Mr Viral
Over the past few years, AI has been the focus of headlines and conversations in the tech industry. It’s not just hype—research has found that organizations have already invested in AI experimentation, with 78% using AI in their software development processes or planning to do so within two years. These organizations are beginning to see a return on their investment through increased productivity, better developer experience, and faster cycle times.
Additionally, the UK’s AI Opportunities Action Plan is crucial to harnessing AI’s transformative potential nationwide. Companies see firsthand how generative AI can revolutionize software development by empowering developers to focus on strategic work rather than routine tasks. However, success requires a balanced approach that addresses the opportunities and risks.
While many organizations have integrated AI tools, true transformation requires adapting organizational cultures and protocols. Tech leaders face a unique challenge: navigating this new era of balancing the risks of AI while upskilling their developers and empowering their teams to begin higher-value projects.
Here are three key areas that tech leaders should prioritize this year.
1. Leaders must rethink risk management with AIOne concern when implementing an AI tool is the risk of plagiarism or using copyrighted training data. In 2025, we could see more lawsuits against LLM providers who cannot prove that their models were not trained on copyrighted data or who fail to adequately protect intellectual property.
Companies must carefully evaluate providers based on how transparent they are about the data used to train models and their guarantees for data protection. Organizations may face increased security risks, fines, customer attrition, and reputational damage unless they factor in how AI tools store and protect proprietary corporate, customer, and partner data.
These risks will motivate AI solution providers to proactively incorporate data protection principles into models from the start, ensuring ongoing compliance and mitigating the potential for future legal challenges.
2. Developers will rely on AI for code testing to shift their focus to higher-value projectsAutomation and AI are particularly well-suited for things like code testing. AI will provide end-to-end testing and gain insights into how well a solution meets the business needs. Developers already anticipate AI impacting their everyday workflows, with 63% of developers reporting that AI will significantly alter their role over the next five years.
Since most developers spend only 20% of their time writing code, there are many opportunities to apply AI to the other 80% of their time. AI features like merge request summaries, vulnerability explanations, and issue summaries allow developers to spend more time on challenging tasks and writing code.
3. AI will create upskilling opportunitiesGenerative AI empowers developers to focus on higher-value, mission-critical tasks that only humans can effectively execute. Rather than spending time on repetitive tasks, developers can now focus on innovative work that allows them to discover and acquire new skills that will help expand their technical expertise and offer opportunities for career advancement.
During this transformational time, it is more critical than ever for tech leadership to work closely with their development teams. Leaders should regularly check in with their teams to understand how these new tools support their work, any concerns, and improvement opportunities.
This year will mark a turning point where organizations focus on sustainable, measurable returns, rethinking how to measure ROI in the age of AI. Organizations must reshape their internal structures and processes to maximize AI’s potential fully, including developing frameworks for risk management and creating upskilling pathways for developers to focus on more strategic projects. Organizations that make these internal shifts will see significant returns on their AI investments and, ultimately, thrive.
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