Liat Ben-Zur, LBZ Advisory
Book a Call

AI

AI

The AI Governance Crisis: Why Boards Can’t Challenge What They Don’t Understand

/

Boards have entered the AI era unprepared. The “AI curiosity” phase is over, replaced by regulatory pressure, rising risk, and a widening gap between management’s AI narrative and the board’s ability to challenge it. Board directors who lack AI experience are trusting the recommendations made by internal AI champions and vendors. As a result, many are now rubber‑stamping initiatives they don’t fully understand — a governance failure with real P&L consequences.
The 3 Cs Framework is the only path out: Clarity, Capabilities, and Capture. Boards that master the 3 Cs move faster, take smarter risks, and build real moats.

AI

Beyond the Binary: The New AI “Definition of Done” for Product Managers

/

Discover why traditional software deployment checklists fail in the age of generative AI and learn how to operationalize a multi-dimensional, probabilistic AI Definition of Done to successfully push enterprise pilots into production.

Moving from traditional software to enterprise AI requires a fundamental shift from specifying explicit logic to defining statistical boundaries. To overcome pilot fatigue and successfully scale, product managers must ditch legacy, binary deployment checklists and implement a multi-dimensional, probabilistic Definition of Done (DoD) that addresses dynamic evaluation, behavioral guardrails, upstream model volatility, and long-term production drift.

AI

The Silicon Ledger: Why AI Unit Economics Are Decoupling from Moore’s Law

/

AI economics are shifting from token price to outcome efficiency. The real question is cost-of-pass: how much compute, rework, correction, and human liability-bearing review it takes to reach one correct, usable result. This essay provides a grounded analysis of AI unit economics, liability floors, cost-of-pass, and the real KPIs leaders use to measure AI productivity, throughput, and verified outcomes.

AI

Ditch the Chatbot: How to Build an AI-Native Operating Model That Actually Acts

/

Most companies are piling up Execution Debt by using AI to draft content instead of redesigning how work gets done. This piece breaks down the four rungs of the Autonomy Ladder, from Intern to Specialist, and how you can move up it. Stop treating LLMs like fancy typewriters and start moving from conversation to outcomes. If you want AI that actually acts without blowing up governance, this is the operating model.

AI

The Silicon Salary: Why Humans are Suddenly the Low-Cost Option

/

AI tools got so good that companies couldn’t stop using them — and now the bills are out of control. Uber burned through its entire 2026 AI budget by April because engineers were using agentic coding tools that charge per every step the AI “thinks,” not per seat. The average engineer cost $150–$250 a month. Heavy users hit $2,000. Microsoft quietly pulled back Claude Code access for the same reason. Meanwhile, an MIT study found that for 77% of vision-based tasks, a human is still the cheaper option. So the math just doesn’t work the way the “automate everything” pitch promised. The companies getting this right aren’t banning frontier AI tools. They’re setting budgets at the task level, matching the model to the job, and putting humans back in the workflows where cost and judgment actually matter.

AI

The Great Agent Sprawl: Navigating the Hidden Complexity of Enterprise AI

/

Blog Excerpt
The Pilot Era is Over. Welcome to the Age of the “Invisible Enterprise.”

For the past two years, the enterprise AI conversation was obsessed with the “pilot”—testing whether LLMs could write an email, summarize a meeting, or draft a line of code. In 2026, that era is officially over. We have crossed the threshold from managing individual AI pilots to orchestrating autonomous AI fleets.

With Gartner predicting that the average Fortune 500 company will soon harbor over 150,000 autonomous agents—dramatically outnumbering human employees—enterprises are facing a massive, silent operational crisis: Agent Sprawl. Unlike traditional Shadow IT, which simply sits there, a rogue agent acts. Left unmanaged, this decentralized explosion of non-human workers levies a heavy “Sprawl Tax” via untraceable security risks, costly algorithmic logic loops, and a fragmented customer experience.

Discover the framework for building a “DMV for AI Agents”—a robust two-tier governance model to regain visibility, mitigate machine-speed liabilities, and successfully pivot from an app-centric to an agent-centric operating model.

AI, Go-to-Market Strategy, marketing

The New KPIs for AI Visibility

/

AI tools are already changing how buyers discover, compare, and choose brands. A buyer can now ask ChatGPT, Gemini, Claude, Perplexity, or Grok what to buy and get a shortlist before ever visiting your website. That means rankings, traffic, and conversions no longer tell the full story. This post breaks down the new KPIs marketers need to track, including AI share of voice, coverage by engine, mention rate versus recommendation rate, prompt-level performance, competitor preference, and what to fix first.

Minimalist, slightly humorous illustration comparing three software delivery models: Waterfall as a paperwork assembly line, Agile as a crowded sticky-note stand-up, and AI-first pods as a small team calmly working with an AI assistant, all supported by guardrails, governance, security, and reusable tools.
AI, culture

The New AI Transformation: Why Agile Is Giving Way to AI-First Pods

/

Software organizations always evolve around their bottlenecks. Waterfall optimized for order. Agile optimized for coordination. AI changes the constraint again. When execution speeds up dramatically, the real bottleneck shifts from production to organizational latency. This piece argues that the next operating model is not bigger teams or better rituals, but smaller pods, stronger shared infrastructure, and leadership designed around leverage instead of process.

Funny claymation-style scene showing a frustrated marketer, AI logos, rising competitor bars, and a sad brand character losing visibility in AI-generated recommendations.
AI

What Is AI Share of Voice? And Why You Should Care

/

AI Share of Voice measures whether AI engines like ChatGPT and Gemini actually recommend your brand when buyers ask real questions. This piece breaks down what the metric means, why one or two prompts are not enough, and what marketers should really be looking for.

AI

Claude 4.7 Coding and Token Efficiency Playbook: Stretch Your Limits, Reduce Bot Blocking, and Make Every Token Count

/

Claude is getting more expensive and many of us are getting “botblocked” sooner and faster than ever before. Claude 4.7 can do harder coding work with less hand-holding, but that creates a trap: teams let sessions run too long, dump too much into context, and pay for noise instead of progress.
The real problem is token waste plus context rot — which accelerates usage limits and lands you in the penalty box.
This playbook shows you how to make Claude need less context, verify more of its own output, and hand off state cleanly so every token buys real progress instead of repetition. The result: stretched usage limits, fewer bot blocks, lower effective costs, and higher reliability.

AI

Tokenmaxxing: Big Tech’s Costly Productivity Trap

/

Big Tech engineers are now competing on internal leaderboards to burn the most AI tokens—racking up $100k+ monthly bills in the name of ‘productivity.’ It’s the new engagement trap: more tokens, more waste, and the same dangerous incentives that once poisoned social media. Why startups with constraints may actually win.

Scroll to Top