--- title: "AI Talent Development Takes More Than Buying New Tools" id: "315" type: "post" slug: "ai-talent-development-rd-teams" published_at: "2026-10-07T15:55:17+00:00" modified_at: "2026-10-07T15:55:17+00:00" url: "https://invetech.io/ai-talent-development-rd-teams/" markdown_url: "https://invetech.io/ai-talent-development-rd-teams.md" excerpt: "ManageEngine’s Vimal Raj argues that R&D skills, ongoing training and customer insights matter as much as AI tools for businesses seeking lasting value." taxonomy_category: - "Workplace Productivity" taxonomy_post_tag: - "AI" - "ai-skills" - "manageengine" - "productivity" - "research-and-development" - "workplace-ai" --- AI talent development is becoming as important as buying AI tools, according to a TechRadar Pro opinion piece by Vimal Raj, UK technical head at ManageEngine. His argument is that organizations need research and development (R&D) professionals who can improve technology and put it to work, not just a budget for adopting it. For businesses, the distinction matters because access to a tool does not automatically create the skills needed to use it well. Raj warns that companies focused on adoption alone could leave themselves short of the expertise required to turn AI investment into useful products and processes. ## Why AI talent development needs a longer view Raj describes a common approach to hiring: a company recruits when a position opens or workloads rise. That may address an immediate problem, but he argues it does little to build lasting R&D knowledge. In his view, a steady supply of skilled people is more valuable than a series of last-minute searches. He suggests building relationships with universities and creating internships and graduate programs to reach people early in their careers. Mentoring and ongoing learning can then help both new recruits and existing staff develop as technology changes. The goal is not simply to fill a role today, but to give teams room to grow with the work they will face later. This approach has a practical productivity angle. A team with relevant skills can spend less effort trying to bridge gaps when a new AI project arrives and more effort developing and implementing it. That is a potential benefit of Raj’s proposal, rather than a measured result reported in the article. ## Continuous learning and employee retention Hiring is only one part of the picture. Raj argues that pay alone may not persuade skilled people to stay if their workplace offers little scope to learn, experiment or collaborate. He presents a supportive culture as a way to attract R&D staff and keep their knowledge within the organization. That culture needs concrete opportunities, not just encouragement. The article points to technical training and certifications in areas such as cloud-native development, AI and DevSecOps, alongside mentoring. It also recommends giving people opportunities to work with AI-assisted development, automation and advanced analytics, then share what they learn with colleagues. For a manager, the takeaway is to consider whether employees can practice new skills in real work and pass useful lessons across teams. Raj emphasizes that research outcomes depend on people with different experience working together. Tools can support that work, but they do not replace the collaboration needed to make sense of problems and develop solutions. ## Customer data as a guide for AI research Raj also points to information businesses already collect from customers. He argues that much of it goes unused, even though AI-driven analytics could help R&D teams find patterns, repeated issues and needs that have not been met. Those findings could give teams a clearer basis for deciding what to develop. The proposed division of work is straightforward: AI analyzes large amounts of information, while people judge what the findings mean and decide how to respond. Raj also sees automation as a way to reduce repetitive work, leaving specialists more time for difficult problems. In that model, productivity comes from combining analysis with human expertise, rather than treating the analysis as the finished product. There is an important limit to the claim. The TechRadar Pro piece is an opinion article, not a report of a particular company’s results. It outlines how customer information and better-trained engineers might support development, but it does not provide figures showing how much time or money a business would save. ## What businesses should watch next The key question raised by the piece is whether investment in people keeps pace with investment in AI systems. Raj’s suggested areas to watch are talent pipelines, training, mentoring and a workplace that lets teams test ideas and exchange knowledge. Together, these would give an organization a stronger foundation for using new technologies as they emerge. Businesses can also examine whether customer insights are reaching the R&D teams able to act on them. If teams have both useful evidence and the skills to interpret it, AI projects may be better connected to real user needs. The broader message of AI talent development is that buying a tool is a starting point; building the people who can apply it is a longer-term task. Source: [TechRadar Pro](https://www.techradar.com/pro/the-next-phase-of-the-ai-era-why-building-r-and-d-talent-is-now-a-make-or-break-investment)