{
  "id": 6282104,
  "title": "Building products is easier than ever, knowing what to build is the hard part",
  "url": "https://urgent.news/2026/09/08/building-products-is-easier-than-ever-knowing-what-to-build-is-the",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-08T13:35:27.000Z",
  "source": {
    "name": "TechRadar",
    "slug": "techradar",
    "url": "https://www.techradar.com/pro/building-products-is-easier-than-ever-knowing-what-to-build-is-the-hard-part"
  },
  "original_language": "en",
  "account": "In the rapidly evolving world of software development, AI-assisted tools have fundamentally transformed how products are created. Gone are the days when engineering capacity was the primary constraint. With AI, ideas that once took weeks or months to develop can now be explored in a fraction of the time. This acceleration in development has enabled more organizations to experiment and bring new products to market at an unprecedented pace.\n\nHowever, building faster does not necessarily equate to creating superior products. While AI can expedite the journey from idea to release, it still falls short of making critical business decisions. Human judgment remains essential in determining which concepts merit investment, which features address genuine customer pain points, and which experiments are not worth pursuing.\n\nResearch from MIT has highlighted this paradox: as AI has fueled a surge in new mobile applications, customer usage has not kept pace. The increased ease of building software has resulted in a proliferation of apps, yet it hasn't led to a corresponding increase in customer engagement. Customers have not suddenly discovered more hours in their day simply because software development has become easier.\n\nThis shift emphasizes the importance of decision-making in the face of AI-driven software development. Success now hinges on quickly learning from customer behavior and discerning what customers genuinely value. With AI streamlining the development process, customer insights have emerged as the most valuable asset for product teams.\n\nAfter each release, product leaders face the perennial questions: what's next? Should we continue investing or pivot? AI has made building and shipping software quicker, but it hasn't simplified the decision-making process. This is where product intelligence comes into play. By understanding how customers behave, decision-makers gain the confidence to prioritize their efforts.\n\nBehavioral data provides valuable insights into what customers repeatedly engage with, where they encounter difficulties, and where they disengage. It helps differentiate between features that garner initial interest and those that become integral to customers' daily workflows. This distinction often signifies long-term value more than mere launch-day engagement or anecdotal feedback. Moreover, behavioral insights often provide stronger evidence than customer opinions, as they reflect actual customer behavior rather than stated preferences.\n\nThese insights enable product teams to make informed decisions about which features to invest in, which ideas fall short, and where the next opportunity may lie. Product decisions become grounded in real customer usage patterns rather than internal assumptions and beliefs.\n\nAI's ability to process large volumes of behavioral data further enhances this process. AI can swiftly identify patterns, unexpected user journeys, and emerging trends that may otherwise go unnoticed. This empowers decision-makers with greater confidence in where to focus their attention, allowing them more time to interpret the data and determine the best course of action.\n\nRather than spending hours analyzing dashboards for answers, product teams can leverage AI to focus on understanding behavior, testing potential improvements, and deciding which ideas to pursue. Each release generates new behavioral data, providing a continuous stream of evidence to guide future releases.\n\nAs AI continues to lower the barriers to software development, its role in product improvement is expanding. AI-assisted development is no longer confined to writing code; it is increasingly becoming an integral part of the product improvement process. By combining AI-assisted creation with AI-assisted product enhancement, organizations can learn faster, respond with greater confidence, and build products that truly meet customer needs.\n\nUltimately, the key to creating products that resonate with users lies in focusing on the data that reveals what customers actually need. While AI has accelerated the speed of product development, the foundation of every release remains the product judgment—knowing what to improve, what to abandon, and where to invest next. Product intelligence, derived from behavioral insights, serves as the guiding light for decision-makers to ensure that each release aligns with what customers actually do, rather than what internal teams assume they will do.",
  "summary": "What should we build? That's becoming one of the most important questions in product development.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}