Spotlight: September 15, 2026


The Weekly Dispatch

Top interviews from this week

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This week’s Dispatch examines what it takes to make AI durable inside the enterprise: people who can adapt, systems teams can trust, and privacy that does not block progress. Paul Sole’s change-management data shows that Amdocs increased internal mobility to 40%, cut recruitment-agency spending by 90%, and improved skill proficiency in four key competencies by 10%—a reminder that AI transformation is also a workforce transformation.

Barr Moses’s trusted-AI research brings a harder reality into view: 64% of AI systems are deployed faster than teams are ready to support, and 64% of respondents say incorrect AI responses limit trust.

David Archer’s homomorphic-encryption work offers a path forward for sensitive data, enabling computation on encrypted information while keeping the underlying data protected.

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AI Change Management Statistics & Data 2026

In a rapidly evolving technological landscape, AI Change Management Statistics 2026 provide crucial insights for understanding how enterprises adapt to AI advancements. Paul Sole, an expert at Amdocs, shares his firsthand experiences and data collected across numerous engagements. “My journey has had a lot of twists and turns, starting with an interest in technology and computers.” — Paul Sole, Expert, Amdocs. Paul Sole's data emphasizes the transformative impact of AI on enterprise operations. His experiences highlight the necessity for robust governance frameworks to manage AI's integration into highly regulated environments. As AI continues to evolve, so do the challenges and opportunities it presents for large organizations.

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2026 Trusted AI Statistics & Data Insights

As we explore Trusted AI Statistics 2026, Barr Moses offers firsthand insights from her work at Monte Carlo. Her data reveals critical trends and challenges in AI deployment and trust. “Observing the full system is the very first step to building trust.” — Barr Moses, CEO, Monte Carlo. The data collected by Barr Moses across numerous engagements highlights the challenges of deploying AI systems at scale. Many organizations, including Fortune 500 companies, face hurdles due to rapid deployment and insufficient preparation. This reflects a broader industry trend towards prioritizing speed over stability, leading to reliability issues.

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2026 Homomorphic Encryption Statistics & Data

David Archer, CTO of Niobium, brings to light the transformative power of fully homomorphic encryption (FHE) in 2026. His insights reveal how FHE is revolutionizing privacy-preserving computing by enabling calculations on encrypted data without decryption, thus closing the window of vulnerability that has long plagued the industry. “FHE isn't a lab curiosity anymore. Its security is built on the same foundation that underpins post quantum encryption.” — David Archer, CTO, Niobium. The data collected by David Archer across numerous industry engagements offers a unique perspective on the advancement of FHE. As of 2026, the integration of FHE is not just about enhancing privacy but fundamentally altering how sensitive data is managed and utilized.

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Three signals stand out across change management, trusted AI, and privacy-preserving computing. First, governance and workforce mobility are practical levers for helping regulated enterprises absorb AI change. Second, speed without observability creates fragile systems; building trust starts with monitoring the full AI system, not just the model. Finally, fully homomorphic encryption is moving beyond theory, enabling privacy-preserving data collaboration—though its substantial compute requirements still make specialized hardware and deliberate use cases essential.

 

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💡 Final Thoughts

The 2026 playbook is clear: prepare people for change, observe systems before scaling them, and protect sensitive data by design.

P.S. Reply with the priority on your roadmap—AI adoption, production trust, or data privacy—and a future Dispatch can go deeper.

Michael Bernzweig
Founder, Software Oasis
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