Ben Foster

One of the most misunderstood frameworks as it relates to the A-player, B-player, etc. framework - here's some ready to re-familiarize yourself https://lnkd.in/gMTT2p3T Only more experience people are A-players, right? Here is a summary - A Players (10-20%): High performers who exceed expectations and solve problems proactively - found at any level, including driven juniors who learn rapidly - B Players (70-80%): Reliable performers meeting expectations, forming organizational backbone - includes experienced staff, some with A-player potential - C Players (10%): Persistent underperformers requiring excessive oversight - not about inexperience but ongoing performance issues despite feedback Key differentiators: A players seek feedback (constantly), take risks, mentor others, and achieve goals resourcefully - traits that transcend seniority Talent implications: Invest in A players, develop B players, address C players decisively - junior A players often outperform senior B/C players. Whats critical to understand is how this relates to hiring - since the frameworks lends itself better to evaluating existing teams Here are some things to think about - A player does NOT mean senior- someone straight out college can be an A player - A track record of past performance and going above and beyond is a good indicator of future behavior and results - Time spent on C players is time poorly spent - over and over again this is the biggest mistake that managers and leaders make. The data tells you that it's not a good use of your time - spend time with the A and B players and get acceleration. Spend time with C players and get lower productivity How do you think about hiring and talent?

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Talking to users and thoughtfully integrating their needs and wants was woven through everything we did at IDEO—threaded through every prototype and product decision. Reading my colleagues Joanne and Leo's latest work brought that to mind. They describe a future where research runs continuously, with AI agents recruiting participants and instantly surfacing insights. Synthetic personas, carefully trained on consumer signals, mirror human behavior closely enough to extend live studies and compress timelines from weeks to hours. Today user research mostly runs in fits and starts, prohibitively expensive and locked away in dedicated project cycles when it could be a constant companion, seamlessly integrated into the product development process. Joanne and Leo lay out the full vision here: https://lnkd.in/gEUBSG_J

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What does it take to create speed in a regulated industry? For Allie Carey, Chief Strategy Officer at SEI, it meant designing a system that could turn hundreds of ideas into real ventures (without adding chaos). Over three years, she: • Built a culture of experimentation across SEI. • Launched a company-wide innovation contest (with real stakes). • Narrowed 30 ideas down to 10 pitches, to 2 experiments, to 1 live business. • Took one idea from concept to paying customers in just 18 months. We tried something new this week on Beyond The Core and built her full playbook live. It’s how SEI increased decision speed, improved throughput, and built a pipeline of repeatable ventures. Catch the full episode, now live wherever you get your podcasts, and download the playbook at https://lnkd.in/gPRvAK_n

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The Shareholder mandate: Cut Cost But Grow Fast! Don't care about team morale, don't care burnt out performers, don't care toxic BOD due to crisis, just cut burning rate and grow top line! Sounds mission impossible ish? Or it sounds like your last BOD meeting? Try hire Fractional CXO. Access the expert, get the battle tested experience, implement changes in your business, and grow fast without full time executive hire! No hiring lead time No CXO level salary cost No “politics” adjustment No load balancing with existing leadership team No onboarding lead time You don’t need another title in the org chart—you need someone who’s been there, done that, and can make real moves starting Day 1. Bring in a Fractional CXO, pay for advisory hours—not full-time salaries—and start scaling smarter, faster, and cheaper. #leadership #fractionalCFO #fractionalCMO #fractionalCISO #fractionalGovRel #fractionalCPO #advisors #boardofadvisors

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Takeaways from Marketplace Risk: 💡 Balancing risk is more complicated than ever: sometimes contradictory regulations, genAI accelerating everything, and uncertainty in tech generally. 💡 A guiding light is your USERS. Suzi R. said something brilliant on a panel (paraphrasing): There's a pardox: The more seamless you make your user experience for your users, the more faceless and inhuman your company seems to them. Be accountable to your users and listen to them. This can include creating advisory councils, thorough appeals flows, and proactive communication. 💡 Another smart thing: Abhi Chaudhuri talked about provenance being the key to building trust online. We talk a lot about safety, but in the age of genAI, trust is becoming more complicated. People want to know where content has come from, and who created it first. 💡 An interesting trend/ insight: on some platforms, spam filters are catching real users along with scammers because BOTH are using the same genAI tools to create their content, so it seems eerily familiar. Interesting! 💡 Always important is using cutting edge AI just as your adversaries are, but to make sure you're building comprehensive human-in-the-loop systems and using your team's best skills. Finally, as said by Nick Tapalansky - YOU ARE NOT ALONE. We are all in this together because we care about internet safety. If you need help, ask for it. There are lots of people who will help you.

