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News articleCrunchbase News

Don’t Just Talk About AI. Measure Business Outputs. Here’s How.

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Crunchbase News - Funding Ma Title: Don’t Just Talk About AI. Measure Business Outputs. Here’s How…
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  • Results are best when choosing the output to improve, varying AI tools until one moves the dial, and measuring Time To Production

    60% confidence
  • Approach AI projects as mathematical optimization problems by defining a target measure, identifying influencing variables, and modeling the mechanism by which the target is moved

    60% confidence
  • Theory on why so many pilots fail is that companies pick an AI tool and pilot duration and qualitatively check in with users, rather than measuring outputs

    60% confidence
  • The theory on why so many pilots fail is that companies tend to pick an AI tool and pilot duration and qualitatively check in with users at the end rather than measuring outputs

    60% confidence
  • AI systems trusted with real decisions are what Peter Drucker would call executives

    60% confidence
  • Last year felt like the Year of the AI Pilot with widespread disappointment in the impact of AI pilots

    60% confidence
  • AI is an invention in the process of becoming an innovation; an invention is a new capability that is not an innovation until it has a business model

    60% confidence
  • The form innovation takes will be AI systems trusted with real decisions, what Peter Drucker would call executives and are referred to as agentic AI

    60% confidence
  • Last year felt like the Year of the AI Pilot with widespread disappointment in the impact of AI pilots

    60% confidence
  • The idea is to approach AI projects as mathematical optimization problems: Define a target measure, ask what variables influence that metric, and model the mechanism

    60% confidence
  • Organizations must focus on outputs rather than activities, anecdotes and initiatives which are inputs

    60% confidence
  • Organizations must focus on outputs rather than activities, anecdotes and initiatives which are merely inputs

    60% confidence
  • AI is an invention in the process of becoming an innovation, not becoming an innovation until it has a business model

    60% confidence
  • 95 percent of generative AI pilots at companies are failing according to MIT report

    60% confidence

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A single-week cluster of large AI/fintech funding rounds (Socure, Stability AI, Emerald AI, Generalist AI, Instinct, Gatik, Regent Craft) shows venture capital still pouring into AI infrastructure, identity, and autonomy plays, while Palantir's Army TITAN contract win coincided with a 6% stock drop — signaling that even flagship AI-defense revenue isn't immune to market reassessment of AI valuations. Efficiency-focused innovations like Multiverse Computing's model compression suggest the sector is also pivoting toward cost/inference economics as capital intensity draws scrutiny.
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EPKINLY Regulatory-Clinical Success Cascade
High probability of expanded label indications, additional combination approvals, and competitive positioning strength in follicular lymphoma market. Predicts positive commercial uptake and potential accelerated review for related indications.
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Where sources disagree
Morgan Stanley & Co. LLC
The same metric (eps) for the same entity (Morgan Stanley & Co. LLC) reported for the identical fiscal period (Q1 2026) and observation date (2026-03-31) has two conflicting values: 3.43 USD_per_share vs 3.08 USD. This is not a temporal change — both observations claim to measure the same point in time. The ~10% discrepancy (0.35 USD difference) is material for a financial metric.
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