without a signal and keeps spoken input on the phone.
Second, Google Images is turning 25, and image generation is moving into Search.
Bello advises checking whether existing tools have quietly added a feature before downloading another app.
Third, OpenAI advises measuring useful work per dollar rather than token prices alone.
The post returns to two simple takeaways: the most useful AI is the kind you stop noticing you are using, and speed is not value.
For brands planning a campaign
This shows Fernando Bello translating AI news for a general business audience. Its beginner-friendly style suits awareness and executive-education campaigns, rather than developer-focused education: it is not a deep technical implementation guide.
This answers what Fernando Bello teaches about leadership behavior when a team is learning to make decisions.
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A concrete personal story pairs with cited research on why delegation often fails.
Fernando Bello describes hiking a familiar trail with a friend who had never led one.
Halfway up, Bello handed over the map.
At a fork, the friend stopped and waited for direction.
Bello said nothing.
The friend chose the wrong path, and they lost twenty minutes, but the friend never got a fork wrong again.
Bello's point: most managers delegate the walking and keep the forks.
The post cites McKinsey finding that only just over a quarter of respondents said their organizations made good delegated decisions, and that leaders who empower others through coaching are nearly four times more likely to make good decisions and outperform peers financially.
It also cites Microsoft's 2026 Work Trend Index of 20,000 workers in 10 countries: 45% say it feels safer to focus on current goals than to redesign how they work, and only 13% say they are rewarded for reinvention if results do not land.
For brands planning a campaign
For an advisory engagement on leadership coaching or AI-era management, this post offers a memorable story and a clear behavioral instruction: name the decision that is now someone else's, name the guardrails, and do not take the decision back.
It can anchor a keynote or workshop segment on delegation.
Ask for the sources behind the cited studies; they are secondary sources, not Bello's own research.
This answers how Fernando Bello connects emotional intelligence to workplace performance, showing the human-skills side of their leadership writing alongside AI and product management.
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Fernando Bello argues that composure is usually treated as a personality trait, but emotional regulation is trainable.
The post cites Yale's RULER framework, where regulating follows recognizing, understanding, labeling, and expressing emotions.
It also cites a study of 180 participants who watched emotional films, with half instructed to suppress their expressions.
Suppression did not create calm; it produced more cardiovascular activation than letting the feeling show.
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.
The post also cites workplace results: after supervisors at one manufacturing plant were trained in emotional competencies, lost-time accidents dropped 50%, formal grievances fell from 15 a year to 3, and the plant beat productivity goals by $250,000.
At a second plant, production rose 17%.
For brands planning a campaign
This broadens Fernando Bello's fit for leadership development and manager-training programs.
A campaign could use the post to build a workshop on emotional regulation as an operational skill, especially for frontline or operations leaders.
Ask for the sources behind the curated studies and cases before presenting the material as research.
Approach: research-cited posts with steps readers can run4
AI adoption versus workflow redesign1
Explaining AI to non-experts1
Delegation and coaching decisions1
Emotional regulation at work1
AI adoption versus workflow redesign
Fernando Bello argues that the AI advantage has shifted from access to redesign: most companies have the tools, but few have rebuilt the work around them.
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The post pairs McKinsey figures (80% of respondents reporting personal productivity gains, only 37% seeing enterprise EBIT impact, and high performers nearly three times more likely to have redesigned workflows) with Gallup numbers on weekly AI use and unclear organizational plans. The advice is concrete: map one workflow as it runs today, cut steps that exist only because a human used to do them, rewrite the definition of done, and measure business outcomes instead of hours saved.
Opens with a counterintuitive claim, supports it with named third-party research and links, lists numbered practical steps, and ends with a question inviting readers to name one workflow step they could remove.
The post cites 2026 McKinsey and Gallup data rather than documented client work. Ask for examples of relevant engagements if client experience is important to the brief.
For brands planning a campaign
A strong fit for a keynote or workshop on AI rollout when the audience has already bought tools and needs help changing processes. The redesign-over-adoption framing gives the event a clear argument and a ready-made exercise attendees can run on a live workflow.
One post translates three AI stories of the week into plain language: Google's on-device Gemma 4 model, image generation arriving inside Google Search, and OpenAI's guidance to measure useful work per dollar rather than token prices.
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Each story includes a short 'try this' action tied to tools the reader already has. The closing argument is that AI is moving into existing products, so advantage comes from judgment and curiosity rather than early access.
A weekly news digest structured as three numbered stories, each with a nuance, a practical action, and a one-line takeaway, closing on a general principle rather than a product pitch.
