{
  "id": 17057449,
  "title": "Are you still piloting?",
  "url": "https://urgent.news/2026/10/29/are-you-still-piloting",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-10-29T11:55:56.000Z",
  "source": {
    "name": "TechRadar",
    "slug": "techradar",
    "url": "https://www.techradar.com/pro/are-you-still-piloting"
  },
  "original_language": "en",
  "account": "The excitement around artificial intelligence is undeniable, with businesses in every industry pouring resources into AI research, testing proof of concepts, and seeking ways to translate advancements into tangible business value. Yet, a concerning gap exists between the enthusiasm for AI and its actual implementation. While many AI projects show promise in the early stages of pilot programs, a significant number stall out and never move from a controlled, experimental environment to a fully deployed solution.\n\nThe difficulty lies in the transition from piloting AI to its successful implementation, which involves dealing with new challenges such as security, governance, culture, skills, integration, and readiness. Research indicates that 71% of organizations remain stuck in the pilot or proof-of-concept phase, highlighting the complexity of turning initial enthusiasm into business reality. This situation is known as the AI implementation gap - the space between demonstrating that AI can work and actually deploying it at scale for measurable results.\n\nTo bridge this gap, organizations must establish the right technical and organizational groundwork, prepare people and processes, and focus on the right use cases. It's not just about speed, but also about creating a secure, scalable, and consistent environment for AI to thrive. The challenge is often not technological but strategic, involving governance and operational hurdles that prevent pilots from becoming production-ready systems. Organizations frequently face analysis paralysis, where they have numerous AI ideas but lack a clear method to identify which will deliver the most value, leading to resources being spread thin across multiple proofs of concept.\n\nFocusing too early on scaling can also be detrimental. Major investments in infrastructure, architecture, and implementation are made without sufficient testing in real-world conditions. If the project needs to change direction, the already-committed investment can make adaptation challenging. Another common issue is the need for greater governance. Regulations like the EU AI Act, GDPR, and DORA add layers of caution, especially in EMEA regions, slowing down decision-making processes due to compliance concerns and uncertainties about the evolving regulatory landscape.\n\nFinally, organizations sometimes underestimate the total cost of moving a successful pilot to widespread deployment. What seems affordable in the proof-of-concept phase can become significantly more expensive once scaled, particularly if the system hasn't been properly stress-tested. Due diligence is crucial, yet over half (56%) of organizations skip a Total Cost of Ownership assessment before committing to large-scale AI projects.\n\nThe key to closing the implementation gap is not moving faster, but moving with confidence and expertise. Organizations often find themselves at two extremes - either waiting until all uncertainties are resolved before taking action or rushing forward without proper validation. Successful companies strike a balance between certainty and speed, making quick moves where evidence supports them while still incorporating thorough validation and governance to mitigate risks. This involves early validation of ideas, rapid prototyping to create tangible experiences for users within days, and testing concepts in real-world conditions to determine their viability before scaling up.",
  "summary": "There's no shortage of enthusiasm for artificial intelligence. Organizations across every sector are investing heavily, experimenting, launching proof of concepts, and finding ways to turn the latest developments into real business value.",
  "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."
}