Cloud computing is the foremost upskilling priority
Whether you’re building something simple or transforming an enterprise, cloud computing isn’t optional, it’s infrastructure. It affects the way your teams build, test, and scale everything. And in 2026, it’s not slowing down. According to Pluralsight’s latest Tech Forecast, more than 1,500 tech leaders and experts have confirmed what most of us already know: cloud is where the real momentum lies. Executives identified cloud computing as the top growth area for their organizations. Tech professionals ranked it as their second most important area to upskill. The numbers back it: from a pool of 2.9 million active learners, cloud computing, not AI, was the number one focus this year.
This tells you two things. First, cloud is no longer something engineers work on in a silo. It’s affecting margins, speed to market, logistics, and innovation cycles. Second, the industry is aligning around specific platforms, mainly AWS and Azure. If you’re leading product or tech strategy at an enterprise, pushing for certifications in AWS Certified Cloud Practitioner or Azure Fundamentals isn’t just a training exercise, it’s a capability investment. These programs offer structured paths and give your teams hands-on labs to work in simulated environments. That means less risk, more confidence.
For early-stage talent, the focus should be on infrastructure building blocks, Linux, Docker, Terraform, Python. For senior roles, prioritize candidates with networking depth and platform experience at the enterprise level. According to Jacob Lyman, Senior MLOps Engineer at Duke Energy Corporation and a Pluralsight author, these skills form a critical foundation within AWS cloud systems. He directly points to Git, Ansible, and advanced networking as essential proficiencies.
Bottom line: cloud isn’t a department, it’s the operational base layer of your company. Getting it right, technically and strategically, isn’t optional. It’s where scalability, security, and speed get decided.
MCP servers are emerging as a critical enabler for agentic AI systems
There’s a protocol you’ll hear more about soon, Model Context Protocol (MCP). It’s not as widely discussed as cloud or Python, but it will be. MCP servers are enabling AI systems, specifically large language models, to interact directly with other tools, databases, APIs, and software systems. What that means is we’ll move beyond AI generating output, to AI acting independently based on that output. This is what people mean when they say “agentic AI.”
As we shift to this architecture, it’s worth noting something: we’re not just increasing software efficiency, we’re completely changing how AI gets integrated into real product stacks. According to James Willett, a Pluralsight Author and expert in AI and software architecture, MCP, combined with standardized AI SDKs, is laying the groundwork for intelligent, agent-driven ecosystems. These will allow models to handle workflows autonomously while maintaining system interoperability. It’s going to change how decisions are made and executed inside enterprise platforms.
There are no formal skill tests for MCP right now, but that’s temporary. Early adopters should be building internal knowledge and running small pilot projects. Pluralsight already has a learning path covering FastMCP fundamentals and hands-on lab environments. That’s a signal that this isn’t hype, it’s the early stage of infrastructure maturity.
If you’re a decision-maker, pay attention to where MCP intersects with AI investment. Integrating LLMs efficiently with your data and systems is key, it’s where most of the ROI from AI investment will actually come from. Inaction in this space could put your digital strategy behind in a very short window.
Cybersecurity, particularly in the AI and cloud realms, is paramount
The attack surface is expanding. Every AI implementation, every cloud migration, and every remote endpoint introduced across your ecosystem increases exposure. In 2026, cybersecurity is not just important, it’s critical infrastructure. In Pluralsight’s latest Tech Skills Report, cybersecurity topped the list of priority skills for tech professionals and was ranked the second most important area for business growth by executives. This prioritization reflects both internal pressures and external threats.
No organization is immune to AI-powered attacks, identity fraud, or third-party vulnerabilities in distributed supply chains. Regulatory frameworks are pushing enterprises to act fast, but the pace of risk is accelerating even faster. Christopher Rees, Principal AI Strategist at Unisys and a leading cybersecurity expert at Pluralsight, calls out the complexity leaders now face: combining education, upskilling, and operational defenses to protect both IT and OT (Operational Technology) environments.
