• Develop and maintain automation workflows using AI tools and platforms such as n8n, Zapier, Microsoft Power Automate, or similar technologies. • Collaborate with cross-functional teams to analyze business processes and identify automation opportunities. • Assist with the implementation of AI-driven solutions to improve operational efficiency and data management. • Monitor and optimize existing automation systems to ensure sustained performance and efficacy. • Prepare documentation, including standard operating procedures (SOPs) and training materials for users. • Provide user training and support for AI tools and automation processes. • Stay updated with industry trends and best practices in AI and automation technologies.

• Map current-state workflows end-to-end to diagnose root causes of operational friction, including handoff failures, governance gaps, and tooling mismatches. • Redesign target-state operations for leaner workflows, clearer decision rights, and effective human-agent collaboration to accelerate throughput using AI. • Design comprehensive operating models, including organizational structure, roles, governance, cross-functional interaction models, and value realization. • Establish performance baselines and build measurement frameworks that track improvement continuously throughout the engagement. • Identify value gaps in stalled implementations, diagnosing where technology deployment has outpaced business adoption to restore the path to ROI. • Build working, AI-powered prototypes directly utilizing LLM APIs, platform-native features, and low-code/no-code environments to create functional solutions. • Utilize working demos and prototypes as the primary tool for stakeholder alignment directly within the client's actual processes. • Evaluate where AI practically enhances a workflow versus where it serves as a distraction, advising clients with realistic, grounded expectations. • Support the rollout of new tools and workflows, ensuring adoption is rooted in process design and change management. • Design enablement materials that ensure new technology implementations effectively connect to and support the intended operating model. • Support complex engagement scoping and conduct diagnostic assessments for value capture, implementation readiness, and adoption strategy. • Contribute reusable frameworks and tools that continuously improve practice qualification and scoping.

• Map current-state workflows end-to-end to diagnose root causes of operational friction, including handoff failures, governance gaps, and tooling mismatches. • Redesign target-state operations for leaner workflows, clearer decision rights, and effective human-agent collaboration to accelerate throughput using AI. • Design comprehensive operating models, including organizational structure, roles, governance, cross-functional interaction models, and value realization. • Establish performance baselines and build measurement frameworks that track improvement continuously throughout the engagement. • Identify value gaps in stalled implementations, diagnosing where technology deployment has outpaced business adoption to restore the path to ROI. • Build working, AI-powered prototypes directly utilizing LLM APIs, platform-native features, and low-code/no-code environments to create functional solutions. • Utilize working demos and prototypes as the primary tool for stakeholder alignment directly within the client's actual processes. • Evaluate where AI practically enhances a workflow versus where it serves as a distraction, advising clients with realistic, grounded expectations. • Support the rollout of new tools and workflows, ensuring adoption is rooted in process design and change management. • Design enablement materials that ensure new technology implementations effectively connect to and support the intended operating model. • Support complex engagement scoping and conduct diagnostic assessments for value capture, implementation readiness, and adoption strategy. • Contribute reusable frameworks and tools that continuously improve practice qualification and scoping.

• Map current-state workflows end-to-end to diagnose root causes of operational friction, including handoff failures, governance gaps, and tooling mismatches. • Redesign target-state operations for leaner workflows, clearer decision rights, and effective human-agent collaboration to accelerate throughput using AI. • Design comprehensive operating models, including organizational structure, roles, governance, cross-functional interaction models, and value realization. • Establish performance baselines and build measurement frameworks that track improvement continuously throughout the engagement. • Identify value gaps in stalled implementations, diagnosing where technology deployment has outpaced business adoption to restore the path to ROI. • Build working, AI-powered prototypes directly utilizing LLM APIs, platform-native features, and low-code/no-code environments to create functional solutions. • Utilize working demos and prototypes as the primary tool for stakeholder alignment directly within the client's actual processes. • Evaluate where AI practically enhances a workflow versus where it serves as a distraction, advising clients with realistic, grounded expectations. • Support the rollout of new tools and workflows, ensuring adoption is rooted in process design and change management. • Design enablement materials that ensure new technology implementations effectively connect to and support the intended operating model. • Support complex engagement scoping and conduct diagnostic assessments for value capture, implementation readiness, and adoption strategy. • Contribute reusable frameworks and tools that continuously improve practice qualification and scoping.