Navigating Growth Challenges in AI Companies

I asked the smartest people I know about AI... I’ve been reading everything I can get my hands on. Talking to AI founders, skeptics, operators, and dreamers. And having some very real conversations with people who’ve looked me in the eye and said: “This isn’t just a tool shift. It’s a leadership reckoning.” Oh boy. Another one eh? Alright. I get it. My job isn’t just to understand disruption. It’s to humanize it. Translate it. And make sure my teams are ready to grow through it and not get left behind. So I asked one of my most fav CEOs, turned investor - a sharp, no-BS mentor what he would do if he were running a company today. He didn’t flinch. He gave me a crisp, practical, people-centered roadmap. “Here’s how I’d lead AI transformation. Not someday. Now.” I’ve taken his words, built on them, and I’m sharing my approach here, not as a finished product, but as a living, evolving plan I’m adopting and sharing openly to refine with others. This plan I believe builds capability, confidence, and real business value: 1A. Educate the Top. Relentlessly. Every senior leader must go through an intensive AI bootcamp. No one gets to opt out. We can’t lead what we don’t understand. 1B. Catalog the problems worth solving. While leaders are learning, our best thinkers start documenting real challenges across the business. No shiny object chasing, just a working list of problems we need better answers for. 2. Find the right use cases. Map AI tools to real problems. Look for ways to increase efficiency, unlock growth, or reduce cost. And most importantly: communicate with optimism. AI isn’t replacing people, it’s teammate technology. Say that. Show that. 3. Build an AI Helpdesk. Recruit internal power users and curious learners to be your “AI Coaches.” Not just IT support - change agents. Make it peer-led and momentum-driven. 4. Choose projects with intention. We need quick wins to build energy and belief. But you need bigger bets that push the org forward. Balance short-term sprints with long-term missions. 5. Vet your tools like strategic hires. The AI landscape is noisy. Don’t just chase features. Choose partners who will evolve with you. Look for flexibility, reliability, and strong values alignment. 6. Build the ethics framework early. AI must come with governance. Be transparent. Be intentional. Put people at the center of every decision. 7. Reward experimentation. This is the messy middle. People will break things. Celebrate the ones who try. Make failing forward part of your culture DNA. 8. Scale with purpose. Don’t just track usage. Track value. Where are you saving time? Where is productivity up? Where is human potential being unlocked? This is not another one-and-done checklist. Its my AI compass. Because AI transformation isn’t just about tech adoption. It’s about trust, learning, transparency, and bringing your people with you. Help me make this plan better? What else should I be thinking about?

AI Adoption: Reality Bites After speaking with customers across various industries yesterday, one thing became crystal clear: there's a significant gap between AI hype and implementation reality. While pundits on X buzz about autonomous agents and sweeping automation, business leaders I spoke with are struggling with fundamentals: getting legal approval, navigating procurement processes, and addressing privacy, security, and governance concerns. What's more revealing is the counterintuitive truth emerging: organizations with the most robust digital transformation experience are often facing greater AI adoption friction. Their established governance structures—originally designed to protect—now create labyrinthine approval processes that nimbler competitors can sidestep. For product leaders, the opportunity lies not in selling technical capability, but in designing for organizational adoption pathways. Consider: - Prioritize modular implementations that can pass through governance checkpoints incrementally rather than requiring all-or-nothing approvals - Create "governance-as-code" frameworks that embed compliance requirements directly into product architecture - Develop value metrics that measure time-to-implementation, not just end-state ROI - Lean into understanability and transparency as part of your value prop - Build solutions that address the career risk stakeholders face when championing AI initiatives For business leaders, it's critical to internalize that the most successful AI implementations will come not from the organizations with the most advanced technology, but those who reinvent adoption processes themselves. Those who recognize AI requires governance innovation—not just technical innovation—will unlock sustainable value while others remain trapped in endless proof-of-concept cycles. What unexpected adoption hurdles are you encountering in your organization? I'd love to hear perspectives beyond the usual technical challenges.

