“Nearly 70 percent of top economic performers, versus just half of their peers, use their own software to differentiate themselves from their competitors. One-third of those top performers directly monetize software. Generative AI (gen AI) offers a tantalizing opportunity to increase this value opportunity by helping software talent create better code faster.”
– The Gen AI skills Revolution: Rethinking your Talent Strategy, McKinsey
According to McKinsey, gen AI has improved product manager (PM) productivity by 40 percent, while halving the time it takes to document and code. At IBM Software, for example, developers using gen AI saw 30 to 40 percent jumps in productivity.
Gen AI can use self-created insights and ideas for new features to create proofs of concept and prototypes, as well as to reduce the cost of testing and unlock higher verification confidence (for example, multiple hypotheses and A/B testing). Over time, gen AI will be able to generate insights from automatically created tests, system logs, user feedback, and performance data.
These developments are expected to significantly reduce Product Development Lifecycle (PDLC) times from months to weeks or even days, improve code quality, and reduce technical debt.
Scaling gen AI capabilities requires companies to “rewire” how they work, with a critical focus on developing the necessary talent for these capabilities. Businesses hoping to operate like software companies will also need to pay special attention to two key roles: the Product Managers, Designers, and Engineers:
Product Managers
Gen AI technology use – be proficient with low-code and no-code tools and iterative prompts to work with models to refine outputs and understanding and developing “agentic” frameworks—large language models (LLMs) that work together to complete a task.
Adoption and trust – identify implicit and explicit barriers to trust (such as not trusting the answers that gen AI solutions provide) and to address them.
Designers
Use AI insights on system logs, customer feedback, and performance data to inform design improvements.
Engineers
Code Review – “train up” AI tools to do complex code reviews.
AI Agent Integration – improve problem-solving speed and solution quality such as using AI systems to analyze the performance of gen-AI-created content by identifying patterns in user engagement, which are then fed back to the model.
As gen AI’s capabilities become more stable and proven, companies need to zero in on skills and adapt their talent management approach. Being flexible enough to learn and adjust, companies can turn their talent challenges into competitive advantages.
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The gen AI skills revolution: Rethinking your talent strategy. McKinsey Digital. August 29, 2024. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-gen-ai-skills-revolution-rethinking-your-talent-strategy