What if the next big helper in your code editor isn’t a human, but a smart AI that can draft, debug, and polish software in minutes? A new pilot study surveying IT workers in the software development arena suggests that Generative AI (GenAI) tools are already widely used and changing how people work. The findings are preliminary, but they offer a glimpse into a future where AI support could boost productivity—while also stirring new concerns about job security and governance.

What GenAI is and why it matters for software development

Generative AI refers to AI systems trained to generate content—like code, text, or even design ideas—based on data they’ve been given. In software development, GenAI can assist with coding, debugging, writing documentation, and other tasks that typically consume time and expertise. This study focuses on the IT sector and specifically on how GenAI adoption interacts with personal productivity, organizational efficiency, and job security.

Key takeaways to keep in mind:

GenAI tools are notably adaptable across core business functions, including IT and software development.In practice, many developers already rely on GenAI to support coding and related activities.Adoption isn’t just about flashy capabilities; it involves how well teams integrate these tools into their workflows, governance, and culture.The study at a glance: what researchers didApproach: Mixed-method, combining expert interviews and a survey.Focus: The IT sector, with an emphasis on software development and the software development life cycle (SDLC).Status: Preliminary results from an ongoing survey (early findings, not a final conclusion).Key metrics explored: personal productivity, organizational efficiency, rate of GenAI adoption, job security concerns, and adoption barriers.

This means we’re looking at early signals about how GenAI is shaping day-to-day work, not a final verdict on its long-term impact.

What the early findings show

These findings come from participants in IT roles who use GenAI tools, with a focus on the most common experiences and perceptions.

1) Widespread use and notable productivity gainsA striking 97% of IT workers surveyed reported using GenAI tools, with ChatGPT being the most common option.Participants describe significant personal productivity gains. In other words, many developers feel they can accomplish more in less time when GenAI assists with tasks such as coding, debugging, or drafting documentation.2) Organizational efficiency rises with GenAI adoptionWhen organizations adopt GenAI more broadly, workers perceive improvements in efficiency at the team or department level.There is a positive relationship between organizational adoption and perceived efficiency gains, suggesting that the way a company implements and supports GenAI matters for outcomes.3) Adoption and job security concerns go hand in handA key and important finding: higher levels of organizational GenAI adoption are associated with increased concerns about job security among employees.In simple terms: as more AI tools are used across the organization, some workers worry more about their future roles. This highlights the need for thoughtful change management and clear communication about how roles may evolve.4) The main barriers to adoptionOutput accuracy issues: 64.2% of respondents flagged that AI-generated outputs can be inaccurate or require careful validation.Regulatory compliance: 58.2% pointed to challenges around meeting regulatory or policy requirements when using GenAI.Ethical concerns: 52.2% raised worries about ethics, such as data privacy, bias, and the responsible use of AI.

These barriers aren’t just “tech problems” — they shape whether and how teams can safely and effectively use GenAI in real work.

How GenAI touches the Software Development Life Cycle (SDLC)

GenAI isn’t just a buzzword; it’s being observed across multiple stages of software development. While the study is in early stages, the direction is clear:

Coding and code generation: GenAI can draft code and offer code suggestions, potentially speeding up development.Debugging and testing: AI-assisted debugging could help identify issues more quickly and propose fixes.Documentation and communication: GenAI can produce or summarize documentation, improving clarity and velocity.Integration and governance: Real-world adoption will require processes to validate outputs, ensure compliance, and address ethical considerations.

The central insight: GenAI’s value in software development grows when it’s integrated with people, processes, and governance—not just when it’s used in isolation.

What this means for developers and the broader IT ecosystemFor developers: GenAI can be a powerful teammate that helps you be more productive, but it’s not a magic wand. You’ll still need expertise to guide, review, and refine AI outputs.For managers and executives: Early gains look promising, but scaling adoption responsibly requires governance, training, and clear communication about how work will evolve.For the job market: As adoption grows, there will be adjustments in roles and skill needs. Upskilling and transparent career planning will be essential to minimize insecurity and maximize opportunity.Conclusion: A cautious but hopeful glimpse into AI-assisted software work

These preliminary findings from an ongoing study in the IT sector point to a compelling pattern: GenAI is already widely used and associated with personal productivity gains, and when embraced at the organizational level, it’s linked to perceived efficiency improvements. However, as adoption expands, concerns about job security rise, underscoring the need for thoughtful governance, ethical considerations, and robust risk management.

In short, GenAI is shaping software development in real time—but its success depends on how well teams integrate it into workflows, address accuracy and compliance challenges, and support people through the changes.

Practical takeaways you can apply todayStart with clear governance: define how GenAI will be used, when human review is required, and how outputs are validated.Build a plan for upskilling: offer training that helps developers leverage GenAI effectively while maintaining high-quality standards.Establish ethical and compliance guidelines: address data handling, bias, and accountability from the outset.Communicate openly about career impacts: involve staff in planning how roles may evolve and how to grow with AI capabilities.Pilot, measure, and iterate: run small-scale pilots, track productivity and efficiency metrics, and adjust practices accordingly.

If you’re curious about exploring GenAI in your own software projects, start with a focused pilot in a single SDLC stage (like coding or debugging), pair it with strong validation steps, and build governance and training around your findings. The future of software development with GenAI is unfolding—showing promise, but requiring careful, people-centered implementation.

The post Early Findings Show 97% of IT Workers Actively Use GenAI Tools appeared first on Jacob Robinson.