Everest Group

Black Hat USA has always been a useful place to separate the Cybersecurity industry’s concerns from its sales pitches. In 2026, that distinction mattered more than usual. Artificial Intelligence (AI) was everywhere in Las Vegas: agentic Security Operations Centers (SOCs), agent identities, AI observability, AI governance, AI security posture management, AI-assisted Application Security (AppSec), autonomous remediation, and AI-powered threat hunting.

The Artificial Intelligence (AI) market has run on one question: whose model is smartest? That question is losing commercial weight, and open weights have finally broken it. On July 24, twenty-five US technology companies signed Open Weights and American AI Leadership, urging Washington not to restrict open-weight models. The signatories included NVIDIA, Microsoft, Meta, IBM, Dell, Palantir, ServiceNow, Mistral, Hugging Face, and Andreessen Horowitz.

The AI investment narrative has been remarkably one-sided. Capital has poured into chips and infrastructure, while investors have aggressively discounted the future of software companies and technology services firms. The assumption appears to be that AI will make software development and implementation so efficient that these industries will steadily lose relevance. However, I believe that conclusion misunderstands what is actually happening inside enterprises.

In Episode 4 of our Future of Workforce series, we shift the lens from how the workforce itself is evolving to how the experience of this workforce and the ways in which they get work done will evolve alongside it. In the previous episodes, we traced this evolution from digital workers in Episode 1 and autonomous physical agents and the emerging phygital workforce in Episode 2, to the changing role of frontline workers in Episode 3.

Payments leaders entered 2026 with a crowded agenda such as instant rails, stablecoins, tokenized credentials, digital identity, and agent-led commerce. As year moves into its second half, focus is moving from which technologies will gain adoption to how they can operate together safely. The first six months have highlighted a common design requirement that each payment must carry stronger evidence of who or what initiated it, what was authorized, and which controls applied.

Food and Beverage (F&B) companies are using Artificial Intelligence (AI) to reduce Research and Development (R&D) bottlenecks. Large Language Models (LLMs) can scan research and ingredient databases; predictive and optimization models can support ingredient, process, formulation, and sensory decisions; and automated labs can connect experiment planning with testing.

Get the latest insights, trends, and strategies delivered straight to your inbox. Join Everest Group’s newsletter to stay at the forefront of what’s next.

Oil and water do not mix. The same was once said of services (water), in the software (oil) world. Artificial Intelligence (AI) has breached that dam. Services have become essential to driving adoption of AI products, and recent Forward-deployed Engineering (FDE) announcements attest to this shift. The question is how far software and frontier model companies are likely to go in scaling services.

Over the past 18 months, the Cloud and Infrastructure Services (CIS) market has witnessed a steady stream of acquisitions, announced transactions, and strategic investments. NTT DATA strengthened its Google Cloud capabilities through Niveus Solutions and announced its intent to acquire Microsoft-focused specialist WinWire.

As India becomes more deeply embedded in global delivery portfolios, the boardroom discussions are shifting beyond its attractiveness as a scaled delivery location to the governance implications of relying heavily on a single market.