85% of executives plan to increase AI spending in supply chain in 2026, with one in five expecting an increase of more than 20%. Supply chain is the function where AI investment is growing fastest - consistent with McKinsey's finding that it is also where cost reductions are most frequently reported.

On investment direction

Companies with AI-mature supply chains are 23% more profitable than their peers and six times as likely to use AI and generative AI widely across the organization. The maturity gap in supply chain AI is a profitability gap - not a technology gap.

On the profitability advantage

AI-enabled distribution operations report 5–20% logistics cost reduction, 20–30% inventory reduction, and 5–15% procurement spend reduction. These are function-level returns from McKinsey's research - specific, measurable, and achievable for organizations that have deployed AI into the right supply chain workflows.

On measurable operational returns

Only 23% of supply chain organizations have a formal AI strategy. The other 77% are making AI investment decisions without a defined framework for where AI belongs in their operation and what outcomes it is expected to produce.

On the strategy gap

57% of US operations organizations have integrated AI into selected functions or throughout the organization - passing the halfway mark for initial integration. But integration into selected functions and full operational impact are different things, and the ROI data suggests most organizations are still on the integration side of that line.

On integration

70% of large-scale organizations will adopt AI-based demand forecasting by 2030. The organizations that build forecasting capability now - on data infrastructure and with models validated against real outcomes - will have a compounding advantage over organizations that begin building later.

On demand forecasting

Supply Chain AI Statistics 2026

The gap between individual AI success and organizational AI value is widening. Individual employees are getting real productivity benefits from AI tools. Those benefits are not accumulating into enterprise-level competitive advantage. The tools work. The integration does not.

On the individual-to-organizational gap

Only 23% of organizations are seeing meaningful ROI from AI. The patterns that separate them from the 77% falling behind are governance-related - not technology-related. The organizations getting returns have defined what AI is authorized to do, built the oversight structures to manage it, and aligned AI deployment with specific business outcomes rather than general capability building.

On governance and the ROI gap

Organizations are deploying AI agents without the infrastructure to manage them. This is the next version of the shadow AI problem - not individual employees using ungoverned consumer tools, but organizations deploying autonomous AI systems without the governance frameworks to control, audit, or correct them.

On agentic AI without infrastructure

Strategy is theater without execution infrastructure. The organizations that have built trust in their AI systems - among employees, among decision-makers, and in compliance with regulatory requirements - are the ones able to scale AI beyond individual productivity into organizational transformation.

On trust

AI Enterprise Adoption Report 2026

57% of enterprises' AI ROI fails to outpace their investment - a number that has been unchanged since 2025 despite significant increases in AI production capability. The plateau is not a capability problem. It is a design and integration problem.

On the ROI plateau

93% of enterprise AI leaders report improved AI production capability in 2026 - up from 88% in 2025. Organizations can build AI faster than ever. They are still not converting that capability into financial returns at the same rate.

On production capability

The report cites independent MIT NANDA research reaching the same conclusion from a different methodology: 95% of organizations running AI pilots saw no measurable P&L impact. Only 5% of integrated pilots generated meaningful financial value. Two independent research methodologies. The same answer.

On the MIT NANDA parallel finding

The report focuses on financial services, life sciences, and the public sector - environments where the gap between AI capability and AI governance is most consequential. In these sectors, an AI system that produces an output the organization cannot audit, explain, or defend is not a solution. It is a liability.

On regulated industries specifically

Fifth Annual Enterprise AI Report 2026

57% of enterprises now have AI embedded in core business processes or deployed broadly across the enterprise - up from 35% just one year ago. Only 11% have achieved both of their top two AI objectives. The deployment curve is steep. The outcomes curve is flat.

On deployment vs. outcomes

Only 23% of business leaders say their workforce is fully prepared for AI - down six points from 2025. As deployment accelerates, confidence in readiness is declining. More AI in the operation, less certainty that the people using it are equipped to use it well.

On workforce readiness

Worldwide AI investment is forecast to total $2.52 trillion in 2026 - a 44% year-over-year increase. The capital is moving fast. The outcomes data is not keeping pace with the spending.

On investment scale

The report identifies a consistent pattern among organizations achieving AI objectives: they redesign work rather than just adding AI to existing processes, they manage the organizational change that AI requires rather than treating it as a technology deployment, and they build governance and trust structures deliberately rather than reactively.

On what works

Nearly four in five respondents agreed that the pace of AI development will outpace their organization's ability to govern it. That is not a future concern - it is a current reality for most organizations in the survey.

On governance

2026 People Readiness Report

Nearly half of health systems have already adopted AI in procurement - but only 18% have fully implemented it across procurement functions. The gap between partial adoption and full implementation reflects the foundational work that most organizations are still completing.

On AI adoption in procurement

Health systems are in the foundational stage - establishing governance models, strengthening IT infrastructure, and investing in data assets before pursuing broader AI transformation. Organizations that skip this stage and deploy AI on top of unprepared data infrastructure are building on sand.

On what is blocking full deployment

Current AI use in healthcare procurement is concentrated in inventory management and product or vendor identification - the lowest-risk, highest-visibility use cases. These are valuable but represent a fraction of the AI opportunity in procurement operations.

