Why Most CEO AI Attempts Are Just Pretending to Make Progress

AI Summary · Serial Founder's Perspective

Five key ailments of AI transformation: overemphasizing pilots at the expense of processes, bolting AI buttons onto legacy workflows, fixating solely on short-term ROI, mistaking vendor capabilities for your own, and preaching radical strategy while maintaining conservative mechanisms. At the core lies a lack of proprietary data, codified workflows, and closed-loop learning systems.

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Why Most CEOs' AI Efforts Are Just "Pretending to Progress"?

Bain & Company recently released a CEO guide consisting of an overview, seven decision points, and conclusions. The guide zeroes in on an awkward reality: companies are running more and more AI pilots, yet few manage to turn them into competitive advantages.

Core Pain Point: There are five classic symptoms.

1. Mistaking the number of pilots for the depth of transformation

The customer service team builds a reply-drafting assistant, the finance team builds a report Q&A bot, and the legal team builds a contract summarization tool. By year-end, the company boasts dozens of AI use cases, and the reporting deck looks impressive. But strip away these projects, and the company's core workflows may remain completely unchanged: agents still copy-paste data across five systems, finance still manually reconciles口径 at month-end, and legal still relies on senior staff to judge edge cases.

2. Adding an AI button to old processes and calling it business transformation

Many AI transformations merely speed up one step within inefficient processes. Instead of the 20 minutes an employee used to spend writing an email, the model now takes 2 minutes. But questions like why the email exists, why four handoffs are needed beforehand, and who has decision-making authority are left entirely unaddressed. The result is that local steps get faster, but end-to-end speed may not improve. Upstream data remains messy, downstream approvals stay congested, and employees still spend time verifying the model's output.

3. Treating short-term ROI as the sole filter

About 85% of CEOs primarily use AI to chase near-term cost cuts and efficiency gains, hoping to fund subsequent transformation with the savings. The fatal flaw is that once every project is required to prove ROI within the same year, the organization will systematically select tasks that are easiest to quantify, closest to existing practices, and least likely to shift the competitive position. Customer journeys, supply chains, or product development processes that require cross-departmental overhaul are often the first casualties of budget cuts because they take longer and involve more complex accountability.

4. Mistaking vendor capabilities for your own

Rolling out an AI-enabled SaaS suite to all employees does not mean the enterprise possesses AI capability. A competitor can buy the same features tomorrow. More problematically, if data semantics, workflow logic, tool integrations, and usage feedback all remain trapped within the vendor's platform, every use may strengthen the vendor's product without leaving behind much asset for your next deployment.

5. Radical strategic slogans paired with extremely conservative operating mechanisms

The most common absurd scenario: the CEO declares AI the top priority for the next three years, yet projects are still required to prove same-year returns one by one; teams claim to be building proprietary capabilities, but key engineering remains fully outsourced; leadership demands rapid iteration, but every launch requires months of serial approvals.

These symptoms point to the same question: companies confuse "doing more projects" with "building stronger capabilities." When a project ends, failures don't become new tests, data remains siloed, veterans' judgment stays locked in their heads, and other teams remain unaware of past pitfalls. The next deployment starts near zero again. The wider the project spread, the broader the repetition of labor.

A "Proprietary Intelligence" Moat?

The reason leading companies pull ahead is that they have built three core assets competitors cannot buy with money:

• Proprietary Data: Customer, operational, and transaction records that belong solely to you, growing richer and more valuable the more the business runs.
• Encoded Workflows: Capturing the expert judgment and playbooks of senior employees into Agents that can execute at scale.
• Closed-Loop Learning Architecture: A human-AI feedback loop where humans guide the AI, the AI elevates human output, and the accumulated data feeds back into the model, creating a "flywheel effect" that continuously widens the gap.

— Excerpted from TG Channel: AI Exploration Guide - Telegram Channel

(Original source: AI Exploration Guide - Telegram Channel; not directly accessible in China; full text embedded above.)