IT leaders foresee generative AI spur job growth in IT rather than simply cutting costs
There’s a widespread misconception that AI, especially generative AI, exists solely to eliminate jobs and reduce headcount. That view is short-sighted. What’s actually happening, and what smart IT leaders are starting to understand, is that AI is shifting the nature of work. Boards might push to cut labor costs by 20%, and it’s true that generative AI can introduce serious efficiencies. But the technology doesn’t run itself. It needs engineers, architects, and operators to design, integrate, and steer these systems.
What’s interesting is this: we’re not looking at traditional roles. We’re entering a stage where organizations will hire for entirely new competencies. Skills in AI model tuning, prompt engineering, data ops, and human-AI interaction design are already rising in value. These aren’t areas where you can just outsource or automate your way toward impact. You need domain expertise and internal alignment. You need people who can turn AI’s raw capability into something that drives results.
This shift should matter to every C-suite executive. If you’re betting only on cost-cutting, you’re missing the long-term growth AI enables. Leaner headcounts may deliver a bottom-line hit in year one. But without reinvestment into talent that can work with these systems, you end up eroding your organization’s capability to evolve. You don’t want to save on payroll only to fall behind your competitors six months later.
Boards may be focused on savings, but most IT leaders are preparing to expand their teams. They recognize that AI’s adoption timeline and complexity will demand more expertise.
In simple terms: the future isn’t fewer jobs. It’s different jobs. And companies that understand this early are getting the edge.
Agentic AI is transforming developer roles
There’s been a lot of noise lately about AI replacing developers. That’s missing the point. The reality isn’t replacement, it’s evolution. Agentic AI, which refers to AI systems capable of autonomous or semi-autonomous action, is changing how developers work. Instead of spending hours solving repetitive bugs or writing boilerplate code, developers are using AI to handle those repetitive tasks, faster and more reliably. That doesn’t eliminate the developer, it elevates them.
What we’re seeing is a shift in what developer time is worth. The value no longer resides in pure output or lines of code. It comes from architectural thinking, system design, creative problem-solving, and the ability to orchestrate multiple AI agents effectively. Developers are becoming more like directors, overseeing workflows, curating AI prompts, and guiding outcomes that still need human intelligence to be relevant, accurate, and aligned with business goals.
This shift is already picking up momentum. Teams aren’t working with one AI tool, they’re managing a set of them. Multi-agent workflows are the next step for engineering teams that want real scale. That requires a learning curve and mindset shift. Developers who stick to old workflows won’t scale. Those who lean into this shift will be leading company-wide transformation far beyond the codebase.
Business leaders need to act on this fast. It’s not enough to “add AI” into a workflow. You need to create space for developers to operate strategically. That means training, access to AI tools, and aligning incentives so engineers can prioritize innovation over task repetition.
Most practitioners welcome the opportunity to focus on high-impact, creative work rather than low-value tasks. The feedback shows a clear pattern: agentic AI is unlocking higher-functioning engineering teams.
AI integration into enterprise platforms is yielding measurable productivity improvements
AI isn’t just transforming front-end functions or user experiences, it’s reengineering enterprise systems from the inside out. One of the clearest examples is in ERP (Enterprise Resource Planning). Traditionally, these platforms are bulky, slow to adapt, and dependent on human intervention for routine data entry, approvals, and process management. That friction is now being reduced, sometimes eliminated, by AI integrations, especially AI agents and copilots built into the system itself.
Organizations are deploying these AI tools to remove time-consuming tasks like invoice processing, report generation, or supply chain updates. These aren’t just marginal gains. Professionals using these systems are reporting faster execution, fewer errors, and better insights in real time. It lets teams redirect attention from operations to outcomes. This means more bandwidth for strategy, decision-making, and innovation, without adding more headcount.
However, automation isn’t the whole story. Some IT leaders see these AI systems not as siloed replacements, but as extensions of their teams. That’s important. Intelligent agents don’t erase the need for humans, they change the kind of work humans do. Enterprise workflows evolve from static sequences into adaptive frameworks that incorporate AI suggestions, carry out tasks independently within guardrails, and surface insights proactively.
Executives need to treat this like an operational shift, not a software upgrade. It takes more than deploying a few bots. True productivity gains come from redesigning internal processes around AI capabilities, training users to work alongside these tools, and ensuring alignment between technology investments and business goals.
Organizations adopting AI agents within ERP and similar platforms are already seeing measurable productivity improvements. These claims, are based on current implementations and observed results across the industry.
Key executive takeaways
- IT headcount is expected to rise with AI adoption: Most IT leaders project workforce expansion as generative AI demands more talent to manage integration, oversight, and system deployment. Leaders should plan for strategic hiring.
- Developer roles are shifting toward higher-value work: Agentic AI is automating routine dev tasks and enabling developers to focus on architecture, innovation, and system design. Companies should invest in upskilling teams to maximize creative and strategic output.
- AI-infused enterprise systems are driving real productivity gains: AI agents and copilots are improving ERP workflows by automating repetitive processes and enabling faster, more accurate decision-making. Executives should align operations around AI capabilities to unlock efficiency at scale.
