The AI Illusion: Why Your Portfolio Needs a Human-First Filter

The current AI frenzy is creating a gap between corporate storytelling and operational reality. Investors are increasingly prone to AI washing, where companies use the buzzword to mask restructuring or cover for failing business models. The non-obvious implication is that the companies shouting the loudest about AI are often the ones furthest from actual implementation. By shifting focus from aggressive marketing headlines to the underlying human infrastructure, specifically psychological safety and decision-making speed, investors can identify the few firms genuinely building structural moats. This approach allows you to bypass the volatile, narrative-driven hype cycle and focus on companies where technology acts as a tool for long-term compounding rather than a temporary shield for declining performance.

The Hidden Cost of Performing Transformation

We are seeing a surge in companies performing transformation rather than sustaining it. According to Julie Averill, former global CIO of Lululemon, this performative behavior is a red flag for individual investors. When a company claims to be AI-forward to justify layoffs, often just right-sizing after pandemic-era overhiring, they are using technology to cover for operational failures.

The downstream consequence is a loss of internal trust. When leaders frame AI as a replacement for human work without fostering psychological safety, they alienate the employees needed to sustain the transformation.

Are they gonna advocate? Are they gonna speak up when something is wrong with the model? Or are they gonna quietly go silent and potentially sabotage the effort?

-- Julie Averill

When employees feel threatened by the technology they are asked to implement, they stop providing the feedback necessary to refine models. This creates a loop where the company AI strategy becomes a hollow exercise in compliance, resulting in expensive tech stacks that fail to move the business forward.

Why the Strategy Question Misleads Boards

Conventional wisdom suggests that boards should demand an AI strategy from their CEOs. Averill argues this is the wrong approach. When a board demands a strategy, the organization responds by appointing an AI lead to collect a list of disparate ideas. This creates a facade of activity that satisfies the board but fails to solve specific business problems.

The system responds by optimizing for the wrong metrics. Instead of focusing on how to solve a core business problem, like supply chain efficiency or real-time decision-making, the organization focuses on generating headlines.

The reality is that many companies have had gains in tremendous gains with AI for their business, but it is not yet coming through in terms of efficiency. Something like 3% of companies are actually seeing the efficiencies play out.

-- Julie Averill

The insight here is that true AI impact is not found in a five-year forecast of efficiency gains; it is found in the ability to document and speed up repeatable processes. If a process is not documented and the decision-making rights are not clear, AI will only accelerate the existing chaos.

The 18-Month Payoff: Where Real Moats Are Built

The most sustainable competitive advantages are not found in the AI-branded pivots that trigger short-term stock spikes. They are found in firms that treat technology as a foundation for better decision-making.

Real transformation is slow, unglamorous, and often invisible to the market in the short term. It involves changing how an organization makes decisions, moving from monthly data reviews to real-time information access. This requires learning companies, those that are comfortable admitting what they do not yet know. As an investor, the advantage lies in identifying companies that treat AI as an internal operational capability rather than an external marketing narrative. If the P&L does not show the impact, the transformation is likely just expensive lipstick.

Key Action Items

• Audit the Why: When a company announces a major AI pivot or layoffs attributed to AI, look at their revenue trends over the last 24 months. If revenue is plummeting, view the AI announcement as a defensive shield, not a growth catalyst. (Immediate)
• Look for Learning Language: In earnings calls, listen for leaders who discuss what is not working. A company that can articulate its failures is likely doing the hard work of transformation. (Ongoing)
• Monitor the So What: For every AI initiative a company highlights, ask: This solves X, so that what can happen? If the answer is vague or lacks a clear business outcome, discount the initiative as performative. (Next 3-6 months)
• Evaluate Decision Velocity: Look for evidence that the company is changing its internal hierarchy. Companies that move from department-based silos to decision-based structures are the ones actually integrating AI. (12-18 months)
• Check the Human Pulse: Research employee sentiment regarding AI adoption. If 80% of the workforce fears replacement, the company AI efforts will likely face silent sabotage and internal friction. (Next 6 months)