Lifsys / field note 01
printf 'thought -> action\n'

01 / RESEARCH

Research that changes the operating decision.

We read primary sources for the boundary they establish, then translate that evidence into an engineering decision a customer can inspect.

PRIMARY-SOURCE CONTEXT / NOT LIFSYS PERFORMANCE OR ORIGINAL RESEARCH

01 / PRIMARY-SOURCE FINDING

The publisher owns the research claim and its evidence boundary.

02 / LIFSYS ENGINEERING INTERPRETATION

We state what the finding means for integration and operating design.

03 / DECISION IMPLICATION

We name the decision and observable artifact that should change next.

PRIMARY-SOURCE CONTEXT / NOT LIFSYS PERFORMANCE OR ORIGINAL RESEARCH03 RECORDS / DIRECT SOURCES
01 / NOTERAND / 2024

Why AI projects fail

RAND reports that, by some estimates, more than 80 percent of AI projects fail.

BUSINESS RELEVANCE

A prototype can work in isolation and still fail when it solves the wrong problem or cannot enter the real workflow.

ESTIMATE + STUDY FRAMERAND / 2024
>80%ESTIMATED AI PROJECT FAILURE RATEESTIMATE CITED BY RAND
STUDY FRAME65

EXPERIENCED DATA SCIENTISTS + ENGINEERS

INDUSTRY INTERVIEWS
50
ACADEMIC INTERVIEWS
15
FIVE LEADING ROOT CAUSESUNRANKED
WRONG PROBLEM OR WORKFLOW
INSUFFICIENT DATA
TECHNOLOGY BEFORE PROBLEM
INSUFFICIENT INFRASTRUCTURE
PROBLEM BEYOND AI LIMITS
02 / NOTEEUROSTAT / 2025

Adoption is not scale

Eurostat reports that 20.0 percent of EU enterprises used AI technologies in 2025, up from 13.5 percent in 2024.

BUSINESS RELEVANCE

Buyers need to distinguish access to AI from a system that changes a governed business process.

EU ENTERPRISE AI USEEUROSTAT / 2025
TIME SERIESSCALE / 0–20%
20217.7%
20238.1%
202413.5%
202520.0%
2025 TECHNOLOGY SHARESSCALE / 0–12%
WRITTEN-LANGUAGE ANALYSIS
11.8%
GENERATED PICTURES / VIDEO / AUDIONEW IN 2025 QUESTIONNAIRE
9.5%
GENERATED LANGUAGE OR CODE
8.8%
SPEECH TO MACHINE-READABLE
7.2%
BOUNDARY / EU ENTERPRISES WITH 10 OR MORE EMPLOYEES · MULTIMEDIA GENERATION WAS NEW IN THE 2025 QUESTIONNAIRE
03 / NOTEMIT CISR / 2025

From pilots to scaled work

MIT CISR reports that the greatest financial impact appears when enterprises progress from building pilots and capabilities to developing scaled AI ways of working.

BUSINESS RELEVANCE

The business decision is whether to invest in an operating system for reuse, not whether to fund another isolated demonstration.

FOUR-STAGE RESPONDENT DISTRIBUTIONMIT CISR / 2025
2025 REAL-TIME BUSINESS SURVEYN=152DISTRIBUTION / NOT CAUSAL PROOF
STAGE 01EXPERIMENT AND PREPARE
202228%
202513%
STAGE 02BUILD PILOTS AND CAPABILITIES
202234%
202523%
STAGE 2STAGE 3
STAGE 03DEVELOP AI WAYS OF WORKING
202231%
202546%
STAGE 04BECOME AI FUTURE READY
20227%
202518%
BOUNDARY / SURVEY DISTRIBUTIONS, NOT A LIFSYS SCORE, CAUSAL CLAIM, OR PROMISED OUTCOME

05 / APPLY THE EVIDENCE

Turn a finding into the next engineering decision.

The first session identifies the operating result, evidence boundary, and one observable artifact. It does not guarantee an implementation or outcome.

BOOK A WORKING SESSION