The market for agentic AI is expanding at a remarkable pace. Analysts project growth of more than 45 percent annually over the next five years. At the same time, Gartner warns that over 40 percent of agentic AI projects will be canceled by 2027 because of escalating costs, unclear business value, inadequate risk controls or breakdowns in human-AI interactions.
This dual reality highlights what makes agentic AI different from earlier waves of automation: these systems are not just tools executing predefined tasks, but designed to plan, adapt and act on their own. That potential makes them powerful but inherently less predictable.
When implemented well, agentic AI can give businesses a decisive competitive advantage, such as accelerating service, reducing costs and opening new ways to engage customers. When implemented poorly, the same autonomy can create financial losses, operational failures and lasting reputational harm. Adoption is no longer a neutral experiment; it is a make-or-break moment.
Cautionary tales: when agentic AI goes wrong
The risks of agentic AI adoption are not theoretical. A series of real-world failures demonstrates how quickly autonomous systems can create financial and reputational damage.
- Air Canada faced legal consequences when its chatbot misled a passenger about refund eligibility for bereavement fares. When the airline refused to honor the bot’s promise, a tribunal ruled that Air Canada was responsible and ordered it to pay damages.
- DPD, the international courier, had to shut down its chatbot after it began swearing at customers, mocking the company in verse and calling DPD “the worst nightmare for a customer.” What started as a customer service enhancement quickly became a public embarrassment.
- Cursor, one of the leading companies at the forefront of AI adoption, lost users after its support bot came up with a fake “one device per subscription” policy. The AI claimed that this restriction was a core feature, which led to subscription cancellations. Cursor later acknowledged that this response was an AI hallucination.
These cases highlight a harsh reality: companies are fully accountable for the information their AI systems generate, the decisions they take and the commitments they make. Defenses such as “the AI was wrong” or “we did not anticipate that behavior” carry little weight with customers, regulators or courts.
Hidden challenges of agentic AI adoption
Implementing the right infrastructure, preparing data and aligning business goals are essential steps, but they are only the beginning. The real test comes once an agentic AI system is live and making decisions in real time. That is when unexpected risks surface — not in theory, but in practice — as the system interacts with customers, employees, and business operations. These hidden challenges often determine whether adoption becomes a breakthrough or a costly failure.
To help leaders turn these risks into practical conversations, each challenge below is paired with a key question. These questions are not technical checklists; they are prompts business and technology leaders can use to test whether the proper safeguards are in place.
Governance and control gaps
Agentic AI is designed to act independently, but autonomy with no boundaries is risky. Without clear rules for when the system should stop, escalate, or seek approval, an agent may make decisions that the business never intended it to make. In customer service, that could mean granting refunds or making commitments outside company policy. In financial operations, it could mean approving a transaction that should have required sign-off. The Air Canada case shows how quickly accountability shifts back to the business when escalation mechanisms are missing.
The question to ask: In what scenarios will our AI hand control back to a human, and how is this enforced in practice?
Reliability and user trust
Even with strong governance, the reliability of AI outputs remains a challenge. Research shows that large language models still generate incorrect or inconsistent responses at notable rates. In a business setting, this might mean an AI assistant giving contradictory answers to two customers asking the same question, or producing guidance that conflicts with company policy. Beyond accuracy, users also need to understand why a recommendation was made. Without transparency, people are less likely to trust or adopt the system, no matter how advanced it is. IBM’s Watson project in healthcare demonstrated this gap: doctors resisted its recommendations because they could not clearly see the reasoning behind them.
The question to ask: How are we ensuring our AI’s outputs are consistent, explainable, and trusted by users?
Data exposure and discovery risks
Agentic AI systems can process large volumes of information far faster than any human. This includes structured sources such as databases, but also unstructured repositories like email archives, shared drives or SharePoint folders. Over years of operations, sensitive information often ends up stored in places where access rules are unclear or outdated. An autonomous system that can instantly search across these repositories may surface data never meant to be exposed to employees or even customers. The risk is not abstract; it comes from the AI doing precisely what it was designed to do: find connections others would miss.