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I’ve spoken to over 2 dozen MDs at PE firms I can confidently say that the arb of figuring out how to implement Vertical AI at portfolio companies is very real right now It will fundamentally change underwriting for those who can do it predictably and unlock generational returns. Most are aware they need to act. Very few have.

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Building a startup is hard. It is especially hard in a country like Nigeria, where the ecosystem is yet to mature, and there are still many challenges to navigate. It is confusing, lonely, and full of uncertain decisions. Founders, especially early-stage founders, need as much support as they can get. Not “inspire to perspire” posts, but practical support on hiring, operations, funding, customer research, and navigating the regulatory landscape. Too many founders are surrounded by noise, surface-level advice, and generic content that does not reflect their current realities. What they need is a community that understands exactly what they are going through and can offer context that is immediately useful. Sierra Collective exists to be that community. A space where founders learn from one another’s live experiences and shorten the painful trial and error that usually defines the early stages of building. At Sierra, we believe that the most valuable support comes from those who are active in the trenches, navigating the same obstacles. If you are an early-stage founder, you should join the Sierra Collective. You do not have to figure everything out alone. As the African proverb says, “If you want to go fast, go alone. If you want to go far, go together.” Join Sierra: https://lnkd.in/d-Nzmtkq

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Is Product Operations really dead because of AI? I don’t think so. I've been tracking responses to my recent post about Product Ops, and while there's broad recognition that something needs to change, the pushback reveals some interesting blind spots. The pattern is undeniable: PMs have become jack-of-all-trades, doing everything except the strategic work they were hired for. Building dashboards, creating templates, reconciling data, aligning stakeholders, and filling gaps left by missing functions. They're the organizational glue, but they're not doing product strategy. The stories are eerily similar across companies. PMs spending months reinventing prioritization frameworks from scratch. Building their own analytics views because systems don't talk to each other. Creating alignment artifacts that should already exist. All while being held accountable for customer discovery and strategic thinking they have no bandwidth to pursue. Then when they burn out around the 12-18 month mark, we blame them for poor performance or lack of strategic vision. But here's where the debate gets interesting. Three main arguments keep surfacing against Product Ops: "𝐈𝐭'𝐬 𝐩𝐫𝐞𝐦𝐚𝐭𝐮𝐫𝐞 𝐟𝐨𝐫 𝐬𝐦𝐚𝐥𝐥 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬." Fair point. If you're a startup with one PM, wearing multiple hats is normal. But even then, ask yourself: what's the minimum viable structure that keeps your PM focused on customers instead of spreadsheets? "𝐅𝐢𝐱 𝐥𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐟𝐢𝐫𝐬𝐭." Some argue that weak decision rights or feature-factory cultures are bigger problems than operational burden. True, but here's the thing: you can't solve strategic alignment if your PMs are drowning in operational work. Product Ops creates the foundation for better leadership decisions. "𝐀𝐈 𝐰𝐢𝐥𝐥 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐭𝐡𝐢𝐬 𝐚𝐰𝐚𝐲." This is where I fundamentally disagree. AI might automate dashboard creation, but it won't solve the organizational design failure that made PMs responsible for operational infrastructure in the first place. If anything, AI makes Product Operations more essential, not less. Someone needs to establish the processes, define the standards, and ensure AI tools actually serve strategic decision-making instead of creating more noise. Product Ops isn't about adding overhead. It's about protecting PM leverage so strategy and customer understanding can actually happen. Are your PMs building the organization, or are they building products? What would change if they could focus purely on strategy and discovery?

