Predictive Analytics in 2026: What the "Minority Report Era" Actually Looks Like

Originally published Jan 2010. Updated June 14, 2026.

Predictive analytics in 2026 is the discipline of using historical data, statistical models, and machine learning to forecast individual-level outcomes — disease progression, customer churn, fraud likelihood, ad response. The original 2010 framing of a "Minority Report era" anticipated pre-crime policing; the actual deployment landscape 16 years later has retired most of the high-profile policing systems while building durable infrastructure in oncology (Tempus AI, Flatiron Health), marketing automation (Salesforce Einstein, Adobe Sensei), and fraud detection.

Where the Minority Report era actually went

The 2010 article anticipated mobile-device personalization and ambient marketing. Some of that arrived. The more consequential predictive-analytics deployments since 2010 fell into four buckets: healthcare and pharmaceutical research (where the technology has genuinely changed outcomes), enterprise marketing and customer-experience automation (where it has changed the operational stack), fraud detection and financial-services risk modeling (where it sits underneath every credit-card transaction), and predictive policing (where high-profile systems have been retired or significantly constrained).

The aggregate market is large and growing. IDC estimated the global AI and machine-learning spending at $235 billion in 2024, projected to reach $632 billion by 2028. The predictive-analytics segment specifically — including platforms, services, and embedded capabilities — represents roughly 30% of that figure.

For adjacent reading, see EPR's marketing coverage and the health tech pillar.

The Chicago Strategic Subject List and the retreat from predictive policing

The most-cited predictive-policing system of the 2010s was the Chicago Strategic Subject List, sometimes called the "heat list" — an algorithm that ranked individuals by their predicted likelihood of involvement in gun violence as either offender or victim. The system was developed in collaboration with researchers at the Illinois Institute of Technology starting in 2012 and reached more than 398,000 individuals by 2019. Independent research published by the RAND Corporation in 2016 found no statistically significant impact of SSL-driven interventions on shooting incidents. The Chicago Police Department officially retired the SSL in January 2020.

The retreat was broader than Chicago. PredPol, the predictive-policing platform marketed widely to US police departments through the 2010s, was rebranded as Geolitica in 2021 amid sustained criticism that its location-based predictions reinforced existing patrol patterns in minority neighborhoods rather than identifying new crime risks. The LAPD discontinued its PredPol contract in 2020. The NYPD's Patternizr system, in operation since 2019, focused on connecting reported crimes rather than predicting individuals; it remains operational but with reduced public profile.

Tempus AI and Flatiron Health — predictive analytics in oncology

Tempus AI, founded in 2015 by Eric Lefkofsky (the Groupon co-founder) and headquartered in Chicago, completed an IPO on Nasdaq in June 2024 (NASDAQ: TEM) at a valuation above $8 billion. The company operates one of the largest libraries of clinical and molecular data in oncology — more than 7.7 million de-identified patient records and growing — and uses machine learning to support treatment selection, clinical-trial matching, and drug development. Tempus reported 2024 revenue of $693 million.

Flatiron Health, founded in 2012 by former Google employees Nat Turner and Zach Weinberg, was acquired by Roche in February 2018 for $1.9 billion. Flatiron's OncoEMR platform serves more than 280 community oncology practices and combines clinical data with proprietary real-world evidence used by Roche, the FDA, and biotech partners for drug development and label expansion. The Flatiron-Foundation Medicine combined oncology data infrastructure is one of the largest in healthcare.

The pattern across these companies illustrates where predictive analytics has worked in healthcare: well-defined clinical decision support, longitudinal patient data with explicit consent, regulated frameworks (FDA Software as a Medical Device, HIPAA), and physician-in-the-loop deployment. The use cases that have failed in healthcare — generalized clinical prediction systems, sepsis-prediction models deployed without local recalibration, IBM Watson Health (sold to Francisco Partners in January 2022 for approximately $1 billion after years of unmet promises) — failed because they violated those constraints.

Salesforce Einstein and Adobe Sensei — predictive analytics in marketing automation

Salesforce Einstein, launched in September 2016, embeds predictive scoring across the Salesforce CRM stack — lead scoring, opportunity insights, churn prediction, send-time optimization, recommended next-best-action for sales reps. Einstein is now bundled into Sales Cloud, Service Cloud, and Marketing Cloud subscriptions, and the Einstein GPT layer (announced March 2023, now consolidated into Agentforce as of October 2024) extends the predictive capability into generative responses for customer service and sales prospecting.

Adobe Sensei, Adobe's competing AI and machine-learning layer, launched in November 2016 and now powers personalization features across Experience Cloud (Adobe Target, Adobe Analytics, Adobe Campaign), creative-tool features across Creative Cloud, and the Sensei GenAI generative layer announced March 2023. Adobe reported $21.5 billion in revenue in fiscal 2024, with the Digital Experience segment (including Sensei-powered Experience Cloud) at $4.85 billion.

The marketing-automation pattern is durable: predictive analytics that operate at population level (predicting average response rates across segments, not individual deterministic outcomes), embedded in software workflows marketers already use, with human override on every recommendation. The systems that have failed in this category were the ones that promised one-to-one personalization at scale without the data infrastructure to support it.

Fraud detection — the largest deployed predictive-analytics use case

Fraud detection is, by transaction volume, the most heavily deployed predictive-analytics application in commercial use. Visa processes more than 700 billion transactions annually and runs every one through machine-learning fraud-scoring systems in milliseconds. Mastercard's Decision Intelligence platform performs similar functions. Stripe's Radar product, integrated into the Stripe payments stack, claims to block more than $19 billion in attempted fraud annually for its customer base.

Specialized fraud-detection vendors — SAS, FICO Falcon, Feedzai, Fraud.net, Riskified — operate at the bank and merchant-acquirer layer. The technical pattern is consistent: real-time scoring on high-dimensional transaction features, periodic model retraining on labeled outcomes, human review for borderline cases, and ongoing adversarial dynamics as fraudsters adapt. The category is the clearest demonstration of where predictive analytics produces durable economic value at scale.

The honest 2026 view — where predictive analytics works and where it doesn't

The pattern across 16 years of deployments is clear. Predictive analytics works where four conditions hold: the outcome being predicted has clean labeled training data; the cost of false positives and false negatives is well understood and the model can be tuned to that asymmetry; the deployment context allows for human review or correction at the decision moment; and the system can be retrained as the underlying phenomenon shifts. Oncology decision support, fraud detection, marketing-automation segmentation, and predictive maintenance in manufacturing all satisfy these conditions.

Predictive analytics fails — sometimes catastrophically — where it is asked to predict individual-level outcomes from population-level signals (Chicago SSL), where the cost of false positives falls on the prediction subject rather than the system operator (predictive policing, automated benefits-eligibility scoring, child-welfare risk systems including the Allegheny Family Screening Tool, which has been subject to ongoing critique since 2018), and where the system replaces rather than supplements human decision-making in high-stakes contexts.

The Minority Report era, in the form 2010 anticipated, did not arrive. The era of embedded statistical infrastructure across healthcare, marketing, and finance did — and is now the durable substrate on which most enterprise AI deployment now builds.

For broader frames, see EPR's digital PR coverage.