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How long does integration take?

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How does outcome tracking work?

Outcomes are detected automatically from follow-up documents, Slack messages, and web intelligence. You confirm with one click. Each reported outcome makes your future analyses more accurate.

How is Decision Intel different from ChatGPT or a general AI assistant?

ChatGPT gives one opinion from one model: ungoverned, untraceable, unaudited. Decision Intel is the reasoning audit platform. Most tools audit your data; we audit your reasoning — and catch the fatal blind spots in strategic memos before the committee does. It measures the noise in your reasoning the same way Kahneman did in the insurance underwriter study, simulates an AI boardroom of CEO, CFO, and board personas, runs what-if interventions against a 143-case public reference library, and compounds every confirmed outcome back into a calibrated Decision Quality Index your audit committee can defend. Not a chatbot; a reasoning audit, checkable from memo to outcome.

Which cognitive biases caused Adobe's exceptional outcome?

3 bias patterns are documented in this case, led by Loss Aversion. The full set: Loss Aversion, Status Quo Bias, Anchoring Bias. Each is mapped to Decision Intel's 22-bias R²F taxonomy with stable IDs and academic citations.

What is Decision Intel's reasoning audit?

Decision Intel runs a 12-node Recognition-Rigor Framework (R²F) pipeline on strategic memos, detecting cognitive biases from a 22-bias canonical taxonomy and producing a Decision Quality Index (DQI) score. The audit produces a hashed, tamper-evident Decision Provenance Record (DPR) suitable for audit-committee review.

All case studies

Exceptional successTechnology2013

Adobe

Adobe Creative Cloud SaaS Transition

Estimated impact: $200B+ in market cap created (2013-2023)

A90

Decision Quality Index (simulated)

Excellent · 3 biases detected

In 2013, Adobe abandoned its perpetual license model for Creative Suite ($2,600/license) and moved to Creative Cloud ($50/month subscription). The stock dropped 8% on announcement day. Revenue declined for two consecutive quarters as perpetual license sales dried up before subscription revenue ramped. By 2017, recurring revenue exceeded 80% of total revenue, the stock had tripled, and Adobe had the most predictable revenue stream in enterprise software.

Decision context

Whether to abandon the profitable perpetual license model generating $4B+/year in favor of a subscription model that would cause immediate revenue decline and Wall Street backlash.

Decision anatomy

Monetary stakes

Very high

Dissent suppressed

Time pressure

None

Unanimous consensus

Decision participants

Dissent encouraged

Yes

External advisors

Yes

Iterative process

Yes

Red = risk factor present · Green = protective factor present

Deep hindsight-stripped analysis coming soon. This case is part of our ongoing backfill to reconstruct what the platform would have flagged at decision time. The summary and bias profile below are drawn from primary sources.

Biases present in the decision

★Loss Aversion

critical

Status Quo Bias

medium

Anchoring Bias

high

★ Primary driver · Severity estimated from bias type and decision outcome

3D Decision Knowledge Graph

Adobe · 3 biases

Drag to rotate · Scroll to zoom · Click to explore

Decision

Critical

High

Medium

Toxic

Bias Intensity Profile

Severity × confidence across 3 detected biases

Reference class base rates

Across all 143 curated case studies in our library:

89%

base failure rate

-0%

partial / mixed

11%

base success rate

■ Failure■ Partial■ Success

Lessons learned

Loss aversion was managed by explicitly modeling the "valley of death" between perpetual and subscription revenue and securing board commitment to the transition period.
Anchoring to current revenue ($4B perpetual) was overcome by reframing the analysis around customer lifetime value, which was 3-5x higher under subscription.
The iterative rollout (pilot → bridge period → full transition) allowed real data to validate the strategy at each stage, reducing uncertainty.

Source: Adobe 10-K filings (2013-2017); Shantanu Narayen interviews, Harvard Business Review (2015); Morgan Stanley research note "Adobe: The SaaS Transformation" (2016) (Earnings Call)

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We caught these patterns in Adobe's own record — before the outcome.

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Workflows that fire on decisions like Adobe’s

The same Recognition-Rigor Framework that documents this case audits memos in the same shape — before the outcome forces the lesson.

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Netflix

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Exceptional success2014

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Microsoft Satya Nadella Cloud Transformation

Exceptional success2010

Domino's Pizza

Dominos "Our Pizza Tastes Bad" Rebrand

Lessons learned

Loss aversion was managed by explicitly modeling the "valley of death" between perpetual and subscription revenue and securing board commitment to the transition period.

Anchoring to current revenue ($4B perpetual) was overcome by reframing the analysis around customer lifetime value, which was 3-5x higher under subscription.

The iterative rollout (pilot → bridge period → full transition) allowed real data to validate the strategy at each stage, reducing uncertainty.