The question to ask: How do we ensure our AI only accesses approved data, and how do we enforce those limits?
Cost explosion in deployment
Like other cloud services, many AI models are billed on a usage basis. What makes it different is how unpredictable that usage can become once agents start operating autonomously. Large language models charge by the number of tokens, fragments of text that represent both input and output. An agent may consume far more tokens than anticipated as it reasons through a task, retries steps or chains multiple tools together. Small increases in autonomy can multiply usage in ways that are difficult to forecast. These are not one-time development costs, but ongoing runtime expenses tied directly to how the system behaves in production, and they can escalate quickly if left unchecked. The result is not just a higher bill, but a moving target that complicates ROI calculations and budgeting.
The question to ask: How are we tracking actual AI usage costs versus projections, and what guardrails are in place to keep spending under control?
From challenges to readiness
Agentic AI readiness is not a checklist. It is a set of engineered controls and operating habits that constrain autonomy, prove reliability, prevent data exposure at the source and keep runtime spend predictable as systems scale.
Governance that constrains autonomy
Define exactly where agents can act, when they must pause and how they escalate. Make autonomy explicit with approval matrices for high-risk actions, policy-as-code that enforces those rules at runtime and a kill switch that halts an agent instantly. Gate releases with shadow mode and canary rollouts allow agents to observe and propose before they are allowed to act. Require tamper-evident audit logs for every tool call, decision, and outbound request. Treat AI incidents like operational incidents, with owners, timelines and root cause analysis.
Reliability and trust by design
Prove that outputs are consistent and explainable before you scale. Build a continuous evaluation pipeline with synthetic and adversarial test suites that cover critical intents, policy boundaries and edge cases. Add live monitors for drift and inconsistency to ensure that the same question cannot yield contradictory answers across channels. Make the agent’s reasoning traceable by recording the plan, tools used, inputs and evidence it relied on, then surface a concise explanation to the user. Set explicit rollback criteria and service levels for answer quality, latency and refusal rates, and A/B test changes behind flags.
Data exposure prevention at the source
Assume the agent will discover what humans overlook. Reduce blast radius with least-privilege access, deny-by-default connectors and attribute-based permissions that are enforced in the retrieval layer, not just the UI. Inventory high-risk stores like shared drives and email archives, then apply data cleansing and labeling so sensitive items are redacted, quarantined or excluded from retrieval. Add pre-query policy checks and post-response scanners that block responses containing regulated or confidential data. Log and review unusual access patterns and require approvals for new data connectors and scopes.
Cost control as an engineering discipline
Treat usage spend as a first-class performance metric. Track token consumption, tool calls and external API requests per agent, per action and user journey. Put hard limits on planning depth, retries and chain length so autonomy cannot silently multiply usage. Use smaller models for routing and classification, reserve larger models for high-value steps and reduce context with summaries and retrieval rather than dumping whole documents. Add caching for prompts, embeddings and frequent answers. Expose a live cost dashboard and budget alerts, and express unit economics in business terms like cost per resolved ticket or cost per qualified lead.
Conclusion: preparing for the agentic AI future
Agentic AI offers a rare mix of promise and peril. The same autonomy that can unlock competitive advantage can also amplify risks that traditional governance and testing methods were never designed to catch. The cautionary tales already on record show that companies cannot disclaim responsibility when an AI system goes off-script.
The difference between failure and advantage lies in preparation. Knowing the challenges of agentic AI adoption and asking the right questions up front equips leaders to contain risks before they turn into incidents. Governance, reliability, data security, and cost control are not optional add-ons; they are the foundation for using agentic AI responsibly and at scale. Organizations that invest early in these readiness levers will be better positioned to turn agentic AI from a liability into a differentiator.
Agentic AI will reshape industries. The winners will be those who prepare for these risks early and design their systems for accountability, trust and resilience.
