The dawn of the Product Engineer
TL; DR: The rise of AI is shaping traditional PM roles into a new archetype — the Product Engineer; combining deep customer empathy…
TL; DR: The rise of AI is shaping traditional PM roles into a new archetype — the Product Engineer; combining deep human empathy, technical fluency, strategic and systems thinking. In this article, I explore what is a Product Engineer, skills and workflows, and today’s PM competencies that may not serve as differentiators or fade away in future.
Supriya and Remy stepped out of their workshop with Future Robotics, their potentially largest customer yet. Supriya felt a surge of excitement — the customer’s ambitious vision for a next-generation manufacturing plant was a significant opportunity for their Robotics product line. Remy immediately synthesized the user interviews, creating a persona journey and mapping key jobs-to-be-done.
At a nearby café, Supriya shared Remy’s design files in a call with Ethan, their resident robotics domain expert, who performed a market analysis and viability study, providing early cost projections and market differentiation insights. Shortly after, Jenn, responsible for technical design, assessed feasibility, sketched initial concepts with Remy, and generated a working prototype. Brainstorming collaboratively, Supriya actively engaged with Ethan, Remy, and Jenn, editing demo code, and shaping the proposal. She confidently presented the compelling product plan to Future Robotics’ head of product, complete with a live working prototype!
Walking out elated, Supriya glanced at her multi-modal device and sent a quick audio note: “बधाई हो टीम, बहुत बढ़िया काम किया!! अब, चलो शिप करने के लिए तैयार हो जाएँ!” (“Congratulations Team, job well done!! Now, let’s get ready to ship!”)
Seems like a hyper-velocity multi-discipline engineering team (Credit: Mike Lanzetta) at work. The only catch? Supriya is the only human — Remy, Ethan, and Jenn are all AI-powered agents seamlessly integrated into her workflow. Supriya is not a product manager or customer engineer; she is a Product Engineer.
Over the years, I’ve written extensively about the “T” in TPM, the evolving role of program managers in AI projects, and what it means to be a product manager in the age of Generative AI. Supriya’s scenario might have felt futuristic just months ago — but it’s rapidly becoming our new reality. By directly shaping product decisions with AI agents, Supriya rapidly transformed customer insights into early product demos, shortening traditional cycles significantly. She acted like a Product Engineer.
This shift raises essential questions for product leaders: What skills should PMs prioritize to effectively navigate and thrive in this rapidly evolving AI landscape? And equally important, what should we confidently delegate for AI to handle? Let’s explore some of those here:
Human Empathy: Beyond Customer Needs
While Customer Empathy focuses on understanding and addressing the specific needs, preferences, and pain points of users to enhance product satisfaction and business outcomes, Human Empathy encompasses a broader perspective. It involves considering the wider societal, ethical, and long-term implications of AI technologies on individuals and communities.
For instance, deploying AI systems in sectors like finance or manufacturing without fully understanding their potential societal impact can lead to unintended consequences. A financial AI system designed to optimize trading strategies might inadvertently contribute to market volatility if it exploits loopholes or behaves unpredictably under certain conditions. Similarly, in manufacturing, AI-driven automation aimed at increasing efficiency could result in significant job displacement if not implemented thoughtfully, affecting the livelihoods of workers and the economic stability of communities.
Applying AI without first deeply understanding the customer’s problem and human impact is like prescribing medicine without diagnosing the patient — it’s ineffective at best, harmful at worst.
This may sound philosophical, but language models (LLMs) have always been vulnerable to alignment issues (aka Hallucination), and now we have evidence of issues like alignment faking, where models might superficially appear aligned with human values but behave harmfully when exploited.
A key Product Engineer skill is to break through the hype of AI and ground our innovations in customer and human empathy. This means considering not just the immediate business outcomes but also the broader societal impacts of our technologies.