You’re Probably Not Ready for AI Transformation I’ve helped organizations implement AI strategies that scaled revenue and transformed operations, but I’ve also seen teams collapse under the weight of poorly executed AI initiatives. AI is a game-changer, but if you rush in unprepared, it can sink your business. Here are the 5 biggest lies companies tell themselves about AI strategy, implementation, and transformation (and how to truly unlock AI’s potential): 1. “We’ll Just Add AI to What We’re Already Doing” AI isn’t a bolt-on feature—it’s a fundamental shift in how you operate. It demands new workflows, infrastructure, and mindsets. Sure, you can use out-of-the-box solutions, but true transformation means aligning AI to your unique business challenges. If you’re not ready to rethink processes, AI won’t deliver transformative results. 2. “Our Current Team Can Handle AI” AI implementation requires cross-functional expertise in data science, engineering, and business strategy. Even with great talent, most teams aren’t ready to bridge the gap between AI’s potential and its practical application. Without proper enablement, adoption will falter, and the shiny new tool will collect dust. 3. “We’ll Just Hire AI tech to Lead the Charge” Good luck. Hiring AI tech specialists isn’t enough—especially if they don’t understand your industry or business model. These hires will spend months ramping up, navigating legacy systems, and explaining concepts to teams unfamiliar with AI. Transformation requires leaders who can marry technical expertise with a deep understanding of your business. 4. “AI Will Solve Our Big Problems Quickly” Not so fast. AI projects live or die on data quality, and most companies’ data is messy, siloed, or incomplete. Before you can expect results, you’ll need to clean, structure, and enrich your data—a slow, unglamorous process that determines whether AI succeeds or fails. 5. “We Just Need to Buy the Right AI Tools” Tools are only as good as the strategy behind them. AI success isn’t about flashy tech—it’s about embedding intelligence into your business processes. Without a clear plan to use AI for specific outcomes, you’ll waste time and money on solutions that fail to deliver meaningful impact. 2025 AI Transformation Plan: Instead of diving headfirst, take an intentional, step-by-step approach: •Start with a clear AI strategy tied to business outcomes •Audit and prepare your data for AI use •Train teams on AI-powered workflows •Build cross-functional alignment for smooth implementation •Invest in AI tools that solve specific problems •Set realistic KPIs and measure progress incrementally AI isn’t just a trend. It’s a paradigm shift. But it’s not a magic bullet. Approach it strategically, and it will unlock new growth, efficiency, and innovation. Rush in without preparation, and you’ll burn time, resources, and credibility. Learn what AI transformation really requires—then execute thoughtfully. No shortcuts.

Why Do Organizations Stall When Scaling AI? I advise both organizations aiming to scale AI enterprise-wide as well as corporate legal departments that must play a leading role—not only supporting enterprise AI use but also adopting AI themselves. I love doing both, as so much of the success in scaling AI is linked to a modernized legal department. And, because I appreciate metaphor, lately I’ve been using a runway analogy. AI’s potential is immense, but many obstacles clutter the runway. Clearing them is key to taking off. Why is this difficult? Based on my experience advising on AI modernization, here are three common reasons organizations (banks, law firms, consumer goods, and life science companies alike) stall: 1. Outdated operating models and ways of working:   AI requires flexibility and collaboration, yet many organizations attempt to scale AI within rigid, siloed structures. Ownership is often unclear—who leads, who supports? How can you get marketing talking with IT, talking with legal, talking with cyber? Companies must rethink their operating models, enabling faster decision-making, earlier risk mitigation, and decentralized leadership (once the strategies and principles, usually center-led, are clear). AI initiatives demand cross-functional integration—there’s no avoiding it. (Also worth noting: faster decision-making is largely driven by good knowledge management—codifying “tenets” once you see patterns of strategic insight.) 2. Legal and Risk readiness lags behind AI growth: AI introduces legal risks that are continually evolving and still somewhat unclear—data privacy, IP, and compliance issues are just the start (and they aren’t small hills to climb). I advise legal departments to get ahead of AI risks by modernizing their approach, ensuring they have talent on their legal teams (or from their law firms) equipped to navigate AI's complex landscape. Legal also needs to play a central role at the organizational level, as ethical AI principles must be integrated into governance from the beginning. 3. Talent gaps in AI and legal modernization: AI isn’t just about technical talent; it’s also about AI-savvy leadership and legal expertise. Legal teams need to be upskilled on AI's impact on brand reputation, performance, safety, contracts, liability, and compliance. Organizations should move quickly to identify and close any gaps, ensuring they have both the tech and legal talent to scale responsibly and protect their interests. (*Also, law firms must offer AI-driven solutions to their clients to effectively partner here. I believe we’re entering a new era of law-firm/legal department collaboration, and law firm leaders will make the first move.*) Scaling AI isn’t just a tech problem—it’s a transformation challenge. Aligning your operating model, legal risk management, and talent strategy with AI’s risks and opportunities is crucial for success. Also curious—what other metaphors do you all use here?

