{
  "id": 16028750,
  "title": "Making the case for context-aware AI",
  "url": "https://urgent.news/2026/10/22/making-the-case-for-context-aware-ai",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-22T10:00:00.000Z",
  "source": {
    "name": "MIT Technology Review",
    "slug": "mit-technology-review",
    "url": "https://www.technologyreview.com/2026/10/22/1146812/making-the-case-for-context-aware-ai/"
  },
  "original_language": "en",
  "account": "Enterprise AI has the potential to revolutionize businesses by generating recommendations, automating tasks, and aiding in faster decision-making. However, without a deep understanding of how a business functions, even advanced AI models may overlook crucial constraints and relationships that affect the validity of decisions. As companies strive to integrate AI into their daily operations, the main hurdle lies in providing the technology with the necessary context to perform effectively within intricate business processes. Devavrat Shah, Celonis’s chief scientist for enterprise AI, emphasizes that this context is essential to what he calls \"decision intelligence.\" AI agents can assist with individual tasks within larger decision-making processes, but they require a comprehension of each task's role and the specific business conditions surrounding it. \"You can't do that in a vacuum,\" Shah asserts. In the absence of proper operational context, agents may neglect vital constraints, make costly errors, or waste computational resources as they attempt to ascertain what they need to know. To address this, embedding AI in the existing processes and data used by enterprises is vital. A contextual layer can assist agents in grasping workflows, adhering to predefined boundaries, and delivering recommendations that can be reviewed and approved by humans. Shah suggests starting with a specific business decision, pinpointing its most challenging steps, and evaluating whether AI can enhance them. By measuring improvements in speed and value, organizations can determine if an application merits scaling. This method could also transform how employees allocate their time. By automating repetitive, manual work and coordinating tasks across teams, employees can dedicate more time to tackling complex strategic decisions. However, realizing this benefit necessitates investing in workforce education, enabling employees to comprehend AI outputs and their implications instead of simply delegating decisions to agents. Looking ahead, Shah envisions AI extending from routine operational tasks to enterprise-wide planning in areas like finance, workforce management, marketing, and sales. With the appropriate context, AI could assist organizations in swiftly evaluating options and making consequential decisions. As Shah puts it, \"Now, perhaps resource planning should not consume too much time, and people can make the right choices quickly.\"",
  "summary": "Enterprise AI can generate recommendations, automate tasks, and help organizations make decisions faster. But without an understanding of how a business actually operates, even powerful models can miss the constraints and relationships that determine whether a decision is the right one. As companies look to move AI from experimentation into everyday operations, the challenge ahead…",
  "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."
}