HP & OpenAI Partnership Shows What Enterprise Agentic AI Requires Beyond the Model

In June, HP announced plans to deploy OpenAI Frontier across customer-facing experiences and internal operations. It expects potential use cases to include customer telemetry insights, employee productivity, and software development.

HP also plans to bring Frontier into its online store, partner channels, chat, and voice services. The aim is to help customers and partners find information and complete tasks more consistently across those channels.

The partnership also highlights a bigger challenge for CIOs: deploying AI agents across the business requires more than selecting a capable model. Agents need access to enterprise data and business apps, while IT teams must control what they can access and do.

Endpoints add another requirement as AI agents take on more routine work. HP is developing devices with dedicated hardware for agentic AI workloads that require continuous inference, while its Workforce Experience Platform (WXP) provides device telemetry and management.

Together, these requirements show why CIOs need to consider the systems and devices surrounding the AI agent as part of their deployment plans.

Why enterprise AI agents need more than a capable model

Generative AI tools initially gave employees a relatively simple interaction: enter a prompt and receive an answer. Agentic systems go further through data retrieval, app interactions, and task completion.

For example, an employee might ask an AI assistant to summarize a customer account, while an agent could retrieve the account history from a CRM, check an internal system for open issues, and use that data for follow-ups. Each step requires clear rules about what the agent can access and do.

To let agents work across business systems, organizations need to prepare the data sources, applications, and permissions required for the task.

OpenAI Frontier’s Business Context capability connects agents to systems such as data warehouses, CRM tools, and internal applications. Agent Execution then allows those agents to carry out tasks across business workflows.

For CIOs, AI planning has to cover more than model selection. IT teams must decide how agents should interact with these systems and where to limit access.

How enterprise data supports AI agents

An AI model may understand a general question, but it cannot automatically know which customer has an unresolved support request or which employee device has recurring app issues. Business applications provide that company-specific context.

Customer relationship management (CRM) systems contain customer histories, while service management platforms track support records. Internal applications may also store company policies or operational information an agent needs to complete a task.

Connecting an agent to these systems raises two questions: which source should be treated as authoritative, and who should be allowed to use the information. IT teams also need to confirm that the data is current enough for the task.

HP says OpenAI Frontier could support customer telemetry insights through WXP, alongside customer and partner experiences.

For organizations developing an AI roadmap, the next step is to review the data sources behind each use case. IT leaders should identify the applications an agent will use and put access policies in place before it can work with sensitive records in production.

Endpoint telemetry can give AI more operational context

For IT operations, agents may also draw on data from employee devices. Endpoint management systems already track device health and application performance, which gives IT teams more insight into recurring issues and potential failures.

According to HP, its AI models analyze telemetry across 48 million endpoints and process 1.9 TB of new data each day. WXP uses that data to detect anomalies, recommend actions, and automate some responses.

With access to endpoint telemetry, IT teams can detect device and app issues sooner. Instead of waiting for an employee to report an app crash, a management platform can identify the issue and trigger a response based on predefined rules.

WXP, for example, can connect alerts to ServiceNow so an incident is automatically created when a specified device health or application issue occurs.

Agentic AI could build on this model by combining device data with information from other business systems before recommending a fix or initiating an approved IT action. For CIOs, this level of automation depends on having enough endpoint visibility and integration with existing IT systems.

Security and governance have to extend to AI agents

Giving agents access to enterprise systems requires rules for what they can access, what actions they can take, and how their activity is monitored. Frontier includes identity and access management, scoped permissions, monitoring, and activity logs. OpenAI also says agent actions are auditable, giving organizations a record of what each agent accessed or changed.

Those controls become more important as companies deploy agents across more departments. A customer service agent may need access to support records but not payroll data, while an IT agent may need device health information and permission to launch approved remediation steps.

CIOs also need to decide which actions can happen automatically and which require human review. Routine, low-risk tasks may be suitable for automation, while changes involving financial records or security settings may need approval.