AI adoption isn’t just about technology. It’s about leadership. Many leaders want AI but struggle to drive change. They face resistance, ethics concerns, and unclear ROI. Here’s how leaders can overcome AI challenges: 1 - Lack of AI Expertise Leaders feel unprepared for AI decisions. Invest in AI literacy and expert guidance. 2 - Resistance to Change Teams fear AI will replace jobs. Communicate benefits and involve employees early. 3 - Integration with Existing Systems Legacy systems create adoption hurdles. Start small and phase AI into workflows. 4 - Managing Ethical Concerns Bias and transparency issues arise. Set AI guidelines and run regular audits. 5 - Balancing Innovation with ROI Short-term wins can slow long-term growth. Set clear goals and focus on scalability. 6 - Building a Collaborative AI Culture Silos slow AI adoption across teams. Foster alignment and celebrate progress. 7 - Navigating Rapid AI Advancements AI evolves faster than most businesses. Stay updated and invest in learning. Great AI leadership isn’t about knowing everything. It’s about creating a culture that embraces change. Found this helpful? Follow Arturo and repost

AI isn’t just a technology shift— it’s a people shift. Inside every company there are Catalysts, Converts, and Anchors. Each need different strategies: In the 10 years of Reforge, we’ve seen inside thousands of transformations. Establishing growth teams, from project to product management, from sales-led to product-led, and many more. Check it out here: https://lnkd.in/gAfDBmP3 There is a pattern that always repeats itself in these transformations. But with the shift to AI, the stakes are much higher. There are three different internal audiences when thinking about AI adoption and transformation: 🎇 Catalysts 🔄 Converts ⚓ Anchors Just like a good product and marketing strategy, you need to segment your audience and have different plans. Catalysts ↳ Early adopters, already tinkering on personal accounts. ↳ They know staying current is non-negotiable for their careers ↳ Intrinsically motivated, deeply curious. Your job: remove friction, hand them bigger problems, then get out of the way. If you slow them down, they’ll bail—and take your future with them. Converts ↳Willing, but hesitant. ↳Crave clear permission, structure, training, and visible incentives. Your job: build the structure to convert them. Provide structured training, highlight internal successes, connect AI objectives to existing KPIs, and include in performance reviews/rewards. With the right scaffolding, they’ll shift their day-to-day habits. Reforge Learning can really help w/ Converts: https://lnkd.in/gAfDBmP3 Anchors Every company has employees who view new tools as threats to hard-won expertise or even to job security. Ignoring that tension lets quiet resistance stall the entire program. How to work with them ↳ Set clear expectations and timelines. Ambiguity breeds rumor mills; specificity forces a decision. ↳ Invest in re-skilling where there’s willingness. Some Anchors simply need structured coaching to pivot their deep domain knowledge into AI-augmented roles. ↳ Know when to cut losses. If an Anchor continues to block progress—even after support—it may be kinder to orchestrate a respectful exit than to let drag become your company’s default speed. The two biggest mistakes companies will make: 1. Believing Everyone Is A Catalyst I can guarantee you they aren’t. As a result, the rest of the company won’t make the shift and the real Catalysts will get frustrated and leave. Founders by nature are Catalysts and over-assume everyone else operates like they do. 2. Assuming Anchors will eventually “get on board.” With incremental shifts, you can wait skeptics out; with AI, you’re racing a clock that rewrites markets in months, not years. A small pocket of resistance can freeze data flows, block experimentation, and hand your advantage to faster-moving rivals. Treating every employee the same may sound fair, but it can be fatal. Segment first, craft distinct paths, and move each group with intention.

