Insights for people running businesses

OutcomeCatalyst partners with MPL Association to provide AI solutions for its members

Databricks CEO Ali Ghodsi says enterprise AI is guessing with false confidence. Only 25% of enterprises run a governed context layer. What Genie Ontology changes.

86% of dealmakers now use generative AI in M&A, yet nearly 40% of GPs expect no material financial impact this year. The nine trends shaping private equity in 2026, and the portfolio data problem behind the gap.

88% of CRE firms are piloting AI and only 5% have achieved their program goals. The nine trends shaping commercial real estate in 2026, from record data center absorption to algorithmic pricing regulation.

80% of health systems are acting on AI in the revenue cycle and 3% report significant ROI. The nine trends shaping healthcare operations in 2026, from rising denials to the 2027 prior authorization API mandate.

Three in four manufacturers plan agentic AI within two years. Only one in five have the data model for it. What to automate, what to fence, and the layer underneath.

Two-thirds of PE firms will put over a quarter of their budget into AI in 2026. Which six agents are worth deploying, what to fence, and the context layer underneath.

74% of CRE firms now use at least one AI tool. Which agent workflows actually pay, what to fence, and the unified data layer that keeps agents from being confidently wrong.

81% of health execs are prioritizing agentic AI for clinical ops, prior auth, and revenue cycle. Only 2% have deployed enterprise-wide. What separates them is context.

Portfolio monitoring fails on data, not dashboards. How PE firms standardize portco KPIs across different ERPs, resolve entities, and reach real-time reporting in 2026.

Your property management, accounting, and underwriting systems disagree because nothing resolves entities between them. How a CRE data layer fixes NOI, rent roll, and lease data.

Denials near 12% and underpayments cost 1% to 11% of net patient revenue. Why RCM AI pilots stall on fragmented data, and what a healthcare data layer changes.

Standard costing hides which SKUs and customers lose money. How to build true margin per SKU and cost-to-serve from ERP, MES, and logistics data in 2026.

AI agents can source deals, read data rooms, and monitor portfolios, but only if they can reach your data. Here is how AI agents, agentic workflows, and knowledge graphs work together across the private equity deal lifecycle, and how to build the connected foundation in weeks.

AI agents can read an offering memo, abstract a lease, and build a first-pass underwriting model in minutes, but only if they can see across the Argus, Yardi, MRI, and VTS systems where your data lives. Here is how AI agents, agentic workflows, and a knowledge graph fit together across origination, underwriting, and NOI and occupancy intelligence.

Most healthcare AI pilots stall on context, not model quality, because revenue cycle data is fragmented across Epic, Oracle Health, clearinghouses, and spreadsheets. Here is how AI agents, agentic workflows, and a HIPAA-aligned knowledge graph work together to recover denials, close charge capture leakage, and normalize multi-site reporting.

AI agents can triage a submission or summarize a claim in minutes, but only when they reason over a connected model of your business instead of siloed systems. Here is how agents, agentic workflows, and a knowledge graph fit together across underwriting, claims, audit, and renewals, and how to deploy them without a two-year platform project.

AI agents can run true SKU margin teardowns, inventory rebalancing, and supplier spend consolidation, but only when they can reason over connected data. Here is how AI agents, agentic workflows, and knowledge graphs fit together, where they create the fastest margin and cash returns, and how to deploy them in weeks.

In a brokerage, AI agents are software workers, not your licensed real estate agents, and they only deliver value when your leads, transactions, listings, and commissions are connected. Here is why most brokerage AI pilots stall and how a knowledge graph turns lead-source ROI, propensity scoring, and deal matching into answers you can trust.

An AI context layer connects your fragmented systems and documents into one source AI can reason over. Learn what it is, why enterprise AI fails without one, and how to build the foundation.

Knowledge graphs ground enterprise AI in your real data, cutting hallucinations and making answers explainable. See how GraphRAG works and why it is the foundation of reliable AI.

How enterprise AI delivers real ROI in private equity, healthcare, manufacturing, insurance, and real estate, built on a context layer and knowledge graph. Real use cases by industry.

Should you build your own RAG stack or buy an AI company brain? A cost, timeline, and risk comparison for operators, including when building in-house is genuinely the right call.

A decision framework for mid-market operators weighing whether to build an AI context layer in-house or buy one, with true 24-month costs and the 90-day test that settles it.

Data unification connects your ERP, CRM, documents, and industry systems into one model your team and AI can trust. What it is, why warehouse and MDM projects stall, the five stages to a production intelligence layer, and what it looks like by industry.