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.
Frequently Asked Questions
What challenges are organizations facing with agentic AI?
Organizations are struggling with the gap between their ambitions for agentic AI and their actual readiness, particularly due to a lack of cohesive data foundations.
Why is data readiness important for deploying agentic AI?
Data readiness is crucial because effective deployment of agentic AI relies on a unified view of data, which is currently hindered by fragmentation across disconnected platforms.
How can organizations improve their return on investment from agentic AI?
Organizations can improve their ROI by switching from activity metrics to outcome metrics, which has been shown to triple measured returns within two quarters for some enterprises.
Who is most affected by the need for agentic AI?
Organizations across various sectors, particularly healthcare, pharma, and financial services, are most affected by the need for agentic AI.
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