In this latest Forbes article, I draw a compelling line from Ada Lovelace’s 19th-century foresight to today’s AI-driven enterprise transformations. Lovelace envisioned machines augmenting human creativity—a vision now realized as #generativeAI reshapes industries. Accenture's experience with over 2,000 gen AI projects reveals that only 13% of companies achieve significant enterprise-wide value, while 36% are scaling AI for industry-specific solutions. Success in this new era hinges on more than just technology investment. Companies must also invest in their people, prioritize industry-specific AI applications, and embed responsible AI practices from the outset. Organizations adopting agentic architecture - digital teams comprising orchestrator, super, and utility agents—are 4.5 times more likely to realize enterprise-level value. Here are five key lessons we’ve learned: 1. Lead with value from the top: Executive sponsorship is crucial. Companies with CEO sponsorship achieve 2.5 times higher ROI from their #AI investments.  2. Invest in people, not just technology: Empower your workforce with the skills to harness AI. Organizations excelling in AI transformation invest in broad AI upskilling, adopt dynamic workforce models, and enable human + agent collaboration.  3. Prioritize industry-specific AI solutions: Tailor AI applications to your sector’s unique needs. Companies creating enterprise-level value are 2.9 times more likely to have a comprehensive data strategy to support their AI efforts.  4. Design and embed AI responsibly from the start: Ensure ethical and effective AI integration. Organizations creating enterprise-level value are 2.7 times more likely to have responsible AI principles and governance in place across the AI lifecycle.  5. Reinvent continuously: Stay adaptable in the face of ongoing change. Companies with advanced change capabilities are 2.1 times more likely to achieve successful transformations. These lessons should serve as a practical playbook for navigating the complexities of #AI integration and achieving sustainable growth. Please read the full article to explore how Lovelace’s visionary ideas are shaping the future of business through #generativeAI. https://lnkd.in/gEVzQeRA

📉 67% of companies fail to scale AI. And nearly half of employees (49%) say their company has done nothing to support them in using it. That’s not an adoption gap...it’s an organizational transformation gap. According to research from Asana’s Work Innovation Lab, AI success depends on crossing 5 critical chasms. From misaligned workflows to missing policies, the teams that fall behind aren't lacking tech—they’re lacking alignment. Here are the key challenges AI leaders must solve: 1️⃣ From AI as a hobby → to AI as a habit 🔹 AI must be embedded into everyday workflows—not treated as an occasional tool. 🔹 Daily AI users report +89% productivity gains; weekly users, +73%. 📊 Insight: Frequency drives fluency. Repetition is what makes AI useful—and usable—at scale. 2️⃣ From top-down buy-in → to all-in adoption 🔹 Leaders are 66% more likely to be early AI adopters than their teams. 🔹 Yet 39% of individual contributors remain skeptical about AI’s benefits. 📊 Insight: Optimism from the C-suite doesn’t guarantee adoption. Teams need role-specific training, clear policies, and space to experiment. 3️⃣ From AI in isolation → to AI in context 🔹 75% of employees report digital exhaustion. 🔹 Workers are 40% more likely to engage with concise AI outputs. 📊 Insight: Low-friction, high-trust workflows are key. AI must reduce—not add to—the noise. 4️⃣ From solo acts → to team sport 🔹 Only 6% of workflows built by individuals scale to peers. 🔹 Co-created AI workflows (the “basketball model”) deliver 651% return on workflow investment (ROWI). 📊 Insight: Centralized solutions scale best early, but long-term success comes from collaborative design and shared ownership. 5️⃣ From acquiring users → to harnessing influencers 🔹 AI workflows built by Bridgers are 96% more likely to be adopted. 🔹 Domain Experts (+27%) and Ops Specialists (+9%) also drive meaningful traction. 📊 Insight: Scale spreads through social influence—not mandates. Find your internal champions early. 💡 So what should People teams do? ➡️ Start tracking AI activity alongside collaboration patterns and workflow performance. ➡️ Segment AI engagement across teams, and surface your internal AI influencers. ➡️ Build habit loops, not just onboarding docs. Make sure to check the comments for the full Asana report. How far along is your organization in crossing these AI chasms? #PeopleAnalytics #HRAnalytics #FutureOfWork #AIAdoption #GenAI

