The Agentic Enterprise Needs Better Data, Not Bigger Promises

§ 01 Executive Snapshot

• What: Companies are struggling with the gap between ambitions for agentic AI and their actual readiness.
• Who: Organizations across various sectors, particularly healthcare, pharma, and financial services.
• Why it matters: The ability to deploy agentic AI effectively depends on having a cohesive data foundation, which is currently lacking in most enterprises.

§ 02 Key Developments

• Eighty-five percent of organizations aim to be agentic within the next three years, but 76% acknowledge their infrastructure cannot support this change.
• A single agentic workflow may involve multiple model calls and integrations, necessitating a different compute foundation than traditional AI.
• Switching from activity metrics to outcome metrics tripled the measured return on investment from agentic AI for one enterprise customer within two quarters.

§ 03 Strategic Context

• The current data fragmentation across disconnected platforms hinders the effectiveness of autonomous AI systems, as they cannot act reliably without a unified view of data.
• The push for agentic AI reflects a broader trend towards automation and efficiency in operations, with companies needing to rethink their existing human-centric models.

§ 04 Strategic Implications

• Immediate consequence: Organizations that view data readiness as infrastructure will likely outpace competitors who treat it merely as a project.
• Long-term implication: By 2030, McKinsey predicts that three-quarters of current jobs will require redesign, upskilling, or redeployment to accommodate agentic AI.

§ 05 Risks & Constraints

• Potential risk: Organizations may face high costs associated with the ongoing operational demands of agentic AI, which were not anticipated in existing budgets.
• Potential risk: A lack of systemic redesign in workflows and processes may lead to ineffective implementation of agentic AI.

§ 06 Watchlist / Forward Signals

• Forward signal: Monitoring organizations' readiness assessments for agentic AI deployment, particularly in terms of data integration and infrastructure.
• Forward signal: Observing the sectors that successfully implement agentic AI and the metrics they use to evaluate success, as these will inform best practices moving forward.