Cloud Computing Insider

AI consulting is being sold as the next great enterprise revolution, but too much of the market looks more like a land grab than real transformation. In this video, I break down why so many consulting firms seem obsessed with partnerships, ecosystems, and "owning the stack," while saying far less about measurable client value. The issue is not that AI lacks potential. It clearly has enormous potential. The issue is that many firms are treating vendor alignments and flashy positioning as if they were proof of capability. They are not.
 
When consulting firms lead with partner logos, push agents before understanding the process, and recommend expensive technology stacks before proving the economics, clients often end up paying for complexity, hype, and experimentation instead of outcomes. That is where the AI consulting gold rush starts to look dangerous.
 
This video explores the incentives driving the market, the gap between AI marketing and AI competence, and the warning signs enterprise buyers should watch for before signing large contracts. If consulting firms want to lead in AI, they need to prove they can create real business value, not just sell the latest version of fear of missing out to drive unnecessary enterprise spending today.

This session explores why digital transformation efforts often stall even after major cloud investments. The core issue is that infrastructure can be modernized faster than culture, governance, and workforce behavior. Rather than treating cloud as a migration exercise, leaders must use it to redesign operating models, decision-making, and value creation. The discussion highlights how the Capability Maturity Model can help organizations move from fragmented practices to continuous monitoring and a strong Cloud Center of Excellence. It also reframes the digital skills gap as a development challenge, showing why structured learning journeys, coaching, and social learning outperform one-off training or a search for rare AI talent. The session further examines why boards need greater digital fluency as AI adoption accelerates, and how governance weakens when leadership ambition outpaces oversight capability. It concludes with a practical look at modern AI governance, including dynamic controls for agentic systems, trust-centered assurance, and guardrails for democratized IT through approved tools, visibility, and risk monitoring. Attendees will leave with a clearer framework for aligning cloud, talent, governance, and trust to drive sustainable transformation. The focus is practical, executive-level, and aimed at organizations seeking to scale innovation without losing accountability, resilience, or strategic clarity across the enterprise today.

Public cloud reliability sucks, and the industry has spent years pretending otherwise. The sales pitch is always the same: better uptime, better resilience, less operational pain. But when public cloud fails, it fails big, and everyone pays for it. These platforms are supposed to reduce risk, yet they often concentrate it. One outage in a major provider can cripple applications, break authentication, disrupt storage, kill APIs, and leave entire businesses frozen. That is not resilience. That is shared fragility at a massive scale.
 
The real problem is that public cloud providers have become too central to too much of the economy. Companies move critical systems into environments they do not control, cannot fully inspect, and cannot quickly recover from when things go wrong. Providers talk endlessly about redundancy, but customers still end up exposed to regional failures, control plane issues, cascading dependencies, and platform-wide mistakes. Public cloud is sold as modern infrastructure, but too often it behaves like an outsourced vulnerability. When it works, everyone congratulates the model. When it breaks, customers discover how little power they actually have. Public cloud reliability does not just disappoint. It fails in exactly the ways businesses were told it would not.

In this video, I break down what I call the Great Agentic Distraction: the growing tendency of cloud providers to pour talent, capital, and roadmap attention into agentic AI while slowing improvement in the core cloud services most enterprises still depend on. For more than a decade, cloud computing won enterprise trust through steady gains in reliability, security, governance, observability, scalability, and cost efficiency. That kind of progress mattered because it improved real-world operations for nearly everyone.
 Now the market narrative has shifted. Providers are racing to retool platforms for agentic AI, autonomous workflows, orchestration layers, and AI-centric architectures. But while that future may be promising, most enterprises are not there yet. They still need better performance, lower complexity, stronger controls, predictable costs, and simpler operations.
 Using a clear visual timeline, I explain how core-service innovation rose steadily, then flattened as the agentic AI push began. The key issue is not whether agentic AI is valuable. It is whether the industry is starving today's enterprise priorities to finance tomorrow's vision. If that imbalance continues, many customers may see agentic AI not as progress, but as a distraction. And that should worry every CIO planning the next five years of investment.

Cloud careers in 2026 are being shaped by more than technical knowledge alone. Employers still want strong capability in platforms like AWS, Azure, and Google Cloud, but they are increasingly looking for people who can connect infrastructure work to security, automation, cost control, AI readiness, and business outcomes. That means career growth now depends on building a solid foundation, adding a specialty, and proving your skills through hands-on work that others can see and trust.
 
Certifications can open doors, but projects, architecture writeups, GitHub repositories, and real operational experience help keep those doors open. Just as important, professionals need to stay visible by keeping LinkedIn and X profiles current, sharing ideas, posting lessons learned, and pointing people toward the work they have done.
 
Networking matters too, especially through live events such as meetups, conferences, and community groups, where many opportunities begin through conversation rather than formal applications. The overall message from today's cloud career advice is simple: learn continuously, build publicly, communicate clearly, and make sure both your skills and your professional presence show that you are ready for what comes next. Professionals who combine curiosity, consistency, and credibility will be best positioned to grow, earn trust, and lead future initiatives.