Building Strong Foundations

Most founders build their investor list like this: → Google top VCs → Scrape names from Twitter → Ask friends for intros → Hope someone bites But here’s the thing: You don’t need 100 intros. You need 10-15 that fit. The founders who raise quickly do one thing really well: They build a high-fit investor pipeline. Here’s how I recommend doing it: Step 1: Match on stage and check size If you’re raising $1M, don’t waste cycles on $10M+ lead investors. Start with funds that regularly lead or follow at your stage. Step 2: Go beyond the thesis Every VC says they invest in “mission-driven founders with bold visions.” Ignore the marketing. Look at actual portfolio companies. → Do they invest in companies like yours? → Have they backed similar business models or industries? Step 3: Find the partner, not just the firm The firm might be right, but the wrong partner = no deal. You want the one who leads in your space and gets excited fast. Step 4: Stalk them (professionally) → Read their blog posts → Watch their past interviews → See who they’ve backed recently This is how you show up to the meeting already speaking their language. At Capwave AI, we help founders find these exact matches by analyzing actual investor behavior, not just their website blurbs. No more guessing who might be a fit. You know who to build with, and why. Have you built your investor list yet? What tools, filters, or questions helped you narrow it down? #fundraising #venturecapital #startups #founders #capitalraising

Harvard Business Review found that founder-led companies outperformed the S&P 500 by nearly 3x. And honestly, I’m not surprised. From 1990 to 2014, the companies in the S&P that were still led by their founders didn’t just perform well, they pulled away from the pack. -The companies studied included Amazon (Bezos), Netflix (Hastings), and Salesforce (Benioff at the time). - These businesses not only delivered outsized shareholder value, but also built more durable, customer-first business models. - The core idea: founder intuition, conviction, and speed beat bureaucracy. We’re seeing this play out in real time again. In the last 2 years, founders are being reinstated across tech and public markets: Bob Iger returned to The Walt Disney Company (not technically a founder, but treated as one culturally). Michael Dell has led Dell Technologies’s public–private–public turnaround with deep founder conviction. Mark Zuckerberg was heavily criticized during the Metaverse pivot, but stuck to his conviction and Meta’s stock rebounded massively in 2023–2024. Brian Chesky at Airbnb led with product obsession during the pandemic when the company was nearly wiped out and emerged stronger. Jensen Huang at NVIDIA (co-founder) became one of the most respected operators in the AI boom, AI now powers 70% of its explosive revenue. Investor sentiment is shifting again toward founder-led conviction. Many VCs now back “founder repeats” or “solo GPs” because they believe speed, resilience, and vision come from proximity, not politics. Startups with strong founder narratives and operating clarity are outperforming peers even in low-capital markets. Founders don’t optimize for next quarter, they’re building something that still makes sense a decade from now. And when you lead like that, everything changes. → You move faster because you don’t wait for consensus. → You listen deeper because you still feel the original customer pain. → You make harder calls because you’re thinking in years, not headlines. → You build for durability, not just velocity. I spent 18 years building my first company without outside capital, that taught me that emotional ownership is the one thing you can’t outsource. And here’s the part that matters most: You don’t need to be a founder to lead like one. You don’t need equity to act like you’re responsible for the outcome. You just need proximity, clarity, and enough care to stay with the problem longer than most people will. What would change if you led your role like the company had your name on it? #founders #leadership #founderledmarketing #startups #marketing

Many solutions to solve the silo problem fail. Companies spend millions on collaboration tools, cross-functional teams, and leadership initiatives to break down silos—yet they persist. A recent Harvard Business Review article explains why. It is because silos aren’t a single problem with a one-size-fits-all solution. The article makes an important distinction: Not all silos are the same. There are three types, each requiring a different approach: 1. Systemic Silos – When departments focus on their own goals rather than the organization’s success. 2. Elitist Silos – When certain teams hoard knowledge, believing others won’t understand or add value. 3. Protectionist Silos – When teams withhold information out of fear, often to maintain control or job security. The real challenge, then, is misdiagnosis. Many companies and leaders throw generic solutions at silos without addressing their root cause. Here is what actually works: Align Goals – If misaligned incentives create silos, shared KPIs and mutual accountability are key. Improve Communication – If knowledge hoarding is the issue, cross-functional learning and embedded collaboration help bridge the gap. Foster Psychological Safety – If fear is driving resistance, leaders must build a culture where transparency is rewarded, not punished. I’ve seen this firsthand in my work. Silos don’t collapse on their own. They require clarity, curiosity, and deliberate action. When team members truly understand each other, momentum happens. #curiosity #collaboration #momentum #understanding #learning #leadership https://lnkd.in/e-nRD8Jv

