The 10 biggest agentic AI challenges and how to fix them

1. Misunderstanding the problem

Challenge: It's still early days for AI, and stakeholders often misidentify the business problem AI agents should solve. Per RAND, this is the top reason AI projects fail: leaders are misaligned or unclear about the domain context and project goals.
Solution: Align leadership and technical teams on project purpose and domain context from the start, and define KPIs rooted in real-world business problems, not abstract technical goals (like F1 model performance).

2. Data issues

Challenge: Lack of clean, high-quality, and accessible data is a major driver of AI agent failure. According to Informatica’s 2025 CDO Insights Report, 43% of AI leaders cite data quality and readiness as their top obstacle. For example, outdated training data can lead to inaccurate answers in customer support interactions, while poor data pipelines can cause agents to hallucinate—leading to unreliable outputs that erode customer trust.
Solution: Invest in data readiness and data governance early, including extraction, normalization, metadata, quality dashboards, and retention controls. This helps ensure agents have the clean, integrated, and contextual data they need to operate reliably.

3. Focusing on tech over business problems

Challenge: Organizations too often fixate on choosing the right AI framework or model rather than ensuring agentic AI addresses their persistent business pain points. Teams may chase higher model accuracy scores, for instance, while neglecting workflow design and integration. As a result, by the time projects reach business review, compliance hurdles feel insurmountable, and ROI remains unproven. In fact, 40% of agentic AI projects are projected to be scrapped by 2027 for failing to link back to measurable business value, according to Gartner.
Solution: Anchor agentic AI initiatives to clear operational and customer pain points from the start. Define KPIs based on real-world outcomes (reduced resolution times, boosting customer satisfaction). By focusing on solutions that lower costs and remove friction, enterprises can prove value early and avoid chasing technical capabilities for their own sake.

4. Fragmented execution

Challenge: Siloed teams can create organizational friction that hampers execution. For instance, product teams chase features, IT teams shore up security, and legal drafts AI compliance policies—often without shared success metrics or coordinated timelines. The result of these disconnected efforts and shadow IT (duplicate systems, orphaned models, redundant data stores) is wasted resources, reduced data quality, and hampered governance.
Solution: Centralize AI oversight and governance to maximize alignment, innovation, and performance. Formalize roles, consolidate platforms, and adopt AI frameworks that enforce visibility, compliance, and shared standards, possibly as part of an agentic AI governance framework.

5. Inadequate infrastructure

Challenge: Organizations may lack the scalable platforms, clear APIs, and orchestration layers needed to support enterprise-grade AI agents. Without robust data plumbing and integration-ready infrastructure, AI agents can’t pursue complex business goals across systems to completion, and so falter due to “immature autonomy.”
Solution: Invest heavily in agent-ready infrastructure before scaling AI pilots. Pairing these investments with agentic governance frameworks (like Sendbird Trust OS) provides both the connective tissue to make AI agent systems not only robust and scalable, but safe and responsible.

6. Workflow & integration failures

Challenge: Poor integration with legacy systems and rigid workflows can cause agents to break down mid-task, especially for cross-system workflows. For example, Salesforce admitted its Einstein Copilot struggled in pilots because it couldn’t reliably navigate across customer data silos and legacy CRM workflows, forcing costly human intervention.
Solution: Rather than “bolting on” AI to legacy processes, re-architect workflows around AI agents before plugging them in. McKinsey's 2025 State of AI Survey found that organizations reporting "significant" ROI from AI projects are twice as likely to have redesigned end-to-end workflows before deploying AI.

7. Balancing human + AI collaboration

Challenge: Full-on AI automation is an alluring idea, but in practice, augmenting humans with AI agents tends to deliver better outcomes, especially in customer experience use cases. By over-automating, organizations risk alienating customers who still expect a human touch. Klarna, for instance, initially touted that its AI agent handled 80% of customer interactions. But after customers complained about the lack of human fallback, the company reverted to amplifying its human capabilities with AI, not replacing them.
Solution: AI leaders tend to design choreographed workflows where AI agents handle FAQs, routine tasks, and upsells, while humans remain in the loop for exceptions or emotionally charged interactions. This involves defining which actions stay human, building in override paths, and capturing user feedback regularly.

8. Task complexity exceeds capability

Challenge: While leaders should choose enduring problems for agentic AI to solve, this evolving technology can be applied to problems too complex for its current capabilities, setting projects up for failure. Importantly, many “agentic” AI companies are overhyped (known as “agent washing”) and can’t reliably deliver enterprise-grade outcomes.
Solution: Business leaders should understand AI's limitations and convene technical experts as needed to assess project feasibility. Also, start with well-defined tasks that AI can realistically automate, then scale to more complex applications once reliability is proven.

9. Overlooking people and processes

Challenge: Many organizations treat AI agent deployment as a purely technical rollout, overlooking the organizational changes required for success. Both RAND and Gartner identify this as a leading agentic AI challenge: leaders underestimate the need for process change and human alignment, leaving human teams disengaged or resistant.
Solution: Approach AI adoption as a process transformation, not just a technology upgrade. This means upskilling employees, redesigning workflows, and defining how responsibilities are shared between humans and machines. Embedding change management, user feedback loops, and governance structures from the start ensures employees see AI as an enabler—not a threat—accelerating adoption and improving business outcomes.

10. Pilot paralysis

Challenge: Many agentic AI initiatives stall in proof-of-concept mode. The technology performs well in a sandbox, but integration tasks like authentication, compliance workflows, and user adoption are pushed aside until executives ask for a production timeline. By then, the pilot feels too fragile to scale, eroding trust and momentum.
Solution: Treat AI pilots not as experiments, but as products from day one. Successful enterprises assign product managers to AI services, define clear SLAs and SLOs (e.g., “ticket summary accuracy >85% with <5s latency, 95% of the time”), and budget for continuous improvement. With standardized AI observability—event logs, drift detection, and user feedback loops tied into dashboards—agents become living systems with uptime, reliability, and customer satisfaction as metrics.