What if technical superiority is not what separates winning AI investments from losing ones? The data is startlingly clear: while 69% of companies have adopted AI in at least one function, only 4% deploy capabilities that consistently produce significant value. This isn't a technology problem: → 64% of AI initiatives stall at the pilot stage (McKinsey) -> 57% of failed implementations cite integration complexity and hidden costs—not model performance—as primary culprits (Deloitte) → 91% of data leaders identify cultural and change management challenges as their main obstacles For VCs, these patterns reveal a critical blind spot in how we evaluate AI companies. We scrutinize technical differentiation while undervaluing implementation capability. When portfolio companies sell AI solutions, they're not just selling technology—they're selling organizational transformation. Yet most vendors leave after implementation, creating: → Dependency relationships that limit expansion revenue → Knowledge gaps that prevent adaptation → Higher total cost of ownership → Barriers to subsequent AI initiatives → Valuation ceilings despite technical excellence The most valuable AI companies aren't those with the best algorithms, but those that bridge capability gaps. The evidence appears in multiple value creation metrics: → Lower customer acquisition costs through improved implementation success rates → Higher expansion revenue from capability-enabled customers → More predictable growth as knowledge transfer accelerates adoption curves → Premium multiples from sustainable competitive advantage beyond the technology itself Consider Colgate-Palmolive's approach: by appointing internal AI champions alongside vendor implementation, they dramatically reduced resistance and accelerated value capture. And resist the temptation to sneer. A legacy company understands legacy problems. When evaluating AI investments, the question isn't just "how good is their technology?" but "how effectively do they bridge capability gaps?" This shifts due diligence priorities considerably. Beyond model accuracy to knowledge transfer methodologies Beyond technical benchmarks to capability-building frameworks.. Beyond initial sales to expansion economics. Beyond features to organizational enablement. Successful AI investing is fundamentally about recognizing that technical differentiation opens doors, but capability transfer builds empires.

Forget the AI hype. A CEO recently asked me, "How do we actually get ROI from AI, without breaking the bank?" Straight from Lean AI principles, my answer surprises: It’s not about chasing the next shiny object but starting lean and being ruthlessly pragmatic. Here are the 3 Lean AI pillars for value-driven AI: 1. Problem-First Approach: Solve a real business pain - Define growth metrics - Ensure AI is the best solution - Establish success criteria upfront Companies often fail by starting with AI, not the problem. Lean AI starts with a high-impact problem, using AI as a precise tool. 2. Minimum Viable AI (MVA): Start small, prove fast - Test with a simple AI model - Verify data availability - Define success metrics MVAs deliver real ROI in high-impact cases, building confidence and momentum. 3. Human + AI Collaboration: Iterate and scale smartly - Establish feedback loops - Train teams to leverage AI strengths - Integrate AI insights into decision-making AI augments humans, unlocking Autonomous Marketing and long-term growth. The Lean AI Bottom Line: Identify a critical growth problem, build an MVA, and iterate based on results to reduce risk, prove ROI, and accelerate growth. 🚀 What’s your biggest hurdle to achieving real AI ROI? Let’s discuss in the comments. 👇 P.S. Excited to hear insights from Andrew Ng, Bernard Marr, and Allie K. Miller on practical AI for growth. #AI #LeanStartups #ArtificialIntelligence #Business #GrowthHacking #ScaleUp #Marketing #Startups