{"slug": "agentic-ai-adoption-is-surging-but-bad-asset-data-could-sink-it", "title": "Agentic AI Adoption Is Surging, but Bad Asset Data Could Sink It", "summary": "A Gartner 2026 CIO and Technology Executive Survey found that only 17% of organizations have deployed AI agents, but more than 60% plan to do so within two years, a faster adoption curve than any other emerging technology tracked. However, practitioners warn that agentic AI systems in asset-intensive industries are being undermined by poor asset data quality, which can scale operational risk as errors propagate at machine speed. A preliminary 2025 MIT Project NANDA report also found that 95% of organizations studied had not achieved measurable profit-and-loss impact from generative AI initiatives, citing integration gaps and tools that failed to retain context.", "body_md": "## Companies race to deploy autonomous systems while operational data quality lags behind\n\n[Agentic AI](https://www.kobaran.com/tag/Agentic-AI) is no longer a concept confined to boardroom presentations. Across utilities, transportation, mining, telecommunications and manufacturing, companies are handing this new class of software real operational tasks: reviewing maintenance histories, checking open work orders, comparing crew schedules and generating action plans without a human stitching every step together manually.\n\nThe shift is happening at a pace that has surprised even seasoned technology leaders. Gartner’s 2026 CIO and Technology Executive Survey found that just 17% of organizations have deployed AI agents so far, yet more than 60% plan to do so within the next two years, a faster adoption curve than any other emerging technology tracked in the survey. For industries where physical equipment, not just dashboards, is on the line, that speed raises a pressing question: is the data behind these systems actually ready for them?\n\nPractitioners who have led enterprise asset management deployments say the honest answer, in many cases, is no. The problem is not that agentic AI cannot handle complex, multi-step operational decisions. It is that the systems can only be as reliable as the asset data feeding them, and in asset-intensive industries, that data has carried known gaps for years.\n\n## Why Agentic AI Raises the Stakes on Data Quality\n\nGenerative AI tools summarize information and draft suggestions for a person to review. Agentic AI works differently. Given a goal, it can break the task into steps, pull information from multiple systems at once, and act on that information with minimal human intervention. That autonomy is precisely what makes flawed data so dangerous.\n\nWhen an AI agent works from incomplete, outdated or inconsistent records, it typically does not fail in an obvious way. Instead, it fails confidently, recommending work on the wrong asset, overlooking a critical dependency, underestimating risk or misjudging what should be prioritized first. Feeding an agent bad data does not scale intelligence. It scales operational risk.\n\n### A Familiar Problem, Moving at Machine Speed\n\nThe data issues now surfacing through agentic AI are not new to enterprise asset management professionals. Duplicated records, missing asset relationships, inconsistent naming conventions, outdated criticality ratings and poor failure coding have shown up in system deployments for well over a decade.\n\nWhat has changed is the scale at which errors can propagate. An experienced technician might catch a bad record before acting on it, relying on institutional knowledge that was never formally entered into any system. An AI agent has no equivalent instinct. It can process thousands of records far faster than any person could review manually, applying the same flawed assumption across an entire operation before anyone notices the mistake.\n\n### The Broader Enterprise AI Gap\n\nThis challenge fits into a wider pattern already visible in enterprise AI rollouts. A preliminary 2025 MIT Project NANDA report found that 95% of organizations studied had not achieved measurable profit-and-loss impact from their generative AI initiatives, attributing much of the shortfall to integration gaps and tools that failed to retain context or adapt to existing workflows.\n\nIn asset-intensive operations, where a flawed recommendation can affect physical infrastructure rather than a spreadsheet cell, that same gap between AI ambition and data reality carries heavier consequences.\n\n## Where Bad Data Turns Into an Unsafe Decision\n\nIn day-to-day operations, data errors stop being abstract and start becoming physical.\n\nConsider maintenance prioritization. If an agent cannot see a recent failure, a delayed work order or a change in an asset’s condition, it may assign the wrong priority level. That miscalculation can generate unnecessary work in a low-risk area while a more serious problem elsewhere goes unaddressed.\n\nThe risk escalates further when the underlying asset relationships are wrong. If a system does not accurately capture which pieces of equipment are connected, what must be isolated before work begins, or what site-specific constraints apply, an AI-generated recommendation can move from inconvenient to genuinely unsafe.\n\n### Regulators and Standards Bodies Are Paying Attention\n\nThat risk is why organizations are being urged to think carefully before granting agentic AI full autonomy over critical decisions. The National Institute of Standards and Technology’s voluntary AI Risk Management Framework lays out a structure for building trustworthiness into an AI system throughout its lifecycle rather than bolting it on after deployment. NIST is also developing a dedicated framework profile specifically for critical infrastructure operators using AI-enabled capabilities, reflecting how unevenly the risks of poor AI decisions are distributed across industries.\n\nAI can gather information quickly, surface options and summarize the evidence behind a recommendation. But where safety, regulatory compliance or service reliability are on the line, industry experts say human judgment still needs to remain part of the final call, at least for now.\n\n## Enterprise Asset Management as the Operational Control Layer\n\nEnterprise asset management, or EAM, is the system where operational reality gets organized: work orders, maintenance strategies, inspections, approvals, asset relationships and equipment histories all live there. A strong EAM foundation gives an AI agent dependable data to draw on. A weak one tends to be exposed almost immediately once an agent starts acting on it.