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- Multi-methodology leadership - because complexity is not one-size-fits-all - I have often heard: “Let’s pick one method - Agile or Waterfall.... and stick with it.” But in practice, that approach breaks down fast. Whether we are handling compliance-heavy updates, pushing innovation, or managing run-the-business tasks, each requires a different rhythm. That is what inspired our latest blog post: a playbook for leadership that respects the unique cadence of each project type, and surfaces all of them on one single execution plane - so nothing falls through the cracks. Spoiler - from our monthly payroll walkthrough with a 50-person SaaS team, you will see how treating each function with its proper methodology can cut errors to approx. 1%, eliminate delays, free up nearly 8 hours per cycle, and restore trust in the workplace. At MindStaq, we built our platform around this principle: empower teams to choose and excel in their own methodology, while keeping leadership in sync. If you are striving for clarity, accountability, and outcomes without compromise, I think you will find value here. Dive in to explore how to lead without methodology silos and build operational harmony.

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The org chart that helped you get to 10 partnerships starts working against you at 50. Unclear ownership, reporting lines that bury the function, specialists brought in too late. Scaling a partner program requires making structural decisions that reflect where you're going, not just where you've been. On Thursday, March 26, Nelson Wang (Partner Principles), Jessica Baker(AchieveUnite), and Eleanor Thompson (Gravitee) are joining Denise Duest, MBA of HubSpot to talk through the real decisions behind building a partner team that scales:   ➡️ When to hire generalists vs. specialists ➡️ How to structure around different partner motions and how that changes as you grow ➡️ What reporting lines signal about whether your organization actually values partners Join us for a candid conversation from people who've built partner teams from startup mode through enterprise scale. Register here to attend live or get the recording:  https://lnkd.in/eFSf_g8v

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I’ve spent the last few years building and advising six dating apps — from large-scale relationship platforms to smaller teams focused on real-life, casual, and identity-based connection, and one pattern keeps surfacing: People aren’t short on options; they’re just exhausted and hesitant to try again. Fatigue looks different everywhere, and AI has made everything look perfect and less believable. Casual dating apps aim for simplicity but expose a disconnect. Women look for intention; men prioritize logistics. One side feels emotional, the other procedural. Long-term apps promise depth but demand emotional work. Profiles become résumés, rewritten by AI, tested on Reddit, optimized for approval. The question shifts from “Do they like me?” to “Is this even me anymore?” Real-world connection apps fight digital fatigue through events and pop-ups, but most users are out of practice. Years of digital curation make real-life interaction feel unfamiliar and awkward. Identity-based spaces create belonging and help people feel seen. But performative authenticity often follows, creating pressure to appear right before feeling real. AI hasn’t eased this burden; it amplifies it. When every message can be generated or polished, users question how much of what they see is real. Authenticity has become the rarest commodity. I’ve worked with smaller teams who often describe a “liquidity problem.” They believe scale will fix engagement, but the opposite is true. More volume speeds up fatigue. More matches don’t build connection—they add noise. Eventually, every user asks a quiet question: “Do I still believe this can work?” That’s where incumbents struggle. For years, they’ve treated connection as a volume problem. Why would users believe that they would solve this? The next frontier in social discovery is sincerity — rebuilding the belief that connection can still feel human. I advise teams working in this space. If you’re building in this space, I’d be interested in comparing notes. 🖤 🖤 🖤

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Leading product at Haast, I've seen skepticism that surrounds AI firsthand, especially when it comes to legal and compliance. In this space, speed isn't enough. Enterprises demand accuracy, explainability, and reliability—without compromise. Building our product is more than great design and code. It requires training AI on real-world compliance scenarios, partnering closely with legal experts, and embedding human-in-the-loop validation at every step. That behind-the-scenes rigor is what makes the difference. Our AI not only flags risks, but also explains *why* they are risks, referencing the exact policy or regulation. We’ve baked transparency and data security into the product from day one, because in compliance, trust isn’t a feature, it’s the foundation. Curious to hear from others: What’s your biggest trust barrier when evaluating new AI-powered tools? #ProductLeadership #AIEthics #EnterpriseSoftware #TrustInTech

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We just published a simple, practical guide for Charitable Fundraising Platforms. It explains what a CFP is, why the rules are changing, and how to stay compliant in California + other states with emerging laws (like Hawaii). You’ll find clear guidance on written consent, nonprofit eligibility checks, payout timelines, disclosures, and reporting so teams can launch and scale with confidence. If you run round-ups, checkout donations, or a giving/crowdfunding platform, give it a read→ https://lnkd.in/eBmhBfbS

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