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- Navigating AI: Insight, Reality, and the Playbooks That Win
Navigating AI: Insight, Reality, and the Playbooks That Win
From warning signs about AI’s erosion of critical thinking, to the sobering reality of GPT-5’s modest gains, to Asana’s playbook for turning skeptics into high-performance teams—these three stories reveal the shifting ground beneath the AI revolution. They highlight a common thread: success with AI isn’t about chasing hype or sheer scale, but about applying it thoughtfully, grounding it in human insight, and embedding it into culture. In an era of inflated promises, the real breakthroughs come from disciplined strategy, measured expectations, and a willingness to learn—and adapt—fast.
GPT-5 Lands with a Thud
After years of soaring expectations driven by the “scaling law” promise, AI’s rapid leaps appear to be slowing. OpenAI’s GPT-5 arrives with refinements rather than revolutions, signaling a shift from building ever-bigger models to fine-tuning what already exists. The New Yorker delves into these post-training tweaks and while they deliver useful, narrow gains, they fall short of the sweeping transformations once predicted—prompting a more measured view of AI’s future and its real economic impact.
The scaling law era in AI may be over. OpenAI’s 2020 “Scaling Laws” paper fueled the belief that ever-larger models would inevitably lead to artificial general intelligence, validated by leaps from GPT-2 to GPT-3 and GPT-4. But the release of GPT-5—after two years without a major breakthrough—showed only modest gains, disappointing many users and critics alike. Industry voices are now questioning whether simply adding more compute and data can continue delivering transformative results.
Post-training has replaced scaling as the main strategy. With diminishing returns from larger pre-training, companies are refining existing models through techniques like reinforcement learning and task-specific tuning. This shift is akin to “souping up” existing cars rather than building entirely faster ones, yielding narrow improvements in coding, writing fluency, and specific benchmarks. However, these gains feel incremental—more like software updates than paradigm-shifting leaps.
Benchmarks may overstate AI’s real-world reasoning progress. Despite improved scores, studies from Apple and Arizona State University suggest that so-called “reasoning models” collapse on tasks beyond their training range. This indicates that current advances don’t translate to broad, generalizable problem-solving. Critics argue that post-training tweaks can make a Camry faster, but won’t turn it into a Ferrari.
The future of AI looks more moderate than hyped. Skeptics predict a $50–$100 billion market, with AI integrated into daily workflows but rarely transforming entire industries. While some fields like programming and academia may see big shifts, most jobs will change gradually, and mass disruption may not happen soon. Nonetheless, experts urge caution, regulation, and ethical preparation for possible future breakthroughs.
AI Trailblazer Takeaways: AI’s growth curve is flattening, shifting from game-changing leaps to incremental tune-ups. GPT-5’s modest gains signal the end of “bigger is better” scaling and the rise of post-training tweaks—useful, but far from transformative. The next era may be defined less by chasing superintelligence and more by extracting practical value from what we already have, with measured expectations and sharper scrutiny.
How Asana Turned AI Skeptics into Marketing Powerhouses
Saastr explores how at Asana, CMO Shannon Duffy has turned AI from a buzzword into a force multiplier—boosting marketing team efficiency by up to 93% through daily use. Her approach blends practical workflow automation with cultural transformation, empowering “super connectors” and rewarding AI-driven innovation. Along the way, she’s learned hard lessons about adoption, strategy, and execution—proving that the real competitive edge comes from starting now, experimenting boldly, and scaling what works.
Daily AI use can massively boost marketing efficiency. Asana’s research with Anthropic showed that marketers using AI monthly saw a 41% effectiveness lift, weekly users 75%, and daily users 93%. Shannon Duffy advises starting with repetitive tasks in intake, planning, execution, and reporting rather than attempting a full overhaul. The goal is to inspire AI adoption culturally, transform promotion criteria to reward AI-driven scaling, and identify “super connectors” who solve real business problems.
AI enables progress toward hyper-personalization at scale. Duffy sees AI as the closest marketers have come to achieving personalization at massive scale, though the goal is not fully realized yet. Cultural hurdles remain, with 38% of marketers skeptical, 19% worried about trust, and 27% fearing they’ll be seen as lazy. Overcoming these fears requires framing AI as a career growth tool rather than a threat.
