{{first_name | Hi}}, your strategic AI update is here.
Anthropic signals the enterprise is ready to hire software, KPMG proved the market is finished paying human rates for robot work, and Reddit confirmed that human friction is now a premium asset. Meanwhile, executive confidence is decoupling from operational reality. Leaders are funding a future they aren't equipped to build, and AI ambition is fast becoming your biggest liability.
“SaaS business products are basically databases with some business logic. When business logic is all going to agents which can work with multiple possible such databases, those will be possible to swap much more easily than before.”
— Satya Nadella.
In today's lineup:
Robots
AI ambition is your biggest liability
Real ROI from Gen AI
The ML observability shortlist
People
How Qasar Younis is making autonomous systems safe
Love
Escape the efficiency trap
Transformative Reads: Competing Against Luck by Clayton M.
ROBOTS 🤖
How are robotics and AI changing industries? We break down the latest news, tools, and innovations for you.
Top Insights
Anthropic’s agents signal the enterprise is ready to hire software instead of people.
KPMG proved the market is finished paying human rates for robot work.
Reddit proved that human friction is now a premium asset.
Amazon’s $200bn pivot confirms legacy media is a hobby while AI is survival.
Big Tech’s $660bn bill proves the market is terrified of the real cost of intelligence.
AI ambition is your biggest liability
Executive AI confidence is soaring while operational readiness is falling. An IBM report shows 79% of leaders expect AI to drive significant revenue, but only a quarter have a clear plan to get there. At the same time, a Deloitte study confirms that preparedness in critical areas like data and talent has actually declined in the last year.
Leaders are funding a future their organizations are not equipped to build.
Your next moves:
Redesign core workflows before you automate them.
Create a clean data layer instead of slow migrations.
Kill pilot projects that don't show value in 90 days.
Tie every dollar of AI spend to a business outcome.
What is the biggest gap in your organization's AI readiness?
Real ROI from Gen AI
How human-centric AI delivers business results
Palo Alto Networks reclaimed 351,000 productivity hours by deploying Moveworks' agentic AI to autonomously resolve IT and HR tickets, freeing their workforce to focus on strategic cybersecurity threats.
Amazon saved 4,500 developer years (~$260M) by using Amazon Q to automate the upgrade of 30,000+ Java applications, effectively modernizing their massive codebase with minimal human intervention.
Hedge funds using generative AI for market analysis earned 2-4% higher annualized returns than non-adopters, according to a 2025 study.
Tool Spotlight
Your weekly briefing on tools that create competitive leverage
The ML observability shortlist
Best for: Enterprises needing a comprehensive platform to troubleshoot and monitor the performance of live machine learning models. Arize is built for ML engineers and data scientists to quickly identify and resolve issues like data drift, performance degradation, and data quality problems.
Choose if you need to:
Automatically monitor model accuracy, drift, and data quality in production.
Get to the root cause of model failures with powerful analytics tools.
Ensure your models are fair, unbiased, and explainable for compliance.
Best for: Teams that want to prevent model failures before they happen with a focus on data health. WhyLabs provides a unified view of data pipelines and models, from development to production, helping teams catch data issues early.
Choose if you need to:
Monitor data quality and drift across the entire ML workflow.
Prevent bad data from ever reaching your production models.
Collaborate between data engineering and data science teams with a shared platform.
3 other platforms to explore...
Fiddler AI, Arthur, and TruEra provide robust solutions for model performance management, explainability, and ensuring responsible AI practices.
PEOPLE 👥
Meet the innovators turning bold ideas into real-world impact.
Transformation Champion
How Qasar Younis is making autonomous systems safe
Deploying autonomous vehicles at scale is stalled by one massive obstacle: proving they are safe without millions of miles of real-world driving. Qasar Younis, CEO of Applied Intuition, is solving this by creating sophisticated simulation software that lets companies test their AI against millions of virtual scenarios. His platform has become the industry standard for safely developing and validating autonomous systems, accelerating the path to market for everything from self-driving cars to automated mining equipment. Read more.
LOVE ❤️
Practical wisdom, growth tactics, and a must-read book that will challenge the way you think.
Escape the efficiency trap
Efficiency doesn't lower costs, it raises ambitions.
Turn efficiency gains into new capabilities:
Instead of tracking cost savings, map the strategic options that are now possible.
Create a dedicated budget for small, fast experiments to test these new capabilities.
Measure your tech spend against the value it creates, whether that's new revenue, market position, or entirely new products.
Transformative Reads
One book, handpicked from my conversations with friends, industry leaders, and tech innovators:
Competing Against Luck by Clayton M. Christensen introduces the "Jobs to be Done" theory. It argues customers "hire" products to solve a specific problem. This reframes innovation away from features and toward a deep understanding of the customer's real context. The insight helps companies create products people reliably choose.
Perfect for: Product managers and innovators focused on customer-centric design.
In Culture
Actual footage of your "Strategic AI Roadmap" hitting the legacy data layer.
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Much Love,
Matt and the Future Works team
