How is sensitive data protected?

All documents are encrypted with AES-256-GCM at rest and TLS 1.3 in transit. A GDPR anonymization layer removes PII before any AI processing. Your data never leaves our SOC 2 certified infrastructure.

How long does integration take?

Less than 30 minutes. Upload documents directly, connect via OAuth for Slack, or use our REST API for bulk processing.

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?

ChatGPT gives one opinion from one model. Decision Intel uses 3 independent judges for noise measurement, a 20×20 bias interaction matrix for compound scoring, 31 domain-specific biases, and an outcome flywheel that gets smarter with every decision.

All case studies

Exceptional successTechnology2013

Adobe

Adobe Creative Cloud SaaS Transition

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

A90

Decision Quality Index

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.

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 AversionStatus Quo BiasAnchoring Bias

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 146 curated case studies in our library:

90%

base failure rate

10%

base success rate

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)

Pilot slots available

See what we'd flag in your next strategic memo.

Upload a strategic memo or board deck. Get the same bias audit you just saw for Adobe, on your own high-stakes call, in under 60 seconds.

Try the Demo — Free Request a Pilot

Or leave your email, we'll run a strategic memo of your choosing and send the readout within a business day.

Ready to audit your own memo right now? Create a free account →

Related cases in Technology

Catastrophic failure1975

Eastman Kodak

Kodak Digital Camera Decision

Catastrophic failure2007

Nokia

Nokia Smartphone Market Collapse

Catastrophic failure2000

Blockbuster

Blockbuster Rejection of Netflix Partnership

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.