Names vendor products directly (Google, OpenAI), so brand mentions can arise naturally, but competitors' tools may appear alongside a sponsor's.
For brands planning a campaign
Useful for audiences that feel behind on AI and want orientation rather than hype. Request this format for a beginner-friendly session, a newsletter series, or onboarding content for non-technical teams.
Bello frames delegation as handing over the decision, not the task, through a personal hiking story about a friend who took the map, chose the wrong fork, and never got one wrong again.
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They support the lesson with McKinsey research on how few organizations delegate decisions well and how coaching leaders are nearly four times more likely to make good decisions, plus Microsoft Work Trend Index data on why employees avoid reinvention (45% prefer focusing on current goals; only 13% feel rewarded if results miss).
Starts with a personal anecdote, then uses research to show the behavior is rare and costly, identifies a behavior to build, and closes with a question about a decision the reader is still holding.
Ask for confirmation of the MBA and Harvard references if verified credentials matter to the brief.
For brands planning a campaign
Suited to leadership development programs, manager training, and events for teams navigating change. The 'delegate the fork, not the walking' line is a memorable anchor for a session on decision ownership during an AI or restructuring push.
A post on composure argues that emotional regulation is trainable, not a personality trait, citing Yale's RULER framework and a 180-participant suppression study showing that hiding feelings costs more physiologically than expressing them.
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The practical section offers four habits ('buy the pause,' regulate inputs like sleep and preparation, treat the skill as trainable, and regulate out loud), plus workplace data from a manufacturing plant where supervisor emotional-competence training cut lost-time accidents by half and reduced grievances from fifteen a year to three.
Challenges a common assumption, uses an academic framework and a controlled study for credibility, then gives four labeled habits with one action each and closes with a reader question.
This topic sits outside the core product management and AI lane, broadening the range but potentially mismatching a strictly product-focused brief.
For brands planning a campaign
Opens a second lane beyond product and AI: wellbeing, manager effectiveness, and culture work. Relevant for HR conferences, people-leadership programs, or a workshop on handling pressure during organizational change.
Approach: research-cited posts with steps readers can run
Across the material, the consistent approach is to anchor an argument in named external research, translate it into plain language, and give the reader numbered actions rather than commentary alone.
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Posts open with a claim or story, carry a takeaway line per section, cite sources with links at the foot, and end with a direct question.
Formats include data-driven analysis, a beginner explainer digest, a leadership story with a lesson, and a framework-based self-improvement post.
Recurring subjects are AI at work, product management practice, leadership and delegation, and emotional intelligence.
Common structure: hook, evidence with linked sources, practical steps or habits, takeaway lines, closing question, and a signature line identifying the author of a product management guide.
For brands planning a campaign
The style adapts to different sponsorship formats: an explainer post, a data-backed argument, or a story-led lesson. Expect an external research citation behind most claims and a discussion prompt at the end, supporting comment engagement while giving the sponsor less control over the exact message.
The posts disclose no sponsorship, affiliate, client, employment, or ownership relationship with a brand.
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Fernando Bello's strongest visible brand connections are the research and news sources cited in their arguments—McKinsey, Microsoft, Google, and OpenAI—and each is treated as a mention, not a partnership.
Bello's authorship of the book Find the Best — A Full Guide for Product Management is self-stated and belongs with their identity rather than in this section.
Ask for confirmation of any undisclosed brand relationships.
McKinsey & Company
Mention
Bello cites McKinsey's 2026 State of AI findings on productivity and EBIT impact to argue that workflow redesign, not adoption, produces returns.
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In the delegation post, they cite McKinsey's 2020 research on delegated decision-making.
Bello draws on Microsoft's 2026 Work Trend Index figures on worker caution about redesigning how they work and on organizational versus individual factors in AI impact.
In a roundup of AI news explained for beginners, Bello describes Google's on-device Gemma 4 model for the Pixel 10 and the addition of image generation into Search.
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The posts present both as news items, not an announced collaboration.
Fernando Bello's posts sit at the intersection of AI adoption, product management and leadership, with a clear thread to follow.
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For the business argument, start with the AI process-redesign post, which pairs McKinsey and Gallup figures with a step-by-step method for rebuilding a process around AI rather than layering a tool on top of it.
For a lighter entry point, the beginner AI news explainer translates three developments into plain language with one action each.
The leadership thread covers delegation as handing over the decision rather than the task, and composure as a trainable skill rather than a personality trait.
Across the posts, the format holds: a claim or a story, named outside research with linked sources, numbered actions and a closing question to the reader.
