In a world awash in headlines warning of AI-driven layoffs, a more hopeful narrative is quietly unfolding. While companies are automating repetitive tasks, many are actually struggling to hire fast enough for AI-related roles. AI isn’t simply eliminating jobs—it’s transforming them and creating entirely new ones.
According to LinkedIn’s 2025 Jobs on the Rise report, “Artificial Intelligence Engineer” ranked #1 among fastest-growing U.S. roles.
And Autodesk’s analysis of nearly 3 million job listings reveals that design-related skills have now overtaken coding and technical skills in AI-job postings — signalling a dramatic shift in what companies are looking for.
As Karin Kimbrough, Chief Economist at LinkedIn, observed, our labour market is entering a “two-speed economy” — one where organisations that anticipate and adapt will unlock competitive advantage while others lag behind.
Change creates opportunity—and the rise of agentic AI shows just how much potential lies ahead.
What is agentic AI — and why it matters now
Agentic AI refers to systems that can reason, plan, and act autonomously toward defined goals. Unlike traditional chatbots that wait for prompts, agentic systems can initiate actions, make decisions, and execute workflows. These systems represent the next leap in enterprise automation — a world where AI doesn’t just assist humans but collaborates with them.
And make no mistake: the race is on. Enterprises that get agentic AI right today will outpace competitors tomorrow — accelerating innovation cycles, cutting costs, and unlocking entirely new sources of value. Those that don’t will find themselves playing permanent catch-up as early adopters redefine entire industries.
Yet, as McKinsey notes, most enterprises aren’t yet ready to seize this opportunity. While nearly 80% of organizations are experimenting with generative AI, few are seeing a tangible bottom-line impact. Why? Because the hardest part of scaling AI isn’t building the model — it’s connecting AI to structured enterprise data in a reliable, governed way.
This is where most initiatives stall. Structured enterprise data is precise, complex, and deeply contextual — qualities that large language models (LLMs) struggle to handle. LLMs are probabilistic by design: “close enough” answers often suffice when drafting copy or summarizing a document. But in the enterprise, close enough doesn’t cut it. When AI is forecasting revenue, reconciling compliance data, or approving a credit risk, accuracy is non-negotiable. Human livelihoods and regulatory standing depend on it.
That’s why forward-thinking professionals now see a powerful new frontier — the intersection of AI and enterprise data. The next generation of data and AI leaders won’t just understand algorithms; they’ll master the governed, contextual data that fuels them.
The hiring surge: What the market is telling us
Across industries, job postings tell the story of agentic AI moving from theory to implementation.
At Amazon, engineers are being recruited to “design and build agents to guide advertisers in conversational and non-conversational experiences.” (Amazon Jobs)
BMO Financial Group is hiring cross-functional engineers “to design and build agentic systems that enable scalable delivery of core AI solutions across the enterprise.” (BMO Careers)
And Hinge Health is looking for developers to “build agentic workflows to enhance the end-to-end experience for members, providers, and operations teams.” (Hinge Health Jobs)
These aren’t isolated cases — they represent a global hiring surge for roles that blend AI, data, and strategy. Companies are searching for people who can not only build agents but also ensure those agents work safely, ethically, and effectively within enterprise systems.
For job seekers, that means opportunities are expanding in every direction — from engineering and design to governance and change management.
From hype to action: Why enterprises are moving fast
After years of AI pilots and proof-of-concepts, organizations are now under pressure to show results. The leap from experimentation to execution requires a shift in focus — from building smarter models to building smarter systems.
Agentic AI sits at the center of that evolution. It promises productivity gains, efficiency breakthroughs, and even new forms of collaboration between humans and machines. But those gains depend on a simple truth: AI is only as trusted as the data it acts on.
For job-seekers, this creates two clear paths to opportunity. The first lies in technical execution — the engineers, architects, and designers who ensure AI drives real business outcomes. Without their expertise and oversight, AI simply delivers wrong answers faster, amplifying poor decisions instead of improving them. Both paths are essential to building the intelligent enterprise.
The hidden challenge: Trusted data and the rise of data products
For all its promise, agentic AI depends on one critical ingredient: trustworthy data. Without it, AI systems can make flawed decisions, amplify bias, or fail to comply with regulations.
To solve this, many organizations are adopting data products — curated, reusable packages of trusted data and metadata designed for AI consumption. Data products give teams the building blocks to scale AI safely and efficiently, ensuring that every model or agent draws on consistent, well-governed data.
This approach is changing the shape of the data workforce. One of the most significant developments of the AI era is the rise of the Data Product Manager — a role that blends product thinking with data expertise. Data Product Managers design how information is packaged, distributed, and governed so that AI systems can act on it and people can trust the results. According to CareerFoundry, the professional role of a Data Product Manager “is growing in importance as tech-based businesses seek to harness the power of their data for better decision-making.”
As companies shift from AI experimentation to operationalizing intelligent systems, organizations increasingly recognise that it’s not simply about modelling—it’s about the products those models depend on. And that’s where Data Product Managers step in.
Alation recently announced an initiative to train 10,000 data product managers, empowering professionals to build the data foundations that fuel trusted AI. For job seekers, learning data product management offers a way to future-proof their careers — bridging the technical and strategic skills enterprises now urgently need.
Explore the data product manager course.
The skills that will matter most in 2026
As AI reshapes every industry, technical expertise alone won’t guarantee success. The professionals who stand out will combine AI fluency with strategic, creative, and ethical thinking. Here are the skills that will define high-demand roles in 2026:
AI literacy and prompt engineering: Understanding how large language models and agentic systems work — and how to guide them effectively.
Data product thinking: Knowing how to design, package, and manage reusable data assets that power trusted AI.
Human-AI collaboration: Developing workflows where humans and agents complement each other, improving outcomes through oversight and iteration.
Ethical and responsible AI: Applying judgment to ensure AI is used transparently, fairly, and in compliance with governance standards.
Cross-functional communication: Translating between data, business, and technology teams to connect AI’s potential with measurable impact.
Continuous learning mindset: Staying adaptable in a field that evolves monthly — not yearly.
These are the capabilities that turn change into opportunity — and position professionals not just to work with AI, but to lead because of it.
Looking ahead: The agentic enterprise era
The next few years will bring a wave of new roles — from AI Systems Designer to Agent Governance Lead — across nearly every industry. The individuals who thrive won’t be defined by a single technical skill, but by their ability to connect dots: between data and AI, design and governance, human and machine.
As companies rush to operationalize AI, those who can translate ambition into action — responsibly, intelligently, and creatively — will lead the way.
Closing thoughts: From signal to opportunity
Yes, AI is reshaping the job market. But that doesn’t mean it’s shrinking. It’s evolving — and with evolution comes opportunity. The organizations hiring for AI roles today are defining how intelligence will work tomorrow.
For job seekers, now is the time to lean in. Learn how AI systems operate. Understand how data builds trust. Explore emerging roles like data product management. The future belongs to those who see change not as a threat, but as a chance to grow.
In this new era of agentic AI, the most valuable skill may not be coding — it’s curiosity.
Tagged with
Loading...