At the tactical level, there are widening gaps in AI security and cloud defense. Professionals need specialized skills that evolve with the threat landscape. Pluralsight currently offers 26 cybersecurity skill IQ tests covering incident response, OT security, secure coding, and architecture. For cloud-specific threats, the “Cloud Security” path, developed by Alexander Shafe and Chris Jackson, builds core awareness of how to detect and prevent breaches in cloud-native deployments. For those working with AI, the “Cybersecurity and Artificial Intelligence” course and the “Generative AI for Security Professionals” track offer executive-level insights and technical depth.
C-suite leaders must adopt a more integrated security strategy. Without it, AI and cloud investments remain vulnerable, and compliance risk increases. Training is no longer optional. The demand for talent is real, but so is the need for internal development. Investing in cross-functional upskilling reduces incident response times, supports proactive posture management, and adds resilience where automation alone falls short.
SQL remains a foundational and increasingly vital skill in data management
SQL has been relevant for decades, but its value hasn’t diminished, and in fact, it’s growing. In 2025, interest in learning SQL surged 27%, based on Pluralsight usage data. That’s a material shift. SQL underpins analytics, fuels reporting, and enables infrastructure decisions across nearly all sectors. It’s also baked into the fabric of every major cloud platform and tightly integrated with services businesses rely on for strategic data operations.
For executives looking to build resilient data teams, this trend matters. SQL isn’t just a basic query language, it’s a capability that ties business intelligence to product choices, marketing performance, customer behavior, and logistics. When employees can rapidly access and manipulate structured data, the organization gains speed and context in decision-making.
Mike McQuillan, Head of IT at Halls and a Pluralsight Author, notes that SQL forms the technical backbone of broader architectural thinking. He highlights the need for foundational database design principles, the ability to work across paradigms (from RDBMS to NoSQL), and a strong understanding of how N-tier systems and APIs interact. This isn’t theoretical knowledge, it’s practical, everyday functionality your teams need to build scalable systems.
Pluralsight offers 14 SQL evaluations, including basics and advanced variations for PostgreSQL, MongoDB, and others. The entry point is the “SQL Essentials” test, but more specialized tracks exist for teams working in more complex environments.
If you want to make informed, real-time decisions that impact revenue or risk, you need a workforce fluent in structured data. SQL isn’t a “nice-to-have” learning opportunity. It’s fundamental to any modern business with serious data ambitions.
Python continues to be essential due to its versatility and industry dominance
Python has earned its place across almost every domain in tech. It’s versatile, clean, and fast to implement. That’s why it’s still a top skill investment heading into 2026. Python doesn’t just automate tasks, it powers AI, machine learning, scripting, and data analysis. It integrates across stacks, supports open-source innovation, and maintains relevance through a growing ecosystem of libraries and tools. In short, it remains core infrastructure for modern software development.
The accessibility of Python makes it ideal for onboarding talent quickly. More importantly, it scales with them. Beginners use it to solve basic automation problems, while senior engineers apply it in neural network design, API frameworks, and real-time systems. Because the syntax is simple and consistent, long-term productivity exceeds that of many languages with higher barriers to entry.
For tech leaders, Python offers a strategic advantage. Organizations that prioritize Python proficiency increase the pace at which they can experiment, ship features, and analyze the impact of strategic initiatives. In fast-growth environments, this matters. From operational automation to full-scale AI pipelines, Python reduces complexity.
Pluralsight provides over 24 Python skill IQ assessments, including general and role-specific tests for developers, data scientists, and cybersecurity professionals. The recommended starting point is the “Python 3 Skill IQ” to benchmark foundational understanding. For deep learning, NLP, and automation workstreams, more advanced tracks are readily available.
C-suite alignment here matters. Even a basic in-house understanding of Python among cross-functional leadership can improve coordination between strategic goals and engineering execution. Python fluency should not be relegated solely to data or backend teams, it has strategic value across product, analytics, and cloud operations.