On where AI is being used

As organizations strengthen data governance and modernize infrastructure, the next wave of AI in healthcare purchasing is poised to move beyond basic visibility into decision support - where AI does not just surface information but informs the procurement decisions themselves.

On what comes next

Procurement is becoming a more strategic function in health systems - with decisions shaped by reliability, scale, and long-term viability rather than short-term cost alone. AI that supports that strategic view requires more sophisticated data infrastructure than AI that simply tracks inventory.

On the strategic shift in procurement

2026 State of Healthcare Procurement

73% of senior US business and finance leaders agree that consolidating their tech stack and reducing tool sprawl is the fastest path to a healthier bottom line. The accumulation of disconnected tools has become a recognized problem rather than a feature of enterprise flexibility.

On tech consolidation

80% of senior US business and finance leaders say AI delivers faster ROI than any other technology investment. That finding sits in tension with the broader ROI data from Deloitte, McKinsey, and Domino - unless you understand what it is measuring. Leaders who have deployed AI in the right places, into the right processes, are seeing faster returns. The challenge is getting AI into those places.

On AI ROI relative to other technology

67% of senior executives say data silos currently hinder decision-making. Not in the abstract - as a current, operational constraint on decisions they are trying to make today. Data silos are not a future problem to solve. They are an active cost in the current operation.

On data silos

92% are redesigning workflows around AI while keeping human oversight central. The organizations making progress are not just deploying AI into existing workflows - they are redesigning the workflows around what AI can do, with humans making the decisions that require human judgment.

On workflow redesign

2026 Enterprise Technology Benchmark Report

Only 34% of organizations are truly reimagining their business with AI. The rest are deploying AI into existing structures and wondering why the numbers are not moving.

On ROI and transformation

Organizations allocate 93% of AI budgets to technology and only 7% to the people and processes expected to use it. The investment is real. The integration is not.

On budget allocation

Worker access to AI rose 50% in 2025. The number of companies with more than 40% of AI projects in production is expected to double within six months. Access is expanding. Preparedness is not keeping pace.

The organizations Deloitte identifies as AI leaders - the "vanguard" - are achieving both additional revenues and lower costs simultaneously. They share three consistent characteristics: they deploy AI across the business rather than in isolated pockets, they align AI initiatives with business strategy rather than running disconnected pilots, and they invest in the governance and data infrastructure that AI requires to work at scale.

On what separates leaders

The State of AI in the Enterprise 2026

AI is transitioning in healthcare from workflow tools into mission-critical infrastructure that drives revenue growth, margin expansion, and clinical outcomes. This is a fundamental change in how healthcare organizations think about AI investment - from optional productivity enhancement to core operational dependency.

On the infrastructure shift

A nascent health AI data infrastructure category is entering significant growth, driven by demand from both model labs and application companies. The organizations building data infrastructure for healthcare AI are not just serving the current wave of AI applications - they are building the foundation that future applications will depend on.

On data infrastructure investment

2025 marked a turning point for healthcare innovation. Six Health Tech companies went public between 2024 and 2025, adding $36.6 billion in fresh market capitalization after a two-year drought. AI is the primary driver of the renewed confidence in healthcare technology investment.

On the market turning point

A new generation of AI-native value-based care organizations is launching and scaling - companies whose business model depends on AI being embedded in clinical and operational processes rather than deployed alongside them.

On value-based care

Digital CROs enabled by AI are beginning to address the cost, speed, and competitiveness trilemma that has constrained pharmaceutical R&D for decades. The combination of real-world data, synthetic data, and AI-assisted trial design is opening research possibilities that were not viable under traditional approaches.

On pharma R&D

State of Health AI 2026

88% of organizations now use AI in at least one business function - up from 72% in early 2024. Generative AI use has surged to 71% of organizations deploying it regularly. Adoption is near-universal. That is the easy part.

On adoption

Only 37% of respondents report AI contributing positively to their organization's EBIT. That number has been essentially unchanged despite growing adoption. More organizations using AI has not translated into more organizations making money from it.

On financial impact

42% of companies abandoned most of their AI projects in 2025 - up from 17% the prior year. The projects that get abandoned are almost always the ones that were deployed on top of existing processes rather than engineered into them.

On project abandonment

When McKinsey examines financial impact at the business function level rather than the enterprise level, the returns become substantial - 10–20% cost reductions in software engineering and IT, similar ranges in manufacturing and supply chain, revenue uplift exceeding 10% in AI-enhanced marketing and sales. The value exists. It is function-specific, not enterprise-wide.

On where value is actually being created

Respondents most frequently report cost reductions from AI in supply chain management, service operations, and manufacturing. Supply chain is the highest-ROI function for AI in the 2025 data - and among the least discussed in general AI coverage.

On supply chain specifically

The shift from generative AI tools toward agentic AI - systems that take autonomous action rather than just generating outputs - is accelerating. 47% of respondents say their organizations are scaling chatbots enterprise-wide. The next wave is systems that act, not just respond.