Enterprise AI is rapidly evolving beyond chatbots toward AI workers that can automate repetitive, information-intensive tasks while meeting enterprise requirements for security, governance, and cost control.
To explore what this looks like in practice, LLMWare.ai collaborated with Intel and the Accenture CIO Office on a proof of concept evaluating how local AI running on Intel AI PCs could complement Accenture's cloud AI environment for endpoint security and compliance. The objective wasn't to replace cloud AI, but to understand how local AI can enhance enterprise productivity and fit into a broader hybrid AI strategy.
The Challenge
The scale of Accenture's environment illustrates why AI-assisted workflows are becoming essential.
The Accenture CIO organization supports more than 809,000 workstations, serving over 779,000 employees across 52 countries, while managing an IT ecosystem powered by more than 12 cloud platforms, with 95% of enterprise applications hosted in the cloud. Security teams are responsible for maintaining endpoint compliance, investigating root causes, adapting to evolving threats, and continuously rolling out new AI capabilities—all while keeping developers focused on prevention rather than manual investigation.
These are precisely the types of repetitive, data-intensive workflows where AI workers can dramatically improve productivity.
The Solution
For the pilot, LLMWare's Model HQ served as the low-code/no-code platform for building and orchestrating an AI worker focused on laptop compliance root cause analysis.
The workflow combined:
Local Small Language Models (Llama-3.2-3B-Instruct)
Retrieval-Augmented Generation (RAG)
Multi-step agentic workflows
Nexthink endpoint management data
Natural language interaction on Intel AI PCs
Rather than creating another chatbot, the goal was to demonstrate how AI could assist engineers by automating investigative workflows while keeping sensitive enterprise data under organizational control.
The Results
The pilot demonstrated measurable productivity improvements for engineering workflows running locally on Intel AI PCs.
Among the findings:
2× faster summarization for model execution explainability supporting security audits.
More than 10% improvement in agentic rule changes and iterative testing.
More than 30% improvement in battery life during development tasks.
Beyond the performance metrics, one of the most important outcomes was identifying where local AI provides the greatest value.
The evaluation found that AI PCs are particularly well suited for rapid prototyping, isolated development environments, air-gapped deployments, privacy-sensitive workloads involving PII, and complex local troubleshooting where cloud AI may not be available. At the same time, cloud inferencing continues to play an important role for globally connected enterprise services and large-scale integrations.
What We Learned
One of the biggest takeaways was that enterprise AI is not an either-or decision between local and cloud deployments.
While Accenture's production AI environment is primarily cloud-based, this collaboration demonstrated how local AI can complement existing infrastructure by bringing AI directly to the endpoint. We also see this same architecture extending naturally beyond AI PCs to private enterprise servers, allowing organizations to choose the optimal deployment model based on performance, security, governance, and business requirements.
From our perspective, enterprise AI should provide deployment flexibility:
On-device AI for low latency, offline operation, privacy, and developer productivity.
Private enterprise servers for shared organizational models, centralized knowledge bases, and departmental AI services.
Cloud infrastructure for globally connected applications, enterprise integrations, and large-scale frontier model workloads.
The future isn't about choosing one deployment model over another—it's about giving organizations the flexibility to run AI wherever it delivers the greatest value.
Looking Ahead
Our collaboration with Intel and the Accenture CIO Office reinforced what we've believed from the beginning: the future of enterprise AI isn't defined by where a model runs—it's defined by how effectively AI is integrated into everyday business workflows.
As organizations continue adopting AI, the most successful deployments will combine local AI, private enterprise infrastructure, and cloud services into a unified architecture that balances productivity, security, governance, and cost. At LLMWare, we're building Model HQ to give organizations that flexibility, enabling AI workers to run wherever they make the most sense—from AI PCs to enterprise servers to the cloud.
Read Accenture’s Case Study Overview Here.