Similar creators below extend the same territory from different angles: executive career visibility, regulated manufacturing quality and project management.
Speciale writes the same kind of leader-facing advice as Bello: a single, rehearsable behaviour is named, illustrated with a real story and tied to who gets recognised in senior rooms.
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Bello argues people decide once they have watched a leader not punish a wrong turn; Speciale argues evaluation starts in the first three seconds of how someone presents who they are. Both locate the obstacle before the substantive conversation, end with a direct question to the reader and invite them to a workshop.
Speciale covers executive recruiting, interview presence and career advancement, drawing on two decades inside recruiting teams at Amazon, McKinsey, Indeed and Google, with client stories such as VP offers within twelve weeks. Bello covers product management, AI process redesign and day-to-day management practice, drawing on third-party research rather than first-person client outcomes.
Cole asks the same question Bello asks about AI at work — whether the capability is being pointed somewhere sound — and reaches a comparable conclusion: in regulated work, human judgment and qualified oversight cannot be handed to a tool.
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Cole describes an FDA finding where AI was used to create drug specifications and records without the required quality-unit approval; Bello describes teams adopting AI without redesigning the steps around it. Both treat oversight as a design requirement rather than a formality.
Cole's frame is medical device and pharmaceutical quality compliance, drawing on a cited Forbes article, an FDA warning letter, ISO 13485 clauses and the qualifications a Quality leader needs. Bello's frame is product management and leadership development, translating studies and index data into numbered steps for product teams.
Le Manh makes the case Bello makes about AI from a different seat: the AI economy will be delivered through redesigned work and projects, not models alone, and AI can automate and recommend but cannot absorb accountability.
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Both turn that position into instructions for practitioners — what to delegate, what needs human oversight and what should not be delegated at all. Both also tie AI value to skills and capability rather than tool access.
Le Manh speaks at the profession level, addressing 500 executives from a stage in Seoul about project management, industrial capacity, reshoring and sustainability at national scale. Bello works at the level of an individual manager or product team, making the case in a written post with cited research and a short action list rather than a conference summary.
For a B2B SaaS campaign about AI workflow redesign and leadership coaching, the topic match is close; the audience match is not yet demonstrated.
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The four posts target product managers, team leads, and operators who decide how work gets organized.
Their arguments are practical rather than technical: remove steps that exist only because a human used to do them, delegate the decision and not just the task, and treat emotional regulation as a trainable skill rather than a personality trait.
One post explains beginner-level AI news in plain language, suggesting the writing also reaches people earlier in their AI adoption.
Based on the writing, a buyer can reasonably expect an audience of working professionals in product, operations, and management roles who are evaluating AI and leadership practice.
The posts provide a LinkedIn follower count but no demographic or professional breakdown, geography, or individual-post engagement numbers.
Ask Fernando Bello, or whoever manages their page, for LinkedIn’s audience breakdown (job seniority, function, industry, and geography), recent comparable results, and any audience survey covering decision-makers in product or engineering.
Request material specifically on AI workflow redesign and leadership coaching, not general reach, to judge whether the audience matches buyers of an AI workflow product.
Fernando Bello’s posts do not establish a partnership offer.
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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.
Need: A B2B SaaS company selling an AI workflow product wants a product management thought leader to advise on AI workflow redesign and leadership coaching, and to propose a keynote or advisory engagement for product and operations leaders.
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Decision: Fernando Bello is a good fit for a campaign on AI workflow redesign and leadership coaching, based on topic, teaching method, and practical usefulness.
The strongest match is the argument in their AI post: the advantage has moved from having AI tools to redesigning the work around them.
They give product teams a sequence they can run: pick one workflow, map it as it actually runs, find steps that exist only because of a human limit AI has removed, cut the step instead of speeding it up, rewrite the definition of done, and measure business outcomes rather than hours saved.
That is directly usable in a keynote and advisory session.
The second match is leadership coaching.
In their delegation post, they teach delegation as handing over the decision, not just the task: name the decision that is now someone else's, name the guardrails, and do not take the decision back.
The hiking story gives a speaker a concrete story, while the cited McKinsey and Microsoft research gives the argument a research spine.
The posts show a repeatable editorial structure: claim or story, cited research, numbered steps, closing question.
They do not document commercial formats, sponsor policy, a media kit, rate card, or inquiry route.
Before committing budget, ask about format, disclosure, review rights, measurement, and documented client outcomes.
The posts show Bello's topics and teaching style, but not whether they have led this work in a paying client engagement.
