Many organizations have already realized meaningful productivity gains from AI. The next stage of enterprise AI, however, depends on enabling agents to execute work across the organization. Agentic AI enables a more transformative approach to enterprise AI, but many organizations may struggle with handling this transformation smoothly.
Using agents enables AI to carry out work on behalf of the organization. An enterprise AI agent can interpret a request, assemble the right business context, decide what to do, and complete authorized steps across applications. That shift from personal AI to organizational AI introduces challenges that many enterprise data environments were never designed to support.
In a recent Wakefield Research survey, 90% of senior technology and data leaders expect to increase their agentic AI investments over the next 12 months. Yet 63% said they had not realized meaningful returns from their AI investments to date.
That gap will not be closed solely by introducing better AI tools. Organizations may find the greatest obstacle to properly scaling autonomous AI agents is the readiness of the business itself. To fully access its potential, agentic AI requires trusted data, shared business meaning, enforceable controls, and connections to the systems where work is completed.
Agentic AI stalls when execution begins
Many agentic AI pilots perform well in controlled environments. Scaling those same agents into production introduces a different set of requirements, where execution depends on trusted data, governance, and connected business systems.
A pilot can flag an inventory shortage using curated data and fixed conditions. A production agent must determine whether the inventory record is current, whether a substitution is permitted, and which approval rules apply. It must then update the order or route the exception to the appropriate reviewer.
Each step adds a dependency that an isolated assistant cannot address on its own. The agent needs current information, permission to use it, and reliable access to the applications involved in the process.
In the same survey, forty percent of technology leaders said more than 40% of their AI pilots fail because existing infrastructure was not designed for the demands of agentic AI. Systems primarily for reporting or periodic analysis may struggle to support organizational AI operating reliably across business functions.
Success should therefore be measured against the performance of the agent’s process. Relevant measures could include the time required to resolve an order, the cost of handling a service request, the rate of processing errors, or the revenue associated with a sales workflow.
Most enterprises remain early in the maturity curve
Enterprise readiness for agentic AI varies widely, but most organizations are still gradually building the foundations required for production-scale deployment. Wakefield Research’s Agentic AI Maturity Index places 28% of organizations in the earliest stage of maturity: “Experimenting.” These organizations are testing localized pilots, learning how AI can help individuals work more efficiently, and beginning to map their broader data strategies.
Another 40% are “Developing.” They may have successful models, but teams still rely on separate data, definitions, and rules. Without a shared way to assemble the right context for an agent and process, successful pilots remain difficult to extend across the organization.
“Building” organizations account for 25%. They have introduced local governance and basic workflow automation, but their data foundations do not yet support agents working reliably across business functions.
Only 7% have reached the final phase, “Operationalizing.” Organizations at this stage have harmonized significant portions of their data, established dynamic safety rules, and begun allowing agents to execute multistep workflows. They are also developing what Teradata calls “Autonomous Knowledge,” or “data with the context, lineage, and governance agents need to act reliably.” Extending that foundation throughout the enterprise, however, remains ongoing work.
AI Maturity Stage | % of Market | Primary Focus |
| Experimenting | 28% | Localized pilots |
| Developing | 40% | Connecting data and context |
| Building | 25% | Governance and workflow automation |
| Operationalizing | 7% | Autonomous Knowledge |
The priorities evolve with each stage, from preparing data for a single, high-value workflow to establishing the governance, shared context, and production controls that allow agents to operate consistently across the entire business.
With more than two-thirds of organizations still in the first two stages, localized pilot success should not be mistaken for enterprise-scale adoption.
Trusted execution requires shared business context
Giving an agent access to enterprise data does not give it the proper business context needed to act successfully.
A record may be available without showing who owns it, whether it is current, or how its fields are defined elsewhere in the company. An agent resolving an order dispute may retrieve every related record and still apply the wrong definition of customer status. It could also choose an action that conflicts with policy if the relevant approval rule is not connected to the data.
According to the research, 77% of executives said that 20% or less of their enterprise data was sufficiently described and contextualized for AI agents. Another 78% reported difficulty unifying data and knowledge across business functions.
Piloting agentic AI on a smaller scale may not signify meaningful readiness for businesswide adoption. Controlled proofs of concept can obscure these gaps because their data and conditions are deliberately constrained. Production records change, permissions vary, and definitions may cross business-unit boundaries.
Authority and governance raise another requirement. An organization must be able to reconstruct the basis for a consequential decision, including the source data and policy the agent applied. Defined limits should determine which actions the agent may complete and which cases require human review.
Execution also depends on application access. If an agent can recommend an action but cannot reach the relevant CRM, ERP, or service system, an employee must transfer the information manually. The workflow then stops before the agent completes the business process.
Scale one governed workflow at a time
Building an enterprise-ready foundation for agentic AI doesn't require solving every data challenge at once. Organizations can make meaningful progress by focusing on a single, high-value workflow and expanding from there.
Preparing an entire data estate before deploying agentic AI would create a project with no fixed endpoint. Enterprise data continually changes as new records, sources, and formats enter the organization.
Technology alone cannot establish enterprise readiness. Business leaders, data teams, and governance stakeholders must agree on how agents should operate before they can be trusted to execute work autonomously.
A more workable starting point is one consequential process whose outcome can justify production investment. Establish cross-functional ownership before technical work begins:
- Business leaders: Define the desired outcome and decide which actions the agent may take without review.
- Data teams: Identify authoritative sources and preserve their definitions, relationships, and lineage.
- Security, legal, and compliance teams: Set access conditions, approval boundaries, and escalation requirements.
- Technology teams: Connect the agent with production applications and monitor its execution.
Map only the information that the process requires. Teradata recommends concentrating on the highest-value 20% to 50% of the data estate instead of preparing everything at once. Give that portion consistent business definitions, ownership, lineage, and appropriate permissions.
Governance should remain with the shared data so access restrictions and decision histories persist as information moves across applications. The same foundation can support new models or deployment environments without requiring teams to reconstruct business context and controls for each change.
Once one bounded workflow performs reliably, leaders have evidence that the agent, data, controls, and infrastructure can produce a defined result. They can then extend the same foundation to another process with a clearer view of the investment and risk involved.
Download Teradata’s Arrested Automation: Why Agentic AI Stalls at the Enterprise Level report for the full maturity findings, the organizational and technical barriers preventing agentic AI from scaling, and strategies for moving from pilots to trusted enterprise workflows.