Research

AI Native Landscape

Market sizing, vendor dynamics, and adoption benchmarks across the enterprise AI stack — from infrastructure to applications.

18 documents

Academic Research on AI-Assisted Programming Productivity: Eight Studies, Three Conclusions

> **Source credibility: HIGH (METR — independent RCT; Science/Daniotti — top-journal observational) / MEDIUM (Google internal RCT; Cui et al. enterprise RCTs) / LOW-MEDIUM (Peng et al.

AI for Customer Retention: Where Mid-Market Companies Are Reducing Churn, Improving NPS, and Protecting Revenue

The math is not subtle. Frederick Reichheld's original research for Bain & Company demonstrated that a 5% increase in customer retention produces a 25-95% increase in profits, depending on industry.

AI and Internal Knowledge Management: The Lowest-Risk, Highest-Satisfaction AI Deployment Available

Mid-market companies suffer from a specific version of the information problem that large enterprises and startups avoid. Startups are small enough that everyone knows everything.

AI-Native Self-Cannibalization: SaaS Vendors Restructuring from Strength

> **Source credibility: MEDIUM-LOW (TIER 2–3).** Primary sources are named CEO public statements (Twitter/LinkedIn), company press releases, and earnings commentary — not peer-reviewed studies.

AI Outcomes by Industry: What the Data Actually Shows for Your Sector

Healthcare AI spending reached $1.4 billion in 2025 — nearly 3x the prior year, per Menlo Ventures' survey of 700+ healthcare executives.

The Academic Evidence on AI Pair Programming: What 10,000+ Developers and 7 Studies Actually Show

> **Source credibility: HIGH (METR — independent, pre-registered RCT) / MEDIUM-HIGH (Cui et al. enterprise RCTs; Faros AI telemetry) / LOW-MEDIUM (vendor surveys — GitHub, AWS, DORA).

AI Partnership Deals Q1 2026: $300B+ in Deals Reshapes Who Controls Enterprise AI

> **Source credibility: MEDIUM-HIGH. TIER 1.**

The Procurement Function's AI Moment: Where 200-500 Person Companies Are Cutting Cycle Times in Half and Recovering 15-25% of Addressable Savings

A 200-500 person company with $50M-$500M in revenue typically manages $10M-$100M in annual procurement spend.

The AI-Ready Company as Acquisition Target: What a Demonstrable AI Program Does to Your Valuation

The most consequential data point for any mid-market executive planning an exit comes from BCG's September 2025 study of over 2,000 companies globally.

AI and the Top Line: Where Mid-Market Companies Are Using AI to Generate Revenue, Not Just Cut Costs

> **Source credibility: MEDIUM-HIGH.

The Boring AI Payoff: Where Traditional ML and Straightforward Automation Actually Move the P&L

The NBER's February 2026 survey of 5,956 C-suite executives across the US, UK, Germany, and Australia found that 89% of managers report zero productivity impact from AI over the past three years — des

Buy, Build, or Both: The Mid-Market AI Decision That Determines Whether You Join the High Performers

> **Source credibility: HIGH.

Searchable Is Not Curated: Why Confluence, Jira, and MCP Are Not Enough for Agentic Enterprise Knowledge

> **Source credibility: MEDIUM-HIGH / TIER 1-2 mix** (June 2026 synthesis).

Enterprise RAG: Separating Signal from Noise in Retrieval-Augmented Generation

> **Source credibility: MEDIUM. TIER 2–3** (April 2026 synthesis; underlying primary studies span 2022–2025).

Leading AI Adoption from the Middle: The VP and Director Playbook for the Layer That Makes or Breaks Deployment

See also (wiki): wiki/leading-ai-from-the-middle.md · wiki/ai-change-management.md · wiki/workflow-redesign.md · wiki/training-architecture.md

Multi-Agent AI Systems: What the Independent Evidence Actually Shows

> **Source credibility: HIGH (academic RCTs and preprints). TIER 1.**

The Stanford AI Index 2026: What the Published Data Says About the Gap Between Capability and Readiness

> **Source credibility: HIGH (Stanford HAI institutional standing) / MEDIUM-HIGH (specific adoption percentages inheriting upstream survey biases). TIER 1.**

What Is AI-Native Engineering?

The "AI-native" framing deliberately echoes "cloud-native":