68% of Firms Deploy AI in Production as Data Quality and Security Challenges Mount

Artificial intelligence has shifted from a test environment to a core element in everyday corporate workflows, yet teams still face persistent deployment obstacles. Across sectors, AI underpins tasks ranging from customer support to strategic planning.

Data from Zogby Analytics for Prove AI reveals that most organisations have moved past simple pilot projects and now operate full-scale, production-ready AI platforms. Survey respondents span finance, healthcare, retail, and manufacturing sectors. Yet they wrestle with basic tasks such as cleaning and standardizing data, protecting sensitive information, and tuning models to meet enterprise requirements.

Examining the figures makes the trend clear: 68% of organisations operate custom AI systems in live settings. That commitment shows in budgets: 81% allocate at least one million dollars each year, and roughly one in four devote more than ten million. This financial backing marks a significant shift from limited tests to sustained AI programs.

That momentum is changing boardroom dynamics. 86% of organisations have named a dedicated head of AI, often carrying the title ‘Chief AI Officer’. In strategic planning, these AI leaders wield influence comparable to CEOs: 43.3% of firms say the CEO retains final say on AI direction, and 42% point to the AI chief as the primary decision-maker.

Yet the path to full AI deployment presents new burdens. More than half of company leaders report that training and fine-tuning AI models proved tougher than anticipated. Data complications—from incomplete records and format mismatches to copyright questions and inconsistent validation routines—are dragging out timelines and cutting into expected gains. Almost 70% of organisations say at least one AI effort has fallen behind schedule owing to such setbacks.

As AI becomes more familiar, organisations are broadening its role. Chatbots and virtual assistants hold 55% share in current deployments. Meanwhile, more technically demanding uses are gaining ground, fueling interest in areas such as code generation and real-time diagnostics.

Software development tops the list at 54%, with predictive analytics for forecasting and fraud detection close behind at 52%. These figures show a move from surface-level interactions toward plugging AI into mission-critical processes. Marketing tools, which once served as a popular launch point, now attract less of the overall investment.

Organisations highlight generative AI as a top focus, with 57% prioritizing its integration for tasks such as automated content creation and code suggestions. Still, many maintain a hybrid model, blending these new capabilities with proven machine learning approaches for established use cases like classification and anomaly spotting.

Google’s Gemini and OpenAI’s GPT-4 dominate the large language model field, though DeepSeek, Claude, and Llama report steady adoption, too. Most firms operate two or three distinct LLMs, indicating that a multi-model lineup is becoming the norm for handling diverse workloads.

Infrastructure choices are under review at many enterprises. Almost nine in ten organisations tap cloud-hosted servers for AI, benefiting from scaling flexibility and managed tooling. Yet rising concerns about cost complexity, compliance rules, and potential exposure of sensitive data push a growing share to rethink on-premises or hybrid architectures.

Two-thirds of executives now believe local or hybrid deployments deliver stronger security and more predictable costs. 67% plan to relocate their AI training workloads to on-site or hybrid environments for tighter data governance. Data sovereignty tops the list of concerns for 83% of respondents when rolling out new AI systems.

Leaders voice high confidence in their AI governance frameworks, with around 90% saying they have policies, guardrails, and auditing in place to monitor usage and data lineage. That confidence stands in contrast to delays that arise as teams wrestle with day-to-day implementation snags.

Tasks like data labeling, model training, and validation continue to derail schedules. The gap between executive optimism and the realities of scaling AI shows up in frequent bottlenecks around annotation processes and quality checks. Organisations point to a shortage of skilled ML engineers and friction when linking new AI tools with legacy systems as key factors in project creep.

AI is no longer confined to R&D or tech pilots; it has become a strategic asset across finance, HR, supply chain, and customer care. Organisations are committing capital to embed AI-driven insights into everyday decision-making, reshaping product roadmaps and service models along the way.

Expanding objectives bring greater demands for robust execution. Transitioning from pilot phases to enterprise-grade rollouts has revealed gaps in data readiness, compute capacity, and security protocols. That trend is driving many to adopt hybrid or on-site deployments, with greater emphasis on system protection, compliance measures, and direct oversight of data stewardship responsibilities.

As AI adoption accelerates, transparency and traceability become core demands for maintaining trust and mitigating risk. Teams must establish clear audit paths, define usage boundaries, and continually check for bias or drift in deployed models. Confidence remains strong among leadership, yet a measured approach to scaling AI appears essential to manage the practical challenges ahead.