\n\nThat view is echoed outside the operations floor. An EY analysis of AI-enabled enterprise asset management points to real-time data and analytics as the key factor moving organizations from reactive maintenance toward proactive, predictive strategies.\n\nRather than treating EAM as a back-office record-keeping function, organizations preparing for agentic AI are increasingly being advised to treat it as part of the operational control layer, the system that shapes what the AI can access, what processes it must follow, what evidence supports its recommendations, and where human sign-off is still required.\n\n### Context Determines the Quality of the Decision\n\nA sound maintenance recommendation rarely depends on a single asset’s condition alone. It typically also depends on:\n\n| Factor | Why It Matters |\n|---|---|\n| Asset criticality | Determines how much risk a delay or failure introduces |\n| Recent failure history | Signals whether a pattern of degradation is emerging |\n| Connected equipment | Identifies dependencies that could be affected by the work |\n| Parts availability | Determines whether the recommended action is actually feasible |\n| Safety procedures | Ensures the work can be performed without introducing new hazards |\n| Crew availability | Affects scheduling and turnaround time |\n| Downtime impact | Measures the effect on the broader operation |\n\nWithout that full context, even a technically capable AI agent can recommend the wrong course of action.\n\n## Is Your Data Actually “AI Ready”?\n\nRather than launching a sweeping, organization-wide data-quality audit, practitioners recommend starting with a specific, narrow use case, such as AI-assisted maintenance planning, and asking targeted questions before expanding further:\n\n| Question | What It Reveals |\n|---|---|\n| Do we trust the asset hierarchy? | Whether relationships between assets are accurately mapped |\n| Is the failure history reliable? | Whether past incidents were logged completely and correctly |\n| Are job plans current? | Whether maintenance procedures reflect present-day conditions |\n| Is crew availability accurate? | Whether scheduling data can support real-time recommendations |\n| Are safety plans up to date? | Whether the agent’s actions will align with current protocols |\n\nData does not need to be flawless. Few organizations will ever reach perfect operational data. It needs to be accurate, current and well managed enough that AI can genuinely support the people making decisions rather than mislead them.\n\nFocusing on a single use case also makes data cleanup more manageable. Instead of trying to scrub every record across an enterprise, teams can concentrate on the specific information that affects the chosen application.\n\n## A Practical Path to Scaling Agentic AI\n\nA workable implementation path starts with a narrow use case where AI can add value without introducing outsized risk. From there, organizations typically:\n\n- Map the data that the use case depends on and honestly assess whether it can be trusted.\n- Correct the most critical asset records and assign clear ownership over them.\n- Standardize how work, failures and operational changes are recorded going forward.\n- Keep the AI operating within existing governance processes rather than around them.\n\nIn the early stages, the agent’s role is generally limited to supporting planners, reliability teams and technicians by gathering information, identifying patterns and recommending possible actions, not making final calls unsupervised. That approach gives organizations time to compare the agent’s recommendations against real-world outcomes, identify remaining data gaps and confirm that necessary controls are functioning as intended.\n\nOnly after those results are understood do most organizations consider expanding the AI’s responsibility, and even then, autonomy is typically increased gradually and only when the associated risks are clear, controlled and auditable.\n\n### Industry Data Backs a Foundation-First Approach\n\nDeloitte’s 2026 State of AI in the Enterprise report reaches a similar conclusion from a leadership perspective, describing a unified, trusted data foundation as essential to scaling AI across an organization. The throughline across these analyses, from operations teams to consulting firms, is consistent: organizations remain responsible for the actions AI recommends or takes, and that responsibility does not disappear simply because a decision was generated by an algorithm.\n\n## The Bottom Line\n\nAgentic AI’s momentum in asset-intensive industries shows no sign of slowing, and the potential upside, faster planning, reduced reliance on institutional memory, and less time lost switching between disconnected systems, is real. But the technology’s success will hinge less on model sophistication and more on whether the data it acts on can actually be trusted.\n\nThe organizations most likely to succeed are starting narrow, checking AI recommendations against real outcomes, fixing the data gaps that surface along the way, and expanding the technology’s role only when the evidence supports it. In industries where a bad decision can affect physical safety and critical services, that patience is what separates AI that earns lasting trust from AI that quietly loses it.", "url": "https://wpnews.pro/news/agentic-ai-adoption-is-surging-but-bad-asset-data-could-sink-it", "canonical_source": "https://www.kobaran.com/agentic-ai-adoption-is-surging-but-bad-asset-data-could-sink-it/", "published_at": "2026-08-11 03:47:38+00:00", "updated_at": "2026-08-11 04:07:08.363723+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "ai-infrastructure"], "entities": ["Gartner", "MIT Project NANDA"], "alternates": {"html": "https://wpnews.pro/news/agentic-ai-adoption-is-surging-but-bad-asset-data-could-sink-it", "markdown": "https://wpnews.pro/news/agentic-ai-adoption-is-surging-but-bad-asset-data-could-sink-it.md", "text": "https://wpnews.pro/news/agentic-ai-adoption-is-surging-but-bad-asset-data-could-sink-it.txt", "jsonld": "https://wpnews.pro/news/agentic-ai-adoption-is-surging-but-bad-asset-data-could-sink-it.jsonld"}}