Real-world use cases show tangible impact across marketing functions. Asana and other companies use AI for campaign management, creative intake, customer insights, product launches, and marketing ops, yielding results like 50% fewer meetings, 96% efficiency gains, and dramatic time savings. Examples include Accor’s multilingual campaign launches, Clear Channel’s creative triage, and Children’s Health’s HIPAA-compliant data integration. These applications show AI’s ability to save time, ensure quality, and improve cross-functional alignment.
AI transformation requires both strategy and cultural change—plus learning from mistakes. Duffy’s missteps included using AI without context (“janky Britney Spears” output), failing to address adoption fears early, misidentifying super connectors, and delaying performance review changes. Each mistake underscored the need for clarity, the right champions, and early integration of AI into career incentives. Her core message: start now, experiment, and evolve, because teams that embrace AI—despite imperfections—gain a long-term competitive advantage.
AI Trailblazer Takeaways: Asana’s AI transformation proves that the real advantage isn’t just in the tools—it’s in how you embed them into culture, workflow, and career growth. By starting small, empowering problem-solving “super connectors,” and tying AI use to professional advancement, Shannon Duffy turned skepticism into adoption and efficiency into scale. The takeaway: AI becomes a force multiplier when it’s woven into both the day-to-day and the DNA of the organization.
The High Cost of Easy Answers in the Age of AI
In an age where AI promises instant answers, marketers risk trading durable strategy for disposable thinking. Zac Stucki of MarTech warns that while generative AI can analyze data and produce content quickly, it can’t deliver the depth, context, and human insight required for effective decision-making. Drawing on research and real-world parallels, he makes the case for resisting over-reliance on AI and protecting critical thinking as marketing’s last true competitive advantage.
AI is eroding critical thinking in marketing. Drawing a parallel to red Solo cups replacing durable glassware, the author warns that AI encourages “disposable thinking” that’s flimsy and short-lived. An MIT study showed that heavy LLM use reduces neural engagement, leading to underperformance in linguistic, behavioral, and cognitive areas. This shift threatens the depth and durability of marketing strategy.
Generative AI can’t deliver true insight for strategic decisions. While revenue leaders hope AI can fill data gaps, its probabilistic outputs are often inaccurate or fabricated. AI is designed to be helpful first, harmless second, and truthful third, making hallucinations a frequent risk. Relying on such outputs is akin to keeping an employee who falsifies data—dangerous and reputation-damaging.
Customers buy outcomes, not products—and AI can’t uncover that. As Theodore Levitt’s drill analogy suggests, people seek solutions to problems, not tools themselves. AI lacks the ability to distinguish between offerings and the deeper motivations behind purchases. Success in marketing comes from generating human-level insight, not shallow, probability-based answers.
Building durable insight is a human skill AI can’t replace. True strategy requires context, pattern recognition, and asking “what’s missing”—something no LLM can replicate. Marketers should study people over dashboards, avoid survivorship bias, and remember AI is not a strategic-level tool. In a world of disposable content, durable human insight remains the ultimate competitive advantage.
AI Trailblazer Takeaways: In the rush to harness AI’s speed and convenience, marketers risk losing the very edge that sets them apart: the ability to think deeply, question assumptions, and see what others miss. While AI can process data and surface patterns, it can’t connect human context, nuance, and intent—the ingredients of lasting strategy. Protecting and sharpening this insight muscle isn’t just prudent; it’s the last sustainable advantage in a marketplace flooded with disposable thinking.
Quote of the Week
“Instead of simply developing AI assistants,… AI with ‘maternal instincts.’”
- Geoffrey Hinton, known as the “Godfather of AI” on the AI4 Conference stage in Las Vegas
Magnificent 7
The AI marketing questions boards must ask - Think with Google (Google Business)
Links of the Week
What If A.I. Doesn’t Get Much Better Than This? (The New Yorker)
AI in retail: How CMOs can transform marketing with AI (Fast Company)
Golden age of Digital Ads, LLM P&L (MBI Deep Dives)
Generative AI drives smarter marketing decisions (Capgemini)
Illinois bans AI therapy as some states begin to scrutinize chatbots (The Washington Post)
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