The world of artificial intelligence is evolving at lightning speed, and agentic AI is quickly becoming a game-changer for businesses worldwide. Experts predict a massive surge in AI spending, with Gartner forecasting a staggering $2.5 trillion globally by 2026, a 44% year-over-year increase. A significant portion of this investment, $31 billion, will go towards AI platforms for data science and machine learning, alongside $3 billion specifically for AI data.
Agentic AI, which involves autonomous agents performing complex tasks, is set to be a major growth driver. Deloitte Digital projects the global agentic AI market to reach $8.5 billion by the end of 2026 and an impressive $40 billion by 2030. Organizations are rapidly embracing these intelligent systems, currently utilizing an average of 12 agents, a figure expected to climb by 67% to 20 agents within the next two years, according to MuleSoft research.
This rapid integration means that by 2026, 40% of all Global 2000 job roles will involve working with AI agents, fundamentally redefining traditional positions across all levels, as IDC reports. However, this transformative journey is not without its challenges. Companies that fail to prioritize high-quality, AI-ready data risk a 15% loss in productivity by 2027, hindering their ability to scale generative AI and agentic solutions effectively.
The Data Dilemma: Why Scaling Agentic AI is a Challenge
While 2025 saw pilot experiments and small deployments, 2026 is emerging as the year for scaling agentic AI. Yet, despite widespread interest, nearly two-thirds of enterprises experimenting with agents have struggled to scale them to deliver measurable value, with fewer than 10% achieving success, according to McKinsey research. The primary culprit? Poor data, cited as a roadblock by eight out of ten companies.
McKinsey’s analysis highlights several critical data limitations hindering AI scaling, alongside operating model and talent constraints, ineffective change management, and tech platform limitations. Agentic AI thrives on a steady flow of high-quality, trusted data to accurately automate complex business workflows. Its success hinges on a data architecture that not only supports autonomy but also allows agents to execute tasks without constant human intervention.
Whether it’s a single agent using multiple tools or a multi-agent workflow where specialized agents collaborate, consistent access to high-quality data is paramount. Data silos and fragmented information often lead to errors and poor decision-making by AI agents. Building a robust data foundation is therefore not just an option, but a critical imperative for businesses looking to harness the full potential of agentic AI.
Four Essential Steps for a Strong Data Foundation
To successfully scale agentic AI, McKinsey identifies four coordinated steps that strategically connect technology, people, and business objectives. These steps are crucial for building the strong foundational data capabilities necessary for truly impactful AI deployment.
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Identify High-Impact Workflows to ‘Agentify’: Begin by pinpointing highly deterministic, repetitive tasks that promise significant value when automated by AI agents. End-to-end workflow mapping often reveals prime opportunities in areas like customer service, marketing, knowledge management, and IT, making it crucial to establish clear metrics and identify reusable data across various tasks and workflows.
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Modernize Each Layer of the Data Architecture for Agents: The existing data architecture must evolve to support interoperability, easy access, and robust governance across all systems. Many business applications today lack cross-platform data sharing, creating significant challenges; for instance, MuleSoft research indicates that while enterprises manage an average of 957 applications (rising to 1,057 for agentic AI leaders), only 27% are currently connected. This fragmentation is a major hurdle for IT leaders aiming to meet their AI implementation goals.
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Ensure Data Quality is in Place: Businesses must guarantee that all data—structured, unstructured, and agent-generated—adheres to consistent standards for accuracy, lineage, and governance. Access to trusted data remains a significant obstacle, evident as IT teams spend 36% of their time on custom integrations, an unsustainable model for scaling AI. Data quality is the top concern for 25% of organizations, and 96% struggle to leverage cross-business data for AI initiatives.
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Build an Operating and Governance Model for Agentic AI: Scaling agentic AI requires a fundamental rethink of how work is done, shifting human roles from execution to supervision and orchestration of agent-led workflows. In this hybrid environment, a well-defined governance model is essential to dictate how agents can operate autonomously in a trustworthy, transparent, and scalable manner. This ensures control and accountability as agentic systems expand.
Data as Your Competitive Edge
In the burgeoning era of agentic AI, access to high-quality data is rapidly becoming a key strategic differentiator. As AI agents generate enormous volumes of information, the importance of data quality, lineage, and standardization will only intensify within the agentic enterprise. Effective governance, in turn, will serve as the primary lever for control as these sophisticated systems scale.
Ultimately, a strong, trustworthy data foundation isn’t just about efficiency; it’s about building a sustainable competitive advantage. Organizations that invest in robust data capabilities will be best positioned to unlock the full potential of agentic AI, driving innovation and shaping the future of work.
Source: ZDNet – AI