HP says the OpenAI Frontier partnership followed pilots that evaluated enterprise integration and security. Staged deployments can help organizations test permissions and review agent behavior before expanding access.

Agentic workloads may change device requirements

Much of enterprise AI still runs in the cloud, but local processing is becoming more relevant for workloads that need lower latency or continued operation when connectivity is limited.

Agentic workloads may also place new demands on endpoints because some agents interact with local apps or device resources. HP says it is developing devices with dedicated hardware optimized for agentic AI workloads that require 24/7 inference. Its broader workplace portfolio includes PCs, workstations, printers, and collaboration technology managed through WXP.

During device refresh planning, IT teams should also account for the AI workloads employees are expected to run. Some roles may rely mostly on cloud-hosted agents, while others may use apps that split processing between the device and cloud.

CIOs should map those workload differences before refreshing large device fleets. Processor and accelerator capacity influence what can run locally, while memory and power requirements affect which device configurations are ideal for each role.

Instead of standardizing on one AI PC configuration, IT teams may need different device profiles for employees who rely on cloud AI and those who run more processing locally.

Workflow integration determines whether agents become part of everyday work

AI agents may see lower adoption if employees have to switch between their usual apps and a separate AI interface. For agentic AI to become part of routine work, agents need access to existing business apps and a way to complete tasks across them.

OpenAI describes Frontier as a platform for connecting agents with enterprise context and business systems. HP plans to apply this approach across store, partner, chat, and voice interactions, while internal use cases could involve device management platforms, service management tools, or CRMs.

The value of this integration depends on whether the agent reduces the amount of work employees have to do themselves. If employees still have to copy information between an AI interface and business software, the agent may simply add another step to the workflow.

What CIOs should prioritize in an agentic AI roadmap

Adopting agentic AI requires decisions around workflow design, access, and infrastructure. As a starting point, CIOs can focus on three priorities.

1. Start with a workflow

Identify the task an agent should perform before deciding where to deploy it. Document how long the process currently takes or how much support it requires so the organization has a baseline for comparison.

A defined workflow also makes access decisions easier because IT can see which systems the agent needs and which actions fall outside its intended role.

2. Set access rules before deployment

List the data sources and applications the agent needs, then define what it can read, change, or initiate. IT teams should also identify which source is authoritative when the same information appears in multiple systems.

Least-privilege access can restrict the agent to only the systems and actions required for its task. Higher-risk actions may still require human approval, and activity logs should record what the agent accessed and changed.

3. Test infrastructure before expanding

Determine whether the workload will run mainly in the cloud or require local processing, then assess whether existing devices and management tools can support it.

Once the infrastructure is in place, measure whether the pilot improves the workflow before expanding it to more users. Completion time, support volume, error rates, and the number of cases requiring human intervention can show where further adjustments are needed.

Enterprise agentic AI depends on more than the model

HP’s OpenAI partnership shows how enterprise AI planning is expanding beyond model selection. Frontier provides the agent platform, while WXP and HP’s device plans address data, management, and endpoint requirements needed to support deployment.

For CIOs, the next phase of agentic AI will depend on whether existing business systems, security controls, and devices can support agents that interact with apps and complete tasks on a user’s behalf.
Those requirements will determine how easily organizations can move from limited pilots to agents that participate in everyday business workflows.

FAQs

What is agentic AI in the enterprise?

Agentic AI refers to systems that can take actions on a user’s behalf, such as retrieving data, updating records, or completing parts of a workflow. In enterprise settings, these agents usually need access to business app and company data, along with rules that specify what they can view, change, or initiate.

What should CIOs prioritize when building an AI roadmap?

CIOs should start with specific business workflows, then identify the data, applications, and access controls each AI use case requires. They should also assess whether existing devices and management tools can support the processing demands of those AI use cases.

How can businesses deploy AI responsibly across teams?

Responsible AI deployment requires limits on what agents can access or change, plus activity logging and human approval for higher-risk actions. Organizations should also test agents in limited workflows before expanding access across departments.