Scaling agentic AI demands a strong data foundation - 4 steps to take first
80% of companies say poor data blocks AI agent scaling, risking a 15% productivity loss by 2027.
McKinsey's latest analysis warns that scaling agentic AI—where autonomous AI agents execute complex workflows—is being severely hampered by poor data foundations. A staggering 80% of companies cite data limitations as their primary roadblock, despite rapid adoption that sees the average organization already using 12 AI agents, a number projected to grow by 67% to 20 agents within two years. The stakes are high: by 2027, companies failing to prioritize high-quality, AI-ready data risk a 15% loss in productivity. This comes as the global agentic AI market is forecast to reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030.
To overcome this, McKinsey identifies four critical, coordinated steps connecting strategy, technology, and people. First, organizations must identify high-impact, deterministic workflows to 'agentify'. Second, they must modernize each layer of their data architecture to support interoperability, easy access, and governance. The scale of the challenge is highlighted by MuleSoft data showing the average enterprise manages 957 applications, with only 27% currently connected, creating massive data silos. The final steps involve systematically improving data quality and advancing operating models and talent to support autonomous systems. Successfully executing this data foundation is essential for transitioning from pilot experiments, which two-thirds of enterprises have conducted, to the fewer than 10% that have scaled agents to deliver measurable value.
- 80% of companies cite data limitations as the top obstacle to scaling AI agent adoption, per McKinsey research.
- The agentic AI market is forecast to hit $8.5B in 2026, with the average firm using 12 agents, projected to grow to 20.
- Firms without high-quality, AI-ready data by 2027 risk a 15% productivity loss, underscoring the urgent need for a modern data architecture.
Why It Matters
Without a robust data foundation, companies cannot scale AI agents to automate high-value workflows, directly impacting productivity and competitive advantage.