Monday Morning Impact – October 5
Gartner Forecasts Worldwide AI Spending to Grow 49.5% in 2026
Worldwide spending on AI is forecast to total $2.7 trillion in 2026, a 49.5% increase year-over-year, according to Gartner.
“Demand for AI infrastructure (including AI-optimized IaaS, AI-optimized servers, AI network fabric, AI processing semiconductors and devices) to support anticipated future workloads remains strong and inelastic to pressures from memory-related pricing increases,” said John-David Lovelock, Distinguished VP Analyst at Gartner. “The buildout of AI data center capacity is the largest infrastructure project humanity has even undertaken. The capacity growth from hyperscalers and service providers purchasing AI-optimized servers will continue to be the largest single area of spending.”
In the software market, Gartner says vendors across different software types are rapidly embedding agentic AI within their existing products to maintain relevance in the market and defend against new cross-functional agents. Enterprises are using these simpler embedded AI features from their incumbent software providers to grow operational efficiency and automate workflows, improve customer engagement and enhance decision making.
“Meanwhile enterprises are turning to service providers less often to help them manage the business transformation, and more often for the smaller indirect projects to exploit AI features of their incumbent software system,” said Lovelock. “The risks associated with vendor lock-in, data sovereignty, and run-away costs are not deterring buyers from adopting these proprietary capabilities. The combination of transformation and indirect projects are forecast to drive a $1.2 trillion opportunity in AI services by 2030.”
The short-term outlook for AI application development platforms has increased from 28% growth in 2026 in the previous forecast to 39% in this quarter’s forecast, as enterprises, software providers and services firms seek to develop custom AI applications tailored to their individual needs. Enterprises are looking to their providers to help them manage their costs and embed usage tracking into their workflows to evaluate success. For model providers, the pressure to offer more cost-efficient models that are aligned to enterprise use cases is opening a small but growing opportunity for domain-specific language models (DSLMs). As a result, the 2026 growth rate for generative AI models has increased from 110% growth in the previous forecast to 117% growth in the current forecast.
Gartner clients can read more in Forecast: AI Spending, Worldwide, 2025-2030, 2Q26.
Channel Impact®
The convergence of AI infrastructure investment and AI embedded into software and services is driving AI spending to unprecedented levels, thereby providing huge potential opportunities for partners adding value in this convergence.
Barracuda Launches AI Security and Governance Solution
Barracuda Networks, a Campbell, California-based security vendor, has launched “Barracuda AI Data Security,” an AI security and governance solution purpose-built for resource-constrained organizations and MSPs. The offering is designed to enable businesses to accelerate AI adoption by protecting sensitive data, enforcing responsible AI use and demonstrating compliance. The solution integrates AI visibility, data protection, threat defense, and governance.
New findings from Barracuda Research reveal that nearly half of senior IT leaders say their teams lack the skills required to securely govern AI already in use across their businesses.
“Organizations need a practical way to secure AI as a new attack surface, protect sensitive data, enforce responsible use, and demonstrate compliance,” said Neal Bradbury, Chief Product Officer at Barracuda. At the same time, they need the confidence to embrace and accelerate AI adoption as a driver of productivity and innovation.”
Capabilities include automatic blockage of sensitive information such as customer data, passwords, financial records, and proprietary code from reaching GenAI services; blockage of prompt injection, jailbreak attempts, hate speech, and policy violations; a compliance-ready audit trail; and support for flexible AI policies.
MSPs also gain centralized multitenant management and bundled pricing.
Barracuda AI Data Security is scheduled to become available this month. It will also be included in Barracuda SecureEdge Premium Access at no additional cost.
Channel Impact®
The offering is intended to help MSPs confidently deliver AI by combining security, governance and compliance into a single solution with full visibility into AI usage.
Andela Research: More than Half of AI Job Postings Seek Skills That Don’t Match Job Title
Andela, a New York-based company with an AI-native talent and services platform, announced that its newly launched Emerging Skills Research found that the industry is inventing roles faster than companies can name them or accurately match skills to job titles.
Andela’s Emerging Skills Research analyzed 47,101 technical job postings from Fortune 500 companies and scored 2,026 distinct skills.
Among the roughly 1,832 postings titled for roles like “AI Engineer” and “ML Engineer,” 53% require skills drawn from at least two different established roles. For familiar titles like “AI Engineer,” companies are seeking a different, unnamed role that includes skills such as LLM orchestration, autonomous agents, and vector databases.
The study also found that 23 skill bundles were identified that recur across Fortune 500 hiring, but don’t map to any standardized job title. Within these findings, Andela identified 8 genuinely new roles, 14 hybrids of old and new, and 1 discarded for accuracy. Two example new roles are MLOps Pipeline Engineer, which bridges five established roles, and LLM Application Engineer, a contemporary AI engineer who builds on foundation models rather than training them.
Of all the emerging skill combinations, 6,758 job postings carry the LLM Application Engineer skill bundle without naming it, reflecting a lag between the technology frontier and what companies know how to hire for.
The findings underscore the challenges companies face, driven by the unprecedented speed of change in the post-AI era, to accurately identify emerging skills, name them, and attach them to job roles. Mismatches result in lost time, money, and opportunity for both employer and employee, according to the company. Job descriptions written for yesterday’s roles filter out the candidates companies actually need. Companies that fail to see emerging needs for human skills, especially as AI changes what it can do, will continually chase the wrong thing, and companies who hire today for yesterday’s roles will likely accrue “talent debt” alongside technical debt.
The most emerging tech roles identified in the research include: MLOps Pipeline Engineer, LLM Application Engineer, FinOps Reliability Engineer, Docs-as-Code Engineer, Product Front-End Engineer, Lakehouse Analytics Engineer, DevSecOps Security Engineer, and SecOps Observability Engineer.
Channel Impact®
MSPs and client companies need to exercise caution in developing titles and language that truly reflect their needs.
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