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Welcome executives and professionals. AI strategy is getting a reality check: Ambition isn’t the constraint to scaling, but infrastructure might be, according to Deloitte’s inaugural AI infrastructure survey.

Since the previous edition, we have reviewed hundreds of the latest insights in agentic and generative AI, spanning best practices, case studies, market dynamics, and innovations.

This briefing outlines what is driving material value — and why it’s important.

In today’s briefing:

• Deloitte’s enterprise AI infrastructure survey.

• Recalibrating technology budgets for AI.

• Accenture-Wharton co-intelligence.

• The next evolution is agentic AIOps.

• Transformation and technology in the news.

• Insights for Executive+ members.

• Career opportunities & events.

Read time: 4 minutes.

MARKET INSIGHT

Deloitte’s enterprise AI infrastructure survey

Brief: Deloitte surveyed 515 U.S. leaders (director level and above, including C-suite and board members) at enterprises with over $500M revenue in Dec 2025, assessing how AI infrastructure strategies are evolving.

Breakdown:

• Over 70% of respondents expect to scale “AI factory” and “AI at the edge” deployments by 2028, doubling adoption within three years.

• The share of respondents with 31+ AI use cases in production is projected to rise from 44% in 2025 to 67% by 2028.

• Closed models are currently most used at 32%, with adoption of more differentiated derivative models expected to increase by 2028.

• Today, 37% of enterprises consume 1-10 billion tokens monthly, while 30% already exceed 10 billion tokens per month.

Why it’s important: Enterprise AI demand is compounding, but scaling remains complex and often unclear for many firms. Decisions around model strategy, token consumption, and where workloads run will increasingly influence the technical and financial posture of future enterprises.

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BEST PRACTICE INSIGHT

Recalibrating technology budgets for AI

Brief: McKinsey examined a core CIO challenge: balancing “run” spend that maintains systems with “change” spend that drives innovation and growth, as rising AI investment increases pressure on capital allocation.

Breakdown:

• McKinsey’s framework assesses tech spend using two metrics: run intensity, revenue share spent on run activities, and change investment.

• Firms are mapped across four archetypes: deliberate modernizers, strained transformers, lean operators, and heavy IT sustainers.

• McKinsey finds deliberate modernizers are best positioned to drive value, typically allocating at least one third of spend to change investment.

• These firms adopt lean architectures, standardise platforms, and reduce tech debt, lowering run costs while freeing capital for agentic AI.

Why it’s important: The companies that outperform will make deliberate decisions about what to simplify or retire. For CIOs, the task now is to reset the run–change balance, ensuring AI unlocks lasting returns with modern capabilities rather than reinforcing today’s complexity.

BEST PRACTICE INSIGHT

Accenture-Wharton co-intelligence

Brief: Accenture, with Wharton, published ‘The Age of Co-Intelligence,’ a 43-page report on how human-AI collaboration is reshaping value creation across the economy, individuals, the workforce, and broader society.

Breakdown:

• AI-enabled ways of working deliver productivity gains, but the true dividend lies in how capacity is redeployed toward innovation and growth.

• Work is shifting from static roles to skills. The Wharton-Accenture Skills Index maps jobs at task/skill level, linking them to economic value.

• Workforce strategy should align with business/tech priorities, redesigning roles around human strengths while AI extends execution.

• As intelligence scales through human-AI systems, responsibility does not. Enterprises must ensure accountability remains human.

Why it’s important: This shift raises a new leadership mandate: redeploy expanded capacity into value and growth. As AI compresses analysis, decision cycles, and delivery, it expands both human and digital capacity, and that capacity can be redirected toward reinvention.

BEST PRACTICE INSIGHT

The next evolution is agentic AIOps

Brief: Capgemini, with IBM, explored how the shift in IT operations to agentic AIOps is less a case of simple tool implementation and more a strategic transformation, best realized with a series of concrete steps.

Breakdown:

• Agentic AIOps moves beyond identifying issues to determining actions, coordinating responses, and executing within defined guardrails.

• Autonomous agents are only as effective as the data they consume, requiring grounding in enterprise-specific context to ensure reliability.

• Human-in-the-loop safeguards should be designed from the outset, with the greatest gains in agentic AIOps realised in early SDLC stages.

• Adopting the Model Context Protocol enables agentic systems to scale across infrastructure without reliance on brittle, proprietary integrations.

Why it’s important: Agentic AIOps marks a shift from reactive maintenance to proactive optimization. By combining specialised AI hardware, frontier foundation models, and orchestration software, organisations can enable more resilient operations while reducing complexity and driving efficiency gains.

BCG published 33 slides on AI-first property and casualty insurers, detailing how agentic AI is reshaping underwriting, claims, and distribution.

Capgemini shared 70 pages on how engineering leaders are navigating trade-offs between speed, agility, and cost efficiency in the age of AI.

McKinsey examined how AI is rewriting the rules of corporate venture building, with a playbook for leaders pursuing high-impact growth opportunities.

Deloitte shared that as global supply chain complexity rises, agentic AI offers manufacturers new ways to manage risk and improve resilience.

Salesforce outlined six lessons from building agents in the enterprise, including focusing on jobs to be done rather than traditional role definitions.

Deloitte outlined what is settled and still in play within the “AI Omnibus,” a legislative package expected to be finalized in Brussels by July 2026.

OpenAI announced a $122B funding round at an $852B valuation, the largest in venture history, alongside plans to build a unified AI “superapp.”

Anthropic launched computer use in Claude Code, enabling AI to open apps, navigate interfaces, and visually verify its own builds directly from the terminal.

Microsoft released Critique and Council, turning Copilot Researcher into a multi-model system that reviews reports and compares outputs.

Anthropic leaked source code behind Claude Code to a public registry, exposing 500K+ lines of code, days after the ‘Mythos’ model leak.

Oracle cut thousands of jobs in a major restructuring, citing a pivot toward AI in what is expected to be the company’s largest ever layoff.

Starcloud raised $170M to build GPU-powered data centers in orbit, betting on SpaceX’s Starship to make space-based compute cost competitive.

CAREER OPPORTUNITIES

JPMorgan - Head of AI Marketing Transformation

OpenAI - Capgemini Alliance Partner Director

NTT DATA - OpenAI Alliance Managing Director

EVENTS

Chief AI Officer Summit - April 14, 2026

MIT Human-Centered AI Leadership - April 14, 2026

Adobe Summit - April 20-22, 2026

Reach enterprise AI decision-makers:

• 66% of readers are C-level executives or VP and Director-level leaders.

• 63.2% of the audience is based in the U.S., EU, UK, ANZ, and Singapore.

• Read by leaders at Microsoft, Deloitte, the Fortune 500, and more.

Guaranteed impression and custom sponsorship packages available, with post-send performance reporting.

Conceived as a practical communication for executives Lewis Walker has worked with, this briefing has become a trusted resource for thousands of senior decision-makers shaping the future of enterprise AI.

We welcome your feedback.