The Product Engineer workflow
Customer Perspective: Product Engineers must prioritize being customer-facing. Partner with UX Designers and build empathy and design-thinking skills by regularly conducting customer interviews using collaborative tools Microsoft Teams and Figma, leveraging built-in transcription and natural language features. AI copilots integrated within these platforms help summarize conversations, feeding directly into well-defined product requirements.
Human Perspective: Actively evaluate how the system could affect broader communities — not just users. Ask: could this harm vulnerable groups? Could this shift market behavior in unpredictable ways? For guidance, refer to tools and frameworks like Microsoft Responsible AI, and UNESCO’s Recommendation on the Ethics of Artificial Intelligence.
Human-in-the-Loop Execution: Involve designers, research, engineers, and legal/compliance teams early. For hyper velocity engineering teams, the Product Engineer becomes the connective tissue — framing the problem, capturing real user goals, and aligning those with responsible AI guardrails.
Product Engineers start and end with the user needs, deeply understanding their lives, workflows, and challenges and AI’s impact on society.
Technical Diplomacy: A Must-Have Skill
There has always been an ongoing debate: How technical should a Product Manager be? I now have a more formed opinion — you must have technical diplomacy to thrive in this new AI-driven world, AND — Yes, you should write code.
Technical diplomacy isn’t about becoming the best coder on your team; rather, it’s about having a practical experience of AI systems and tools and their implications, how they integrate into broader systems, and how they impact user experiences.
There is a reason I now say you should write code — the barrier to entry is rapidly lowering. Tools like GitHub Copilot, Cursor, and low-code platforms such as v0, lovable have made coding accessible even to those with minimal prior experience. These AI tools offer ready-to-use natural language user experiences, code snippets and agents, freeing Product Engineers from syntax details and enabling natural problem-solving. And keep in mind — these tools are at their worst today; they’ll only get better.
Product Engineer Workflow:
Product Engineers leverage technical fluency to prototype, test, and iterate rapidly. They actively code quick proofs-of-concept (POCs), assess technical feasibility, and clearly communicate product ideas to engineering teams
Rapid prototyping and validation: Using AI-assisted coding tools to quickly build functional prototypes, enabling early validation and demo’s based on customer feedback.
Evaluating and applying AI models: Gain practical knowledge of key AI architectures—transformers, retrieval-augmented generation (RAG), and fine-tuning. Take ownership of model evaluations by actively participating in assessment processes. Tools like Azure AI Foundry provide built-in metrics to assess response quality, safety, and security.
Responsible AI Champions: As mentioned earlier, ensure alignment with Responsible AI practices, embedding fairness, transparency, and ethical considerations directly into product decisions. Microsoft’s Responsible AI principles offer a comprehensive framework for this
Systems Thinking: Seeing the Big Picture
AI systems are becoming increasingly complex. With the rise of model ensemble pipelines, multiple tooling integrations, and agent orchestration frameworks, managing aspects like security, observability, traceability, dependencies, and performance is more challenging than ever. Adding to this complexity are evolving regulatory and sovereignty requirements, such as the EU AI Act, GDPR, and DORA.
Most organizations aren’t AI-ready; aligning their current capabilities and system maturity with new AI workflows is a critical role for Product Engineers
Product Engineer workflow
Assess Readiness: Evaluate existing infrastructures to determine AI integration feasibility, considering factors like data quality, system scalability, and compliance requirements. For insights on establishing effective AI governance structures, refer to The AI Governance Gambit.
Implement AI Security Measures: Collaborate with security teams to determine how to identify vulnerabilities in AI systems. Tools like Microsoft’s PyRIT can facilitate this process, enabling proactive risk identification in generative AI systems.
Enhance AI System Interpretability: Work closely with engineering teams to improve the interpretability, explainability, and testability of AI agents and systems. For example, choosing between open-source models and proprietary solutions like OpenAI may depend on industry-specific requirements, user needs, or government policies.
