A practical view of the modern PM skillset across digital and physical products. After a recent conversation with a PM at a large tech company, it was obvious how fast expectations are shifting...
A practical view of the skillset of a modern PM for digital and physical platforms
When I recently spoke with a PM from a large bank, he told me something that stopped me: "I only use AI for writing emails." Meanwhile his competitors are already achieving 40% higher productivity thanks to AI integrated across the whole product process. In five years this gap will be existential.
The data from McKinsey, Gartner and BCG are unambiguous. 72% of data leaders worry that their organisation will lose its competitive advantage if it doesn't adopt AI. Yet most product managers still work with methods from 2020.
The problem: the traditional PM approach is no longer enough
The gap between AI-first and AI-laggards is deepening dramatically. McKinsey data show that the AI maturity gap between leaders and laggards grew by 60% over three years. Digital and AI leaders today achieve 2-6× higher total shareholder return than their competitors.
The risks of non-adoption are concrete and measurable. Product managers without AI competencies face 40% lower productivity, a 5% slower time-to-market and a growing inability to compete with teams that integrate AI into their daily work.
Companies that don't invest in the AI competencies of their PM teams fall into a stagnation trap. While their competitors automate routine tasks and use AI for strategic decisions, they stay trapped in manual processes that are ever more expensive and slower.
The solution: five key AI competencies for a modern PM
According to research by McKinsey, Gartner and Product School, every product manager needs to master five essential areas. It's not about becoming a data scientist, but about understanding AI well enough to use it strategically.
AI & Data Literacy means understanding how AI models work, what data they need and where they can fail. It's not about technical details, but about the ability to identify which problems suit an AI solution and how to ensure the quality of outputs.
Analytical Thinking & AI-Powered Decisions represents the shift from intuitive decision-making to a data-driven approach. A modern PM must be able to interpret AI analyses, design experiments for AI-driven features and translate AI outputs into product strategy.
AI Tool Fluency & Integration includes practical knowledge of generative AI tools from ChatGPT to specialised PM platforms. It's about the ability to integrate these tools into the daily workflow and use them to automate routine tasks.
Strategic AI Product Development is about designing products that use AI as a competitive advantage. A PM must be able to balance accuracy, latency, costs and user experience when working with data science and ML teams.
Responsible AI Awareness covers ethical aspects, bias management and compliance with regulations. At a time when AI decisions affect millions of users, responsibility for transparency and fairness is key.
Results: measurable impact on the business
The numbers speak clearly. A McKinsey study showed a 40% increase in PM productivity when using generative AI tools and a 5% speed-up in time-to-market across a six-month product cycle. All study participants reported a 100% improvement in customer experience.
At the macroeconomic level, AI can contribute 0.5-0.9 percentage points of annual productivity growth by 2030. Companies that commit to AI transformation can achieve a 15-20% improvement in digital maturity within 2-3 years and a 10-20% increase in EBIT in targeted domains.
Case Studies: concrete numbers from practice
The best evidence comes from companies that took AI integration seriously. Their results show what is possible when AI becomes part of the DNA of a product team.
Klarna: a warning against hasty AI implementation
Klarna became a textbook example of how not to bring AI into product processes. In 2022 it laid off 700 employees and replaced them with AI systems. CEO Sebastian Siemiatkowski claimed that AI could handle everything from customer support to executive decision-making.
Reality was different. By 2024 Klarna faced a rise in customer complaints, a drop in satisfaction and user frustration with generic AI answers. The AI systems failed on more complex problems requiring empathy and human understanding. The CEO later admitted: "We went too far" and the company is now actively hiring human employees back.
Toyota: thoughtful AI integration
Toyota represents the opposite approach: thoughtful AI integration. It uses AI to optimise production and saved 10,000 hours a year thanks to automation. Generative AI replaces physical manuals, which brings material savings across millions of vehicles. A hybrid cloud approach to AI infrastructure also reduces investment in facilities.
General Mills: AI as a tool, not a replacement
General Mills implemented AI models to optimise its supply chain and achieved $20+ million in savings since fiscal year 2024. Real-time performance data in production bring a planned $50 million reduction in waste and a 20 to 30% cut in production waste, which represents almost $20 million of annual savings. The key is that artificial intelligence is used as a tool for improving human decisions, not as their replacement.
The future: a framework for the AI-enabled PM
The transformation of product management is not just about tools, but about a fundamental change of mindset. By 2030 all product managers will be de facto AI product managers. The difference will be in who starts investing in these competencies now.
The framework of the future PM combines traditional business acumen with AI fluency, customer insights with agile leadership. It's not about replacing existing skills, but about extending them with an AI dimension.
A challenge for leadership
The time to act is now. Every month of delay means a bigger gap to be caught up. Whoever doesn't start investing in the AI skills of their PM teams today will have an existential problem in five years.
The concrete steps are clear: invest in AI upskilling of PM teams, redefine hiring criteria for AI-fluent candidates, equip teams with the right AI tools and create a culture of AI-driven decision-making.
The question is not whether AI will become the standard in product management. The question is whether you will be among those who lead this change, or among those who catch up with it.
The article was first published on LinkedIn. Original article on LinkedIn
