As AI becomes part of everyday work, IT leaders are being asked to give employees more capable, responsive, and secure experiences without creating unnecessary cloud cost or management complexity. This raises a practical question: how can organizations use the computing resources already deployed across the enterprise PC fleet to support more AI work closer to employees and business data?
This interactive roundtable will explore where local AI can improve employee workflows, how IT can decide which workloads belong on the PC versus in the cloud, and what is required to scale responsibly. The discussion will stay focused on IT outcomes: employee productivity, data control, cost predictability, fleet readiness, governance, and the ability to introduce new AI capabilities without disrupting existing operations.
Engineers and developers are one example of an employee group at the forefront of putting AI into practice. Local AI solutions can help these teams analyse and optimise code, shorten iteration cycles, reduce dependence on cloud-based workflows, and work with sensitive code and data closer to the device. For IT, enabling these capabilities means identifying where local processing can improve productivity while supporting the security, privacy, and management requirements of the enterprise. Starting with a focused employee group can also help IT evaluate where local AI adds value and what it could mean for the broader PC fleet.
Join Intel and TurinTech for a peer discussion on how IT leaders can make local AI practical for employees and turn the enterprise PC fleet into a more active part of their AI strategy. Explore where on-device AI can add value, what IT needs to manage it responsibly, and how to identify a focused use case that can move from experimentation to a measurable enterprise deployment.
Speakers
Mike Basios
Discussion Topics
Improving Employee Productivity with Local AI – Identify where local AI can support employee workflows, with developers and engineers as a practical starting point.
Getting More Value from the Enterprise PC Fleet – Explore fleet readiness, coordinated hardware and software optimisation, and how existing devices can support local AI.
Building a Secure and Practical Hybrid AI Strategy – Determine what should run locally or in the cloud, while balancing cost, privacy, security, governance, deployment, and support.
About TurinTech
If it can be measured, it can be optimized.
That belief has guided TurinTech since our founding. We are computer and data scientists building AI that makes production systems measurably better, with every improvement validated before it counts.
TurinTech began at UCL with research in genetic optimization and software engineering. Frustrated by the inefficiencies in traditional optimization workflows, our founders set out to build something better: AI that makes real systems measurably faster, cheaper and more reliable.
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