Strategic Mindset and Thought Leadership
The AI goalpost moves every day. First, it was LLMs; then, agents; next, it will be Physical AI. As a Product Engineer, staying ahead of these shifts is critical — not just to anticipate trends, but to thoughtfully guide your teams and products through continuous waves of change.
Product Engineer Workflow
Learning is part of the job: Allocate regular time each week for learning, coding, reading research, and understanding industry trends. I know it’s easier said than done but I cannot emphasis this more, things are changing rapidly, and you cannot be an expert, but you can be aware. Here are some things to try: identify tasks you can delegate, things you can automate using AI (that could be a project by itself), remove things that are only busy work. I set aside 1–2 hours daily for learning — be it reading blogs, watching YouTube tutorials, coding, listening to customer feedback, or exploring research papers. It’s not about mastering everything but staying tuned to the evolving AI landscape and shape my opinion.
Integrate AI Tools into Daily Workflows: AI assistants like ChatGPT and Copilot have become integral to my daily routine. I use them to brainstorm ideas, validate assumptions, refine communications, and accelerate tasks both at work and in my personal life. For example, I created a writing assistant agent in ChatGPT called Alfred (more than a valet ;)) who worked with me to co-author this article. Alfred knows my style of writing, helps me in research and we debate on viewpoints all the time!
Develop Core Soft Skills: Strengthen essential skills such as communication, negotiation, and critical thinking to enhance collaboration and leadership. It’s easy to think and AI will do this for you, but You are always in the driving seat here, and if you don't have clear thinking on the outcomes, AI will only follow your instructions. Consider enrolling in courses like Negotiation Skills and Effective Communication on Coursera or Critical Thinking for More Effective Communication on LinkedIn Learning to build these competencies.
Now that we’ve explored the essential skills for a Product Engineer, let’s discuss that with AI increasingly handling tasks that previously differentiated PM skills, which competencies alone can no longer serve as a competitive advantage.
What’s No Longer a differentiator?
Deep Industry Expertise as a Standalone Advantage: Industry expertise remains valuable, but relying solely on it is insufficient. AI democratizes domain knowledge, legacy biases can limit innovation, and retirements of experienced professionals create critical knowledge gaps. Complementing traditional domain expertise with adaptable, AI-driven insights is now essential.
Deep industry knowledge when combined with robust AI skills becomes exceptionally powerful — and often unbeatable — combination for Product Engineers.
Sole Focus on Product Metrics: Relying exclusively on traditional product metrics ownership without actively engaging in customer and design workshops or prototyping is no longer a sustainable competitive advantage. Metrics remain essential—but effective Product Engineers blend quantitative data with rich, qualitative customer feedback loops for informed decisions, augmented by AI-driven insights and tools.
What’s Likely to Fade Away?
These are my personal projections and primarily apply to high-performing product teams. Teams burdened with organizational debt or slower AI adoption may still rely on these skills, exclusively focusing on them risks becoming irrelevant in the future.
Traditional Backlog Management: AI-driven tools significantly reduce the need for manual story writing and backlog management, enabling Product Engineers to focus more on customer focussed, strategic and creative tasks.
Dedicated Tooling Champions: Specialized tooling knowledge alone no longer offers substantial differentiation due to integrated AI capabilities in platforms like Jira, Asana, or Azure DevOps.
Standalone Agile Coaches: Agile coaching roles diminish as agile methodologies become embedded directly into Product Engineers’ skill sets, supported by AI-enhanced workflows.
Conventional Project Management: Routine project management tasks will get automated as AI automates coordination and task tracking.
Traditional People Management without Customer Accountability Administrative people management roles without direct customer accountability become less relevant. Product Engineers will potentially be leading smaller teams or work in an Individual Contributor leader capacity.
As AI reshapes the landscape, the Product Engineer role emerges as central to innovation and customer value creation. Embracing this transition by continually refining skills and strategically adopting AI tools positions you to thrive in an increasingly dynamic and competitive environment.