Advisory Solution Consultant at ServiceNow, working on the Autonomous IT portfolio. I work in presales and build custom AI solutions on top of large-scale enterprise platforms — pairing deep technical detail with executive-level storytelling.
Senior solution consultant and architect with 14+ years in enterprise software — spanning IT Asset Management, technology consulting, and applied AI.
Equally at home in audit, compliance, and platform-migration programs, and driven by turning complex technology into measurable business outcomes. I extend more than a decade of enterprise delivery into production-minded AI — bridging model capability and enterprise constraint: data, security, adoption.
Transformer-based and large language models that automate and augment ITAM, presales, and delivery workflows — bridging model capability with data, security, and adoption constraints.
Led a large-scale platform migration end to end — architecture, data migration, process redesign, and user adoption — coordinating technical and business stakeholders.
Presales, architecture, planning, and adoption; ITAM deployments and process definition to ensure correct, value-driving usage.
Architecture and presales; adaptation and deployment of inventory methods, optimization, data processing, and reporting across all technical aspects.
Compliance, information security, software portfolio, and risk management; contributed to data-center compliance and risk mitigation.
Led team-building and knowledge-sharing initiatives, established a culture of continuous learning, and interviewed talent during hiring.
Enterprises rarely lack models. They lack the asset, contract, and entitlement data those models need — and the process to act on the output.
Language models read contracts, product-use rights, and vendor terms, then reconcile them against discovered installs — turning weeks of manual entitlement work into a reviewable draft position.
Surface the handful of publishers, contracts, and data-quality gaps that actually drive audit risk, with the evidence trail attached — so remediation is prioritised by exposure, not by alphabet.
Messy CMDB, discovery, and procurement records mapped to a clean catalogue. Normalised data is the precondition for every downstream automation — and the step most programmes skip.
Solution drafts, demo data, and value narratives generated from the customer's own environment — shortening the path from discovery call to a defensible business case.
Agentic patterns wired into platform workflows, with human approval where the decision has cost or contractual weight — automation that operations teams are willing to trust.
Independent advice on ITAM, ServiceNow architecture, and applied AI — from discovery through adoption.