Agentic AI represents the upcoming wave in autonomous systems
The rise of agentic AI is redefining how we think about software automation, at a system level. These are AI entities that don’t just generate output but act on it as well. They can perform tasks, make decisions, and integrate dynamically with APIs, backends, and data pipelines. What this unlocks is end-to-end task execution without continuous human intervention. That’s a big shift in how AI is applied in real enterprise workflows.
In 2026, demand will grow for professionals who can design and operate these systems. The technical footprint includes orchestration layers like LangChain, integration pipelines, and domain-specific logic built with large language models. Developers need to go beyond prompt engineering. They need to design agents that interact with business systems, with traceability, resource allocation, and fallback logic.
The practical use cases range across industries. For example, agentic AI can be applied to dynamically process insurance claims, personalize client communications, or coordinate logistics tasks. These aren’t just theoretical capabilities, they’re active development areas in many AI-forward companies.
Pluralsight is already responding to the shift. Courses like “Agentic LLMs for Developers” and “Integrating Agentic AI for Developers” offer targeted guidance through real project workflows. Testing availability now includes a LangChain-based skill assessment as a baseline for agent development readiness.
For senior executives and innovation officers, workload automation is a strategic objective, whether for operational cost-savings or improving employee leverage. Having internal leadership aware of what agentic AI enables improves planning, speeds up deployment cycles, and aligns AI risk oversight.
This is a domain that will scale quickly. Organizations should invest now in building both internal literacy and capability to operate these emerging systems. What’s being built today will become standard enterprise tooling sooner than expected.
Continuous, individualized learning is essential for sustained career and organizational growth
Technology doesn’t pause. Neither should learning. The most effective professionals in 2026 will be those who continue to expand their skills, adapt to changing tools, and contribute beyond their original scope. This isn’t about chasing trends, it’s about aligning with a market that expects capability, creativity, and execution. The same holds true for companies. Organizations that prioritize evolution outperform ones that stick with what worked last year.
Personalized learning matters more now than ever. Not everyone on your team needs to be an AI engineer or network architect, but everyone needs to be progressing. The emphasis should be on relevance, not volume. Match the skill to the role and the trend to the business goal. That’s how you build high-leverage talent.
One of the most overlooked issues at the executive level is unused learning programs. Leaders invest in resources, platforms, and paid learning time, then see low engagement. That won’t improve unless leadership enforces strategic priority. Continuous education needs to be managed with the same urgency as technical debt or performance metrics. Otherwise, it becomes a checkbox.
You don’t need everyone to master the most popular frameworks. What you need is steady improvement at all levels. Pluralsight’s platform supports this with benchmarking tools and diagnostics like Skill IQ tests, adaptable across roles from entry-level support to product leadership. Whether someone’s advancing in DevOps or data security, there are targeted paths and simulation labs that show direct results.
If you’re leading at the C-level, the takeaway is simple: treat learning velocity as a performance metric. Encourage quarterly skills development at every level of your organization. Invest time, not just money. Skill depth, adaptability, and learning momentum are competitive advantages, especially when the tech environment introduces change faster than your organization chart updates.
Recap
Technology isn’t standing still, and neither are the skills that power it. Whether it’s the expansion of cloud infrastructure, the rise of autonomous AI systems, or the evolving demands of cybersecurity, the message is clear, static teams fall behind. The most valuable organizations in 2026 won’t just have the latest tools. They’ll have people who know how to use them, adapt to change, and build systems that move when the market does.
For decision-makers, the opportunity is straightforward. Upskilling isn’t a side project; it’s a core business function. Prioritizing technical literacy across departments, starting at the top, makes everything else run smoother, smarter, and faster. You don’t need everyone to become an engineer. But everyone should understand enough to make smarter decisions, reduce friction, and align with where the industry’s actually going.
Keep your teams sharp. Encourage continuous, role-relevant learning. Invest in tools that benchmark progress and identify gaps before they slow you down. The companies that do this best will lead the next wave, not follow it.