Putting pressure on data science teams to deliver analytical value with LLMs is cruel and unusual punishment without a scalable data foundation. Over time, the best LLMs will be able to write queries as effectively or more effectively than an analyst - or at minimum make writing the query easier. However, the most cost-intensive aspect of answering business questions is not producing SQL, but deciding what the query inputs should be and determining whether or not the inputs are trustworthy. Thanks to the rapid evolution of microservices and data lakes, data teams find themselves living in a world of fragmented truth. The same data points might be collected by multiple services, defined in multiple different ways, and could actually be going in opposite and contradictory directions. Today, data developers must do the hard work of understanding and resolving those discrepancies, which comes in the form of 1-to-1 conversations with the engineers managing logs and databases. Very few if any service teams at a company have documented their data for the purpose of analytics. That results in a giant gap in documentation across 1000s of datasets across the business. Without this gap being filled, data scientists will ultimately have to manually hand-check any prediction that an LLM makes in order to ensure it is accurate and not hallucinating. The model is doing a job with the information it has, but the business is not providing enough information for the model to deliver trustworthy outcomes! By investing in a scalable data foundation, this paradigm flips on its head. Data is well documented, clearly owned, and structured as an API enforced by contracts that define the use case, constraints, SLAs, and semantic meaning. A quality-driven infrastructure is a subset of all data in the lake, which reduces the surface area LLMs need to make decisions only to the nodes in the lineage graph which have clear governance and change management. Here's what I suggest: 1. Start by identifying which pipelines are most essential to answering the business's most common questions (you can do this by accessing query history) 2. Identify the core use cases (datasets/views) that are leveraged in these pipelines, and which intermediary tables are of critical importance 3. Define semantically what the data means at each level in the transformation. A good question to ask is "What does a single row in this table represent?" 4. Validate the semantic meaning with the table owners 5. Get the table owners to take ownership of the dataset asn API, ideally supported programmatically through a data contract 6. Define the semantic meaning and constraints within the data contract spec, mapped to a source file 6. Limit any usage of an LLM to the source files under contract Good luck! #dataengineering

In the race to adopt the latest technologies, many companies are jumping on the AI bandwagon. But here's the truth: You don't need an "AI" strategy – you need a solid data strategy. + AI can only be as good as the data it processes. Without high-quality, well-organized data, even the most advanced AI systems will fall short. Start by ensuring your data is accurate, comprehensive, and easily accessible. + Invest in the tools and processes that allow you to collect, store, and analyze data effectively. This includes data governance, data quality management, and scalable storage solutions. + Break down silos within your organization. Ensure that data from different departments and sources can be integrated and analyzed cohesively. A unified data approach will provide a more complete and actionable view of your business. + A successful data strategy requires collaboration between IT, data science, and business units. Ensure everyone understands the value of data and works together to harness its potential. + With a solid data strategy in place, you'll be in a prime position to adopt AI technologies. Your AI initiatives will be more effective and deliver better results because they're built on a strong foundation of reliable data. In conclusion, before you think about implementing AI, make sure you have a robust data strategy. It's the backbone of successful AI applications and will drive long-term value for your organization. #DataStrategy #AI #DataDriven #BusinessIntelligence #DataQuality #TechStrategy #Innovation

Working with as many CRE sponsors raising capital as I have, I’ve seen how challenging it can be to balance structure with innovation in investor outreach. Traditional wisdom says you have to choose: stick to tried-and-true methods (the traditional in-person approach) or experiment with something new (like scaling by raising capital online). But what if the new way wasn’t as different as it seems? That’s why I developed the *Investor Acquisition System* for sponsors: blending the familiarity of traditional methods with the scalability of online strategies. Here’s how it works: Step 1: Build Familiar Foundations ↳ Start with what you know works—building relationships. The same trust and personal touch you bring to in-person meetings can be integrated into online campaigns through personalized emails, thoughtful messaging, and consistent follow-ups. Step 2: Leverage the Power of Scale ↳ Online marketing doesn’t replace your personal touch—it amplifies it. By using digital systems, you can reach more investors at once, nurture relationships automatically, and keep investors engaged without needing to be everywhere in person. Step 3: Test and Adapt for Results ↳ Just like refining your pitch over time, online marketing thrives on iteration. By testing what works—messaging, channels, and timing—you can improve your results and expand your reach without extra effort. This approach shows sponsors that moving online isn’t about abandoning what they know; it’s about scaling it. The personal connections and reliability you’ve built in person can now reach more people, more effectively, without sacrificing quality. The results have been transformational for sponsors: ↳ More active investors. ↳ Faster capital raises. ↳ More money raised. ↳ less time spent traveling. ↳ investor pipelines that grow steadily with less manual effort. Online marketing isn’t just a new way—it’s the evolution of what has always worked by bringing efficiency, scale, and results to sponsors who are ready to grow. *** If you are already raising capital online, what was the biggest challenge you faced initially; and if you’re still relying strictly on in-person capital formation, what holds you back from scaling online?

