Enterprises are investing heavily in AI (a reported $700B market) but lack the tools to measure its ROI, leading to a belief among 70% of IT leaders that they are wasting money.
The current AI adoption phase mirrors the early days of digital advertising, creating a massive opportunity for a new infrastructure of measurement, governance, and analytics tools to be built.
Laredon's thesis is that AI's primary value is not job replacement but productivity augmentation, shifting focus from cost-cutting to enhancing the capabilities and efficiency of the existing workforce.
There is a strong sense of urgency among companies, with 85% believing they have a critical 18-month window to become an AI leader, which drives rapid, often unmanaged, adoption and creates significant security risks.
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Concerns Raised
Enterprises are wasting a significant portion of their AI budgets due to a lack of measurement and strategy.
Unmanaged "shadow AI" usage is creating widespread security and compliance risks.
Companies are relying on ineffective, ad-hoc training methods for a world-changing technology.
Measuring the wrong productivity metrics (e.g., lines of code) can create perverse incentives and poor outcomes (Goodhart's Law).
Opportunities Identified
A massive market exists for tools that measure the ROI and govern the use of enterprise AI, analogous to the ad-tech infrastructure built for the internet.
AI can dramatically increase knowledge worker productivity and solve systemic coordination problems within large organizations.
Shifting a fraction of corporate labor budgets to more efficient AI and software tools represents a multi-trillion dollar market expansion.
Data-driven platforms that prove AI's value can replace traditional, less effective management consulting frameworks.