Redesigning AI-first development teams
Development teams are expected to continue to be remade throughout 2026.18 Gartner predicts that, “80% of organizations will evolve large software engineering teams into smaller, AI-augmented teams by 2030”.19 Those that leverage agentic AI capabilities in an integrated way across the full software development life cycle (SDLC)—including coding, requirements development, deployment, monitoring, and testing—may be able to unlock more value. Deloitte expects that AI could potentially drive productivity gains of 30% to 35% across the SDLC.20 To maximize this value and help AI tools and agents improve outcomes, rather than introduce new risks, development teams should revamp their strategies.
Several AI-centric challenges should be addressed as part of this shift. Cultural resistance, trust issues and ambiguity about expectations could throw strategies off track. In addition, skill erosion is a possibility in the long-term, which could hinder innovation and adaptability.21
What does this mean for individual developers and teams? For mid- and senior-level developers, the demand for intangible skills related to customer experience, cross-functional engineering, systems thinking and cross-product management is expected to grow. With respect to the future of entry-level developers, opinion is far from consensus among those we interviewed.22 Some expect such roles to maintain their status quo, others anticipate a significant change of focus.
Teams can expect changes to structure and composition as organizations retool to optimize collaboration between humans and AI systems. Conventional team structures will likely shift with fewer entry-level developers and more mid-level and specialized professionals, with a broader supervisory span for managers. New roles may be added as well, like AI governance specialists, prompt engineers and context designers, and AI-augmented user experience designers. In addition, functional teams are beginning to add AI-native, domain-specialized engineers who can quickly build capabilities without help from IT.
To get the productivity gains from an AI-empowered SDLC, some tech companies interviewed are implementing more AI-first training programs and upskilling initiatives. A senior vice-president at an enterprise applications company highlights this shift, “We brought in 500 interns this year globally… It’s an AI-first internship class where we’re training them to focus on AI capabilities for the first time in our history.”23
Mentorship should also get more attention in 2026. Responses to a recent internal survey of coders showed concerns that AI tools are driving fewer mentoring and collaboration opportunities with AI tools taking the place of interaction with colleagues.24 AI might be causing challenges, but it can also provide talent-related solutions. In a recent Deloitte survey, 60% of respondents said that AI can help experienced workers share their knowledge and skills.25
Finally, conventional models of performance evaluation and incentives might not be enough for software companies going forward. Individual objectives and key results and team key performance indicators may need to shift to reinforce AI adoption and innovation. Addressing these shifts and challenges could help software companies get the AI integration and gains they seek. Those who create effective and sustainable AI-first development teams will likely be the ones who win in the long run.
Strategic questions to consider:
- What should our AI-enabled SDLC operating model ultimately look like? How will we leverage our own agentic capabilities for internal operations?
- How will we redesign our talent pipeline and team structures for both human and digital workers across entry, mid and senior levels? How will we restructure training and mentorship?
- How can we better measure and incentivize performance in an AI-augmented environment to drive outcomes?
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