When to choose a specialist
Need: A developer tools company wants a technical education series for machine learning engineers, including code walkthroughs, model deployment benchmarks, and architecture tradeoffs for production systems.
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Decision: Bello's posts do not support this need.
Their AI writing is aimed at non-specialists and business readers, not ML engineers.
The beginner AI news post translates three stories out of technical language for beginners: it explains that Google's Gemma 4 runs on the phone's own chip and describes image generation arriving inside Google Search, with practical actions for ordinary users rather than implementation detail.
There is no code, benchmark, model architecture discussion, or production deployment walkthrough in the posts.
The other two posts cover leadership and workplace performance: one is about delegation and coaching, the other about emotional regulation and composure.
Neither can stand in for developer education.
The audience section contains one follower count and no occupational or seniority breakdown, so the posts do not show that readers are ML engineers or that a technical series would reach them.
Before reconsidering this need, look for technical writing, code repositories, conference talks, or results with engineering audiences.
The posts also contain no video, podcast, or long-form technical formats.
A worked campaign example
Worked campaign angle for advising on AI workflow redesign and leadership coaching: a keynote and workshop titled 'Redesign the Work, Not the Tool.' A 45-minute keynote plus a 90-minute working session for a B2B SaaS customer summit or internal product leadership offsite.
Open with the gap from the AI workflow post: 80% of respondents say AI improved their own productivity, but only 37% report enterprise-level EBIT impact, and the high performers are the ones redesigning workflows.
Move quickly to the method: participants map one workflow as it runs today, mark the steps that exist only because a human used to do them, and choose one step to cut rather than accelerate.
Then use the delegation story to make the leadership half concrete: the manager hands over the decision and the guardrails, not just the task, and does not take the decision back at the first wrong turn.
Close by rewriting the definition of done for the chosen workflow and choosing a business outcome to measure instead of hours saved.
Deliverable: a one-page workflow redesign canvas that each participant completes during the session. Follow-up: a short written recap with the five-step sequence and the delegation prompt, so the internal team can run the same exercise on a second workflow.
What the buyer gets: a speaker who can connect both halves of the need—the AI workflow argument and the leadership behavior that makes redesign stick.
The posts do not document client outcomes or a published sponsorship format; settle those directly, and keep claims about Bello's client results out unless supported by primary sources.
The external statistics in the posts are from McKinsey, Gallup, and Microsoft, not from Bello's own studies or engagements.
Choose Jennifer Speciale when the buyer’s real problem is how product leaders are read in senior rooms, not how their workflows run.
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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.
The tradeoff is depth beyond career positioning.
Fernando Bello’s posts teach how leaders are evaluated and how to prepare an entrance, but not AI workflow redesign, product management practice, or a five-step method a product team can apply to its own work.
A buyer would trade the workflow canvas and delegation exercise for interview and visibility coaching.
Their posts offer client anecdotes about results, not a published outcome study.
Ask directly about sponsorship, rates, and advisory format.
Choose Mark Cole when the AI need sits inside regulated medical device or pharmaceutical manufacturing rather than a software product organization.
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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.
The tradeoff is scope.
Fernando Bello’s posts cover quality management, FDA and ISO requirements, and oversight by qualified personnel, but offer no product management method, workflow redesign sequence for knowledge work, or leadership coaching for product organizations.
If the need is B2B SaaS product leaders redesigning how their teams work, this regulatory depth answers a different question and would need translation the buyer cannot rely on.
Ask directly about audience composition, commercial format, and engagement terms.
Choose Pierre Le Manh when the engagement is enterprise-scale transformation rather than a single product team’s workflow.
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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.
The tradeoff is altitude.
Fernando Bello’s posts work at the level of programs, portfolios, and national buildouts, but do not give a product team a specific workflow to cut, a definition of done to rewrite, or a coaching exercise to run.
A buyer choosing Le Manh trades the concrete five-step workflow method and delegation drill for executive framing on how AI changes delivery and where human accountability stays.
Ask directly about commercial format and audience breakdown.
Fernando Bello's LinkedIn posts cover AI adoption and workflow redesign, beginner-level AI news, delegation and leadership coaching, and emotional regulation.
The posts here have no publication dates, and the outside research they cite spans 1997 to 2026.
No video, podcast, newsletter or long-form writing is available here, and the book Find the Best — A Full Guide for Product Management is known only by its title, so its contents cannot be assessed here.
The posts here do not include audience demographics, engagement figures, firsthand client outcomes, or documented sponsorship, affiliate, or employment relationships.