I’ve created over 70 brand positionings. Here’s what I’ve learned.     It started with Coke Zero, where I spent endless hours refining positioning, narrative, and insights. At Gallo, I worked on 60+ brands—many distressed, talking to themselves, or built from scratch using trends, insights, and creativity.   Here’s what I’ve learned. 3 𝐓𝐡𝐢𝐧𝐠𝐬 𝐓𝐡𝐚𝐭 𝐖𝐨𝐫𝐤: ➜ Positioning rooted in foundational emotions: The strongest ideas tap into deep, universal human needs—not just the category you’re selling in. ➜ The opposite should also work: If your positioning couldn’t be flipped into its opposite and be good, it’s probably too vague or uninspired. ➜ Clarity above all else: A positioning statement that requires paragraphs to explain won’t resonate—it has to be sharp and clear. 3 𝐓𝐡𝐢𝐧𝐠𝐬 𝐓𝐡𝐚𝐭 𝐃𝐨𝐧’𝐭 𝐖𝐨𝐫𝐤: ➜ Confusing what you want to sell with what the consumer needs: Saying “20-somethings want a fun, healthy drink” doesn’t mean they need you. It means you want to sell it. ➜ No clear problem to solve: If consumers have no unmet need, why would they care? I always challenge, “Why do they need us if their life is already perfect?” ➜ Trying to appeal to everyone: Broad, general positioning connects with no one. The more focused, the more powerful.   𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 Good positioning is the difference between brands that break through and products that disappear. It separates the 5% 𝐭𝐡𝐚𝐭 𝐬𝐮𝐜𝐜𝐞𝐞𝐝 from the 95% 𝐭𝐡𝐚𝐭 𝐝𝐨𝐧’𝐭. = I’m Courtney, and I help brands transform from scattered or invisible to breakout stars with sharp, differentiated positioning that’s rooted in insights and emotion. Want to connect in the new year?

⛓️ Break the Silo! Finance and talent development can no longer exist independently of each other. In the #FutureOfWork, talent development is no longer a "soft" skill. It's a strategic asset. Data analytics back this up and finance needs to wake up to this reality -- not by cutting budgets, but by asking HR to show an ROI on talent development. The most forward-thinking companies are breaking down traditional silos between HR and finance, and building strategic, cross-functional partnerships to co-create programs tied to measurable business outcomes like productivity, retention, and ROI. ❓ What does that sound like in action? Francie Jain and I demonstrate this with an imagined conversation between the head of L&D of a company and its CFO. L&D begins by pitching professional development with the same rigor and ROI framing used for R&D or capital investments. It’s not about asking for more budget—it’s about making a business case for growth through learning. When finance and talent align on how human capital drives value, we move from “nice to have” to “need to scale.” Read the full Association for Talent Development (ATD) article below and then share in the comments below: Is your finance team treating L&D like an asset or an afterthought? #HumanizingHumanCapital #TalentDevelopment #BusinessStrategy Many thanks to Tony Bingham for letting Francie and me share out thoughts!

Most workplaces today focus on “me” (the employee) to “handle it”. Through the EAP, therapy, self-awareness, mindfulness, meditation, self reflection, EQ and resilience training. All those things are good and needed. But there’s a catch. And it’s a big one. All self-care “me” practices are built off of one critical foundation: A solid support system - the “we”. And our support systems are crumbling. If we don’t have a support system we will feel: 🔅 Unseen and misunderstood 🔅 Uncared for and unvalued 🔅 Lonely & isolated 🔅 Unmotivated The very things that increase the stress we are seeking self-care to solve. Its a dangerous cycle that we can’t “me” our way out of. ⬇️ In our aggregated data from 2021-2023, we found that anywhere from 22-35% of your workforce is beginning their workday empty. Because they don’t have a solid support system outside of work. Others have such a high stress level at work that adding one more “thing to do” to their plate will go undone. Even if that’s self-care. Therefore expecting employees in these situations to “handle” their wellbeing at work without your support will add to their stress and backfire. It’s scientifically impossible to thrive and flourish without a support system. And we desperately need them in our organizations today. Leadership support counts more than peer support. We need them both. ⬇️ Leaders need to learn to be the “we” for the “me”. Or the “me” will burnout. #ShowingUp #SupportiveLeadership