If you want to lead in a fast-moving industry, you have to move faster. That’s what AI enables. Developers and executives know this, and the data proves it. In GitLab’s report, 83% of industry leaders say AI is essential to staying competitive.
But enthusiasm alone doesn’t close the gap between vision and execution. AI tools need access to vast amounts of data to function effectively, and that brings risk. Security, intellectual property, and privacy concerns remain top challenges, with 79% of executives worried about sensitive information being exposed. There’s no point in improving speed if you compromise security in the process.
The companies that win will be the ones that balance rapid adoption with security-first thinking. AI must be built into development lifecycles while safeguarding proprietary code and data integrity. That’s not optional. It’s the baseline for maintaining trust and competitive advantage in a world increasingly driven by intelligent automation.
AI boosts developer productivity but raises security risks
AI is changing how developers work. The numbers are clear, 51% of GitLab survey respondents say AI improves productivity. It automates repetitive tasks, accelerates code creation, and allows developers to focus on more complex problems. Companies that adopt AI effectively see faster development cycles, greater efficiency, and ultimately, a sharper competitive edge.
But speed comes with challenges. Security professionals are raising concerns, and for good reason. AI-generated code isn’t perfect. It can introduce vulnerabilities that developers might not catch immediately. Right now, developers spend only 7% of their time identifying and fixing security issues, while they allocate 11% to testing. That gap raises critical questions about risk exposure. If AI is generating more code but security isn’t scaling alongside it, vulnerabilities will pile up.
The solution isn’t to slow down AI adoption, it’s to integrate security into the AI-driven workflow. Companies need security teams involved early, making sure AI-driven development includes continuous monitoring and vulnerability checks. Businesses that get this right will maximize AI’s productivity benefits without opening the door to security threats. Those that ignore the risks will face bigger problems down the road.
Data privacy and intellectual property are top priorities in AI adoption
AI is powerful, but without the right safeguards, it creates risk. Businesses know this. According to GitLab’s report, 95% of senior technology executives prioritize data privacy and intellectual property protection when selecting AI tools. The reason is simple—AI systems process vast amounts of data, and if not properly managed, that data can be exposed, misused, or compromised.
Security isn’t the only issue. Intellectual property rights are another growing concern. AI-generated code exists in a legal gray area, and 48% of respondents are worried it may not receive the same copyright protections as human-written code. Additionally, 39% of developers fear that AI-generated code could introduce security vulnerabilities, further complicating adoption decisions. These concerns make it clear that AI solutions can’t just be efficient—they must also be trustworthy.
Companies that take AI security and intellectual property seriously will have a competitive edge. That means vetting AI vendors, ensuring compliance with legal frameworks, and implementing strict data governance policies. AI should accelerate development, not expose a business to unnecessary risk. The organizations that understand this—and take decisive action—will be the ones leading in the AI-driven future.
AI training exists, but it’s not enough
AI is only as effective as the people using it. Many organizations recognize this, with 75% of GitLab survey respondents stating that their companies offer AI training and resources. However, the same percentage also reports searching for independent learning materials, signaling that what’s currently available isn’t sufficient.
The demand for AI expertise is growing. A striking 81% of respondents say they need more training to effectively integrate AI into their workflows. Companies are aware of this gap, with 65% of those planning to implement AI in software development also intending to hire new talent to manage the transition. That’s a strong indication that internal training alone isn’t solving the problem.
Businesses that want to lead in AI must go beyond basic training. Structured, ongoing education programs are necessary to make sure teams use AI effectively and securely. Organizations investing in AI talent, whether through upskilling existing employees or recruiting AI specialists—will have a clear advantage. AI is a force multiplier, but only for those who know how to wield it.
AI needs to be integrated across the entire software development lifecycle
AI has the potential to transform the entire software development process. Yet many companies are still limiting AI use to isolated tasks rather than embedding it across workflows. To unlock AI’s full potential, it must be integrated at every stage, from planning and coding to security testing and deployment.
David DeSanto, Chief Product Officer at GitLab, highlights this shift, noting that while only 25% of developers’ time is spent on code generation, AI has the potential to enhance nearly 60% of their daily work. This means AI’s biggest impact is improving collaboration, streamlining testing, and helping security teams detect vulnerabilities earlier. The companies that leverage AI across the full development cycle will see greater efficiency, fewer errors, and stronger security.
To make this happen, AI adoption must be a company-wide initiative, not just an engineering decision. IT, security, and development teams need to work together to ensure AI-driven improvements benefit everyone involved in software creation and deployment. Businesses that take this integrated approach will move faster and will build more secure, reliable, and scalable software.
Key executive takeaways
- AI is essential for staying competitive, but security risks must be managed: AI is now a necessity in software development, with 83% of executives recognizing its competitive edge. However, 79% are concerned about security and intellectual property risks, making it crucial to balance innovation with strong data protection strategies.
- AI boosts productivity but introduces security challenges: While 51% of developers cite AI as a major productivity driver, security teams warn that AI-generated code could introduce vulnerabilities. Leaders must ensure security measures evolve alongside AI adoption to prevent increased risk exposure.
- Data privacy and intellectual property concerns are a major barrier: With 95% of technology executives prioritizing data security in AI adoption, unprotected AI-generated code and legal uncertainties around copyright pose real risks. Executives should enforce strict governance policies to safeguard proprietary data and compliance.
- Current AI training is inadequate, creating a skills gap: Although 75% of companies provide AI training, the same percentage of employees seek additional learning independently, and 81% report needing more expertise. Investing in structured AI education and hiring specialized talent will be critical for long-term success.
- AI must be fully integrated across the software development lifecycle: AI can improve up to 60% of daily developer tasks, including testing and security. Organizations that embed AI into every stage of development will achieve faster innovation, stronger security, and higher efficiency.
